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authorKen Kellner <ken@kenkellner.com>2018-02-16 20:05:52 (GMT)
committerKen Kellner <ken@kenkellner.com>2018-02-16 20:05:52 (GMT)
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+@article{AbrahamsonLayne2003,
+author = {Abrahamson, W. G. and Layne, J. N.},
+title = {Long-term patterns of acorn production for five oak species in xeric {F}lorida uplands},
+journal = {Ecology},
+volume = {84},
+year = {2003},
+pages = {2476-2492},
+}
+
+@article{Abrams1992,
+author = {Abrams, M. D.},
+title = {Fire and the development of oak forests},
+journal = {BioScience},
+volume = {42},
+year = {1992},
+pages = {346-353},
+}
+
+@article{Abrams2003,
+author = {Abrams, M. D.},
+title = {Where has all the white oak gone?},
+journal = {BioScience},
+volume = {53},
+year = {2003},
+pages = {927-939},
+}
+
+@article{AdlerTamarin1984,
+author = {Adler, G. H. and Tamarin, R. H.},
+title = {Demography and reproduction in island and mainland white-footed mice (\textit{{P}eromyscus leucopus}) in southeastern {M}assachusetts},
+journal = {Canadian Journal of Zoology},
+volume = {62},
+year = {1984},
+pages = {58-64},
+}
+
+@article{AldrichParkerRomeroSeversonMichler2005,
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+journal = {Forest Science},
+volume = {51},
+year = {2005},
+pages = {406-416},
+}
+
+@article{AndersonFolk1993,
+author = {Anderson, D. C. and Folk, M. L.},
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+year = {1993},
+pages = {656-664},
+}
+
+@article{Andersson1992,
+author = {Andersson, C.},
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+journal = {Forest Ecology and Management},
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+year = {1992},
+pages = {247-251},
+}
+
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+author = {Annand, E. M. and Thompson, F. R. III.},
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+journal = {Journal of Wildlife Management},
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+year = {1997},
+pages = {159-171},
+}
+
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+author = {Ash, A. N.},
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+}
+
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+}
+
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+}
+
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+author = {Bellocq, M. I. and Jones, C. and Dey, D. C. and Turgeon, J. J.},
+title = {Does the shelterwood method to regenerate oak forests affect acorn production and predation?},
+journal = {Forest Ecology and Management},
+volume = {205},
+year = {2005},
+pages = {311-323},
+}
+
+@article{Bowers1995,
+author = {Bowers, M. A.},
+title = {Use of space and habitats by the eastern chipmunk, \textit{{T}amius striatus}},
+journal = {Journal of Mammalogy},
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+pages = {12-21},
+}
+
+@article{Blair1942,
+author = {Blair, W. F.},
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+}
+
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+year = {2001},
+pages = {491-504},
+}
+
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+year = {1998},
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+}
+
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+journal = {Forest Ecology and Management},
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+year = {2008},
+pages = {3017-3018},
+}
+
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+}
+
+@article{Chao1988,
+author = {Chao, A.},
+title = {Estimating animal abundance with capture frequency data},
+journal = {Journal of Wildlife Management},
+volume = {52},
+year = {1988},
+pages = {295-300},
+}
+
+@article{Chao1989,
+author = {Chao, A.},
+title = {Estimating population size for sparse data in capture-recapture experiments},
+journal = {Biometrics},
+volume = {45},
+year = {1989},
+pages = {427-438},
+}
+
+@article{ChaoLeeJeng1992,
+author = {Chao, A. and Lee, S. M. and Jeng, S. L.},
+title = {Estimating population-size for capture recapture data when capture probabilities vary by time and individual animal},
+journal = {Biometrics},
+volume = {48},
+year = {1992},
+pages = {201-216},
+}
+
+@article{Clayton2003,
+author = {Clayton, J. C.},
+title = {Effects of clearcutting and wildfire on shrews ({S}orcidae: \textit{{S}orex}) in a {U}tah coniferous forest},
+journal = {Western North American Naturalist},
+volume = {63},
+year = {2003},
+pages = {264-267},
+}
+
+@article{CostelloYamasakiPekinsLeakNeefus2000,
+author = {Costello, C. A. and Yamasaki, M. and Pekins, P. J. and Leak, W. B. and Neefus, C. D.},
+title = {Songbird response to group selection harvests and clearcuts in a {N}ew {H}ampshire northern hardwood forest},
+journal = {Forest Ecology and Management},
+volume = {127},
+year = {2000},
+pages = {41-54},
+}
+
+
+@article{Dalgleish2011,
+author = {Dalgleish, H. J. and Swihart, R. K.},
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+journal = {Restoration Ecology},
+year = {2011},
+}
+
+@article{Demaynadier1998,
+author = {Demaynadier, P. G. and Hunter, M. L.},
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+volume = {12},
+year = {1998},
+pages = {340-352},
+}
+
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+title = {A suggested approach for design of oak (\textit{{Q}uercus} {L}.) regeneration research considering regional differences},
+journal = {New Forests},
+volume = {37},
+year = {2009},
+pages = {123-135},
+}
+
+@article{DiamondGilesKirkpatrickGriffin2000,
+author = {Diamond, S. J. and Giles, R. H. Jr. and Kirkpatrick, R. L. and Griffin, G. J.},
+title = {Hard mast production before and after the chestnut blight},
+journal = {Southern Journal of Applied Forestry},
+volume = {24},
+year = {2000},
+pages = {196-201},
+}
+
+@article{DixonJohnsonAdkisson1997,
+author = {Dixon, M. D. and Johnson, W. C. and Adkisson, C. S.},
+title = {Effects of weevil larvae on acorn use by blue jays},
+journal = {Oecologia},
+volume = {111},
+year = {1997},
+pages = {201-208},
+}
+
+@article{ElkintonHealyBuonaccorsiBoettnerHazzardSmith1996,
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+title = {Interactions among gypsy moths, white-footed mice, and acorns},
+journal = {Ecology},
+volume = {77},
+year = {1996},
+pages = {2332-2342},
+}
+
+@article{ElliottBoringSwankHaines1997,
+author = {Elliott, K. J. and Boring, L. R. and Swank, W. T. and Haines, B. R.},
+title = {Successional changes in plant species diversity and composition after clearcutting a {S}outhern {A}ppalachian watershed},
+journal = {Forest Ecology and Management},
+volume = {92},
+year = {1997},
+pages = {67-85},
+}
+
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+journal = {Frontiers in Ecology and the Environment},
+volume = {3},
+year = {2005},
+pages = {479-486},
+}
+
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+author = {Fantz, D. K. and Renken, R. B.},
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+journal = {Wildlife Society Bulletin},
+volume = {33},
+year = {2005},
+pages = {293-301},
+}
+
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+author = {Ford, W. M. and Menzel, M. A. and McGill, D. W. and Laerm, J. and McCay, T. S.},
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+journal = {Forest Ecology and Management},
+volume = {114},
+year = {1999},
+pages = {233-243},
+}
+
+@article{FordRodrigue2001,
+author = {Ford, W. M. and Rodrigue, J. L.},
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+journal = {Forest Ecology and Management},
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+year = {2001},
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+}
+
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+}
+
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+journal = {Forest Ecology and Management},
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+year = {1999},
+pages = {129-139},
+}
+
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+pages = {373-386},
+}
+
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+author = {Gates, J. E. and Gysel, L. W.},
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+year = {1978},
+pages = {871-883},
+}
+
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+author = {Gibson, L. P.},
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+journal = {Annals of the Entomological Society of America},
+volume = {57},
+year = {1964},
+pages = {521-526},
+}
+
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+}
+
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+}
+
+
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+author = {Greenberg, C. H.},
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+volume = {164},
+year = {2002},
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+}
+
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+}
+
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+}
+
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+year = {2004},
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+}
+
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+}
+
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+author = {Healy, W. M.},
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+year = {1997},
+pages = {152-156},
+}
+
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+}
+
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+}
+
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+year = {1998},
+pages = {57-74},
+}
+
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+pages = {231-235},
+}
+
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+pages = {385-397},
+}
+
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+}
+
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+author = {King, D. I. and Griffin, C. R. and DeGraff, R. M.},
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+journal = {Forest Ecology and Management},
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+year = {1998},
+pages = {151-156},
+}
+
+@article{Kirkland1977,
+author = {Kirkland, G. L.},
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+journal = {Journal of Mammalogy},
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+year = {1977},
+pages = {600-609},
+}
+
+@article{Kirkland1990,
+author = {Kirkland, G. L.},
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+journal = {Oikos},
+volume = {59},
+year = {1990},
+pages = {313-320},
+}
+
+@article{LarsenJohnson1998,
+author = {Larsen, D. R. and Johnson, P. S.},
+title = {Linking the ecology of natural oak regeneration to silviculture},
+journal = {Forest Ecology and Management},
+volume = {106},
+year = {1998},
+pages = {1-7},
+}
+
+@article{LombardoMcCarthy2008,
+author = {Lombardo, J. A. and McCarthy, B. C.},
+title = {Silvicultural treatment effects on oak seed production and predation by acorn weevils in southeastern {O}hio},
+journal = {Forest Ecology and Management},
+volume = {255},
+year = {2008},
+pages = {2566-2576},
+}
+
+@article{LombardoMcCarthy2009,
+author = {Lombardo, J. A. and McCarthy, B. C.},
+title = {Seed germination and seedling vigor of weevil-damaged acorns of red oak},
+journal = {Canadian Journal of Forest Research},
+volume = {39},
+year = {2009},
+pages = {1600-1605},
+}
+
+@article{Lorimer2001,
+author = {Lorimer, C.},
+title = {Historical and ecological roles of disturbance in eastern {N}orth {A}merican forests: 9,000 years of change},
+journal = {Wildlife Society Bulletin},
+volume = {29},
+year = {2001},
+pages = {425-439},
+}
+
+@article{LuskSwihartGoheen2007,
+author = {Lusk, J. L. and Swihart, R. K. and Goheen, J. R.},
+title = {Correlates of interspecific synchrony and interannual variation in seed production by deciduous trees},
+journal = {Forest Ecology and Management},
+volume = {242},
+year = {2007},
+pages = {656-670},
+}
+
+@article{MaserNussbaumTrappe1978,
+author = {Maser, C. and Nussbaum, R. A. and Trappe, J. M.},
+title = {Fungal small mammal interrelationships with emphasis on {O}regon coniferous forests},
+journal = {Ecology},
+volume = {59},
+year = {1978},
+pages = {799-809},
+}
+
+@article{Maeto2003,
+author = {Maeto, K. and Ozaki, K.},
+title = {Prolonged diapause or specialist seed-feeders makes predator satiation unstable in masting of \textit{{Q}uercus crispula}},
+journal = {Oecologia},
+volume = {137},
+year = {2003},
+pages = {392-398},
+}
+
+@article{McShea2000,
+author = {McShea, W. J.},
+title = {The influence of acorn crops on annual variation in rodent and bird populations},
+journal = {Ecology},
+volume = {81},
+year = {2000},
+pages = {228-238},
+}
+
+@article{JHealyDeversFearerKochStaufferWaldon2007,
+author = {McShea, W. J. and Healy, W. M. and Devers, P. and Fearer, T. and Koch, F. H. and Stauffer, D. and Waldon, J.},
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+}
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+}
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+}
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+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northeastern Research Station},
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+}
+
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+ AUTHOR = {Gibson, L. P.},
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+ NUMBER = {NE-RP-492},
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+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northeastern Research Station},
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+}
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+ NUMBER = {TB-NC-1},
+ TYPE = {Res. Paper},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station},
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+ YEAR = {1994},
+ PAGES = {1-2},
+}
+
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+ ADDRESS = {Upper Darby, PA},
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+}
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+ NUMBER = {NE-RP-356},
+ TYPE = {Res. Paper},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station},
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+ YEAR = {1976},
+ PAGES = {1-8},
+}
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+ NUMBER = {SRS-GTR-74},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Southern Research Station},
+ ADDRESS = {Asheville, NC},
+ YEAR = {2004},
+ PAGES = {60-70},
+}
+
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+ AUTHOR = {Sander, I. L.},
+ TITLE = {Size of oak advance reproduction: key growth following harvest cutting},
+ NUMBER = {NC-RP-79},
+ TYPE = {Res. Paper},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station},
+ ADDRESS = {St. Paul, MN},
+ YEAR = {1972},
+ PAGES = {1-6},
+}
+
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+ NUMBER = {NC-RP-251},
+ TYPE = {Res. Paper},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station},
+ ADDRESS = {St. Paul, MN},
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+ PAGES = {1-8},
+}
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+}
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+}
+
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+ YEAR = {2003},
+ NUMBER = {NC-GTR-234},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station},
+ ADDRESS = {St. Paul, MN},
+}
+
+@TECHREPORT{Gribko1995,
+ AUTHOR = {Gribko, L. S.},
+ TITLE = {The effect of acorn insects on the establishment and vigor of northern red oak seedlings in north-central {W}est {V}irginia},
+ YEAR = {1995},
+ NUMBER = {NE-GTR-197},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station},
+ ADDRESS = {Radnor, PA},
+ PAGES = {430-441},
+}
+
+@TECHREPORT{Riccardi2004,
+ AUTHOR = {Riccardi, C. L. and McCarthy, B. C. and Long, R. P.},
+ TITLE = {Oak seed production, weevil ({C}oleoptera:{C}urculonidae populations, and predation rates in mixed-oak forests of southeast {O}hio},
+ YEAR = {2004},
+ NUMBER = {NE-GTR-316},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northeastern Research Station},
+ ADDRESS = {Newtown Square, PA},
+ PAGES = {10-20},
+}
+
+@TECHREPORT{Jenkins2012,
+ AUTHOR = {Jenkins, M. A.},
+ TITLE = {The history of human disturbance in forest ecosystems of {S}outhern {I}ndiana},
+ YEAR = {2012},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northern Forest Experiment Station},
+ ADDRESS = {Newtown Square, PA},
+}
+
+@TECHREPORT{Kalb2012,
+ AUTHOR = {Kalb, R. A. and Mycroft, C. J.},
+ TITLE = {The {H}ardwood {E}cosystem {E}xperiment: goals, design, and implementation},
+ YEAR = {2012},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northern Forest Experiment Station},
+ ADDRESS = {Newtown Square, PA},
+}
+
+@TECHREPORT{Urban2012,
+ AUTHOR = {Urban, N. A. and Swihart, R. K.},
+ TITLE = {A pre-treatment assessment of the small mammals in the {H}ardwood {E}cosystem {E}xperiment},
+ YEAR = {2012},
+ TYPE = {Gen. Tech. Rep.},
+ INSTITUTION = {U.S. Department of Agriculture, Forest Service, Northern Forest Experiment Station},
+ ADDRESS = {Newtown Square, PA},
+}
+
+@BOOK{IN1984,
+ AUTHOR = "{Indiana Department of Natural Resources}",
+ TITLE = {Indiana forest soils handbook},
+ PUBLISHER = {Indiana Department of Natural Resources, Division of Forestry},
+ YEAR = {1984},
+ ADDRESS = {Indianapolis, IN},
+}
+
+@BOOK{Siegel1988,
+ AUTHOR = {Siegel, S. and Castellan, N. J.},
+ TITLE = {Nonparametric statistics for the behavioral sciences},
+ PUBLISHER = {McGraw-Hill},
+ YEAR = {1988},
+ ADDRESS = {New York, NY},
+ PAGES = {213-214},
+}
diff --git a/appendices.tex b/appendices.tex
new file mode 100644
index 0000000..a64cffd
--- /dev/null
+++ b/appendices.tex
@@ -0,0 +1,34 @@
+\appendices
+\chapter{{CHAPTER 2 WINBUGS CODE}}
+\section{Poisson-lognormal model of acorn production}
+\begin{scriptsize}
+\lstinputlisting{"appendices/chapter1code1.R"}
+\end{scriptsize}
+
+\section{Logistic regression model of weevil infestation}
+\begin{scriptsize}
+\lstinputlisting{"appendices/chapter1code2.R"}
+\end{scriptsize}
+
+\section{Logistic regression model of acorn removal}
+\begin{scriptsize}
+\lstinputlisting{"appendices/chapter1code3.R"}
+\end{scriptsize}
+
+\chapter{{CHAPTER 3 WINBUGS AND R CODE}}
+\section{\textit{N}-mixture model}
+\begin{scriptsize}
+\lstinputlisting{"appendices/chapter2code1.R"}
+\end{scriptsize}
+
+\section{R code for simulation comparing \textit{N}-mixture and MRR models}
+\begin{scriptsize}
+\lstinputlisting{"appendices/chapter2code2.R"}
+\end{scriptsize}
+
+\chapter{{CHAPTER 4 WINBUGS CODE}}
+\section{Multi-season, multi-species trap occupancy model}
+\begin{scriptsize}
+\lstinputlisting{"appendices/chapter4code.R"}
+\end{scriptsize}
+
diff --git a/appendices/chapter1code1.R b/appendices/chapter1code1.R
new file mode 100644
index 0000000..fbd8aee
--- /dev/null
+++ b/appendices/chapter1code1.R
@@ -0,0 +1,79 @@
+model {
+
+#####################Priors######################
+#Intercept
+beta0 ~ dnorm(0,0.001)
+
+#Tree and year random effect hyperparameter priors
+tree.sigma ~ dunif(0,7)
+tree.tau <- sqrt(1/(tree.sigma*tree.sigma))
+year.sigma ~ dunif(0,7)
+year.tau <- sqrt(1/(year.sigma*year.sigma))
+
+#Lognormal error term hyperparameters
+eps.sigma ~ dunif(0,7)
+eps.tau <- sqrt(1/(eps.sigma*eps.sigma))
+
+#Slope parameters for included covariates
+b.species ~ dnorm(0,0.001)
+b.sampledate ~ dnorm(0,0.001)
+b.datec2 ~ dnorm(0,0.001)
+b.even ~ dnorm(0,0.001)
+b.uneven ~ dnorm(0,0.001)
+b.dbh ~ dnorm(0,0.001)
+
+
+###############Generate Random Effects###########
+for (m in 1:ntrees){ #trees
+ alpha.tree[m] ~ dnorm(0,tree.tau)
+ }
+
+for (n in 1:3){ #years
+ alpha.year[n] ~ dnorm(0,year.tau)
+ }
+
+####################Likelihood################################
+for (i in 1:ntrees) {
+ for (j in firstyear[i]:lastyear[i]){
+ for (k in first[i,j]:last[i,j]){ #Avoids missing data problems
+ #lognormal error term (allows for better model fit)
+ eps[i,k,j] ~ dnorm(0, eps.tau)
+ #Poisson regression
+ log(lambda[i,k,j]) <- beta0 + alpha.tree[i] + alpha.year[j]
+ + b.sampledate*sampledate[i,k,j] + b.datec2*datec2[i,k,j]
+ + b.species*species[i] + b.dbh*dbh[i]
+ #Management covariates and error term
+ + b.even*even[i] + b.uneven*uneven[i] + eps[i,k,j]
+
+ #Model collected count data
+ count[i,k,j] ~ dpois(lambda[i,k,j])
+
+ ############Posterior Predictive Checks############
+
+ #Pearson Residuals for actual dataset
+ res.raw[cucount[i,k,j]] <- pow((count[i,k,j] - lambda[i,k,j]), 2)
+ res[cucount[i,k,j]] <- res.raw[cucount[i,k,j]] / sqrt(lambda[i,k,j]+0.5)
+ #Simulate new dataset based on model
+ c.new[i,k,j] ~ dpois(lambda[i,k,j])
+ #Pearson residuals for simulated dataset
+ res.new.raw[cucount[i,k,j]] <- pow((c.new[i,k,j] - lambda[i,k,j]), 2)
+ res.new[cucount[i,k,j]] <- res.new.raw[cucount[i,k,j]] /sqrt(lambda[i,k,j]+0.5)
+
+ #Maximum and Median Values: Ranked dataset
+ c.new.vec[cucount[i,k,j]] <- c.new[i,k,j]
+
+ #Proportion of Zeros in Dataset
+ c.neg[i,k,j] <- (-1)*(c.new[i,k,j])
+ zero.raw.new[cucount[i,k,j]] <- step(c.neg[i,k,j])
+
+}}}
+
+#Derived Parameters for Posterior Predictive Checks
+maxedcount <- ranked(c.new.vec[], 2084)
+lambda.mean <- mean(lambda.mean.raw[])
+zerofit.new <- mean(zero.raw.new[])
+fit <- sum(res[]) #discrepency of actual dataset
+fit.new <- sum(res.new[]) #discrepency of simulated dataset
+
+#End model specification
+} \ No newline at end of file
diff --git a/appendices/chapter1code2.R b/appendices/chapter1code2.R
new file mode 100644
index 0000000..895dd43
--- /dev/null
+++ b/appendices/chapter1code2.R
@@ -0,0 +1,50 @@
+model {
+
+#####################Priors######################
+#Intercept
+beta0 ~ dnorm(0,0.001)
+
+#Covariate slopes (year was a fixed effect)
+b.species ~ dnorm(0,0.001)
+b.sampledate ~ dnorm(0,0.001)
+b.datec2 ~ dnorm(0,0.001)
+b.even ~ dnorm(0,0.001)
+b.uneven ~ dnorm(0,0.001)
+b.year07 ~ dnorm(0,0.001)
+b.year08 ~ dnorm(0,0.001)
+
+#Tree random effect hyperparameters
+tree.sigma ~ dunif(0,10)
+tree.tau <- sqrt(1/(tree.sigma*tree.sigma))
+
+###############Generate Random Effects########################
+for (m in 1:ntrees){ #trees
+ alpha.tree[m] ~ dnorm(0,tree.tau)
+ }
+
+####################Likelihood################################
+for (i in 1:nacorns) { #Iterate over all acorns
+#Logistic regression
+logit(p[i]) <- beta0 + alpha.tree[treecode[i]] + b.year07*y07[i] + b.year08*y08[i]
+ + b.sampledate*sampledate[i] + b.datec2*datec2[i]
+ + b.species*species[i]
+ #management type covariates
+ + b.even*even[i] + b.uneven*uneven[i]
+
+ #Model my infestation data, y
+ y[i]~dbern(p[i])
+
+ #########Posterior Predictive Check#######################
+ res[i] <- abs(y[i] - p[i]) #absolute residual
+ y.new[i] ~ dbern(p[i]) #Simulate new dataset based on model
+ res.new[i] <- abs(y.new[i] - p[i]) #absolute residual of simulated data
+}
+
+############Derived Quantities###################
+
+#for Posterior Predictive Check
+fit <- sum(res[]) #discrepency of actual dataset
+fit.new <- sum(res.new[]) #discrepency of simulated dataset
+
+#end model specification
+} \ No newline at end of file
diff --git a/appendices/chapter1code3.R b/appendices/chapter1code3.R
new file mode 100644
index 0000000..d20d1a8
--- /dev/null
+++ b/appendices/chapter1code3.R
@@ -0,0 +1,74 @@
+model {
+
+#####################Priors######################
+#Intercept
+beta0~dunif(-5,5)
+
+#Random Effects of Tree and Year - Hyperparameters
+tree.sigma ~ dunif(0,7)
+tree.tau <- sqrt(1/(tree.sigma*tree.sigma))
+year.sigma ~ dunif(0,7)
+year.tau <- sqrt(1/(year.sigma*year.sigma))
+
+#Covariate slope parameters
+b.broken~dunif(-5,5)
+b.germ~dunif(-5,5)
+b.weevil~dunif(-5,5)
+b.age~dunif(-5,5)
+b.species~dunif(-5,5)
+b.open~dunif(-5,5)
+b.deer~dunif(-5,5)
+b.squirrel~dunif(-5,5)
+b.even~dunif(-5,5)
+b.uneven~dunif(-5,5)
+b.date~dunif(-5,5)
+b.length~dunif(-5,5)
+b.datec2~dunif(-5,5)
+
+###############Generate Random Effects########################
+for (m in 1:ntrees){ #trees
+ alpha.tree[m] ~ dnorm(0,tree.tau)
+ }
+for (p in 1:nyears){ #years
+ alpha.year[p] ~ dnorm(0,year.tau)
+ }
+
+####################Likelihood################################
+for(i in 1:nacorns){
+ for(j in (first[i]+1):last[i]){
+ #Logistic regression
+ logit(phi[i,j]) <- beta0+alpha.tree[treecode[i]]+alpha.year[yearcode[i]]
+ #Acorn characteristics
+ + b.broken*broken[i,j-1] + b.germ*germinated[i,j-1]
+ + b.weevil*weevil[i,j-1] + b.species*species[i]
+ #Exclosure types
+ + b.open*open[i] + b.deer*deer[i] + b.squirrel*squirrel[i]
+ #Proposed management
+ + b.even*even[i] + b.uneven*uneven[i]
+ #Time-related covariates
+ + b.date*date[treecode[i],j-1,yearcode[i]]
+ + b.length*length[treecode[i],j,yearcode[i]]
+ + b.datec2*datec2[treecode[i],j-1,yearcode[i]]
+
+ #Presence of acorn conditional on presence in previous time step
+ mu[i,j]<-phi[i,j]*y[i,j-1]
+ #Model 'encounter history' acorn data - y is my dataset
+ y[i,j]~dbern(mu[i,j])
+
+ #########Posterior Predictive Check#######################
+ #Absolute residuals of real data set
+ res[cucount[i,j]] <- abs(y[i,j] - phi[i,j])
+ #Generate new simulated dataset based on model
+ y.new[i,j] ~ dbern(mu[i,j])
+ #Absolute residuals of simulated dataset
+ res.new[cucount[i,j]] <- abs(y.new[i,j] - phi[i,j])
+
+}}
+############Derived Quantities###################
+
+#For posterior predictive check
+fit <- sum(res[]) #discrepency of actual dataset
+fit.new <- sum(res.new[]) #discrepency of simulated dataset
+
+#End model specification
+} \ No newline at end of file
diff --git a/appendices/chapter2code1.R b/appendices/chapter2code1.R
new file mode 100644
index 0000000..c2dc32f
--- /dev/null
+++ b/appendices/chapter2code1.R
@@ -0,0 +1,103 @@
+model {
+
+#####################Priors######################
+
+#Intercept priors
+mu.lam ~ dnorm(0,0.001)
+mu.p ~ dnorm(0,0.001)
+sigma.lam ~ dunif(0,10)
+tau.lam <- pow(sigma.lam,-2)
+sigma.p ~ dunif(0,10)
+tau.p <- pow(sigma.p,-2)
+
+#Detection Covariate Priors
+b.temp~dunif(0,1)
+mu.temp <- log(b.temp/(1-b.temp))
+s.temp~dunif(0,7)
+tau.temp <- pow(s.temp,-2)
+
+b.jd~dunif(0,1)
+mu.jd <- log(b.jd/(1-b.jd))
+s.jd~dunif(0,10)
+tau.jd <- pow(s.jd,-2)
+
+b.eff~dunif(0,1)
+mu.eff <- log(b.eff/(1-b.eff))
+s.eff~dunif(0,7)
+tau.eff <- pow(s.eff,-2)
+
+#Abundance Priors
+b.aspect~dunif(0,1)
+mu.aspect <- log(b.aspect/(1-b.aspect))
+s.aspect~dunif(0,7)
+tau.aspect <- pow(s.aspect,-2)
+
+b.ccl~dunif(0,1)
+mu.ccl <- log(b.ccl/(1-b.ccl))
+s.ccl~dunif(0,7)
+tau.ccl <- pow(s.ccl,-2)
+
+b.csh~dunif(0,1)
+mu.csh <- log(b.csh/(1-b.ccl))
+s.csh~dunif(0,7)
+tau.csh <- pow(s.csh,-2)
+
+b.cgr~dunif(0,1)
+mu.cgr <- log(b.cgr/(1-b.cgr))
+s.cgr~dunif(0,7)
+tau.cgr <- pow(s.cgr,-2)
+
+b.mast~dunif(0,1)
+mu.mast <- log(b.mast/(1-b.mast))
+s.mast~dunif(0,7)
+tau.mast <- pow(s.mast,-2)
+
+####################Likelihood################################
+for (i in 1:nspecies){
+ #Generate random intercepts and slopes by species
+ alpha.lam[i] ~ dnorm(mu.lam,tau.lam)I(-5,5)
+ alpha.p[i] ~ dnorm(mu.p, tau.p)I(-5,5)
+ beta.aspect[i] ~ dnorm(mu.aspect,tau.aspect)I(-5,5)
+ beta.c.cl[i] ~ dnorm(mu.ccl,tau.ccl)I(-5,5)
+ beta.c.sh[i] ~ dnorm(mu.csh,tau.csh)I(-5,5)
+ beta.c.gr[i] ~ dnorm(mu.cgr,tau.cgr)I(-5,5)
+ beta.temp[i] ~ dnorm(mu.temp,tau.temp)I(-5,5)
+ beta.jd[i] ~ dnorm(mu.jd,tau.jd)I(-5,5)
+ beta.eff[i] ~ dnorm(mu.eff,tau.eff)I(-5,5)
+ beta.mast[i] ~ dnorm(mu.mast,tau.mast)I(-5,5)
+ #Likelihood for first 3 years (all sites)
+ for (j in 1:nsites){
+ for (t in 1:3){
+ #Unobserved model subunit
+ #Model true abundance N as Poisson
+ N[j,i,t] ~ dpois(lambda[j,i,t])
+ #Poisson regression
+ log(lambda[j,i,t]) <- alpha.lam[i] + beta.aspect[i]*aspect[j]
+ + beta.c.cl[i]*treat[j,1,t] + beta.c.sh[i]*treat[j,2,t]
+ + beta.c.gr[i]*treat[j,3,t]+ beta.mast[i]*mast[units[j],t]
+
+ #Observed model subunit
+ for (k in start[i]:5){
+ #Model counts as binomial random variable with parameters N and p
+ counts[j,k,i,t] ~ dbin(p[j,k,i,t],N[j,i,t])
+ #Logistic regression on the probability of detection
+ logit(p[j,k,i,t]) <- alpha.p[i] + beta.temp[i]*temp[j,t]
+ + beta.jd[i]*jd[j,t]+ beta.eff[i]*eff[j,i,t]
+ }}
+
+ #Likelihood for years 4-5 (indexing eliminates sites with missing data)
+ for (t in first[j]:5){
+ N[j,i,t] ~ dpois(lambda[j,i,t])
+ log(lambda[j,i,t]) <- alpha.lam[i]+ beta.aspect[i]*aspect[j]
+ + beta.c.cl[i]*treat[j,1,t] + beta.c.sh[i]*treat[j,2,t]
+ + beta.c.gr[i]*treat[j,3,t] + beta.mast[i]*mast[units[j],t]
+ for (k in start[i]:5){
+ counts[j,k,i,t] ~ dbin(p[j,k,i,t],N[j,i,t])
+ logit(p[j,k,i,t]) <- alpha.p[i] + beta.temp[i]*temp[j,t]
+ + beta.jd[i]*jd[j,t]+ beta.eff[i]*eff[j,i,t]
+ }}
+}}
+
+}
+
+#End model specification \ No newline at end of file
diff --git a/appendices/chapter2code2.R b/appendices/chapter2code2.R
new file mode 100644
index 0000000..c609e32
--- /dev/null
+++ b/appendices/chapter2code2.R
@@ -0,0 +1,205 @@
+#Simulation code for comparing binomial/poisson mixture model of abundance
+#with traditional capture models
+
+#Libraries used
+library(R2WinBUGS)
+library(Rcapture)
+
+#########Necessary custom functions#####################
+
+# Simulate a site's individual encounter histories with this function;
+# Every individual gets a history with 5 occasions
+# The output from this can be put directly into Rcapture once NAs are removed
+gen.data = function(Nvec,p){
+nsites = length(Nvec)
+eh = array(data=NA, dim=c(max(Nvec),5,nsites))
+for (j in 1:nsites){
+ for (i in 1:Nvec[j]){
+ for (k in 1:5){
+ eh[i,k,j] <- rbinom(1,1,p)
+ }}
+}
+return(eh)
+}
+
+#Obtain count data for Binomial-Poisson mixture model with this function
+#Generates counts of observed individuals at a site at each sampling occasion
+gen.mix = function(data){
+nsites = dim(data)[3]
+counts = array(data=NA, dim=c(nsites,5))
+for (i in 1:nsites){
+ for (j in 1:5){
+ counts[i,j] <- sum(na.omit(data[,j,i]))
+}}
+return(counts)
+}
+
+#################Simulation Function#####################
+#Specify the number of iterations, Nvec (vector of true site abundances),
+#p, and WinBUGS parameters
+
+fitsim = function(nsims, Nvec, p, ni=30000, nb=15000, nt=30){
+
+nsites = length(Nvec)
+#Create empty output container
+output = array(data=NA, dim=c(nsites,7,4,nsims))
+
+for (i in 18:nsims){
+ #generate encounter histories based on Nvec and p
+ raw = gen.data(Nvec,p)
+
+ #Convert raw data to counts
+ bugs.inp = gen.mix(raw)
+
+###############Begin setting up WinBUGS n-mixture analysis##############
+
+#set up WinBUGS input
+ counts=bugs.inp
+ #Bundle data
+ data <- list("counts","nsites"
+ )
+ #Initial value for N at each site for WinBUGS: max count+1 to ensure no 0s
+ Nst = array(data=NA,dim=c(nsites))
+ for (k in 1:nsites){
+ Nst[k] = max(counts[k,1:5])+1
+ }
+ # Initial values function
+ inits <- function(){
+ Nst=Nst
+
+ list(mu.lam=rnorm(1),mu.p=rnorm(1),
+ sigma.lam=runif(n=1,min=0.5,max=1),
+ N=as.numeric(Nst)
+
+ )}
+ # Parameters to save
+ params <- c(
+ 'mu.lam','p',
+ #Detection parameters
+ #Abundance parameters
+ #Derived parameters
+ "N"
+ )
+
+###############Begin Actual WinBUGS Model##################
+ modelFilename = "bin_mix.txt"
+ cat(
+ model {
+############Priors##################
+ mu.lam ~ dnorm(0,0.01) # intercept
+ mu.p ~ dnorm(0,0.01)
+ sigma.lam ~ dunif(0,10)
+ tau.lam <- pow(sigma.lam,-2)
+ sigma.p ~ dunif(0,10)
+ tau.p <- pow(sigma.lam,-2)
+
+#############Likelihood#############
+
+ for (i in 1:nsites){
+ alpha.p[i] ~ dnorm(mu.p,tau.p)I(-5,5)
+ alpha.lam[i] ~ dnorm(mu.lam,tau.lam)I(-5,5)
+ N[i] ~ dpois(lambda[i])
+ logit(p[i]) <- alpha.p[i]
+ log(lambda[i]) <- alpha.lam[i]
+ for (j in 1:5){
+ counts[i,j] ~ dbin(p[i],N[i])
+ }}
+ }
+ , fill=TRUE, file=modelFilename)
+
+ ##########Send everything to WinBUGS##############
+ fit = bugs(data, inits, params, modelFilename, n.chains=3, n.iter=ni,
+ n.burnin=nb, n.thin=nt, bugs.seed=sample(1:9999,size=1), debug=FALSE,
+ DIC=FALSE, bugs.directory="")
+
+ ##############Begin generation of output file################
+ #Structure is as follows: nsites x generated values x model type x
+ #iteration. Values for site j:
+ #1. Actual N, 2. estimated N, 3. est. N se, 4. est. p, 5. est. p se,
+ #6. %bias 7. Is actual N in 95% confidence interval for est? (indicator)
+
+ #Generate output for traditional capture models at each site j
+ #Currently using M0 (true, since p is constant), Mhc=MhChao, MthC=MthChao
+ #More or different models can easily be added
+
+for (j in 1:nsites){
+
+ #Clean up data (i.e., remove individuals that were never detected)
+ site.data = na.omit(raw[,,j])
+ #Run analysis for selected models in Rcapture
+ if(sum(site.data)>0){
+ cap.fit <- closedp.t(site.data)
+ position = c(NA,1,3,7)
+ mnames = c(NA,"M0","MhC", "MthC")
+
+ try(output[j,1,1:length(position),i] <- Nvec[j],silent=TRUE)
+
+ #Move results to output file for each model (if reasonable)
+ for (m in 2:length(position)){
+ try(nest <- cap.fit$results[position[m],1],silent=TRUE)
+ try(se <- cap.fit$results[position[m],2],silent=TRUE)
+ try(lower <- nest-(1.96*cap.fit$results[position[m],2]),silent=TRUE)
+ try(upper <- nest+(1.96*cap.fit$results[position[m],2]),silent=TRUE)
+ try(pest <- eval(parse(text=paste("cap.fit$parameters$",mnames[m],"[,2]",
+ sep=""))),silent=TRUE)
+
+ try(output[j,2,m,i] <- nest, silent=TRUE)
+ try(output[j,3,m,i] <- se,silent=TRUE)
+ try(output[j,4,m,i] <- pest, silent=TRUE)
+
+ #Calculate bias
+ try(c.bias <- 100*(nest-Nvec[j])/Nvec[j],silent=TRUE)
+ try(output[j,6,m,i] <- c.bias, silent=TRUE)
+
+ #In confidence interval?
+ if(Nvec[j]>=lower){
+ if(Nvec[j]<=upper){output[j,7,m,i]=1}
+ else{output[j,7,m,i]=0}}
+ else{output[j,7,m,i]=0}
+
+ if(output[j,2,m,i]>100){output[j,2:7,m,i] = NA}
+ }}
+ if(sum(site.data)==0){output[j,2:7,m,i] = NA}
+
+ #Move WinBUGS results to output file
+ bugs.out <- fit$sims.list$N[,j]
+ bugs.p <- fit$sims.list$p[,j]
+ nhat = mean(bugs.out)
+ sdev = sd(bugs.out)
+ phat = mean(bugs.p)
+ sdevp = sd(bugs.p)
+ interval = quantile(bugs.out, c(0.025,0.975))
+ output[j,2,1,i] = nhat
+ output[j,3,1,i] = sdev
+ output[j,4,1,i] = phat
+ output[j,5,1,i] = sdevp
+
+ #Calculate bias
+ bias <- 100*(nhat-Nvec[j])/Nvec[j]
+ output[j,6,1,i] = bias
+
+ #In credible interval?
+ if(Nvec[j]>=interval[1]){
+ if(Nvec[j]<=interval[2]){output[j,7,1,i]=1}
+ else{output[j,7,1,i]=0}}
+ else{output[j,7,1,i]=0}
+ }
+}
+return(output)
+#End simulation function
+}
+
+###########Run Simulation#########################
+
+#Generate vector of abundance values
+nsites = 150
+lambda = 10
+Nvec= rpois(n=nsites,lambda=lambda)
+
+#Run simulation for range of p-values
+results7 = fitsim(10,Nvec,p=0.7,30000,15000,30)
+results5 = fitsim(10,Nvec,p=0.5,30000,15000,30)
+results3 = fitsim(10,Nvec,p=0.3,30000,15000,30)
+results2 = fitsim(10,Nvec,p=0.2,30000,15000,30)
+
+#End simulation \ No newline at end of file
diff --git a/appendices/chapter4code.R b/appendices/chapter4code.R
new file mode 100644
index 0000000..5a0ba8c
--- /dev/null
+++ b/appendices/chapter4code.R
@@ -0,0 +1,208 @@
+model {
+
+##Intercept Priors
+psi.mean ~ dunif(0,1)
+beta <- log(psi.mean) - log(1-psi.mean)
+sigma.u ~ dunif(0,7)
+tau.u <- pow(sigma.u,-2)
+
+p.mean ~ dunif(0,1)
+alpha <- log(p.mean) - log(1-p.mean)
+sigma.p ~ dunif(0,7)
+tau.p <- pow(sigma.p, -2)
+
+r.mean ~ dunif(0,1)
+mu.rho <- log(r.mean) - log(1-r.mean)
+sigma.rho ~ dunif(0,7)
+tau.rho <- pow(sigma.rho, -2)
+
+#Detection probability for initializing occupancy
+p0 ~ dunif(0,1)
+s.site ~ dunif(0,10)
+tau.site <- pow(s.site,-2)
+
+##Detection Covariate Priors
+b.temp~dunif(0,1)
+mu.temp <- log(b.temp/(1-b.temp))
+s.temp~dunif(0,7)
+tau.temp <- pow(s.temp,-2)
+
+b.jd~dunif(0,1)
+mu.jd <- log(b.jd/(1-b.jd))
+s.jd~dunif(0,10)
+tau.jd <- pow(s.jd,-2)
+
+b.precip~dunif(0,1)
+mu.precip <- log(b.precip/(1-b.precip))
+s.precip~dunif(0,7)
+tau.precip <- pow(s.precip,-2)
+
+b.eff~dunif(0,1)
+mu.eff <- log(b.eff/(1-b.eff))
+s.eff~dunif(0,7)
+tau.eff <- pow(s.eff,-2)
+
+##Site-level Occupancy Covariate Priors
+b.aspect~dunif(0,1)
+mu.aspect <- log(b.aspect/(1-b.aspect))
+s.aspect~dunif(0,7)
+tau.aspect <- pow(s.aspect,-2)
+
+b.5~dunif(0,1)
+mu.5 <- log(b.5/(1-b.5))
+s.5~dunif(0,7)
+tau.5 <- pow(s.5,-2)
+
+b.10~dunif(0,1)
+mu.10 <- log(b.10/(1-b.10))
+s.10~dunif(0,7)
+tau.10 <- pow(s.10,-2)
+
+#Trap-level occupancy covariates
+b.cent~dunif(0,1)
+mu.cent <- log(b.cent/(1-b.cent))
+s.cent~dunif(0,7)
+tau.cent <- pow(s.cent,-2)
+
+b.edge~dunif(0,1)
+mu.edge <- log(b.edge/(1-b.edge))
+s.edge~dunif(0,7)
+tau.edge <- pow(s.edge,-2)
+
+b.herb~dunif(0,1)
+mu.herb <- log(b.herb/(1-b.herb))
+s.herb~dunif(0,7)
+tau.herb <- pow(s.herb,-2)
+
+b.wood~dunif(0,1)
+mu.wood <- log(b.wood/(1-b.wood))
+s.wood~dunif(0,7)
+tau.wood <- pow(s.wood,-2)
+
+b.cwd~dunif(0,1)
+mu.cwd <- log(b.cwd/(1-b.cwd))
+s.cwd~dunif(0,7)
+tau.cwd <- pow(s.cwd,-2)
+
+b.lit~dunif(0,1)
+mu.litter <- log(b.lit/(1-b.lit))
+s.lit~dunif(0,7)
+tau.litter <- pow(s.lit,-2)
+
+#Begin Model Specification
+
+#Observation Submodel
+for (i in 1:nspecies) {
+ #Generate species random slopes and intercepts
+ eta[i] ~ dnorm(alpha, tau.p)I(-5,5)
+ beta.temp[i] ~ dnorm(mu.temp,tau.temp)I(-5,5)
+ beta.jd[i] ~ dnorm(mu.jd,tau.jd)I(-5,5)
+ beta.precip[i] ~ dnorm(mu.precip,tau.precip)I(-5,5)
+ beta.eff[i] ~ dnorm(mu.eff,tau.eff)I(-5,5)
+ #Likelihood
+ for (j in 1:nsites) {
+ #Year one (all sites)
+ for (t in 1:1) {
+ for (k in 1:ntraplocs[i]) {
+ #Logistic regression on probability of detection
+ logit(p[tr[i,k],i,j,t]) <- eta[i] + beta.temp[i]*temp[j,t] + beta.jd[i]*jd[j,t]
+ + beta.precip[i]*precip[j,t] + beta.eff[i]*eff[tr[i,k],i,j,t]
+ #1-product of all trap-level detection probabilities
+ #Yields probability of detecting at least 1 individual at a site at a given time
+ plog[tr[i,k],i,j,t] <- log(1-p[tr[i,k],i,j,t])
+ }}
+ #Years 2-3 (varied numbers of sites)
+ for (t in first[j]:3) {
+ for (k in 1:ntraplocs[i]) {
+ logit(p[tr[i,k],i,j,t]) <- eta[i] + beta.temp[i]*temp[j,t] + beta.jd[i]*jd[j,t]
+ + beta.precip[i]*precip[j,t] + beta.eff[i]*eff[tr[i,k],i,j,t]
+ plog[tr[i,k],i,j,t] <- log(1-p[tr[i,k],i,j,t])
+ }}
+}}
+
+#Random site effect
+for (j in 1:nsites){
+ eps[j] ~ dnorm(0,tau.site)I(-5,5)
+}
+
+#Process Submodel: Occupancy in first year sampled
+for (i in 1:nspecies) {
+ #Species random slopes and intercepts
+ phi[i] ~ dnorm(beta, tau.u)I(-5,5)
+ rho[i] ~ dnorm(mu.rho, tau.rho)I(-5,5)
+ beta.aspect[i] ~ dnorm(mu.aspect,tau.aspect)I(-5,5)
+ beta.5[i] ~ dnorm(mu.5,tau.5)I(-5,5)
+ beta.10[i] ~ dnorm(mu.10,tau.10)I(-5,5)
+ beta.cent[i] ~ dnorm(mu.cent,tau.cent)I(-5,5)
+ beta.edge[i] ~ dnorm(mu.edge,tau.edge)I(-5,5)
+ beta.herb[i] ~ dnorm(mu.herb,tau.herb)I(-5,5)
+ beta.wood[i] ~ dnorm(mu.wood,tau.wood)I(-5,5)
+ beta.cwd[i] ~ dnorm(mu.cwd,tau.cwd)I(-5,5)
+ beta.litter[i] ~ dnorm(mu.litter,tau.litter)I(-5,5)
+ #First year submodel
+ for (j in 1:nsites) {
+ #Generate site effects with site-level covariates
+ site.effect[i,j] <- phi[i] + beta.aspect[i]*aspect[j] + eps[j]
+ for (k in 1:ntraplocs[i]) {
+ #Trap-level
+ for (t in 1:1) {
+ #Initialize occupancy at time = 0
+ z0[tr[i,k],i,j] ~ dbern(p0)
+ #Logistic regression on probability of occupancy
+ #Rho represents temporal autocorrelation covariate
+ logit(psi[tr[i,k],i,j,t]) <- site.effect[i,j] + rho[i]*z0[tr[i,k],i,j]
+ + beta.cent[i]*pos[k,1] + beta.edge[i]*pos[k,2]
+ + beta.herb[i]*herb[k,j,t] + beta.wood[i]*wood[k,j,t]
+ + beta.5[i]*size[j,3]*pos[k,4] + beta.10[i]*size[j,4]*pos[k,4]
+ + beta.cwd[i]*cwd[k,j,t] + beta.litter[i]*litter[k,j,t]
+ #Link to actual observed dataset
+ Z[tr[i,k],i,j,t] ~ dbern(psi[tr[i,k],i,j,t])
+ mu.p[tr[i,k],i,j,t] <- p[tr[i,k],i,j,t]*Z[tr[i,k],i,j,t]
+ occ[tr[i,k],i,j,t] ~ dbin(mu.p[tr[i,k],i,j,t], noccas[i])
+ }}}
+
+ #Years 2-3 (for sites sampled both years)
+ for (j in 1:8) {
+ for (k in 1:ntraplocs[i]) {
+ for (t in 2:3) {
+ logit(psi[tr[i,k],i,y4[j],t]) <- site.effect[i,y4[j]]
+ + rho[i]*Z[tr[i,k],i,y4[j],t-1] + beta.cent[i]*pos[k,1]
+ + beta.edge[i]*pos[k,2] + beta.herb[i]*herb[k,y4[j],t]
+ + beta.wood[i]*wood[k,y4[j],t] + beta.cwd[i]*cwd[k,y4[j],t]
+ + beta.litter[i]*litter[k,y4[j],t]+ beta.5[i]*size[j,3]*pos[k,4]
+ + beta.10[i]*size[j,4]*pos[k,4]
+ Z[tr[i,k],i,y4[j],t] ~ dbern(psi[tr[i,k],i,y4[j],t])
+ mu.p[tr[i,k],i,y4[j],t] <- p[tr[i,k],i,y4[j],t]*Z[tr[i,k],i,y4[j],t]
+ occ[tr[i,k],i,y4[j],t] ~ dbin(mu.p[tr[i,k],i,y4[j],t], noccas[i])
+ }}}
+
+ #Year 3 (for sites sampled only in year 3)
+ for (j in 1:10) {
+ for (k in 1:ntraplocs[i]) {
+ for (t in 3:3) {
+ logit(psi[tr[i,k],i,y5[j],t]) <- site.effect[i,y5[j]] + beta.cent[i]*pos[k,1]
+ + beta.edge[i]*pos[k,2] + beta.herb[i]*herb[k,y5[j],t]
+ + beta.wood[i]*wood[k,y5[j],t] + beta.cwd[i]*cwd[k,y5[j],t]
+ + beta.litter[i]*litter[k,y5[j],t]+ beta.5[i]*size[j,3]*pos[k,4]
+ + beta.10[i]*size[j,4]*pos[k,4]
+ Z[tr[i,k],i,y5[j],t] ~ dbern(psi[tr[i,k],i,y5[j],t])
+ mu.p[tr[i,k],i,y5[j],t] <- p[tr[i,k],i,y5[j],t]*Z[tr[i,k],i,y5[j],t]
+ occ[tr[i,k],i,y5[j],t] ~ dbin(mu.p[tr[i,k],i,y5[j],t], noccas[i])
+ }}}
+}
+
+#Derived Parameters
+
+#Example code for generating site-level p and occupancy from trap-level parameters
+for (i in 1:4) {
+ for (j in 1:nsites) {
+ for (t in 1:1) {
+ #Site-level probability of detection (1-(1-p)^N)
+ psite[i,j,t] <- 1-exp(sum(plog[1:27,i,j,t]))
+ #Site-level occupancy
+ #See WinBUGS manual for step function: takes on value of either 0 or 1
+ Zsite[i,j,t] <- step(sum(Z[1:27,i,j,t])-1)
+ }
+ }
+#End model specification
+} \ No newline at end of file
diff --git a/chapter2.tex b/chapter2.tex
new file mode 100644
index 0000000..fd32006
--- /dev/null
+++ b/chapter2.tex
@@ -0,0 +1,256 @@
+\chapter{{OAK MAST PRODUCTION AND ANIMAL IMPACTS ON ACORN LOSS IN THE CENTRAL HARDWOODS}}
+
+\section{Introduction}
+
+Oak (\textit{Quercus}) is a dominant overstory species group in the Central Hardwoods region. Oaks have been labeled as both a 'keystone' and 'foundation' species in eastern deciduous forests (Fralish 2004, Ellison et al. 2005), performing a wide variety of functions in forest ecosystems. For example, oak-dominated forests promote biodiversity in the herbaceous understory because oak branch structure allows a large amount of sunlight to reach the forest floor (Horn 1971, Fralish 2004). Oaks also provide habitat for many species of insects, fungi, and vertebrates and play an important role in hydrology and nutrient cycling (Brandle and Brandle 2001, Johnson et al. 2002). Among the most important functions of oak in the Central Hardwoods is the production of hard mast (i.e., acorns), an important food source for at least 44 species of birds, small mammals, and larger vertebrates such as white-tailed deer (\textit{Odocoileus virginianus}) and black bear (\textit{Ursus americanus}; McShea et al. 2007). The importance of oak as a source of hard mast has greatly increased in the past century due to the decline of American chestnut (\textit{Castanea dentata}; Diamond et al. 2000, McShea et al. 2007, Dalgleish and Swihart 2011).
+
+Oaks generally are shade-intolerant species, requiring disturbance to regenerate effectively (Larsen and Johnson 1998). An active cycle of natural and/or anthropogenic fire disturbance is thought to have promoted oak dominance in the Central Hardwoods before European settlement (Abrams 1992). Following settlement, cycles of land clearing for agriculture and subsequent abandonment maintained oak presence in the overstory (Fralish 1997). However, the advent of fire suppression and the creation of protected national and state parks in the 20th century have greatly reduced the frequency of disturbance in eastern forests (Abrams 1992, 2003). The result has been regeneration failure of oaks across the Central Hardwoods region, as well as shifts in species assemblages within the oak genus (Abrams 2003, Aldrich et al. 2005). This altered disturbance regime favors the establishment of shade-tolerant climax species such as maple (\textit{Acer}) and American beech (\textit{Fagus grandifolia}; Fralish 2003).
+
+Loss of oak as a canopy dominant would have important ecological and economic impacts (Johnson et al. 2002, McShea 2007). As a result, researchers have begun to develop forest management (i.e., timber harvest) strategies to promote oak regeneration (Loftis 1990, Dey and Parker 1996, Dey et al. 2008). Examples of such management strategies include even-aged methods (clearcuts and shelterwood harvests) and uneven-aged methods (patch cuts, group selection, and single-tree selection harvests). Management efforts have met with mixed success probably because ecological variables such as soil quality and moisture likely play a role in the outcome (Dey et al. 2009). Oak regeneration success is also heavily influenced by animals, including acorn weevils, small mammal seed predators, and deer (Marquis et al. 1976).
+
+Weevils (genera \textit{Curculio} and \textit{Conotrachelus}) infest the acorns of all oak species in the Central Hardwoods. In a given year, as many as 50-90\% of all acorns produced may be infested (Marquis et al. 1976, Gribko 1995, Riccardi et al. 2004, Lombardo and McCarthy 2008). Infested acorns are both less likely to be dispersed (Steele et al. 1996) and less likely to germinate successfully (Andersson 1992, Lombardo and McCarthy 2009). Infested acorns that do germinate are likely to yield a less vigorous seedling (Gribko 1995). The interaction of oaks with small mammal seed predators is more complex. A large percentage of fallen acorns are eventually removed by seed predators such as white-tailed deer, gray squirrels (\textit{Sciurus carolinensis}), eastern chipmunks (\textit{Tamius striatus}) and mice (\textit{Peromyscus}; McShea 2000, McShea et al. 2007). However, some seed predators (e.g. \textit{S. carolinensis}) make many small caches of acorns that may promote germination success (Barnett 1977, Smallwood et al. 2001, Steele et al. 2006).
+
+Regardless of the influence of animals on oak regeneration success, little is known about the impact of timber harvest strategies on oak mast production and subsequent predation by insects and small mammals. The Hardwood Ecosystem Experiment, a long-term replicated study of forest ecosystem responses to timber harvest, provides an excellent experimental framework to address this knowledge gap. Our objectives in this study were to compare mast production by black (\textit{Q. velutina}) and white (\textit{Q. alba}) oaks at several sites in southern Indiana over a 3-year period, and subsequently to assess the impacts of acorn weevils and seed predators on the acorn crop. Following the final year of this preliminary study, the experimental sites were harvested under several different management strategies with the goal of improving oak regeneration. Ultimately, our results will serve as a baseline for identifying changes in mast production and seed predation by weevils and small mammals following the timber harvests.
+
+Prior to the application of harvest treatments, we tested several hypotheses. We expected that acorn production would vary among the 3 years of the study, and that production would be different between the two oak species. Oaks are a masting species group, synchronizing with other trees in a region to produce very large or very small mast crops (Janzen 1971), but often lacking interspecific synchrony (Abrahamson and Layne 2003, Lusk et al. 2007, Lombardo and McCarthy 2008). We also expected that black and white oaks may be affected differently by acorn weevils; Lombardo and McCarthy (2008) found that a higher proportion of acorns in the red oak section (Lobatae) were infested than those in the white oak section (Quercus), but there are few studies that have compared infestation levels between oak species.
+To address the impacts of predators on the fate of fallen seeds, we sought to isolate the individual contributions of several acorn predators on the total amount of removals observed, to assess if those contributions were additive or compensatory. We expected that small mammals, particularly gray squirrels, would be most prolific at removing fallen acorns (Bellocq et al. 2005). In addition, we expected that in years of high mast availability less desirable acorns (i.e., acorns that were broken, germinated, or infested with weevils) would be less likely to be taken by small mammals. Gray squirrels are more likely to remove and cache acorns that are uninfested and less perishable (i.e., on a longer germination schedule) and selectively consume others (Steele et al. 1996, Smallwood et al. 2001). However, when mast is abundant they may focus on removing intact, sound acorns and ignore less desirable seeds.
+
+\section{Study Area and Design}
+
+This study was conducted as part of the Hardwood Ecosystem Experiment, located in Morgan-Monroe and Yellowwood State Forests and Brown County State Park in south-central Indiana, USA. Nine research units were selected in 2006 and divided among three treatments: even-aged management, uneven-aged management, and control (unmanaged). Harvests were scheduled for Fall 2008. Within each even-aged unit, two stand-level silvicultural treatments were planned: clearcuts and shelterwood harvests. Similarly, within uneven-aged units, single-tree selection and a series of patch cuts were planned. A more detailed summary of site characteristics and the selection of research areas can be found in Kalb and Mycroft (2012).
+
+Following the delineation of the research units, we selected mature black and white oak trees to be included in the study. We chose this pair of oak species because they are among the most dominant tree species in the region (Jenkins and Parker 1998), and to ensure that both oak sections were represented. Individual trees of reproductive age were chosen based on their location relative to future harvests. The number and location of selected trees depended on the assigned management treatment of the unit. In each of the three control units, six trees of each species were selected, with two near the center of the unit and four > 100 m away near the unit edge. In the three units assigned uneven-aged management, six trees of each species were selected, with all trees adjacent to (but outside) the proposed 1, 3, and 5 acre timber harvest areas. In the even-aged units, the arrangement of trees differed between harvest types. Each even-aged unit contained four trees of each species associated with shelterwood harvests; half were adjacent to the future shelterwoods and half were selected as overstory trees within the shelterwoods to be retained during the harvest. Each even-aged unit also contained two trees of each species located adjacent to future clearcut areas.
+
+Across all experimental units, there were 108 trees sampled in 2006, divided evenly among the three harvest treatments. In 2007, an additional experimental unit (for a total of 10) was added in Brown County State Park; four additional control trees were selected in this unit for a total of 112 trees sampled. Due to active timber harvesting in uneven and even-aged units in the fall of 2008, 20 trees could not be sampled, yielding a sample size of 96 in the third year of the experiment. Overall, black and white oaks had diameters at breast height (d.b.h.) of 20.0 $\pm$ 4.4 cm (mean $\pm$ standard deviation) and 19.5 $\pm$ 4.5 cm, respectively.
+
+\section{Materials and Methods}
+\subsection{Mast Production}
+
+At each tree, two mast collection traps were established. Traps consisted of a 52 x 33 x 32 cm plastic bin mounted atop a 2 m high PVC pipe driven into the ground. Bins were covered with chicken wire to prevent pilferage of seeds by animals while still allowing mast to fall into the trap. Traps were placed midway between the trunk and canopy edge underneath a limb. For trees adjacent to proposed harvest areas, one mast trap was placed on the side of the tree facing the harvest and one on the opposite side. For all other trees, one mast trap was oriented to the north of the trunk and the other to the south.
+
+In the first year of the study (2006), traps were set up in October and checked five times at weekly intervals until the beginning of December. In 2007 and 2008, traps were set up in late August and checked eight times at 1-2 week intervals until December. Some traps were not sampled in uneven-aged units in 2008 due to ongoing timber harvests in the area. At each trap check, all acorns in the collection buckets were counted, identified to species, and removed.
+
+\subsection{Weevil Infestation}
+
+The majority of acorns removed from mast collection bins were examined for weevil infestation. In 2007 and 2008, a small number of additional acorns were removed from the forest floor underneath the mast trees to increase the sample size. Acorns were X-rayed to identify damage caused by weevils (Dixon et al. 1997, Lombardo and McCarthy 2009). Acorns were marked according to source tree and collection date and x-rayed in groups of 50 using a Specimen Radiography X-ray System (Faxitron X-ray Corporation, Lincolnshire, IL). Weevil damage is indicated on the X-ray film by characteristic dark patterns within the acorn (Figure 2.1). We classified an acorn as 'infested' if any portion of the acorn interior exhibited weevil damage.
+\begin{figure}[t]
+\centering
+\includegraphics{figures/Ch2_Fig1_final}
+\caption{X-ray of two black oak acorns; the acorn on the left is infested with weevil larvae and the acorn on the right is sound.}
+%\label{Figure 1.}
+\end{figure}
+
+\subsection{Acorn Removal}
+
+A set of four semipermeable exclosures was established underneath each mast tree to assess the impacts of several groups of acorn predators. In the study forests, acorn predators include white-tailed deer, gray squirrels (and a few fox squirrels, \textit{S. niger}), eastern chipmunks, and white-footed mice. Each exclosure was a 0.75 x 0.75 m square. The first exclosure allowed access to all wildlife species (deer, squirrels, chipmunks and mice, denoted A) and simply consisted of four wooden stakes designating a 0.75 x 0.75 m area on the ground. The second excluded deer only (allowing access to squirrels and smaller animals, denoted S) and consisted of four wooden stakes covered on the sides and top by 3.8 cm wide hexagonally meshed chicken wire. The wire began 15 cm above the ground to allow access to squirrels and smaller wildlife. The third exclosure (M) excluded squirrels and larger wildlife but allowed access to mice and chipmunks and was a 0.75 x 0.75 x 0.2 m wooden frame covered in 3.8 cm chicken wire. The final control exclosure (N) was designed to prevent access by all vertebrate wildlife and was a wooden frame of the same size but covered instead with 0.6 cm mesh hardware cloth.
+
+The four exclosures were arranged randomly on a north-to-south transect bisecting the trunk of the mast study tree. Two exclosures were placed to the north of the tree and two to the south. On each sampling occasion, acorns that had fallen into each exclosure were located and numbered with a black marker. Since it was impossible for acorns to fall naturally into the control exclosures, the (N) exclosures were provisioned with a number of acorns equal to the average number inside the other three. On subsequent visits, the presence or absence of the marked acorns was determined. If an acorn was present, information about the acorn including the presence of weevil exit holes, germination status, and integrity of the acorn (broken or intact) was recorded.
+
+\subsection{Analysis}
+
+\subsubsection{Mast Production}
+
+The total number (count) of acorns collected at a given tree during a given sampling occasion was the response variable in this analysis. Overdispersion of this count data, with a variance ~5 times greater than the mean value, prevented analysis with simple Poisson regression. Instead, we fit a Poisson-lognormal model (K\'{e}ry 2010) which introduced an additional free parameter to allow the variance of the distribution to differ from the mean. Briefly, we modeled $C_{ijk}$, the observed acorn count at tree \textit{i} during sampling period
+\textit{j} of year \textit{k} as $C_{ijk} \sim \operatorname{Poisson} \left({\lambda_{ijk}}\right)$. The linear predictor for the Poisson intensity parameter $\lambda$ was
+\begin{equation}
+\lambda_{ijk} = \operatorname{Exp} \left({\beta_qX_{q,ijk} + \epsilon_{ijk}}\right)
+\end{equation}
+where $\beta$ is a vector of slope parameters, $X$ is a matrix of \textit{q} covariate values and $\epsilon$ is distributed $\operatorname{N} \left({0, \sigma^2}\right)$. We included covariates for tree species, d.b.h., the length and date of sampling periods (in Julian day format), and proposed treatment in the linear predictor. In addition to the random error $\epsilon$, we considered tree and year to be random effects.
+
+\subsubsection{Weevil Infestation}
+
+We estimated the effects of acorn-specific covariates on the infestation status $z_{i}$ of individual collected acorns. Define
+\begin{equation}
+\operatorname{logit} \left({x}\right) = \operatorname{ln} \left({x \over 1-x}\right)
+\end{equation}
+Infestation $z_{i}$ was modeled as
+\begin{equation}
+z_{i} | p_{i} \sim \operatorname{Bernoulli} \left({p_{i}}\right)
+\end{equation}
+where $\operatorname{logit} \left({p_{i}}\right)$ is a linear function of covariates. The linear predictor included year, Julian day of sampling, Julian day centered squared, proposed management for the source site of acorn \textit{i}, and species of acorn \textit{i} as fixed effects, and source tree as a random effect.
+
+\subsubsection{Acorn Removal}
+
+We related site- and acorn-specific covariates to the probability $\phi_{ijk}$ that acorn \textit{i} would be removed between sampling occasions \textit{j}-1 and \textit{j} in year \textit{k}. The presence $z_{ijk}$ of a given acorn \textit{i} in an exclosure at sampling occasion \textit{j} in year \textit{k} took on a value of 0 or 1 and was modeled conditional on the acorn's state at the previous sampling occasion $z_i,j-1,k$ and the probability of removal $\phi_ijk$; that is,
+\begin{equation}
+z_{ijk} | z_{i,j-1,k}, \phi_{ijk} \sim \operatorname{Bernoulli} \left({\phi_{ijk}}\right)
+\end{equation}
+The function $\operatorname{logit} \left({\phi_{ijk}}\right)$ was set equal to a linear predictor containing acorn- and site-specific covariates. Explanatory variables considered in this analysis were the fixed effects of Julian day of sampling for occasion \textit{j}, Julian day of sampling centered squared, exclosure type for acorn \textit{i}, proposed management treatment for acorn \textit{i}, and the germination, weevil infestation, and shell status (i.e., broken or intact) of acorn \textit{i} at time \textit{j}-1. Length of time between sampling periods \textit{j}-1 and \textit{j} was considered as a nuisance variable, and source tree and year were included as random effects.
+
+\subsubsection{Model Fitting}
+
+All regression models were fit in a Bayesian framework with non-informative priors. Slope parameters were assigned a normal prior distribution with mean 0 and variance 1000, and variance parameters were assigned a log normal distribution with mean 0 and variance 1 following K\'{e}ry (2010). All covariates were standardized by subtracting the mean and dividing by the standard deviation. Bayesian analyses were conducted using the software package WinBUGS (Spiegelhalter et al. 2003, Cambridge, UK; version 1.4.3) which uses the Markov Chain Monte Carlo (MCMC) method to generate posterior distributions for the model parameters. WinBUGS was called from within R 2.10.0 (R Foundation for Statistical Computing, Vienna, Austria) using the R2WinBUGS library (Sturtz et al. 2005).
+
+MCMC simulations were conducted with a minimum of 3 chains, 30,000 iterations, and a burn-in of 10,000 iterations. Convergence was assessed by calculating the Gelman-Rubin diagnostic \^{R} (Brooks and Gelman 1998) for each estimate parameter. If we did not observe convergence in the distributions of all posterior parameters (\^{R} \textgreater 1.1; Brooks and Gelman 1998), MCMC chain length and burn-in were increased until convergence was achieved. Posterior predictive checks were performed on each regression model to assess goodness of fit; for the mast production model Pearson's chi-square residuals were compared between the actual data and data simulated based on the model, and for the other two analyses the sums of absolute residuals were compared. Bayesian p-values were calculated for each posterior predictive check. An individual covariate was considered to have an important effect if the 95\% credible interval of its slope parameter did not contain 0.
+
+\section{Results}
+
+\subsection{Mast Production}
+
+We collected a total of 1140 acorns over the 3 years of the experiment (Table 2.1). The log-normal Poisson model had acceptable fit; a posterior predictive check yielded a Bayesian p-value of 0.41 (0.5 is ideal). In general, white oaks produced fewer acorns than black oaks in our study area (Figure 2.2), except in 2008, when there was a mast failure in black oak. The 95\% credible interval for the species coefficient in the Poisson regression included 0 (Table 2.2). However, 74\% of the posterior distribution of the species coefficient was below 0, providing marginal support for lower production by white oak. Production by individual trees was positively correlated with d.b.h.; larger trees produced more acorns. Counts were negatively correlated with both Julian day of sampling and Julian day squared, indicating that production was highest early in the sampling period (September-October), declining later in the season. Proposed harvest treatment had no effect on acorn production among the trees we sampled.
+
+% Table generated by Excel2LaTeX from sheet 'Sheet1'
+\begin{table}[htbp]
+ \centering
+ \caption{Number of trees sampled and acorn sample sizes for the mast production, weevil infestation, and mast removal experiments in 2006-2008.}
+ \begin{tabular}{rcccc}
+ \toprule
+ \multicolumn{1}{l}{\textbf{Parameter}} & \textbf{2006} & \textbf{2007} & \textbf{2008} & \textbf{Total} \\
+ \midrule
+ \multicolumn{1}{l}{Trees sampled} & 108 & 112 & 96 & \\
+ \multicolumn{5}{c}{\textbf{Collected in traps}} \\
+ \multicolumn{1}{l}{Black oak} & 235 & 414 & 9 & 658 \\
+ \multicolumn{1}{l}{White oak} & 120 & 256 & 106 & 482 \\
+ \multicolumn{1}{l}{Total} & 355 & 670 & 115 & 1140 \\
+ \multicolumn{5}{c}{\textbf{X-rayed for weevils}} \\
+ \multicolumn{1}{l}{Black oak} & 265 & 423 & 8 & 696 \\
+ \multicolumn{1}{l}{White oak} & 101 & 172 & 106 & 379 \\
+ \multicolumn{1}{l}{Total} & 366 & 595 & 114 & 1075 \\
+ \multicolumn{5}{c}{\textbf{Monitored for removal}} \\
+ \multicolumn{1}{l}{Black oak} & 468 & 1013 & 30 & 1511 \\
+ \multicolumn{1}{l}{White oak} & 573 & 814 & 210 & 1597 \\
+ \multicolumn{1}{l}{Total} & 1041 & 1827 & 240 & 3108 \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch2_Fig2_final}
+\caption{Total acorns collected by species and year. Since sampling began in October in 2006, Only acorns collected in the months of October through December are included to allow direct comparisons between years.}
+%\label{fig:}
+\end{figure}
+
+% Table generated by Excel2LaTeX from sheet 'Sheet2'
+\begin{table}[htbp]
+ \centering
+ \caption{Estimated parameter values for the Poisson-lognormal model of oak mast production. The value \textit{f} represents the proportion of the posterior distribution for the parameter which has the same sign (positive or negative) as the mean. Bolded parameters have 95\% credible intervals which do not include 0.}
+ \begin{tabular}{lccc}
+ \toprule
+ \textbf{Parameter} & \textbf{Mean} & \textbf{SE} & \textit{\textbf{f}} \\
+ \midrule
+ Intercept & -3.21 & 0.98 & 1.00 \\
+ Tree species (WO=1) & -0.17 & 0.26 & 0.74 \\
+ d.b.h. & \textbf{0.33} & 0.13 & 0.99 \\
+ Sample day (Julian) & \textbf{1.06} & 0.12 & 1.00 \\
+ Julian day squared & \textbf{1.75} & 0.13 & 1.00 \\
+ Even-aged treatment & -0.46 & 0.31 & 0.93 \\
+ Uneven-aged treatment & 0.12 & 0.32 & 0.64 \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+\subsection{Weevil Infestation}
+
+A total of 1075 acorns were X-rayed to identify weevil infestation (Table 2.1). The number of x-rayed acorns does not correspond exactly to the number removed from buckets because small numbers of acorns were lost following collection and/or added from the ground beneath the bucket. A posterior predictive check of the logistic regression model fit to the data yielded an acceptable Bayesian p-value of 0.39. Black oak acorns had a higher probability of infestation in all three sample years compared to white oak (Figure 2.3, Table 2.3). Infestation probability increased from 2006 to 2008 (Table 2.3). The high probabilities of infestation in both species in 2008 (Figure 2.3) coincided with low mast production in white oak and a mast failure in black oak in 2008 (Figure 2.2). In addition to the year effects, there was a negative correlation between Julian day of sampling and probability of infestation (Table 2.3); acorns collected later in the fall were less likely to have weevil damage. There were no differences in weevil infestation between areas selected to receive different harvest treatments.
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch2_Fig3_final}
+\caption{Posterior distributions for the mean probabilities of acorn infestation, by acorn species and year. Error bars represent 95\% credible intervals.}
+%\label{fig:}
+\end{figure}
+
+% Table generated by Excel2LaTeX from sheet 'Sheet3'
+\begin{table}[htbp]
+ \centering
+ \caption{Estimated parameter values for the model of probability of acorn infestation. The value \textit{f} represents the proportion of the posterior distribution for the parameter which has the same sign (positive or negative) as the mean. Bolded parameters have 95\% credible intervals which do not include 0.}
+ \begin{tabular}{lccc}
+ \toprule
+ \textbf{Parameter} & \textbf{Mean} & \textbf{SE} & \textbf{\textit{f}} \\
+ \midrule
+ Intercept & -2.02 & 0.26 & 1.00 \\
+ 2007 year effect & \textbf{0.72} & 0.22 & 1.00 \\
+ 2008 year effect & \textbf{1.96} & 0.34 & 1.00 \\
+ Acorn species (WO=1) & \textbf{0.52} & 0.25 & 0.98 \\
+ Sample day (Julian) & \textbf{-0.18} & 0.09 & 0.98 \\
+ Even-aged treatment & -0.08 & 0.27 & 0.62 \\
+ Uneven-aged treatment & -0.07 & 0.26 & 0.62 \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+\subsection{Acorn Removal}
+
+Over the 3-year study period, 3108 acorns were marked and monitored for removal (Table 2.1). Of these, 1200 (39\%) were removed from the exclosures at some point; 595 of 1511 black oak acorns (39\%) and 605 of 1597 white oak acorns (38\%). We used logistic regression to model the probability that an individual acorn was removed during a given time period [\textit{j}-1,\textit{j}]. Our model fit the data adequately, with a posterior predictive check of absolute residuals yielding a Bayesian p-value of 0.38.
+
+There were no differences in the probability of removal between acorn species. However, acorn damage, weevil infestation, and germination were all negatively correlated with the probability of acorn removal (95\% credible intervals excluding 0; Table 2.4). There were no differences in probability of removal between proposed treatment areas.
+
+Acorns in all three exclosures accessible to at least some vertebrates (open, deer, and squirrel exclosures) had a higher probability of removal than acorns in the control, inaccessible exclosure (Table 2.4, Figure 2.4). Differences between the three accessible exclosure types were more difficult to ascertain. The posterior distributions for the exclosure regression coefficients overlapped (Figure 2.5), with the coefficient corresponding to squirrel exclosures having a smaller mean value, corresponding to a lower probability of removal. In 2007 and 2008, the 95\% credible interval for removal probability from squirrel exclosures did not overlap with the open or deer exclosures credible intervals, providing further evidence that excluding access to squirrels reduced the probability of acorn removal (Figure 2.4). In the same years, probability of removal was higher from the exclosures that excluded only deer. In general, probabilities of removal were higher in 2008, corresponding to reduced mast production (Figures 2.2 and 2.4).
+
+% Table generated by Excel2LaTeX from sheet 'Sheet4'
+\begin{table}[htbp]
+ \centering
+ \caption{Estimated parameter values for the model of probability of acorn removal by predators. The value \textit{f} represents the proportion of the posterior distribution for the parameter which has the same sign (positive or negative) as the mean. Bolded parameters have 95\% credible intervals which do not include 0.}
+ \begin{tabular}{lrrr}
+ \toprule
+ \textbf{Parameter} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{SE}} & \multicolumn{1}{c}{\textit{\textbf{f}}} \\
+ \midrule
+ Intercept & \multicolumn{1}{c}{-2.02} & \multicolumn{1}{c}{0.26} & \multicolumn{1}{c}{1.00} \\
+ Sample day (Julian) & \multicolumn{1}{c}{0.05} & \multicolumn{1}{c}{0.06} & \multicolumn{1}{c}{0.82} \\
+ Time between samples & \multicolumn{1}{c}{-0.06} & \multicolumn{1}{c}{0.04} & \multicolumn{1}{c}{0.92} \\
+ Even-aged treatment & \multicolumn{1}{c}{0.12} & \multicolumn{1}{c}{0.26} & \multicolumn{1}{c}{0.67} \\
+ Uneven-aged treatment & \multicolumn{1}{c}{-0.01} & \multicolumn{1}{c}{0.27} & \multicolumn{1}{c}{0.52} \\
+ \textbf{Acorn characteristics} & \textbf{} & \textbf{} & \textbf{} \\
+ Acorn species (WO=1) & \multicolumn{1}{c}{-0.08} & \multicolumn{1}{c}{0.22} & \multicolumn{1}{c}{0.66} \\
+ Integrity (broken=1) & \multicolumn{1}{c}{\textbf{-0.48}} & \multicolumn{1}{c}{0.15} & \multicolumn{1}{c}{1.00} \\
+ Germ. status (germ=1) & \multicolumn{1}{c}{\textbf{-0.49}} & \multicolumn{1}{c}{0.15} & \multicolumn{1}{c}{1.00} \\
+ Weevil status (infested =1) & \multicolumn{1}{c}{\textbf{-0.78}} & \multicolumn{1}{c}{0.21} & \multicolumn{1}{c}{1.00} \\
+ \multicolumn{4}{l}{\textbf{Exclosure-type parameters (relative to exclosure N)}} \\
+ Mouse \& Smaller Access (M) & \multicolumn{1}{c}{\textbf{3.39}} & \multicolumn{1}{c}{0.24} & \multicolumn{1}{c}{1.00} \\
+ Squirrel \& smaller access (S) & \multicolumn{1}{c}{\textbf{4.09}} & \multicolumn{1}{c}{0.24} & \multicolumn{1}{c}{1.00} \\
+ All species access (A) & \multicolumn{1}{c}{\textbf{3.91}} & \multicolumn{1}{c}{0.24} & \multicolumn{1}{c}{1.00} \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch2_Fig4_final}
+\caption{Mean probabilities of removal from each exclosure type (for an individual sampling period) during the 3 years of the study. Exclosure A was open to all animals, S excluded deer, M excluded everything squirrel-sized and larger, and N (the control) excluded all vertebrates. Error bars represent 95\% credible intervals.}
+%\label{fig:}
+\end{figure}
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch2_Fig5_final}
+\caption{Comparison of posterior distributions for the three exclosure-type parameters. The three parameter values are relative to removal from a control, i.e., the completely inaccessible exclosure. Significant overlap between the open exclosure and the exclosure denying access to deer indicate deer had a minimal effect on removal probability. When squirrels were excluded, removal probability was slightly reduced.}
+%\label{fig:}
+\end{figure}
+
+\section{Discussion}
+
+\subsection{Mast Production}
+
+This 3-year study included 2 years of abundant mast (2006-2007) and 1 year of mast failure in black oak and partial mast failure in white oak (2008; Figure 2.2). More black oak acorns were collected per sampling occasion (traps ? sample periods) in the non-failure years, indicating that individual black oaks in our study region may be more valuable than white oaks for wildlife food production. This is consistent with other studies that found black oak to generally be a larger producer of acorns than chestnut oak and white oak (Sork and Bramble 1993, Sork et al. 1993, Lombardo and McCarthy 2008). Overall, however, our model did not identify a species effect that was statistically different from 0 (Table 2.2). In the short duration of our study, production by black oak appeared to be more variable than white oak (Figure 2.2), though the literature suggests it should be a relatively consistent producer (Sork and Bramble 1993, Sork et al. 1993, Lombardo and McCarthy 2008). A longer record of production is necessary to accurately compare variability in production of acorns between these species. In addition, since our study did not incorporate population-level information on oaks in the study area, we cannot make conclusions about the overall importance of the two oak species at our sites.
+
+Though white and black oaks are common at our study sites, it is important to note that the total amount of mast available also depends on other hardwood species (especially chestnut oak, \textit{Q. prinus}, but also Northern red oak, \textit{Q. rubra}, and the hickories,\textit{ Carya} spp.). We did not monitor mast production by these species but since there is often a lack of interspecific synchrony in masting (Abrahamson and Layne 2003, Lusk et al. 2007, Lombardo and McCarthy 2008), they may buffer the total mast crop available for wildlife in the study area.
+
+\subsection{Weevil Infestation}
+
+We found higher rates of weevil infestation in black oak than in white oak in each year of the study (Figure 2.3), and overall there was a significant species effect for white oak (Table 2.3)). Our results are consistent with the findings of Lombardo and McCarthy (2008) who found higher rates of infestation in black oak than in chestnut oak (which belongs to the white oak section) in southeastern Ohio. Black oak may be a more consistent mast producer than chestnut oak (Lombardo and McCarthy 2008) and white oak (Sork and Bramble 1993, Sork et al. 1993) which may allow the black oak population to support a larger number of weevils. A limitation of this study is that weevils were not identified to the species level, and different members of the genus \textit{Curculio} appear to specialize on certain oak species (Gibson 1972 Gibson 1982). It is possible that the particular assemblage of individual Curculio species at our study sites is responsible for higher infestation in black oak. Since nearly all acorns were collected from raised buckets, we do not believe many of the acorns were infested with weevils in the genus \textit{Conotrachelus}, which generally lays eggs in previously infested or broken acorns that have already fallen to the ground (Gibson 1964).
+
+While there were year effects for both 2007 and 2008 (Table 2.3), infestation rates were highest for both species in 2008 (Figure 2.3), corresponding to a mast failure in black oak and a reduced mast year in white oak (Figure 2.2) across the study sites. The previous two years were good mast years for both white and black oak (Figure 2.2), likely resulting in a large population of \textit{Curculio} larvae undergoing diapause in the soil. When this population emerged from the soil in fall 2008, only a few acorns were available resulting in very high rates of infestation. Lombardo and McCarthy (2008) identified a similar pattern in chestnut oak in southeastern Ohio. They found 86\% of chestnut oak acorns were infested by weevils in a non-mast year but only 26\% were infested in a mast year; unlike our study, however, they found black oak to have relatively consistent weevil predation regardless of the quantity of mast produced. The mean probabilities of black oak acorn infestation that we observed across the three years of the study (0.12-0.48 for black oak, and 0.08-0.38 for white oak) were generally lower than in similar studies. For example, Lombardo and McCarthy (2008) reported that on average 79\% of black oaks were infested over a 4-year period. Riccardi et al. (2004) found that 55 and 34\% of black oaks were infested, respectively, in the 2 years of their study. The lower numbers we report may be due to differences in the assemblage of acorn weevil species between sites. Differences in host tree and weevil predator densities may also have an impact on weevil populations. For example, Anderson and Folk (1993) found that small mammal predators, especially white-footed mice and short-tailed shrews (\textit{Blarina brevicauda}) reduced overwinter survival of acorn weevils in Indiana.
+
+\subsection{Acorn Removal}
+
+Overall, 39\% of acorns were eventually removed from the exclosures across the 3 years of the study. Seed predators did not seem to preferentially remove either acorn species, but avoided acorns that were damaged, weevil-infested, or germinated (Table 2.4). Previous studies have demonstrated that gray squirrels, the primary dispersal agent in our system, are sensitive to acorn condition and are less likely to cache infested acorns (Steele et al. 1996). In addition, since white oak begins to germinate in the fall, squirrels are less likely to cache them for later use than acorns from the red oak section (Smallwood et al. 2001). The most likely explanation for this behavior is that broken, infested, and germinated acorns are more perishable and therefore less valuable to store for the winter months. Infested or damaged acorns may still be removed and eaten immediately by the predator (Steele et al. 1996), but in years when mast is plentiful (2006-2007 in this study), predators may ignore less desirable acorns that are not intact. In the year of mast failure (2008), removal probability during a given sample period was very high (Figure 2.4), and nearly all fallen acorns were removed by the end of the experiment regardless of their condition. The design of this experiment did not allow us to ascertain the fate of acorns post-removal, so we were unable to identify differences in the ultimate fate of acorns of varying species and conditions. Secondary dispersal affects germination and seedling success of oak, so we cannot equate removal with predation (Vander Wall et al. 2005, Moore and Swihart 2008).
+
+The system of semipermeable exclosures used in this study allowed us to identify the impacts of several groups of acorn predators on overall removal rates. When deer and turkeys were excluded, removal probabilities actually increased in each year (compare exclosures A and S in Figure 2.4), though differences between the two exclosures were not different from 0 (Table 2.4). Posterior distributions for the slope parameters of the deer and open exclosure indicator variables overlapped almost completely (Figure 2.5). Therefore, large animals like deer do not appear to be an important predator of acorns in this system, since squirrels and smaller predators completely compensate for their effect when they are excluded. However, any predation by deer is a dead end for acorns, whereas removal by squirrels and other small mammals could result in either consumption or caching and later germination. Bellocq et al. (2005), Haas and Heske (2005), and Perez-Ramos and Maranon (2008) also observed that removal rates were similar when deer were excluded than when they had access to the acorns, further evidence that deer are not an important acorn predator. Increased rates of removal when deer are excluded appear to be counter-intuitive, since fewer animals in total have access to the acorns. One possible explanation is that the deer exclosure itself provided shelter from airborne predators, increasing the probability that squirrels and mice would forage inside the exclosure.
+
+We did not observe a strong difference in removal probability when squirrels were excluded (Table 2.4), but the trend (Figure 2.4) indicates that removal probability was reduced; the posterior distributions for the slope parameters corresponding to the deer and squirrel exclosures did not overlap greatly (Figure 2.5). Differences in small mammal community composition and demographics (e.g. very high mouse and chipmunk populations) could be the reason we did not observe an additive effect of squirrel predation as strong as the one observed by Bellocq et al. (2005).
+
+\subsection{Treatment Effects and Predictions}
+
+One of the primary objectives of this study was to identify any differences in tree-level mast production and predation between HEE sites prior to applying the timber harvest treatments. In general, we did not observe many differences between trees in areas with different future management treatments. The parameters corresponding to proposed uneven and even-aged treatment effects on probability of weevil infestation and probability of acorn removal were not statistically different from 0 (Tables 2.3 and 2.4). Mast production was similar between trees in future control, uneven, and even-aged management sites (Table 2.2). However, tree-level mast production was slightly lower at future even-aged sites (95\% of the posterior distribution for the even-aged treatment parameter was less than 0). Mast production in oaks can be highly variable from year to year (Sork et al. 1993), so it is possible that observed differences in production between trees are due to the short duration of the study. All sites at HEE are relatively close together so it is unlikely that weather or site quality is responsible for lower production in trees at future even-aged treatment stands. Unfortunately, we are unable to scale our results up from tree-level to site-level comparisons because we did not account for population-level differences in oak between sites in this study. Still, these results provide a basis for attributing future observed differences in tree-level mast production and predation to harvest treatments, rather than to pre-existing site characteristics.
+
+We anticipate that timber harvest with the goal of regenerating oak will increase acorn production. Harvesting at the clearcut and patch cut sites will increase light penetration to oaks on the edge of the cut, which will increase branch density and therefore acorn production (Verme 1953, Johnson 1994). However, these acorns play a minimal role in regeneration inside the clearcut (4.0 ha) and patch cut (0.4, 1.2, and 2.0 acre) harvest sites. In general, the primary dispersal agents in this system (small mammals and the blue jay, \textit{Cyanocitta cristata}) do not move acorns deep into recently harvested areas (Nixon et al. 1980). More importantly, any seedlings that do develop from dispersed acorns following harvest will not be competitive without silvicultural intervention (Sander 1972). The primary source of regeneration in the harvested areas will be seedlings of sufficient size (\textgreater 1.4 m tall) established prior to harvest and stump sprouts (Sander 1972, Sander et al. 1984).
+
+Dominant and codominant oaks are retained in the overstory during the first phase of the shelterwood method for regeneration (Loftis 1990). Removal of the midstory and some competing trees should increase light availability for oaks in the overstory, increasing acorn production (Johnson 1994). Evidence in the literature is inconclusive. Thinning had a positive effect on red oak acorn production in New England (Healy 1997), but had minimal effects on chestnut and black oak production in Ohio (Lombardo and McCarthy 2008). Bellocq et al. (2005) found that production of red oak in Ontario appeared to increase when visual estimates were used, but no differences between treatments were observed in the number of acorns collected in traps. Responses in production to shelterwood harvests may be species-specific (Lombardo and McCarthy 2008); in addition, any increases in production due to increased light may have begun after the time period following harvest covered by these studies.
+
+Currently, little is known about the effects of harvest treatments on vertebrate and invertebrate acorn predators. Thinning and shelterwood harvests do not appear to change weevil infestation rates (Bellocq et al. 2005, Lombardo and McCarthy 2008). Weevil infestation of acorns produced by trees on the edge of clearcuts and patch cuts is also unlikely to be affected (Govindan et al. 2012). Predation by small mammals following harvest likely depends on population levels relative to the amount of mast available. In the shelterwoods, increased available light will result in increases in understory vegetation, which is correlated with higher probabilities of seed removal (Pons and Pausas 2007, Perez-Ramos and Maranon 2008). However, Bellocq et al. (2005) found no differences in rates of acorn removal by predators between shelterwoods and control stands, but there were also no treatment effects on the abundance of small mammals. A limitation of this and previous studies is that the ultimate fate of removed acorns was not determined. Future work should focus on identifying differences in seed fate between treatments, since removal could have either a positive (if the acorn is cached and unrecovered) or negative (if the acorn is eaten) effect on seedling establishment.
+
diff --git a/chapter3.tex b/chapter3.tex
new file mode 100644
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+++ b/chapter3.tex
@@ -0,0 +1,286 @@
+\chapter{{SHORT-TERM POPULATION RESPONSES OF SMALL MAMMALS TO EVEN-AGED AND UNEVEN-AGED SILVICULTURE IN SOUTHERN INDIANA}}
+
+\section{Introduction}
+Identifying the effects of land management on plant and animal communities is an important task for researchers and managers seeking to preserve ecosystem function. Forest management, specifically timber harvesting, is no exception. Following timber harvest, soil and air temperatures increase and moisture is more variable (Zheng et al. 2000), leaf litter is reduced, and understory composition is altered (Ash 1995, Fredericksen et al. 1999). These changes in habitat conditions and structure affect insect communities (Summerville and Crist 2002), breeding bird abundances (Annand and Thompson 1997), amphibians (Grialou et al. 2000), and small mammals (Urban and Swihart 2010).
+Most studies of plant and animal responses to timber harvest occur over a short time period after the harvest and focus on relatively small spatial units (e.g. forest stands) due to cost and logistical constraints (Brudvig 2008). Recently, a small number of long-term, large-scale studies have begun to study the effects of management at the landscape scale. For example, the Missouri Ozark Forest Ecosystem Project (MOFEP) began in 1990 to measure the responses of a wide range of plants and animals to management over a 105 year period (Brookshire and Shifley 1997). The Hardwood Ecosystem Experiment (HEE) was established in 2006 in south-central Indiana with similar goals. Forest management conducted as a part of the HEE is intended to regenerate oak (\textit{Quercus}), which is experiencing regeneration failure across much of the Central Hardwood Forest due in part to changes in disturbance regimes (Abrams 2003). The HEE is a 100-year study of different management practices: even-aged (clearcut and shelterwood harvests), uneven-aged (single-tree selection and patch cuts) and control (no harvest).
+
+The scale and intensity of these harvests differ, as do their potential impact on habitat for wildlife. Clearcutting typically involves removal of all trees of a minimum diameter over a large area (\textgreater 1 cm diameter, 4 ha openings at HEE sites; Kalb and Mycroft 2012). Openings created by patch cutting are similar (all trees above a minimum diameter are removed) but multiple smaller openings ($\le$ 2 ha for the HEE; Kalb and Mycroft 2012) are created instead. Shelterwood harvests differ from openings created by clearcutting and patch cutting in that trees are harvested in stages. Some overstory trees are retained and provide shade, seeds, and structural complexity to the harvested area until they are remove in the final phase of the harvest, 10-15 years after the initial cut (Franklin et al. 1997, Work et al. 2003). Shelterwood and other silvicultural systems in which overstory trees are harvested in stages are increasingly seen as means to promote biodiversity and more accurately simulate natural disturbances (Franklin et al. 1997). The retention of live overstory trees provides habitat patches which are similar to intact forest, potentially acting as a refuge for forest wildlife in the harvested area (Franklin et al. 1997, Zwolak 2009); thus they may have a smaller effect on small mammal populations than other harvest methods.
+
+In conjunction with the harvests, the HEE includes a series of experiments to monitor biotic and abiotic components of the affected forest and responses of plants, insects, songbirds, reptiles, amphibians, and small mammals (Kalb and Mycroft 2012). Small mammals are an important component of food webs in eastern forests as granivores, herbivores, predators and prey (Whitaker and Hamilton 1998). They also function as crucial dispersal agents of seeds and fungi (Maser et al. 1978, Moore et al. 2007), influence transmission of diseases (Ostfeld 1996, Page et al. 2001), and impact the life cycles of pest insects (Anderson and Folk 1993, Elkinton et al. 1996). This study focused on the short-term (?3 years following harvest) response of several common small mammals in the Central Hardwood Forest (eastern chipmunk \textit{Tamius striatus}, white-footed mouse \textit{Peromyscus leocupus}, pine vole \textit{Microtus pinetorum}, and northern short-tailed shrew \textit{Blarina brevicauda}) to management. The literature is inconclusive on the magnitude and direction of short-term responses to timber harvest (Zwolak 2009); in addition, very little information is available on small mammal responses to silvicultural systems like shelterwoods that remove the overstory in stages.
+
+In hardwood forests, both positive (Kirkland 1990, Urban and Swihart 2010) and negative (Kirkland 1977, Schmid-Holmes and Drickamer 2001) responses of eastern chipmunks to clearcuts have been reported. In general, they may respond more positively to smaller openings like the ones created by patch cuts (Kirkland 1990, King et al. 1998); however, Urban and Swihart (2010) found no effect of opening size on chipmunk occupancy.
+
+White-footed mice, considered habitat generalists (Adler and Tamarin 1984) also exhibited a range of responses to clearcutting; positive (Buckner and Shure 1985, Fantz and Renken 2005), neutral (Kaminski et al. 2007, Urban and Swihart 2010), and negative (Schmid-Holmes and Drickamer 2001) effects on mouse abundance have been reported. Further, the response of white-footed mice does not appear to vary with opening size (Buckner and Shure 1985, Schmid-Holmes and Drickamer 2001, Urban and Swihart 2010), so the effects of clearcuts and smaller patch cuts are not likely to be different. There is little information on the response of white-footed mice to shelterwood harvest; however, Von Trebra et al. (1998) reported minimal effects of a shelterwood harvest (treatments of 30 and 50\% basal area removal) on deer mice (P. maniculatus) immediately following the harvest.
+
+The available literature, while limited, indicates short-tailed shrews and pine voles may be more sensitive to harvesting than chipmunks and mice. Short-tailed shrews appear to respond negatively clearcut openings (Kirkland 1977, Fuller et al. 2004), and pine vole occupancy and abundance was redcuced in recently burned (Ford et al. 1999), and harvested stands (Urban and Swihart 2010). There is some evidence that pine voles may be more likely to use small openings on the scale of patch cuts (Johnson et al. 1979), but Urban and Swihart (2010) did not find a relationship between occupancy of either species and opening size. Short-tailed shrews responded positively to partial harvest of 52-59\% of basal area in one study, perhaps due to higher densities of understory vegetation than in mature forest (Fuller et al. 2004), but they exhibited a neutral response in a second study in which roughly 50\% of the canopy was removed (Ford and Rodrigue 2001).
+
+Inconsistencies in small mammal responses among studies may be due to several factors. First, climate and the rate of vegetation recovery following silvicultural treatments differ across regions, possibly leading to spatial variation in short-term responses of small mammals to silviculture (Clayton 2003). A second, related limitation is that while harvests conducted at different sites and as part of different experiments may use similar terminology (e.g. clearcut, shelterwood), this does not imply that the effects of the harvests on habitat for wildlife will be identical; this can lead to significant confusion in the interpretation of results (Nyland and McNulty 2004). Reporting measurements of habitat variables following harvest and providing detailed descriptions of the silviculture applied may reduce this confusion. Even when silvicultural terminology is consistently applied, many studies may not have the statistical power to detect differences following harvest; meta-analysis techniques may be useful in elucidating differences, if they exist, by looking at many studies together (Zwolak 2009, Holloway and Smith 2011).
+
+Third, many studies fail to account explicitly for temporal fluctuations in resource availability independent of a silvicultural treatment. For instance, in the suite of species we consider, populations of white-footed mice and eastern chipmunks are highly correlated with the production of hard mast (Ostfeld et al. 1996, Wolff 1996, McShea 2000). Mast availability may vary greatly between years (Lusk et al. 2007) and cause fluctuations in populations of these species that could obscure effects of silvicultural treatments. Finally, most previous studies have failed to account for imperfect detection of animals, which can lead to substantial bias in estimates of occupancy and abundance and potentially erroneous inferences regarding responses to silvicultural prescriptions (MacKenzie et al. 2006). This issue is discussed in depth elsewhere (Gu and Swihart 2004, Urban and Swihart 2010). In this study, we addressed the latter two limitations by measurement of mast availability at each site in the fall preceding each year of trapping, and by a novel application of an abundance modeling framework that explicitly incorporated imperfect detection.
+
+We developed several predictions for the numeric response of small mammal species to even- and uneven-aged timber harvest, based on a study of small mammal communities in a chronosequence of nearby harvested stands (Urban and Swihart 2010) and a pre-treatment assessment of small mammals at our study sites (Urban and Swihart 2012). We predicted a positive short-term response of eastern chipmunks to clearcuts and patch cuts, like that observed by Urban and Swihart (2010) in a very similar forest system. We expected that the abundance of white-footed mice, a generalist species, would be similar under all treatments. The abundance of short-tailed shrews and pine voles, which are sensitive to moisture and leaf litter conditions (Goertz 1971, Greenberg et al. 2007), should be reduced inside clearcuts and patch cuts, and this effect should be greater on sites with southwestern aspects that are likely to be warmer and drier (Urban and Swihart 2010). Overall, small mammal responses to clearcuts and smaller patch cuts should be similar, since there is little evidence of a relationship between small mammal populations and opening size (Buckner and Shure 1985, Schmid-Holmes and Drickamer 2001, Urban and Swihart 2010). Likewise, we expected small mammal populations at unharvested and shelterwood harvest sites to be similar. At the time this study was conducted, only midstory trees had been removed from the shelterwood sites; large overstory trees remained and the canopy was essentially intact, so habitat for small mammals was likely very similar to the surrounding intact forest.
+
+\section{Materials and Methods}
+
+\subsection{Study Area}
+
+The Hardwood Ecosystem Experiment is located in Morgan-Monroe State Forest (39\textdegree 25'N, 86\textdegree 25'W), Yellowwood State Forest (38\textdegree 50'N, 86\textdegree 30'W) and Brown County State Park (39\textdegree 8'N, 86\textdegree 13'W), Indiana. In total, the forests span \textgreater 18,000 ha in two counties of southern Indiana, and are part of the Brown County Hills, with slopes typically 23\% - 35\% (Homoya et al. 1984, Jenkins and Parker 1998). Silt-loam is the predominant soil type in the region (Indiana Department of Natural Resources 1984). At mesic sites, the overstory is dominated by white oak (\textit{Quercus alba}) American beech (\textit{Fagus grandifolia}), sugar maple (\textit{Acer saccharum}), and hickory (\textit{Carya} spp.), with most of the understory made up of \textit{A. saccharum} and \textit{F. grandifolia} saplings. Drier sites are dominated by \textit{Q. alba}, black oak (\textit{Q. velutina}) and \textit{A. saccharum} with an understory of \textit{A. saccharum}, \textit{F. grandifolia}, and flowering dogwood (\textit{Cornus florida}) (Jenkins and Parker 1998). Past management in the state forests has consisted of mainly group and single-tree selection (Jenkins and Parker 1998). A more detailed description of the study area can be found in Jenkins (2012).
+
+\subsection{Silvicultural Treatments}
+
+In fall 2006, nine study units were created within the two parks, with a tenth control unit added in Brown County State Park in 2007. Units were made up of a core research area surrounded by a buffer region. Research cores ranged in size from 83.4 to 110.9 ha, and buffers from 219.3 to 427.0 ha. Buffer widths ranged from 100 - 1,190 m. Each unit was randomly assigned to one of three treatments: even-aged management, uneven-aged management, or unharvested control (Figure 3.1).
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=1.5]{figures/Ch3_fig1_final}
+\caption{Map of Morgan-Monroe and Yellowwood State Forests showing the locations of experimental units in the Hardwood Ecosystem Experiment. A 4th control unit (not shown) was added in Brown County State Park in 2007.}
+%\label{fig:}
+\end{figure}
+
+In each unit selected for even-aged management, four 4-ha areas were designated for harvests. At two of the four areas, silvicultural clearcuts were applied; specifically, trees \textgreater 30.48 cm diameter were harvested, and all remaining trees with diameter \textgreater 1 cm were removed via timber stand improvement. At the remaining two areas, a 3-stage shelterwood system was implemented. The first stage consisted of a removal of midstory trees, to be followed by an establishment cut in 3-5 years and the removal of remaining overstory trees in 10-15 years. At the time of this study, only the midstory removal phase had been completed. In units receiving uneven-aged management, harvests consisted of a combination of patch cuts and single-tree selection in the matrix surrounding the openings. Within each unit, eight patch cuts were made in a range of sizes: four were 0.4 ha, two were 1 ha, and two were 2 ha in size.
+
+In both even and uneven-aged management units, harvest sites were selected so that half had a northeast aspect (5-85\textdegree) and half had a southwest aspect (185-265\textdegree). All harvests occurred between November 2008 and March 2009. A more detailed description of management implemented as part the Hardwood Ecosystem Experiment can be found in Kalb and Mycroft (2012).
+
+\subsection{Trapping}
+
+In June 2007 (prior to timber harvest), 32 permanent trapping grids were established. Four grids were established in each of the even- and uneven-aged management units, and two grids were established in each of the control units. More specifically, 6 grids were located at future clearcut areas, 6 were located in future shelterwood areas, 12 were located at future patch cut areas, and the final 8 were located in control areas. Of the 12 grids in patch cut sites, 8 were located in future 0.4 ha openings and 4 were located in future 2 ha openings. Grids were evenly divided between northeast and southwest aspects in all units and for all treatments.
+
+Each grid consisted of 18 trap stations arranged in a 3 x 6 lattice. Transects were laid out across the slope and were separated by 50 m, with traps in each transect separated by 20 m. Therefore, each grid was 1 ha in size. Following harvest, grids of this size did not fit inside the 0.4 ha openings in the uneven-aged units; instead, these grids had transects 40 m apart and the traps in each transect were 10 m apart, yielding a size of 0.4 ha. Each point on the grid was flagged to allow for relocation in subsequent years; grid corners were also mapped with a handheld GPS unit (Earthmate PN-40).
+
+A Sherman trap (H.B. Sherman Trap, Inc., Tallahassee Fla.) was placed within 1 meter of the center of every trap station. At 3 of the 6 traps on the center transect, a pitfall trap was placed within 1.5 m of the station center. Pitfall traps consisted of a buried coffee can (15.7 cm diameter) and were placed whenever possible along a natural drift fence such as a fallen log. If raccoon (\textit{Procyon lotor}) or opossum (\textit{Didelphis virginiana}) disturbance became a problem within a grid, 1-2 Tomahawk traps (Tomahawk Live Traps Co., Tomahawk, WI) were set at central locations. Tomahawks were baited with cat food, and all captured animals were relocated at least 10 kilometers away from the nearest trapping grid.
+
+Every grid was trapped for 1 week in each of the 5 years of the study between mid-June and late July. The only exception was summer 2010, when only 20 of the 32 grids were sampled following a poor mast year. Traps were prebaited for 3 days; following prebaiting, grids were sampled twice daily (morning and evening) for 5 consecutive days. Sherman traps were baited daily with rolled oats and sunflower seeds, and pitfall traps were provisioned with an earthworm. During each sampling occasion, the mass, sex, and reproductive status of captured small mammals were recorded before the animals were released. Rodents were considered reproductively active if they were lactating or had an enlarged pubic symphysis (females) or descended testes (males). At most sites, captured animals were not tagged in order to increase sampling efficiency and minimize animal stress. However, at a subset of 6 grids in 2008, 2010, and 2011, all captured mice and chipmunks were eartagged (Hasco Tag Company, Dayton, Kentucky) to allow for mark-recapture estimates of abundance.
+
+\subsection{Collection of Covariate Data}
+
+Mast production by two of the most common mast-producing species at our sites, black (\textit{Quercus velutina}) and white (\textit{Q. alba}) oak was monitored in each experimental unit as an index of available hard mast. Within each of the 6 units receiving even- or uneven-aged timber harvests, six mature trees (3 of each species) were selected. Within each of the 4 control units, 4 mature trees (2 of each species) were selected. Two mast collection traps, consisting of 52 x 33 x 32 cm plastic bins mounted on 2 m tall PVC pipes, were established under each tree. Acorns that fell into the traps were collected and counted 5-8 times from September through December in each year of the study.
+
+For each trapping grid, we recorded the site aspect and harvest treatment applied. On each visit to the grid during the week it was trapped each year, we recorded the temperature and the presence or absence of precipitation in the 8 hours preceding the visit. Once per year, in a 1 m radius adjacent to each trap station, we measured a suite of microhabitat variables: percent herbaceous and woody cover, leaf litter depth, and total length of coarse woody debris \textgreater 10 cm in diameter.
+
+\subsection{Analysis}
+
+\subsubsection{\textit{N}-mixture Approach}
+
+Traditionally, estimates of small mammal abundance are obtained using mark-release-recapture (MRR) methods (e.g. Nichols and Pollock 1983). We did not individually mark captured animals at most sites, and we rarely captured enough individuals at a given site to allow for reasonable MRR estimates of abundance. Instead, we used a binomial-Poisson mixture model (also called an \textit{N}-mixture model) based on Royle (2004) and K\'{e}ry (2010) that incorporates imperfect detection to estimate site-level abundance for the species we trapped. We summarized our capture data as the observed counts, $y_{ijkt}$, of species \textit{i} captured at site \textit{j} on occasion \textit{k} (\textit{k} = 1,...,5) in year \textit{t}. Counts were modeled as binomial random variables:
+\begin{equation}
+y_{ijkt} \sim \operatorname{Binomial} \left({N_{ijt},p_{ijt}}\right)
+\end{equation}
+where \textit{p} is the species, site, and year-specific probability of detection, and $N_{ijt}$ is a latent (unobserved) variable representing true abundance of species \textit{i} in a given site and year. The true abundance \textit{N} was modeled as
+\begin{equation}
+N_{ijt} \sim \operatorname{Poisson} \left({\lambda_{ijt}}\right)
+\end{equation}
+We assumed \textit{N} remained constant during the 5-day sampling period at each site and that animals were equally likely to be caught in any trap at the site. An obvious limitation of this approach is that \textit{N} x \textit{p} (the total number of observed animals in a given count) must be $\le$ 18, since that is the number of trap stations in each grid available on a given trapping occasion. In this study, values of \textit{N} x \textit{p} never exceeded 10 for any check, which suggests that trap saturation was not an issue.
+
+We used generalized linear mixed models (GLMMs) to determine the effects of covariates on parameters $\lambda$ (mean abundance) and \textit{p} (probability of detection). Mean abundance $\lambda$ was set equal to the log-linear predictor
+\begin{equation}
+\log(\lambda_{ijt}) = \alpha_i + \displaystyle\sum\limits_{n=1}^r \beta_{n,i}x_{n,ijt}
+\end{equation}
+where $\alpha_i$ is a species-specific random intercept distributed as a normal random variable (i.e., $\alpha_i \sim N(\mu,\sigma^2_{u})$, $\beta_{n,i}$ (n = 1,...,\textit{r}) are species-specific random slopes for \textit{r} covariates (distributed $\beta_{ni} \sim N(v_{ni},\sigma^2_{v,ni}$), and $x_{n,ijt}$ are the corresponding covariate values. We considered stand-level silvicultural treatment (clearcut, shelterwood, and the two sizes of patch cuts), site aspect, and mast availability as covariates on $\lambda$. We set $\operatorname{logit} \left({p}\right)$, where
+\begin{equation}
+\operatorname{logit} \left({x}\right) = \operatorname{ln} \left({x \over 1-x}\right)
+\end{equation}
+ equal to a linear predictor in a similar fashion, again with species-specific random slopes and intercepts. Temperature, precipitation, Julian day, and trapping effort were included as covariates on \textit{p}. Effort was calculated as the number of trap-nights (corrected for disturbance) per unit area. An advantage of this multi-species model structure (i.e., random slopes and intercepts for each species coming from a common distribution) is that the information contained in the random intercept and slope parameters can be shared between abundant and rare species since they come from common distributions, thereby yielding more accurate estimates (Alldredge et al. 2007).
+
+The abundance analysis was conducted in a Bayesian framework. Hyperparameters were assigned non-informative prior distributions based on those used by K\'{e}ry (2010), to ensure that inference from posterior distributions was driven by collected data.
+
+\section{Results}
+
+\subsection{Abundance Model}
+
+Trapping over 5 years yielded 4,608 small mammals of 6 different species captured during 27,972 trap-days/nights (Table 3.1). The most commonly captured species were the eastern chipmunk, white-footed mouse, northern short-tailed shrew and pine vole; only these 4 species were considered in the analysis. Available mast varied greatly between years during the experiment, with 3 years of moderate to high mast availability and 2 years of mast failure in black and white oak (Figure 3.2). Abundances of the four small mammal species also varied over this time period (Figure 3.3).
+
+% Table generated by Excel2LaTeX from sheet 'capture counts'
+\begin{table}[htbp]
+ \centering
+ \caption{Total captures of small mammals at study sites, 2007-2011. In 2010 only 20 of the 32 sites were sampled.}
+ \begin{tabular}{lcccccc}
+ \toprule
+ \multicolumn{1}{l}{\textbf{Species}} & \textbf{2007} & \textbf{2008} & \textbf{2009} & \textbf{2010} & \textbf{2011} & \textbf{Total} \\
+ \midrule
+ White-footed mouse & 712 & 555 & 674 & 58 & 516 & 2515 \\
+ Eastern chipmunk & 270 & 401 & 668 & 177 & 370 & 1886 \\
+ Pine vole & 33 & 27 & 16 & 0 & 0 & 76 \\
+ Short-tailed shrew & 30 & 23 & 18 & 19 & 37 & 127 \\
+ Smoky shrew & 2 & 0 & 0 & 0 & 0 & 2 \\
+ Southeastern shrew & 1 & 1 & 0 & 0 & 0 & 2 \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch3_fig2_final}
+\caption{Total number of acorns collected, October-December, across all sites by species and year.}
+%\label{Figure 2.}
+\end{figure}
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch3_fig3_final}
+\caption{Yearly mean density of small mammals in the four silvicultural treatments. The vertical line in each graph corresponds to the implementation of timber harvests.}
+%\label{Figure 3.}
+\end{figure}
+
+Multiple predictors of detection and abundance were important and varied among species of small mammals (Table 3.2). Higher daily temperatures reduced detectability of chipmunks and white-footed mice, and short-tailed shrews were more likely to be detected earlier in the trapping season. A higher trapping effort increased detectability of white-footed mice. Overall, mean \textit{p} was 0.43 for chipmunks, 0.48 for white-footed mice, 0.13 for short-tailed shrews, and 0.17 for pine voles.
+
+% Table generated by Excel2LaTeX from sheet 'model output'
+\begin{sidewaystable}[htbp]
+ \centering
+ \caption{Fitted parameter values from the \textit{N}-mixture model of abundance. Bolded values have 95\% credible intervals that do not include 0.}
+ \begin{tabular}{rcrcrcrcr}
+ \toprule
+ \textbf{} & \multicolumn{2}{c}{\textbf{Eastern chipmunk}} & \multicolumn{2}{c}{\textbf{White-footed mouse}} & \multicolumn{2}{c}{\textbf{Short-tailed shrew}} & \multicolumn{2}{c}{\textbf{Pine vole}} \\
+ \midrule
+ \multicolumn{1}{l}{\textbf{Covariate}} & \textbf{βj} & \multicolumn{1}{c}{\textbf{SE}} & \textbf{βj} & \multicolumn{1}{c}{\textbf{SE}} & \textbf{βj} & \multicolumn{1}{c}{\textbf{SE}} & \textbf{βj} & \multicolumn{1}{c}{\textbf{SE}} \\
+ \multicolumn{1}{c}{\textit{\textbf{p}}} & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{} \\
+ \multicolumn{1}{l}{Intercept} & \textbf{-0.29} & \multicolumn{1}{c}{0.1} & \textbf{1.1} & \multicolumn{1}{c}{0.29} & \textbf{-1.8} & \multicolumn{1}{c}{0.29} & \textbf{-1.5} & \multicolumn{1}{c}{0.34} \\
+ \multicolumn{1}{l}{Temperature} & \textbf{-0.11} & \multicolumn{1}{c}{0.04} & \textbf{-0.13} & \multicolumn{1}{c}{0.03} & -0.07 & \multicolumn{1}{c}{0.09} & -0.19 & \multicolumn{1}{c}{0.14} \\
+ \multicolumn{1}{l}{Julian day} & -0.05 & \multicolumn{1}{c}{0.06} & 0.07 & \multicolumn{1}{c}{0.05} & \textbf{-0.49} & \multicolumn{1}{c}{0.13} & 0.27 & \multicolumn{1}{c}{0.17} \\
+ \multicolumn{1}{l}{Effort} & -0.03 & \multicolumn{1}{c}{0.11} & \textbf{1.31} & \multicolumn{1}{c}{0.26} & -0.26 & \multicolumn{1}{c}{0.19} & -0.14 & \multicolumn{1}{c}{0.48} \\
+ \multicolumn{1}{c}{\textbf{λ}} & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{} & & \multicolumn{1}{c}{} \\
+ \multicolumn{1}{l}{Intercept} & \textbf{1.3} & \multicolumn{1}{c}{0.09} & \textbf{2.23} & \multicolumn{1}{c}{0.07} & 0.11 & \multicolumn{1}{c}{0.25} & \textbf{-0.67} & \multicolumn{1}{c}{0.29} \\
+ \multicolumn{1}{l}{Mast Index} & 0.05 & \multicolumn{1}{c}{0.04} & \textbf{0.1} & \multicolumn{1}{c}{0.03} & \textbf{0.28} & \multicolumn{1}{c}{0.1} & -0.11 & \multicolumn{1}{c}{0.16} \\
+ \multicolumn{1}{l}{Aspect (NE=1)} & -0.06 & \multicolumn{1}{c}{0.09} & 0.1 & \multicolumn{1}{c}{0.06} & \textbf{0.44} & \multicolumn{1}{c}{0.23} & 0.42 & \multicolumn{1}{c}{0.27} \\
+ \multicolumn{1}{l}{Clearcut (4 ha)} & \textbf{0.51} & \multicolumn{1}{c}{0.13} & -0.06 & \multicolumn{1}{c}{0.1} & \textbf{-2.41} & \multicolumn{1}{c}{0.88} & \textbf{-1.94} & \multicolumn{1}{c}{0.88} \\
+ \multicolumn{1}{l}{Patch cut (2 ha)} & \textbf{0.35} & \multicolumn{1}{c}{0.19} & 0.24 & \multicolumn{1}{c}{0.13} & -1.14 & \multicolumn{1}{c}{0.9} & -1.01 & \multicolumn{1}{c}{0.9} \\
+ \multicolumn{1}{l}{Patch cut (0.4 ha)} & \textbf{0.35} & \multicolumn{1}{c}{0.17} & \textbf{-0.35} & \multicolumn{1}{c}{0.11} & 0.2 & \multicolumn{1}{c}{0.39} & -0.9 & \multicolumn{1}{c}{0.7} \\
+ \multicolumn{1}{l}{Shelterwood} & 0.22 & \multicolumn{1}{c}{0.14} & 0.09 & \multicolumn{1}{c}{0.1} & -0.25 & \multicolumn{1}{c}{0.35} & -0.27 & \multicolumn{1}{c}{0.44} \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{sidewaystable}%
+
+The abundance of eastern chipmunks was greater in clearcuts (4 ha) and in both sizes of patch cuts (0.4 ha and 2 ha) relative to controls (Table 3.2, Figures 3.3-3.4). There was some evidence of an effect of opening size on shrew abundance; they had lower abundance in clearcuts and were marginally less abundant in the 2 ha patch cuts (most of the posterior distribution of the coefficient was less than 0), but were unaffected by the smaller 0.4 ha patch cuts. Pine voles tended to have had lower abundance in all three opening sizes relative to controls, although the effect was statistically different from zero only for the 4 ha (clearcut) opening size (Table 3.2, Figures 3.3-3.4). White-footed mouse abundance was similar between controls and all harvest treatments (Figure 3.3), except that their abundance was reduced in the 0.4 ha patch cuts (Table 3.2, Figure 3.4). We were unable to detect a difference in abundance for any species following the first stage of the shelterwood harvests. Mast availability in the previous fall was positively related to the abundance of white-footed mice and short-tailed shrews. Shrew abundance was also higher at sites with a northeastern aspect relative to sites with a southwestern aspect (Table 3.2).
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch3_Fig4_final}
+\caption{Parameter values for the treatment effects included in the \textit{N}-mixture model, and their 95\% credible intervals. Values next to each interval represent the proportion of the parameter's posterior distribution which has the same sign as the mean.}
+%\label{Figure 4.}
+\end{figure}
+
+\subsection{Model Validation}
+
+We were unable to obtain usable MRR estimates of abundance for 3 of the 18 possible sites (17\%) for mice and 10 of the sites (55\%) for chipmunks. MRR abundance estimates (from the $M_{0}$, $Chao_{h}$ and $Chao_{th}$ models) fell within the 95\% credible interval around the estimate from the \textit{N}-mixture model at 12 of the 15 sites (80\%) for which we were able to obtain reasonable MRR estimates for mice, and 7 of the 8 sites (88\%) for chipmunks. Estimates of \textit{N} for chipmunks and mice obtained from the \textit{N}-mixture model were strongly correlated with estimates obtained through MRR (Figure 3.5), and the slopes of the regression lines were close to 1. In contrast, the count-based indexes of abundance used in other studies were more biased (a slope higher or lower than 1) and not as strongly correlated with the MRR estimates (Figure 3.5).
+ Simulation results indicated that the \textit{N}-mixture model did well in recovering estimates of abundance relative to the suite of MRR models. In general, for all models, mean percent bias of model estimates (and variability in bias between simulations) increased as probability of detection (\textit{p}) decreased (Table 3.4). High bias is also partially attributable to the simulated populations being small ($\lambda$=10). Still, for all values of \textit{p}, mean percent bias for the \textit{N}-mixture model was similar to the MRR models. Actual estimates of \textit{p} generated by the model were close to the values chosen for the simulation: near 0.5 for the common species (eastern chipmunk, white-footed mouse) and near 0.2 for the more rare species (short-tailed shrew, pine vole). The 95\% credible intervals around estimates obtained from the mixture model contained the true abundance in $\ge$ 95\% of the simulations with better coverage than MRR models in most cases (Table 3.5).
+
+\begin{landscape}
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.5]{figures/Ch3_Fig5_final}
+\caption{Comparison of mark-release-recapture estimates of site abundance (using $Chao_{t}$) with \textit{N}-mixture model estimates and two alternative abundance indices: total captures and the maximum number of captures during a single trapping occasion. Regression lines were forced through the origin.}
+%\label{Figure 5.}
+\end{figure}
+\end{landscape}
+
+% Table generated by Excel2LaTeX from sheet 'bias'
+\begin{table}[htbp]
+ \centering
+ \caption{Mean percent bias of four alternative abundance models, based on simulated data generated using varying probabilities of detection. Standard error values are shown in parentheses.}
+ \begin{tabular}{lcrrr}
+ \toprule
+ \textbf{} & \multicolumn{4}{c}{\textbf{Detection Probability}} \\
+ \midrule
+ \textbf{Model} & \textbf{0.2} & \multicolumn{1}{c}{\textbf{0.3}} & \multicolumn{1}{c}{\textbf{0.5}} & \multicolumn{1}{c}{\textbf{0.7}} \\
+ N-mixture & 29 (44) & \multicolumn{1}{c}{20 (36)} & \multicolumn{1}{c}{4.1 (15)} & \multicolumn{1}{c}{1.2 (9)} \\
+ M0 & 16 (52) & \multicolumn{1}{c}{8.7 (32)} & \multicolumn{1}{c}{1.0 (7.9)} & \multicolumn{1}{c}{0.1 (1.9)} \\
+ Chao (Mh) & 34 (87) & \multicolumn{1}{c}{23 (63)} & \multicolumn{1}{c}{5.8 (18)} & \multicolumn{1}{c}{1.81 (10)} \\
+ Chao (Mth) & 23 (77) & \multicolumn{1}{c}{17 (56)} & \multicolumn{1}{c}{4.4 (17)} & \multicolumn{1}{c}{1.5 (10)} \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+
+% Table generated by Excel2LaTeX from sheet 'coverage'
+\begin{table}[htbp]
+ \centering
+ \caption{Percent of sites across all simulations in which the 95\% credible interval (\textit{N}-mixture model) or 95\% confidence interval (other models) around the population estimate contained the known, true population value.}
+ \begin{tabular}{lcrrr}
+ \toprule
+ \textbf{} & \multicolumn{4}{c}{\textbf{Detection Probability}} \\
+ \midrule
+ \textbf{Model} & \textbf{0.2} & \multicolumn{1}{c}{\textbf{0.3}} & \multicolumn{1}{c}{\textbf{0.5}} & \multicolumn{1}{c}{\textbf{0.7}} \\
+ N-mixture & 95 & \multicolumn{1}{c}{95} & \multicolumn{1}{c}{99} & \multicolumn{1}{c}{99} \\
+ M0 & 89 & \multicolumn{1}{c}{92} & \multicolumn{1}{c}{92} & \multicolumn{1}{c}{97} \\
+ Chao (Mh) & 92 & \multicolumn{1}{c}{95} & \multicolumn{1}{c}{96} & \multicolumn{1}{c}{98} \\
+ Chao (Mth) & 91 & \multicolumn{1}{c}{92} & \multicolumn{1}{c}{94} & \multicolumn{1}{c}{98} \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+
+\subsection{Microhabitat Variables}
+
+There were significant differences between years for most microhabitat variables in harvested areas, but not in control areas (Table 3.5). Notably, herbaceous cover increased after harvest in clearcuts and patch cuts, as well as in shelterwoods following midstory removal; woody understory cover was also greater after clearcut and shelterwood harvests but unchanged in patch cuts. Coarse woody debris length was greatest immediately following harvest (2009) for clearcut and shelterwood harvest sites and in the following year (2010) for patch cuts. In general, litter depth was reduced following harvest in clearcut and patch cut areas but unchanged following shelterwood harvest (Table 3.5).
+
+% Table generated by Excel2LaTeX from sheet 'Sheet1'
+\begin{sidewaystable}[htbp]
+ \centering
+ \caption{Mean values for microhabitat variables by silvicultural treatment and year. Values in 2007 and 2008 were prior to timber harvest. Kruskal - Wallis tests were performed to determine if there were differences between years for each treatment. Mean values denoted by the same letter were not statistically different based on post-hoc comparisons.}
+\begin{scriptsize}
+ \begin{tabular}{rcrrrrrrrrrrrr}
+ \toprule
+ \multicolumn{1}{c}{\textbf{}} & \textbf{} & \multicolumn{2}{c}{\textbf{2007 (pre-harvest)}} & \multicolumn{2}{c}{\textbf{2008 (pre-harvest)}} & \multicolumn{2}{c}{\textbf{2009}} & \multicolumn{2}{c}{\textbf{2010}} & \multicolumn{2}{c}{\textbf{2011}} & \multicolumn{2}{c}{\textbf{Kruskal - Wallis}} \\
+ \midrule
+ \multicolumn{1}{l}{\textbf{Variable}} & \textbf{Unit} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{SE}} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{SE}} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{SE}} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{SE}} & \multicolumn{1}{c}{\textbf{Mean}} & \multicolumn{1}{c}{\textbf{SE}} & \multicolumn{1}{c}{\textbf{Χ2}} & \multicolumn{1}{c}{\textit{\textbf{p}}} \\
+ \multicolumn{1}{c}{\textbf{Control}} & & & & & & & & & & & & & \\
+ \multicolumn{1}{l}{Herbaceous plants} & \% cover & \multicolumn{1}{c}{17.18} & \multicolumn{1}{c}{5.84} & \multicolumn{1}{c}{9.11} & \multicolumn{1}{c}{1.41} & \multicolumn{1}{c}{7.27} & \multicolumn{1}{c}{1.71} & \multicolumn{1}{c}{12.53} & \multicolumn{1}{c}{1.97} & \multicolumn{1}{c}{14.38} & \multicolumn{1}{c}{4.52} & \multicolumn{1}{c}{7.25} & \multicolumn{1}{c}{0.12} \\
+ \multicolumn{1}{l}{Woody plants} & \% cover & \multicolumn{1}{c}{8.19} & \multicolumn{1}{c}{1.09} & \multicolumn{1}{c}{14.85} & \multicolumn{1}{c}{2.91} & \multicolumn{1}{c}{13.69} & \multicolumn{1}{c}{2.48} & \multicolumn{1}{c}{9.44} & \multicolumn{1}{c}{1.82} & \multicolumn{1}{c}{12.89} & \multicolumn{1}{c}{2.03} & \multicolumn{1}{c}{0.88} & \multicolumn{1}{c}{0.06} \\
+ \multicolumn{1}{l}{Coarse woody debris} & m & \multicolumn{1}{c}{0.92} & \multicolumn{1}{c}{0.19} & \multicolumn{1}{c}{0.93} & \multicolumn{1}{c}{0.17} & \multicolumn{1}{c}{0.71} & \multicolumn{1}{c}{0.16} & \multicolumn{1}{c}{0.88} & \multicolumn{1}{c}{0.04} & \multicolumn{1}{c}{0.64} & \multicolumn{1}{c}{0.11} & \multicolumn{1}{c}{2.95} & \multicolumn{1}{c}{0.57} \\
+ \multicolumn{1}{l}{Litter depth} & cm & \multicolumn{1}{c}{3.98} & \multicolumn{1}{c}{0.51} & \multicolumn{1}{c}{3.53} & \multicolumn{1}{c}{0.53} & \multicolumn{1}{c}{3.23} & \multicolumn{1}{c}{0.38} & \multicolumn{1}{c}{2.57} & \multicolumn{1}{c}{0.1} & \multicolumn{1}{c}{2.82} & \multicolumn{1}{c}{0.16} & \multicolumn{1}{c}{2.45} & \multicolumn{1}{c}{0.65} \\
+ \multicolumn{1}{c}{\textbf{Clearcut}} & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & \\
+ \multicolumn{1}{l}{Herbaceous plants} & \% cover & \multicolumn{1}{c}{8.27a} & \multicolumn{1}{c}{2.91} & \multicolumn{1}{c}{11.3a} & \multicolumn{1}{c}{1.93} & \multicolumn{1}{c}{16.04ab} & \multicolumn{1}{c}{2.77} & \multicolumn{1}{c}{53.36b} & \multicolumn{1}{c}{7.65} & \multicolumn{1}{c}{54.31b} & \multicolumn{1}{c}{7.36} & \multicolumn{1}{c}{21.02} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \multicolumn{1}{l}{Woody plants} & \% cover & \multicolumn{1}{c}{7.19a} & \multicolumn{1}{c}{3.33} & \multicolumn{1}{c}{25.42ab} & \multicolumn{1}{c}{4.9} & \multicolumn{1}{c}{17.44ab} & \multicolumn{1}{c}{2.09} & \multicolumn{1}{c}{35.93ab} & \multicolumn{1}{c}{8.19} & \multicolumn{1}{c}{54.45b} & \multicolumn{1}{c}{3.41} & \multicolumn{1}{c}{17.55} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \multicolumn{1}{l}{Coarse woody debris} & m & \multicolumn{1}{c}{0.76a} & \multicolumn{1}{c}{0.1} & \multicolumn{1}{c}{1.12ab} & \multicolumn{1}{c}{0.13} & \multicolumn{1}{c}{2.95b} & \multicolumn{1}{c}{0.49} & \multicolumn{1}{c}{1.58ab} & \multicolumn{1}{c}{0.52} & \multicolumn{1}{c}{1.1ab} & \multicolumn{1}{c}{0.07} & \multicolumn{1}{c}{13.1} & \multicolumn{1}{c}{\textbf{0.01}} \\
+ \multicolumn{1}{l}{Litter depth} & cm & \multicolumn{1}{c}{3.45ab} & \multicolumn{1}{c}{1.17} & \multicolumn{1}{c}{3.37a} & \multicolumn{1}{c}{0.22} & \multicolumn{1}{c}{2.1ab} & \multicolumn{1}{c}{0.23} & \multicolumn{1}{c}{0.96b} & \multicolumn{1}{c}{0.38} & \multicolumn{1}{c}{1.36b} & \multicolumn{1}{c}{0.14} & \multicolumn{1}{c}{17.88} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \multicolumn{1}{c}{\textbf{Shelterwood}} & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & \\
+ \multicolumn{1}{l}{Herbaceous plants} & \% cover & \multicolumn{1}{c}{7.67a} & \multicolumn{1}{c}{2.52} & \multicolumn{1}{c}{8.66ab} & \multicolumn{1}{c}{1.83} & \multicolumn{1}{c}{8.05a} & \multicolumn{1}{c}{1.82} & \multicolumn{1}{c}{15.49ab} & \multicolumn{1}{c}{3.12} & \multicolumn{1}{c}{23.29b} & \multicolumn{1}{c}{2.24} & \multicolumn{1}{c}{15.12} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \multicolumn{1}{l}{Woody plants} & \% cover & \multicolumn{1}{c}{6.58a} & \multicolumn{1}{c}{2.36} & \multicolumn{1}{c}{14.00ab} & \multicolumn{1}{c}{3.14} & \multicolumn{1}{c}{16.87ab} & \multicolumn{1}{c}{4} & \multicolumn{1}{c}{14.39ab} & \multicolumn{1}{c}{2.65} & \multicolumn{1}{c}{22.93b} & \multicolumn{1}{c}{3.85} & \multicolumn{1}{c}{9.86} & \multicolumn{1}{c}{\textbf{0.04}} \\
+ \multicolumn{1}{l}{Coarse woody debris} & m & \multicolumn{1}{c}{0.63a} & \multicolumn{1}{c}{0.1} & \multicolumn{1}{c}{0.88ab} & \multicolumn{1}{c}{0.13} & \multicolumn{1}{c}{0.98ab} & \multicolumn{1}{c}{0.15} & \multicolumn{1}{c}{1.58b} & \multicolumn{1}{c}{0.29} & \multicolumn{1}{c}{0.94ab} & \multicolumn{1}{c}{0.11} & \multicolumn{1}{c}{10.55} & \multicolumn{1}{c}{\textbf{0.03}} \\
+ \multicolumn{1}{l}{Litter depth} & cm & \multicolumn{1}{c}{2.32} & \multicolumn{1}{c}{0.31} & \multicolumn{1}{c}{2.92} & \multicolumn{1}{c}{0.42} & \multicolumn{1}{c}{2.57} & \multicolumn{1}{c}{0.2} & \multicolumn{1}{c}{1.54} & \multicolumn{1}{c}{0.3} & \multicolumn{1}{c}{2.5} & \multicolumn{1}{c}{0.2} & \multicolumn{1}{c}{7.58} & \multicolumn{1}{c}{0.11} \\
+ \multicolumn{1}{c}{\textbf{Patch cut}} & & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & & \\
+ \multicolumn{1}{l}{Herbaceous plants} & \% cover & \multicolumn{1}{c}{9.76ab} & \multicolumn{1}{c}{1.27} & \multicolumn{1}{c}{14.2a} & \multicolumn{1}{c}{4.66} & \multicolumn{1}{c}{35.28ab} & \multicolumn{1}{c}{3.63} & \multicolumn{1}{c}{62.88ab} & \multicolumn{1}{c}{4.95} & \multicolumn{1}{c}{67.30b} & \multicolumn{1}{c}{4.96} & \multicolumn{1}{c}{27.61} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \multicolumn{1}{l}{Woody plants} & \% cover & \multicolumn{1}{c}{9.71} & \multicolumn{1}{c}{2.71} & \multicolumn{1}{c}{18.86} & \multicolumn{1}{c}{3.41} & \multicolumn{1}{c}{15.2} & \multicolumn{1}{c}{3.52} & \multicolumn{1}{c}{16.88} & \multicolumn{1}{c}{3.9} & \multicolumn{1}{c}{29.94} & \multicolumn{1}{c}{4.61} & \multicolumn{1}{c}{6.97} & \multicolumn{1}{c}{0.14} \\
+ \multicolumn{1}{l}{Coarse woody debris} & m & \multicolumn{1}{c}{0.65ac} & \multicolumn{1}{c}{0.14} & \multicolumn{1}{c}{0.87ac} & \multicolumn{1}{c}{0.07} & \multicolumn{1}{c}{1.82b} & \multicolumn{1}{c}{0.13} & \multicolumn{1}{c}{0.71c} & \multicolumn{1}{c}{0.05} & \multicolumn{1}{c}{1.11abc} & \multicolumn{1}{c}{0.14} & \multicolumn{1}{c}{25.79} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \multicolumn{1}{l}{Litter depth} & cm & \multicolumn{1}{c}{3.43ab} & \multicolumn{1}{c}{0.42} & \multicolumn{1}{c}{4.16a} & \multicolumn{1}{c}{0.33} & \multicolumn{1}{c}{1.9b} & \multicolumn{1}{c}{0.09} & \multicolumn{1}{c}{1.98ab} & \multicolumn{1}{c}{0.17} & \multicolumn{1}{c}{1.34b} & \multicolumn{1}{c}{0.2} & \multicolumn{1}{c}{30.45} & \multicolumn{1}{c}{\textbf{< 0.01}} \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{scriptsize}
+\end{sidewaystable}%
+
+\section{Discussion}
+
+Forest managers increasingly are interested in understanding the effects of timber harvest and other management tools on wildlife and plant communities. This study quantifies effects of timber harvest on small mammal populations in the short term, and introduces a method of modeling abundance that may be a useful alternative to more traditional and time-consuming MRR methods.
+
+\subsection{Response of Small Mammals to Management}
+
+Eastern chipmunks increased in abundance at patch cuts (both 0.4 and 2 ha) and clearcut sites (Figures 3.3-3.4), consistent with our expectations based on a chronosequence study in southern Indiana (Urban and Swihart 2010). Past studies have found that chipmunks are more abundant in edge habitat (Johnson et al. 1979, King et al. 1998), which increased at harvested sites in this study. Chipmunk populations were highest in the smaller openings created by patch cutting (Figure 3.3), which had the greatest proportion of edge habitat. Though the increase in edge and decrease in canopy closure may increase predation risk for chipmunks (Bowers 1995, Nupp and Swihart 1998), the available coarse woody debris at recently harvested sites may shield them from predators (Zollner and Crane 2003). Coarse woody debris did increase in the year following harvest in patch cuts and clearcuts (Table 3.5). In addition, chipmunks may prefer disturbed habitat with abundant seedlings and less leaf litter (Kaminski et al. 2007); harvest openings generally had a higher percentage of woody cover in the understory and less leaf litter (Table 3.5).
+
+The white-footed mouse is a habitat generalist (Adler and Tamarin 1984), so we did not expect treatment effects on mouse populations. Mice were ubiquitous at our study sites, and there were no differences in abundance based on treatment, except that mice were less abundant in the smallest patch cuts (0.4 ha; Figures 3.3-3.4). Other studies in similar hardwood forests (Fantz and Renken 2005, Urban and Swihart 2010) also reported minimal impact of timber harvest on white-footed mouse populations. The negative response of mice to the smallest openings is surprising. White-footed mice were found in high densities in small habitat patches (Nupp and Swihart 1996); however this was the reverse situation: a small patch of forest surrounded by agriculture. Nupp and Swihart (1996) suggested that the reduced number of predators in small patches may have allowed for higher mouse populations. In this system, smaller openings may be more accessible to predators, resulting in reduced densities of mice. Hard mast, an important food source for mice (McShea 2000), is unavailable except on the edges of recent clearcuts and patch cuts. However, mice consume a wide variety of seeds, berries, and insects, and the increase in the amount of available soft mast (Perry et al. 2004, Reynolds-Hogland et al. 2006) following harvest likely helps to offset the reduction in availability of hard mast. White-footed mice are not highly selective in microhabitat use, but they do prefer some vertical stratification of vegetation, presumably to reduce predation risk (Yahner 1986). The rapid growth of early-successional vegetation at the clearcut and patch cut sites in this study (Table 3.5) likely provided sufficient protection from aerial predators within the openings, promoting their use by mice.
+
+In contrast to chipmunks and mice, short-tailed shrews responded negatively to the clearcut treatments and to the 2 ha patch cuts. However, they did not show a strong response to the smaller 0.4 ha patch cuts (Table 3.2, Figures 3.3-3.4). Shrews rapidly lose water to evaporation for thermoregulation (Ochocinska and Taylor 2005) and therefore prefer cooler, moist sites with sufficient leaf litter (Greenberg et al. 2007); typically, clearcuts are warmer and drier (Zheng et al. 2000) and have less leaf litter (Ash 1995) than intact forest. We found that leaf litter was indeed reduced following the clearcut and patch cut harvests (Table 5). Shrew abundance was further reduced at sites with a southwestern aspect (Table 2), which have higher temperatures due to more direct sunlight. Shrews must eat frequently to maintain their high metabolic rate (Ochocinska and Taylor 2005). The composition of soil invertebrates, the primary food source for shrews, can be altered following clearcuts (Niemela et al. 1993), potentially reducing available food and further discouraging use of recent clearcuts. However, increased levels of course woody debris in openings (Table 5) harbor large populations of insects which may serve as a valuable food source for shrews (Kirkland 1990). In general, our results are consistent with several other studies that reported fewer short-tailed shrews in recent clearcuts (Fuller et al. 2004, Urban and Swihart 2010). The 0.4 ha patch cuts, with less 'interior' opening habitat, may have been more accessible to shrews for foraging because they allow for quicker access to the cooler temperatures and deeper litter layer of the surrounding intact forest.
+
+Pine voles were also negatively affected by harvest openings (Table 3.2, Figures 3.3-3.4), with reductions in abundance in response to clearcuts and patch cuts. Pine voles prefer sites with well-developed leaf litter (Goertz 1971), and we suspect that the negative response to these silvicultural treatments was due in part to a loss of leaf litter in the openings (Table 3.5). In addition, when selecting nest locations, pine voles prefer soil types that are moist and minimize burrowing effort (e.g., loam, peat moss) (Rhodes and Richmond 1985). Reduced moisture (Zheng et al. 2000) and increased soil compaction from logging equipment following harvest may make clearcut and patch cut sites unsuitable for the establishment of pine vole nests.
+
+\subsection{Patterns of Response to Opening Size and Shelterwood Harvests}
+
+Overall, we observed few differences between opening size, though responses for white-footed mice and short-tailed shrews were different for the smallest openings (0.4 ha) compared to the larger 2 ha and 4 ha (clearcut) openings (Table 3.2). It appears that smaller opening sizes may be less detrimental to some small mammal species sensitive to management (the short-tailed shrew in this study). However, our ability to make conclusions about the effect of opening size is limited because we considered only three size categories (4 ha, 2 ha, and 0.4 ha).
+
+Consistent with our predictions, none of the small mammal species in this study were strongly affected by the midstory removal phase of shelterwood harvest (Figure 3.4). The midstory harvest left the canopy and leaf litter mostly intact, preserving habitat structure (Work et al. 2003). However, continued monitoring of small mammal populations at these sites is necessary to determine the effects of the final two shelterwood phases, culminating in the removal of large overstory trees.
+
+\subsection{Response to Mast Availability}
+
+Independent of treatment effects, we observed considerable annual variation in small mammal abundance over the 5 years of the study (Figure 3.3). This variation was especially pronounced in eastern chipmunk and white-footed mouse populations, which depend on hard mast (McShea 2000), but the effect of mast on population size was statistically important only for mice (Table 3.2). Following 2 years of low mast production in the fall of 2008 and 2009 (Figure 3.2), chipmunk and mouse populations declined in 2010 (Figure 3.3). Mast availability was also positively correlated with short-tailed shrew abundance (Table 3.2). This relationship is surprising because shrews are insectivores and do not consume hard mast (Whitaker and Hamilton 1998); in addition, shrew populations at our sites did not show strong annual variation between years of high and low mast availability (Figure 3.3). However, short-tailed shrews consume the larvae of acorn weevils (\textit{Curculio}) (Anderson and Folk 1993), and weevil populations may be positively linked to acorn availability (Govindan et al. 2012). Thus, weevil resources may act as an indirect link between mast production and shrew population size.
+
+\subsection{\textit{N}-mixture Model}
+
+In this study, we did not individually tag small mammals at all sites, precluding mark-release-recapture estimates of abundance. The choice to forego marking was made so we could increase the number of grids sampled and minimize handling stress for captured animals. Estimates derived from the \textit{N}-mixture model, when compared with MRR models, enabled more sites to be sampled, performed as well, and more reliably produced usable estimates. Specifically, the \textit{N}-mixture model yielded estimates of abundance that were similar in magnitude and bias to those from alternative MRR models, indicating that marking of individuals may not be an essential component of studies that examine population responses to treatments. Moreover, a substantial percentage (17\% for white-footed mice, 55\% for eastern chipmunks) of sampled populations failed to yield usable MRR estimates, whereas estimates were always obtained with the \textit{N}-mixture mode (though the paucity of data led to wider credible intervals).
+
+Estimates from the \textit{N}-mixture model were more accurate than simple counts of trapped individuals (Tables 3.3-3.4, Figure 3.5). Although we did not determine the effective area trapped, na\"{\i}ve estimates of population density for chipmunks (2-10 individuals/ha) and white-footed mice (5-15 individuals/ha) were comparable to estimates from studies in similar habitats (Ostfeld et al. 1996, Whitaker and Hamilton 1998, Ostfeld et al. 2006).
+
+\textit{N}-mixture models have been used to estimate abundance of a variety of species from repeated counts, including birds (K\'{e}ry et al. 2005) and fish (Webster et al. 2008). However, we believe that this is the first use of the \textit{N}-mixture model to estimate abundance from data collected in a terrestrial trapping array. In future trapping studies for which tagging individuals is difficult, or total number of captures is low, \textit{N}-mixture models may more reliably produce an estimate of abundance than MRR models and can provide a better estimate abundance than methods that do not incorporate detection probability (e.g., captures/100 trap nights).
+
diff --git a/chapter4.tex b/chapter4.tex
new file mode 100644
index 0000000..0c1ddbe
--- /dev/null
+++ b/chapter4.tex
@@ -0,0 +1,150 @@
+\chapter{{SMALL MAMMAL HABITAT USE IN HARVEST OPENINGS AS A FUNCTION OF OPENING SIZE AND MICROHABITAT VARIABLES}}
+
+\section{Introduction}
+
+The impacts of forest management on wildlife are an increasing concern for managers seeking to preserve ecosystem function in conjunction with silviculture. Common forms of both even- and uneven-aged forest management, including clearcutting and patch cutting, result in openings in which the majority of the overstory and midstory has been removed. This disturbance greatly alters many biotic and abiotic components of forest habitat. For example, removal of the midstory and overstory increases available sunlight, which can increase temperature and decrease moisture in both the air and soil (Zheng et al. 2000, Redding et al. 2003). Increased insolation and the removal of competing trees also change the composition of vegetation, both inside the opening and on the newly created edge (Phillips and Shure 1990, Elliott et al. 1997). Following harvest, a dense understory typically composed of opportunistic, fast-growing species emerges inside the opening (Elliott et al. 1997). Remnants of the logging process also remain once the harvest is completed; for example, the heavy machinery involved in logging compacts soil (Kimmins 1997) and facilitates the erosion of the leaf litter layer, which is reduced in recent clearcuts (Ash 1995). In addition, many logging operations leave behind downed smaller trees and treetops, increasing the amount of coarse woody debris.
+
+Small mammals are a group of forest species with varied dietary, behavioral, space use, and shelter requirements (Whitaker and Hamilton 1998). The abundance of some common small mammal species changes following the creation of new open and edge habitat by silviculture (see Chapter 3). These shifts in abundance may be due to changes in habitat following harvest. For example, increases in the abundance of the eastern chipmunk (\textit{Tamius striatus}) in clearcuts and patch cuts (Chapter 3) may occur because chipmunks use edge habitat (Johnson et al. 1979, King et al. 1998) with plenty of coarse woody debris to shield them from predators (Zollner and Crane 2003). Likewise, shrews (e.g., the short-tailed shrew, \textit{Blarina brevicauda}) may be reduced in abundance in openings (Chapter 3) because the amount of leaf litter, which they use for movement and foraging, is reduced (Ash 1995, Greenberg et al. 2007).
+
+Many studies of small mammal responses to forest management (the majority of which measure responses to clearcutting) report changes in small mammal relative abundance, but do not experimentally assess which habitat variables (e.g. vegetative cover, availability of coarse woody debris) may be contributing to these patterns (e.g. Fantz and Renken 2005). In addition, these studies rarely consider opening size as an explanatory variable. Harvests of all sizes create new edge habitat on the boundary of the opening. As the size of the harvest increases, the proportion of habitat within the opening that can be considered 'edge' will be reduced. The availability of edge habitat can affect the abundance and distribution of species, since edges provide quick access to adjacent habitat types (and may provide an 'intermediate' between them) and facilitate interactions between species normally confined to one habitat or the other (Ries et al. 2004). For some small mammal species, edges created by clearcuts may provide access to both food resources in the opening and cover in the nearby forest (King et al. 1998), though the dense layer of early successional vegetation typical of recent openings may provide sufficient cover. Other species, e.g. nest predators, may be attracted to increases in the abundance of prey species near edges (Gates and Gysel 1978). Species that use edge habitat may therefore respond positively to harvest openings, but with reduced magnitude as harvest size increases, since the proportion of edge habitat is concurrently reduced. In general, however, the studies that did consider opening size did not find a relationship between size and site occupancy and/or abundance of small mammals (Buckner and Shure 1985, Schmid-Holmes and Drickamer 2001, Urban and Swihart 2010). In contrast, I found some evidence of a relationship between the abundance of two species, the white-footed mouse and short-tailed shrew, and opening size; mice had lower abundance in small openings (0.4 ha) than in controls, while shrew populations were unchanged in 0.4 ha openings but reduced in larger openings (Chapter 3). Identifying relationships between habitat variables altered by silviculture and small mammal habitat use can be difficult, particularly because small mammals are likely reacting to changes at the spatial scale of the microhabitat, making data collection difficult.
+
+The objective of this study was to identify relationships between opening sizes, microhabitat, and use of local sites by four small mammal species: the eastern chipmunk (\textit{T. striatus}), white-footed mouse (\textit{Peromyscus leucopus}), short-tailed shrew (\textit{B. brevicauda}), and pine vole (\textit{Microtus pinetorum}). We measured a suite of variables at the microhabitat scale, and measured small mammal habitat use at a corresponding small spatial scale - the individual trap station. We assumed that probability of use (i.e., probability a trap station is 'occupied') is positively correlated with use of the microhabitat surrounding the station by small mammals. Other studies have made similar assumptions; for example, Greenberg (2002) used capture success as a surrogate for microsite use by white-footed mice. We improved upon previous efforts by implementing a hierarchical model that explicitly incorporated imperfect detection, which if ignored can be a substantial source of bias (Gu and Swihart 2004, MacKenzie et al. 2006).
+
+We predicted that opening size would not have an effect on trap-level use (Buckner and Shure 1985, Schmid-Holmes and Drickamer 2001, Urban and Swihart 2010). We expected the chipmunk to be more likely to use edge habitat (i.e., a higher probability of trap-site use on edges, Johnson et al. 1979, King et al. 1998) and the short-tailed shrew and pine vole, which decreased in abundance following harvest (Chapter 3) to have higher probabilities of use inside the forest. We predicted that probabilities of use would be positively correlated with coarse woody debris abundance for chipmunks and mice, since they may use fallen logs and branches for travel and avoiding predators (Greenberg 2002, Zollner and Crane 2003), and that use by short-tailed shrews and pine voles should be correlated with litter depth, since litter is used by both species for movement and foraging (Goertz 1971, Brannon 1997). We expected few relationships between white footed mice and microhabitat variables, since mice are typically able to make use of a variety of habitats (Adler and Tamarin 1984).
+
+\section{Materials and Methods}
+
+\subsection{Study Area}
+
+This study was conducted as part of the Hardwood Ecosystem Experiment (HEE), located in Morgan-Monroe and Yellowwood State Forests in south-central Indiana. A detailed description of the ecology of this area can be found in Chapters 2-3 and Jenkins (2012). The HEE focuses on the impacts of even- and uneven-aged management on forest ecology at a landscape scale. This study took advantage of a series of forest openings created by timber harvest under these management regimes; details of these harvests can be found in Kalb and Mycroft (2012). Specifically, six 4-ha openings (clearcuts) were created at HEE experimental units receiving even-aged management, and a series of smaller 0.4, 1, and 2 ha openings (patch cuts; Leak and Filip 1975) were created at units receiving uneven-aged management. We selected a subset of 18 of these openings of varying sizes in which to conduct this study of small mammal responses to opening size and microhabitat variables.
+
+\subsection{Trapping}
+
+Prior to timber harvest in winter-spring 2009, 18 trapping grids were established at the locations of future clearcuts and patch cuts. Of these 18 grids, 6 were located at the 4-ha clearcut sites (2 grids in each even-aged experimental unit) and 12 were at patch cut sites (4 in each uneven-aged unit). Initially, our goal was to have the 12 grids in patch cuts be evenly divided between opening sizes (0.4, 1, and 2 ha). However, following harvest, the actual opening sizes created resulted in 8 grids at 0.4 ha patch cuts and 4 grids at 2 ha patch cuts. Of these 18 grids, 10 were on northeast-facing slopes, and 8 had the opposite aspect (southwest).
+
+Each grid consisted of 27 trapping stations arranged in a 3 x 9 lattice. These grids were identical to the ones used in Chapter 3 in 2009-2011, but also incorporated several extra traps on the edge and interior of the surrounding forest to allow examination of the effect of position relative to the harvest. Specifically, each of the three transects of 9 traps was laid out across the slope, beginning inside the opening and extending into the surrounding forest matrix. On each grid, traps 1-6 were inside the opening, trap 7 was located on the harvest boundary and traps 8 and 9 were located in the forest matrix adjacent to the harvest. At the sites with larger openings (4 and 2 ha), transects were separated by 50 m. and traps 1-6 within each transect were separated by 20 m and traps 6-9 were separated by 50 m. At the sites with 0.4 ha openings, grids of this size would not fit. Therefore, transects were instead separated by 40 m and traps 1-7 within each transect were separated by 10 m. Therefore, the larger grids had an area of approximately 2.5 ha, and the smaller grids at 0.4 ha sites had an area of 1.6 ha. Each grid location was flagged and grid corners were also marked with GPS (Earthmate PN-40).
+
+The arrangement of traps at each point on an individual grid is described in detail in Chapter 3. Briefly, each point on the grid received a Sherman live trap (H.B. Sherman Trap, Inc., Tallahassee Fla). Grid locations 1, 3, 5, and 7-9 on the central transect also had a pitfall trap fashioned from a coffee can. In each of the 3 years of the study, microhabitat variables were recorded at every trap station. A 1-meter radius circle was placed adjacent to each Sherman trap. Within the circle, the percent herbaceous and woody cover for plants \textless 50 cm tall, and the length of all coarse woody debris \textgreater 5 cm in diameter, were recorded. At the center of the circle, the depth of the leaf litter was measured to the nearest 0.1 cm.
+
+Each grid was trapped for a 1-week period in each of the 3 years of the study, in the months of June-August 2009-2011. In 2010, we trapped only 8 of the 18 sites due to low small mammal populations following several consecutive poor mast years. Traps were baited with a mixture of sunflower seeds and oatmeal. Following 3 days of prebaiting, traps were checked twice daily in the morning and evening for 5 consecutive days. The species, sex, mass, and reproductive status of each captured individual was recorded before release. In the summers of 2010-2011, captured chipmunks and white-footed mice at 4 of the 18 grids were eartagged (Hasco Tag Company, Dayton, Kentucky).
+
+\subsection{Analysis}
+
+Probability of use was modeled using within a hierarchical multi-species, multi-season occupancy framework based on Royle and Dorazio (2008) and Urban and Swihart (2010). These two studies modeled small mammal occupancy at the spatial scale of the site (i.e., the trapping grid). In this study, we modeled small mammal occurrence at the spatial scale of the individual trap station. This approach allowed for direct use of the finest resolution data collected in the study - encounter histories at individual traps. In addition, since we collected trap-level microhabitat variables at three types of habitat within each trapping grid (harvest interior, edge, and adjacent forest), modeling occurrence at the trap station level allowed us to compare probability of habitat use between these three different habitats. The alternative, consolidating all trap-level encounter histories for a given species into a single site-level encounter history, ignores a large amount of data.
+
+The form taken by the multi-species, multi-season model closely resembles the one presented in Urban and Swihart (2010). It is a hierarchical model, with one subunit modeling the unobserved 'true' state of the system, and a second subunit for the actual observations made, conditional on the 'true' state. In each of the \textit{t} (=5) years of the study, \textit{j} (=18) sites each containing \textit{k} (=27) trap stations were sampled on \textit{l} (=5) consecutive days (sampling occasions). The true occupancy \textit{z} for species \textit{i} at a given site \textit{j} and trap station \textit{k} in a given year \textit{t} is modeled as a latent variable,
+\begin{equation}
+z_{ijkt} \sim \operatorname{Bernoulli} \left({\phi_{ijkt}}\right)
+\end{equation}
+where $\phi$ is the probability of occupancy.
+
+The number of times y that a given species \textit{i} was detected at site \textit{j} and trap location \textit{k} in year \textit{t} can be modeled, conditional on \textit{z}, as
+\begin{equation}
+y_{ijkt}|z_{ijkt} \sim \operatorname{Binomial} \left({n_i,z_{ijkt}\cdot p_{ijkt}}\right)
+\end{equation}
+where $n_{i}$ is the number of trapping occasions for species \textit{i} and \textit{p} is the species- and site-specific probability of detection. With this model structure, if species \textit{i} is truly absent from a given location ($z_i$ = 0), the observation $y_i$ will always be 0 as well. However, nondetections ($y_i$ = 0) do not imply absence; the species may be present ($z_i$ = 1) and undetected.
+
+We modeled occurrence and the probability of detection at the trap level; it is also possible, however, to scale up to site-level parameters. The probability of detecting a least one individual of a given species \textit{i} at site \textit{j} and time \textit{t} is equal to 1 minus the probability that the species is not detected at any trap station within the site; that is,
+\begin{equation}
+p_{(site)ijt} = 1 - \displaystyle\prod\limits_{k=1}^{27} p_{(trap)ijkt}
+\end{equation}
+Similarly, a site is occupied by a given species ($z_{(site)ijt} = 1$) as long as at least 1 trap is occupied by that species, that is,
+\begin{equation}
+\displaystyle\sum\limits_{k=1}^{27} z_{(trap)ijkt} \ge 1
+\end{equation}
+
+We used generalized linear mixed models (GLMMs) to determine the effects of covariates on parameters $\phi$ (probability of occupancy) and \textit{p} (probability of detection). Define
+\begin{equation}
+\operatorname{logit} \left({x}\right) = \operatorname{ln} \left({x \over 1-x}\right)
+\end{equation}
+and let $u_{ijkt} = \operatorname{logit} \left({\phi}\right)$ and $v_{ijkt} = \operatorname{logit} \left({p}\right)$. Transformed parameters \textit{u} and \textit{v} were set equal to linear functions of site- and time-based covariates. Parameter \textit{u} (transformed occupancy $\phi$) was a linear function of trap position (harvest interior, edge, forest matrix), a suite of microhabitat variables (herbaceous and woody cover, coarse woody debris, and litter depth) and random effects of site and species. The site random effect was itself a linear function of site aspect and opening size. In addition, we included in the predictor a parameter $\rho$ which represented the correlation between the presence of a species at a given trap station at time \textit{t} (that is, \textit{z}) with its presence there at time \textit{t} - 1. Estimates of $\rho$ \textgreater 0.5 indicate that a species was likely to 'persist' at a given trap station from time \textit{t} - 1 to \textit{t}, while $\rho$ \textless 0.5 suggests that presence is cycling between 0 and 1 with each time step (Russell et al. 2009). Parameter \textit{v} (logit transformed \textit{p}) was set equal to a linear function with temperature, precipitation, Julian day of sampling, and trapping effort as covariates, and a random effect of species. Effort was calculated as the number of trapping occasions a given species was exposed to at a trap station after accounting for disturbance.
+
+The analysis of trap-site use was fit in a Bayesian framework with non-informative priors in a manner similar to that found in Chapter 3. Briefly, intercept and slope parameters in the model had normal priors with mean and variance parameters selected from uniform [0,1] and uniform [0,7] distributions, respectively. Posterior distributions for parameters were generated using MCMC in the program WinBUGS (Spiegelhalter et al. 2003, Cambridge, UK; version 1.4.3) using the R2WinBUGS library (Sturtz et al. 2005) in R 2.10.0 (R Foundation for Statistical Computing, Vienna, Austria). The analysis consisted of 3 parallel MCMC chains, each with a burn-in of 6,000 iterations, a thinning rate of 30, and 11,000 total iterations. We considered model convergence to be adequate when all parameters had R values \textless 1.1 (Brooks and Gelman 1998). We considered covariates to have important effects if the 95\% credible interval around the corresponding slope parameter did not include 0.
+
+\section{Results}
+
+Over 3 years, we captured 2,156 small mammals of 5 different species during 13,068 trap days/nights (Table 4.1). White-footed mice and eastern chipmunks were by far the most common species captured, but we also captured short-tailed shrews, pine voles, and a single smoky shrew (\textit{Sorex fumeus}). We limited our analysis to the four most common species.
+
+% Table generated by Excel2LaTeX from sheet 'Sheet1'
+\begin{table}[htbp]
+ \centering
+ \caption{Total captures of small mammals at 18 study sites, 2009-2011. In 2010, following a poor mast year, only 8 of the 18 sites were sampled.}
+ \begin{tabular}{lcccc}
+ \toprule
+ \multicolumn{1}{l}{\textbf{Species}} & \textbf{2009} & \textbf{2010} & \textbf{2011} & \textbf{Total} \\
+ \midrule
+ White-footed mouse & 619 & 20 & 433 & 1072 \\
+ Eastern chipmunk & 576 & 119 & 362 & 1057 \\
+ Pine vole & 6 & 0 & 0 & 6 \\
+ Short-tailed shrew & 11 & 2 & 7 & 20 \\
+ Smoky shrew & 0 & 1 & 0 & 1 \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{table}%
+
+Important predictors of probability of detection included temperature, effort, and Julian day of sampling (Table 4.2). The probability of detecting chipmunks and white-footed mice was negatively related to temperature. Julian day of sampling was negatively related to p for chipmunks, indicating they were more likely to be detected early in the trapping season (June). Trapping effort was positively related to detection probability for white-footed mice. There were no statistically important predictors of \textit{p} for short-tailed shrews or pine voles.
+
+% Table generated by Excel2LaTeX from sheet 'Sheet2'
+\begin{sidewaystable}[htbp]
+ \centering
+ \caption{Fitted parameter values from the multi-species, multi-season occupancy model. Bolded values have 95\% credible intervals that do not include 0.}
+ \begin{tabular}{rcccccccc}
+ \toprule
+ \textbf{} & \multicolumn{2}{c}{\textbf{Eastern chipmunk}} & \multicolumn{2}{c}{\textbf{White-footed mouse}} & \multicolumn{2}{c}{\textbf{Short-tailed shrew}} & \multicolumn{2}{c}{\textbf{Pine vole}}\\
+ \midrule
+ \textbf{Covariate} & \textbf{$\beta_j$} & \textbf{SE} & \textbf{$\beta_j$} & \textbf{SE} & \textbf{$\beta_j$} & \textbf{SE} & \textbf{$\beta_j$} & \textbf{SE} \\
+ \multicolumn{1}{c}{\textit{\textbf{p}}} & & & & & & & & \\
+ \multicolumn{1}{l}{Temperature} & \textbf{-0.15} & 0.04 & \textbf{-0.13} & 0.03 & -0.07 & 0.09 & -0.19 & 0.14 \\
+ \multicolumn{1}{l}{Precipitation} & 0.04 & 0.03 & -0.03 & 0.03 & -0.11 & 0.14 & 0.02 & 0.17 \\
+ \multicolumn{1}{l}{Julian day} & \textbf{-0.18} & 0.05 & 0.10 & 0.06 & 0.30 & 0.31 & 0.37 & 0.55 \\
+ \multicolumn{1}{l}{Effort} & -0.02 & 0.15 & \textbf{0.52} & 0.17 & 0.05 & 0.49 & 0.05 & 0.90 \\
+ \multicolumn{1}{c}{\textbf{$\phi$}} & & & & & & & & \\
+ \multicolumn{1}{l}{$\rho$} & \textbf{-0.93} & 0.24 & \textbf{-3.29} & 0.36 & -1.83 & 1.56 & -1.36 & 1.92 \\
+ \multicolumn{1}{l}{Aspect (NE=1)} & 0.26 & 0.32 & \textbf{0.80} & 0.34 & \textbf{1.51} & 0.83 & 0.92 & 0.83 \\
+ \multicolumn{1}{l}{Harvest edge} & 0.32 & 0.31 & -0.27 & 0.36 & -1.13 & 1.33 & 0.01 & 1.05 \\
+ \multicolumn{1}{l}{Harvest interior} & 0.43 & 0.25 & 0.06 & 0.29 & \textbf{0.33} & 0.57 & 0.09 & 0.64 \\
+ \multicolumn{1}{l}{2 ha opening} & -0.25 & 0.30 & -0.02 & 0.33 & -0.57 & 0.78 & -0.18 & 0.73 \\
+ \multicolumn{1}{l}{4 ha opening} & -0.39 & 0.23 & 0.17 & 0.29 & -0.84 & 0.69 & -0.56 & 0.81 \\
+ \multicolumn{1}{l}{Herb cover} & \textbf{-0.22} & 0.09 & \textbf{-0.31} & 0.10 & -0.16 & 0.26 & -0.21 & 0.27 \\
+ \multicolumn{1}{l}{Woody cover} & -0.43 & 1.25 & -0.69 & 1.35 & -0.56 & 1.26 & -0.60 & 1.34 \\
+ \multicolumn{1}{l}{CWD} & \textbf{0.25} & -0.90 & 0.17 & 0.10 & 0.10 & 0.25 & 0.04 & 0.36 \\
+ \multicolumn{1}{l}{Litter depth} & 0.68 & 1.25 & 0.41 & 1.35 & 0.44 & 1.24 & 0.48 & 1.31 \\
+ \bottomrule
+ \end{tabular}%
+ %\label{tab:addlabel}%
+\end{sidewaystable}%
+
+Neither opening size (relative to the smallest openings, 0.4 ha) nor transect position (opening interior, harvest edge, and forest matrix) were important predictors of trap-level occurrence for any species (Table 4.2, Figures 4.1-4.2). Although there was no relationship between occurrence and opening size in the model, overall mean trap use was reduced for mice in the largest opening size (4 ha, Figure 4.1). Inference about the relationship between opening size and white-footed mouse use of sites is confounded by conditions in the second year of the experiment. In 2010, only 2 sites smaller than 4 ha were sampled, and only 20 mice were captured after two consecutive poor mast years (Table 4.1, Chapter 3). This paucity of data that year is likely pulling the mean probability of trap-site use for 4 ha sites down relative to the smaller opening sizes. Therefore, we confidently claim, consistent with the model results, that there were no effects of opening size on any species.
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch4_Fig1_final}
+\caption{Mean trap-level occupancy for the four most common small mammal species at sites with different opening sizes.}
+%\label{fig:}
+\end{figure}
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch4_Fig2_final}
+\caption{Mean trap-level occupancy for four small mammal species by position relative to the harvest (harvest interior, harvest edge, adjacent forest matrix).}
+%\label{fig:}
+\end{figure}
+
+There was a trend of increased chipmunk occurrence in the interior and on the edge of harvest openings, while white-footed mice and short-tailed shrews tended to be found less frequently on edges. Site aspect was an important predictor of occurrence for white-footed mice and short-tailed shrews, with both responding positively to northeastern slopes. The parameter $\rho$ (temporal autocorrelation at a given trap location) was negative for both chipmunks and white-footed mice, indicating 'cycling' between occurrence states 0 and 1 between years for these species (Table 4.2, Russell et al. 2009).
+
+In general, microhabitat variables were not strongly related to trap-site use (Table 4.2). For chipmunks, trap-site use ($\phi$) was highest at sites with low herbaceous cover and high amounts of coarse woody debris. White-footed mice were also associated with low herbaceous cover.
+
+\section{Discussion}
+
+Use of trap-site microhabitats was not related to opening size nor position relative to the opening (edge or interior; Figures 4.1-4.2). Several studies have shown that the abundance of white-footed mice was similar in openings and intact forest (Chapter 2, Kaminski et al. 2007, Urban and Swihart 2010). A habitat generalist, the white-footed mouse appears to make use of a diverse range of habitats including the thick understory vegetation found in recent harvest openings of a range of sizes (Buckner and Shure 1985, Yahner 1986, Greenberg 2002). Therefore, it is not surprising that we were unable to detect a difference in use based on either opening size or position relative to the opening.
+
+Chipmunks appear to respond positively to openings created by harvest (Urban and Swihart 2010, Chapter 2), but the probability of site use was similar at all opening sizes (Figure 4.1), and there were no differences in use between edge and harvest interior habitats (Figure 4.2). Therefore, it appears that chipmunks at our study sites were not using either harvest 'interior' habitat (which increases as the size of the opening increases) or edge habitat preferentially. It is possible that the chipmunks, which have home ranges as large as 1 ha (Blair 1942, Yerger 1953, Whittaker and Hamilton 1998) may perceive much of even the largest openings as edge habitat. Chipmunks were more likely to occupy trap stations where the quantity of adjacent coarse woody debris increased; they may prefer microhabitat with plenty of coarse woody debris for easier travel in the dense vegetation of the opening as well as protection from predators (Zollner and Crane 2003).
+
+Site use was negatively related to the amount of herbaceous cover for both chipmunks and white-footed mice and unrelated to the amount of woody cover around trap stations (Table 4.2). This is somewhat surprising, since both herbaceous and woody-stemmed plants should provide small mammals cover from predators as well as food in the form of soft mast (Reynolds-Hogland et al. 2006). This ought to be particularly important inside openings where hard mast is unavailable and the removal of the overstory increases the risk from avian predators. High densities of understory vegetation inside openings could impair the movement or sight range of small mammals, resulting in the pattern of reduced occupancy we observed. Alternatively, herbaceous cover may be negatively correlated with another habitat variable to which white-footed mice and chipmunks are responding. Specifically, microhabitats with large amounts of coarse woody debris (to which chipmunks responded positively, Table 4.2) may have reduced herbaceous cover due to shade and reduced growing space.
+
+Inference about short-tailed shrews and pine voles is limited by the small sample sizes we obtained for these species (Table 4.1). Litter depth was not positively related to short-tailed shrew site use, as we expected, though there was a trend in this direction (Table 4.2). Though shrews travel and forage in the leaf litter (Brannon 1997), at least one study found a significant negative correlation between shrew captures and litter depth (Ford et al. 1997). One possible explanation is that shrews have a unimodal, rather than linear, response to litter depth. Alternatively, we may have lacked the statistical power (only 20 shrews were captured) to identify an effect of litter depth.
+
+Overall, we found little evidence for differences in small mammal habitat use as a function of opening size or between edge and interior habitat created by harvest. Though several species responded either positively (eastern chipmunk) or negatively (short-tailed shrew, pine vole) in abundance to the creation of openings by timber harvest in a related study (Chapter 3), these effects were unchanged by the range of opening sizes (0.4 ha - 4 ha) that we studied. From a management perspective, even-aged management using clearcuts ($\ge$ 4 ha) appears to affect the small mammal community at our sites similarly to smaller (0.4-2 ha) patch cuts made as part of an uneven-aged management regime.
+
+
diff --git a/conclusion.tex b/conclusion.tex
new file mode 100644
index 0000000..3efc261
--- /dev/null
+++ b/conclusion.tex
@@ -0,0 +1,39 @@
+\chapter{{CONCLUSION}}
+
+\section{Interactions of Oak, Mammals, and Insects}
+
+As a keystone species in eastern deciduous forests (Fralish 2004), oaks (\textit{Quercus}) are known to have a disproportionately large impact on many species of animals and insects (Wolff 1996, Ostfeld et al. 1996, Lombardo and McCarthy 2008). My thesis further elucidates these interactions, providing information on the complex relationship between acorn production and populations of small mammals and acorn weevils. Since weevils and many small mammals act as seed predators (and also as dispersal agents in the case of some small mammals), there are implications for oak populations as well.
+
+The abundances of the most common small mammal species in my study system (the white-footed mouse, \textit{Peromyscus leucopus}, and the eastern chipmunk, \textit{Tamius striatus}) were closely linked to mast availability (Figure 3.3, Table 3.2). Following good mast years in 2006 and 2007, the resulting large populations of small mammals faced a shortage of mast in 2008 (Figures 3.2 and 3.3). The probability of acorn removal was highest in the fall of 2008, reflecting the increased value of acorns (even less desirable acorns) when mast was scarce (Table 2.4, Figure 2.4). Although we do not know the ultimate fate of removed acorns, the shortage of mast likely meant that nearly all removed acorns were eventually eaten. Surprisingly, small mammal populations did not immediately crash following the poor mast year in 2008; abundances were high in summer 2009 (Figure 3.3). However, a second consecutive poor mast year in 2009 did precipitate a crash in white-footed mouse and chipmunk populations in 2010 (Figures 3.2 and 3.3). I did not examine acorn removal probabilities in 2010 as part of this thesis, but I expect that they will be low, reflecting low small mammal abundance. If so, the data collected in these two studies (Chapters 2 and 3) provide some support for the predator-satiation hypothesis, which posits that episodic mast production benefits plants by creating scenarios in which high seed production coincides with low predator abundance, maximizing the chance seeds will escape predation and successfully germinate (Wolff 1996).
+
+A similar pattern of interactions between acorn production and acorn weevils is evident based on data collected for this thesis, although weevil populations were not measured explicitly. As with acorn removal, probability of infestation was maximized in the year (2008) in which mast was most scarce (Figure 2.3). Presumably, weevil populations were large following several good mast years (2006-2007), and emerged to find few suitable acorns for infestation in 2008, resulting in high probabilities of acorn infestation. When forthcoming post-harvest data from the experiment detailed in Chapter 2 is examined, I expect that following the poor mast years of 2008-2009 (Figure 3.2), infestation probability was low in 2010, reflecting declines in the weevil population. However, weevils can remain in diapause in the soil for multiple years, potentially avoiding the predator-satiation strategy (Maeto and Ozaki 2003)
+
+\section{Implications for Successful Oak Regeneration}
+
+The creation of canopy openings via silviculture creates light conditions favorable to the growth of shade-intolerant oak (Dey and Parker 1996, Larsen and Johnson 1998). However, in order for oak regeneration to be successful at a harvested site, there must be adequate advanced oak reproduction; both seedlings and saplings existing before the harvest and stump sprouts created by the harvest are crucial components of this advance regeneration (Sander 1972, Sander et al. 1984). The amount of advance oak reproduction in the understory prior to harvest is tied directly to the interactions of oak and seed predators like small mammals and weevils.
+
+Understanding the effects of seed predators on successful establishment of oak seedlings is important managers seeking to successfully regenerate oak. Seedling success is likely to be maximized when predation and infestation are minimized. As described above, this may occur when a large acorn crop is produced following several poor crops which suppressed predator populations. Within a few years, this seedling cohort will reach sufficient size (\textgreater 1.4 m) to compete following harvest (Sander 1972, Sander et al. 1984) and contribute to regeneration of oak at the site. Of course, there are many other factors that influence the survival and growth of oak seedlings; notably, herbivory by deer (\textit{Odocoileus virginianus}) and other browsers (Marquis et al. 1976).
+
+\section{Recommendations and Lessons Learned}
+
+\subsection{Acorn Production and Fate}
+
+There are two important limitations to the study described in Chapter 2. First, I modeled acorn production without considering detailed tree characteristics (e.g., canopy volume) or accounting for differences in tree populations between stands. Since acorn production is closely tied to tree characteristics (Greenberg and Parresol 2002), and there are likely to be differences in tree composition between study sites (stands), these factors will need to be considered before we can determine if there are differences in acorn production based on silvicultural treatment. This data has been collected, and will be used in future studies that compare acorn production before and after harvest at HEE.
+
+Second, I used acorn removal probability as a proxy for acorn predation. Removal rates are much easier to measure than ultimate acorn fate; however, there is consensus that examining removal is generally not a good substitute for directly studying acorn predation (Vander Wall et al. 2005, Moore and Swihart 2008). Secondary dispersal (i.e., removal) by some seed predators is actually beneficial to oak germination success; we cannot necessarily equate the removal of an acorn to mortality. Further experiments that follow seeds to their final fate are necessary to fully understand the potential impacts of silviculture on acorn predation.
+
+\subsection{Small Mammal Trapping}
+
+After 5 years of small mammal trapping at the Hardwood Ecosystem Experiment (3 of which I participated in myself), I have a few observations and suggestions for future direction. Two directions can be taken when conducting small mammal sampling in the context of a long-term study. The first is to individually tag and record information (e.g. sex, weight) for all individuals. This method maximizes the potential amount of information gained, but also increases the effort required and potentially reduces the number of sites sampled. The second option is to target abundance information only (perhaps using the \textit{N}-mixture model described in Chapter 3) and forego recording any information about captured individuals beyond species; handling would not be necessary. This would minimize sampling effort and allow more sites to be sampled more quickly. At HEE, we chose a sampling strategy between these two extremes. Specifically, most animals were not individually marked, but we did collect some information unique to each individual (sex, weight). Since all animals were handled anyway, tagging would not have required that much additional effort; we neither maximized the information obtained from the study nor minimized the effort required.
+
+The efficiency of small mammal sampling could be improved in future years of this project by instead picking one of the two options I described above. Alternatively, a compromise would be to conduct intensive small mammal trapping with individual marks in the years immediately before and after a harvest is conducted at a site. This would allow for estimates of survival and reproduction. In intervening years, changes in abundance could be monitored more efficiently using the second option in which animals were not handled. This would allow an unbroken sequence of years in which small mammals were sampled (a requirement for some grant applications) without requiring a large technician workforce in every year of the study.
+
+\subsection{\textit{N}-mixture Model}
+
+In Chapter 3, I presented an argument that the \textit{N}-mixture model is a good alternative to mark-release-recapture (MRR) models when animals have not been individually marked. Both empirical data and simulation modeling backed up this claim, as abundance estimates from the two models are similar (Figure 3.5). I believe the \textit{N}-mixture model could be useful for other small mammal studies; however, researchers should carefully consider their goals and characteristics of the system they are studying before choosing to use it.
+
+First, researchers should be certain they are only interested in estimates of abundance. The \textit{N}-mixture model does not allow examination of any other population characteristics like survival or recruitment. While this is an important limitation, there are many research contexts in which abundance may be sufficient; for example, long-term studies like the HEE. Second, researchers should ensure that they have considered the assumptions made by the \textit{N}-mixture model. Trap saturation is one important problem to consider. If there are many more animals than traps at a given site and detection probability is high, nearly all traps will be filled and abundance will be consistently underestimated by the model. Solving this problem simply requires that adequate traps are set at each site, a number which can be determined by pilot studies. Rather than decreasing the space between traps in order to include more in a grid, it may be preferable to set several traps at each trap station.
+
+An additional problem is the assumption that all animals have an equal probability of capture in a given time-step. This is not likely to be true in any study, since small mammals typically exhibit a variety of potential trap responses. Without individual marking, it is impossible to measure these responses. The best way to determine if this is introducing bias into abundance estimates is to apply both MRR models and the \textit{N}-mixture model to a subset of sites in which animals are individually marked (as in Chapter 3). If the estimates are very similar, the added precision in estimates based on individual marking may not be worth the extra effort - the \textit{N}-mixture model may be sufficient.
+
+
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diff --git a/front.tex b/front.tex
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+\begin{dedication}
+For my parents, the greatest teachers I will ever have.
+\end{dedication}
+
+\begin{acknowledgments}
+My greatest thanks go to my advisor, Robert Swihart, for his wise and patient counsel. My committee members, Harmony Dalgleish, Michael Saunders, Michael Steele, and Patrick Zollner, provided invaluable advice and numerous improvements to this manuscript. Also, I would like to thank my lab group, Nathanael Lichti, Natasha Urban, Byju Govindan, Mekala Sundaram, Rita Blythe, and Timothy Smyser, for their support.
+
+I am grateful to Jeff Riegel, Rebecca Kalb, and Cortney Mycroft for their role in collecting the data used in this thesis. Numerous field technicians also contributed to this effort.
+
+This thesis is a contribution of the Hardwood Ecosystem Experiment, a
+partnership of the Indiana Department of Natural Resources, Purdue University,
+Ball State University, Indiana State University, Drake University, Indiana
+University-Pennsylvania, and the Nature Conservancy. Funding for the project
+was provided by the Indiana Division of Forestry, the Department of Forestry
+and Natural Resources at Purdue University, and the Purdue University Graduate School.
+\end{acknowledgments}
+
+\tableofcontents
+\listoftables
+\listoffigures
+
+\begin{abstract}
+
+Oaks (\textit{Quercus}) are a keystone species in eastern deciduous forests. One of their most important functions is the production of hard mast (acorns), a crucial food resource for many wildlife species. Oaks are relatively shade-intolerant and have been maintained as a dominant overstory species in many forests through a cycle of disturbance including fire and land clearing for agriculture. Recently, changing disturbance regimes have resulted in oak regeneration failure. Eventually, oak-dominated stands may instead be dominated by other tree species, such as maple (\textit{Acer}). This shift may have important negative consequences for other forest species that rely on oak, especially those that consume hard mast. Forest managers have begun to use silviculture (i.e., timber harvest) as an artificial disturbance mechanism in an effort to promote oak regeneration and maintain oak dominance in deciduous forests, with mixed results.
+
+Understanding the effects of timber harvest on wildlife species is an important corollary to these efforts, particularly in the case of wildlife species that themselves affect successful oak regeneration (e.g., small mammals and acorn weevils (\textit{Curculio})). In this thesis, I examined the impact of two groups of acorn predators, small mammals and acorn weevils, on the survival of acorns prior to the implementation of timber harvest (Chapter 2). I then studied the effect of several methods of silviculture on common small mammal species, two of which (the white-footed mouse, \textit{Peromyscus leucopus}, and the eastern chipmunk, \textit{Tamius straitus}) are consumers of acorns (Chapters 3-4). I used two recently developed statistical techniques, an \textit{N}-mixture model of abundance and a multi-species occupancy model, to make inferences about small mammal responses to harvest. Both models explicitly incorporated variability in detection probability, which if ignored can be a significant source of bias.
+
+I found that acorn mortality due to both weevils and removal by small mammals was maximized when mast production was lowest, reflecting the scarcity of this crucial food resource. In years when mast was abundant, small mammal predators preferred to remove more valuable acorns (e.g., acorns that were undamaged). Not surprisingly, abundance of granivorous small mammals were closely linked to mast availability throughout the study.
+
+Of the common small mammals that were the focus of the second portion of this thesis, one (the eastern chipmunk) generally responded positively to the creation of openings through timber harvest. A second, the generalist white-footed mouse, generally did not respond strongly to any type of silviculture. In contrast, the short-tailed shrew (\textit{Blarina brevicauda}) and the pine vole (\textit{Microtus pinetorum}) generally responded negatively to the creation of openings via harvest. In general, small mammals did not respond strongly to the first stage of a shelterwood harvest. Some of these shifts may have been due to changes in habitat variables following harvest; notably, the eastern chipmunk was associated with areas containing a large amount of coarse woody debris, which increased following the creation of openings.
+
+The results of this thesis will be useful in furthering our understanding of how silviculture affects insects and wildlife that play important roles in the oak life cycle. Successful oak regeneration depends on proper implementation of silviculture, but also on ensuring that potential sources of oak mortality, such as small mammals and weevils, do not create a bottleneck in the oak life cycle. My results lay the groundwork for future studies that examine the effects of animals and insects on the entire life cycle of oak in managed forests.
+
+\end{abstract}
diff --git a/introduction.tex b/introduction.tex
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+++ b/introduction.tex
@@ -0,0 +1,31 @@
+\chapter{{INTRODUCTION}}
+\section{Importance of Oak in Hardwood Forests}
+
+Now recognized as a 'keystone' and 'foundation' species group in eastern deciduous forests (Fralish 2004, Ellison et al. 2006), oaks (\textit{Quercus}) are capable of regulating forest nutrient and hydrology cycles, and providing habitat and food for a variety of invertebrate and vertebrate species (Brandle and Brandle 2001, Johnson et al. 2002, McShea et al. 2007). Production of a hard mast crop (i.e., acorns) is an important function of oaks; at least 44 species of forest mammals and birds rely on mast as a food source (McShea et al. 2007). The importance of oak as a food source for wildlife has increased greatly following the extirpation of American chestnut (\textit{Castanea dentata}), historically a prolific producer of hard mast, due to chestnut blight (Diamond et al. 2000, McShea et al. 2007, Dalgleish and Swihart 2012).
+
+Oak is a shade-intolerant genus (Larsen and Johnson 1998) requiring sufficient light to successfully regenerate. In the past, oaks have been maintained as dominant species in eastern forests through a regime of natural and anthropogenic disturbance events, which opened the forest canopy to allow sufficient light for successful regeneration (Abrams 2003). Prior to European settlement, this disturbance came in the form of wildfire (Abrams 1992, Abrams 2003) and land management by Native Americans (Whitney 1994, Lorimer 2001). Following the spread of Europeans through eastern forests, a cycle of clearing for agriculture and subsequent land abandonment created light conditions ideal for successful oak regeneration (Fralish 1997). Recently, fire suppression and fragmentation due to agriculture and development has interrupted this disturbance regime, stifling oak regeneration and instead encouraging the growth of shade-tolerant species like maple \textit{(Acer}; Aldrich et al. 2005, Fralish and McArdle 2009). For example, in many deciduous stands in southern Indiana the overstory is dominated by oak species (e.g., black oak, \textit{Quercus velutina}), while the understory is dominated by maple (Figure 1.1). Without disturbance, the overstory at these sites will eventually be dominated by shade-tolerant species.
+
+\begin{figure}[t]
+\centering
+\includegraphics[scale=0.6]{figures/Ch1_Fig1_final}
+\caption{Size distributions for two tree species, shade-intolerant black oak (\textit{Quercus velutina}) and shade-tolerant sugar maple (\textit{Acer saccharum}), in southern Indiana deciduous forest stands. Vertical black lines are the distribution medians.}
+%\label{fig:}
+\end{figure}
+
+To maintain oak as an important component of eastern forests, managers have begun to use silviculture as a tool to create disturbance regimes that favor oak regeneration (Larsen and Johnson 1998). Harvesting overstory and midstory trees creates early successional habitat with light conditions that favor the growth of oak seedlings. However, silviculture with the goal of promoting oak has met with mixed success; the type of harvest employed and ecological characteristics of the site are likely important factors (Dey et al. 2009).
+
+\section{Interactions of Oak, Wildlife, and Forest Management}
+
+In addition to identifying the silvicultural techniques that best encourage oak regeneration, researchers and forest managers are increasingly concerned with the effects of timber harvest on wildlife communities. The effects of silviculture on species that directly impact the oak life cycle are of particular interest, since changes in the abundance or distribution of these species following harvest could have implications for successful oak regeneration.
+
+A number of vertebrate and invertebrate forest species rely heavily on oak, and in turn influence the oak life cycle. Acorn weevils (genera \textit{Curculio} and \textit{Conotrachelus}) and granivorous mammals (e.g., the white-footed mouse \textit{Peromyscus leucopus}, eastern chipmunk \textit{Tamias striatus}, and the eastern gray squirrel \textit{Sciurus carolinensis}) are particularly reliant on acorns produced by oaks. The abundance of these species is often closely tied to the availability of mast (Wolff 1996, Ostfeld et al. 1996, McShea 2000), and as acorn predators (or, in the case of the weevil, parasites), they greatly impact the oak life cycle (Marquis et al. 1976). For example, weevil larvae may infest 50-90\% of the acorn crop (Gribko 1995, Riccardi et al. 2004, Lombardo and McCarthy 2008); these infested acorns are less likely to germinate successfully (Andersson 1992, Lombardo and McCarthy 2009) and even when they do germinate the resulting seedling may be less vigorous (Gribko 1995). Many of the acorns that escape infestation will subsequently be eaten by granivores, particularly small mammals (McShea 2000, McShea and others 2007). However, some seed predators (e.g. \textit{S. carolinensis}) create small caches of acorns, which, if not recovered, contribute to germination and establishment (Smallwood et al. 2001, Steele et al. 2006).
+
+Changes in habitat following silviculture affect a wide variety of species. For example, timber harvest reduced abundance and richness of salamanders and amphibians (Demaynadier and Hunter 1998) and altered species composition of birds (Annand and Thompson 1997, Costello et al. 2000). More importantly for the oak life cycle, silviculture can have an effect on the abundance of granivorous small mammals; for example, eastern chipmunks have responded positively to openings created by harvest in several studies (Kirkland 1990, Urban and Swihart 2010). In contrast, silvicultural treatments had no effect on probability of infestation by acorn weevils (Lombardo and McCarthy 2008), although there is limited information available on weevil responses to timber harvest. Further study in this area is needed to better understand the effects of silviculture on species that affect the oak life cycle as predators or dispersal agents.
+
+
+\section{Thesis Summary}
+
+I examined interactions of oak and species that impact the oak life cycle, including acorn weevils, white-tailed deer (\textit{Odocoileus virginianus}), and small mammals, in a series of studies across a landscape in the Central Hardwood Forest Region. All of these studies took place in a long-term, large-scale experimental framework, the Hardwood Ecosystem Experiment, which has the goal of identifying responses of forest ecosystems to management for oak regeneration.
+
+First, I identified factors affecting the primary sources of acorn mortality: weevil infestation and consumption by seed predators. This study was carried out prior to the application of a series of silvicultural treatments and will provide a baseline for future comparisons in factors affecting acorn mortality between these treatments. Next, I examined the short-term responses of small mammal populations to silviculture using a novel technique for estimating abundance without the use of individual marks. This analysis explicitly incorporated imperfect detection, a source of error unaccounted for in previous studies (Gu and Swihart 2004, Urban and Swihart 2010), and considered the role of variation in mast availability. Finally, I identified several habitat variables affected by silvicultural treatments that may be driving the changes in abundance observed for small mammal species.
+
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+ \global\@mparbottom\z@
+ \pagegoal\vsize
+ \endgraf\penalty\z@\addvspace\LTpost
+ \ifvoid\footins\else\insert\footins{}\fi}
+\def\LT@nofcols#1&{%
+ \futurelet\@let@token\LT@n@fcols}
+\def\LT@n@fcols{%
+ \advance\LT@cols\@ne
+ \ifx\@let@token\LT@nofcols
+ \expandafter\@gobble
+ \else
+ \expandafter\LT@nofcols
+ \fi}
+\def\LT@tabularcr{%
+ \relax\iffalse{\fi\ifnum0=`}\fi
+ \@ifstar
+ {\def\crcr{\LT@crcr\noalign{\nobreak}}\let\cr\crcr
+ \LT@t@bularcr}%
+ {\LT@t@bularcr}}
+\let\LT@crcr\crcr
+\let\LT@setprevdepth\relax
+\def\LT@t@bularcr{%
+ \global\advance\LT@rows\@ne
+ \ifnum\LT@rows=\LTchunksize
+ \gdef\LT@setprevdepth{%
+ \prevdepth\z@\global
+ \global\let\LT@setprevdepth\relax}%
+ \expandafter\LT@xtabularcr
+ \else
+ \ifnum0=`{}\fi
+ \expandafter\LT@LL@FM@cr
+ \fi}
+\def\LT@xtabularcr{%
+ \@ifnextchar[\LT@argtabularcr\LT@ntabularcr}
+\def\LT@ntabularcr{%
+ \ifnum0=`{}\fi
+ \LT@echunk
+ \LT@start
+ \unvbox\z@
+ \LT@get@widths
+ \LT@bchunk}
+\def\LT@argtabularcr[#1]{%
+ \ifnum0=`{}\fi
+ \ifdim #1>\z@
+ \unskip\@xargarraycr{#1}%
+ \else
+ \@yargarraycr{#1}%
+ \fi
+ \LT@echunk
+ \LT@start
+ \unvbox\z@
+ \LT@get@widths
+ \LT@bchunk}
+\def\LT@echunk{%
+ \crcr\LT@save@row\cr\egroup
+ \global\setbox\@ne\lastbox
+ \unskip
+ \egroup}
+\def\LT@entry#1#2{%
+ \ifhmode\@firstofone{&}\fi\omit
+ \ifnum#1=\c@LT@chunks
+ \else
+ \kern#2\relax
+ \fi}
+\def\LT@entry@chop#1#2{%
+ \noexpand\LT@entry
+ {\ifnum#1>\c@LT@chunks
+ 1}{0pt%
+ \else
+ #1}{#2%
+ \fi}}
+\def\LT@entry@write{%
+ \noexpand\LT@entry^^J%
+ \@spaces}
+\def\LT@kill{%
+ \LT@echunk
+ \LT@get@widths
+ \expandafter\LT@rebox\LT@bchunk}
+\def\LT@rebox#1\bgroup{%
+ #1\bgroup
+ \unvbox\z@
+ \unskip
+ \setbox\z@\lastbox}
+\def\LT@blank@row{%
+ \xdef\LT@save@row{\expandafter\LT@build@blank
+ \romannumeral\number\LT@cols 001 }}
+\def\LT@build@blank#1{%
+ \if#1m%
+ \noexpand\LT@entry{1}{0pt}%
+ \expandafter\LT@build@blank
+ \fi}
+\def\LT@make@row{%
+ \global\expandafter\let\expandafter\LT@save@row
+ \csname LT@\romannumeral\c@LT@tables\endcsname
+ \ifx\LT@save@row\relax
+ \LT@blank@row
+ \else
+ {\let\LT@entry\or
+ \if!%
+ \ifcase\expandafter\expandafter\expandafter\LT@cols
+ \expandafter\@gobble\LT@save@row
+ \or
+ \else
+ \relax
+ \fi
+ !%
+ \else
+ \aftergroup\LT@blank@row
+ \fi}%
+ \fi}
+\let\setlongtables\relax
+\def\LT@get@widths{%
+ \setbox\tw@\hbox{%
+ \unhbox\@ne
+ \let\LT@old@row\LT@save@row
+ \global\let\LT@save@row\@empty
+ \count@\LT@cols
+ \loop
+ \unskip
+ \setbox\tw@\lastbox
+ \ifhbox\tw@
+ \LT@def@row
+ \advance\count@\m@ne
+ \repeat}%
+ \ifx\LT@@save@row\@undefined
+ \let\LT@@save@row\LT@save@row
+ \fi}
+\def\LT@def@row{%
+ \let\LT@entry\or
+ \edef\@tempa{%
+ \ifcase\expandafter\count@\LT@old@row
+ \else
+ {1}{0pt}%
+ \fi}%
+ \let\LT@entry\relax
+ \xdef\LT@save@row{%
+ \LT@entry
+ \expandafter\LT@max@sel\@tempa
+ \LT@save@row}}
+\def\LT@max@sel#1#2{%
+ {\ifdim#2=\wd\tw@
+ #1%
+ \else
+ \number\c@LT@chunks
+ \fi}%
+ {\the\wd\tw@}}
+\def\LT@hline{%
+ \noalign{\ifnum0=`}\fi
+ \penalty\@M
+ \futurelet\@let@token\LT@@hline}
+\def\LT@@hline{%
+ \ifx\@let@token\hline
+ \global\let\@gtempa\@gobble
+ \gdef\LT@sep{\penalty-\@medpenalty\vskip\doublerulesep}%
+ \else
+ \global\let\@gtempa\@empty
+ \gdef\LT@sep{\penalty-\@lowpenalty\vskip-\arrayrulewidth}%
+ \fi
+ \ifnum0=`{\fi}%
+ \multispan\LT@cols
+ \unskip\leaders\hrule\@height\arrayrulewidth\hfill\cr
+ \noalign{\LT@sep}%
+ \multispan\LT@cols
+ \unskip\leaders\hrule\@height\arrayrulewidth\hfill\cr
+ \noalign{\penalty\@M}%
+ \@gtempa}
+\def\LT@caption{%
+ \noalign\bgroup
+ \@ifnextchar[{\egroup\LT@c@ption\@firstofone}\LT@capti@n}
+\def\LT@c@ption#1[#2]#3{%
+ \LT@makecaption#1\fnum@table{#3}%
+ \def\@tempa{#2}%
+ \ifx\@tempa\@empty\else
+ {\let\\\space
+ \addcontentsline{lot}{table}{\protect\numberline{\thetable}{#2}}}%
+ \fi}
+\def\LT@capti@n{%
+ \@ifstar
+ {\egroup\LT@c@ption\@gobble[]}%
+ {\egroup\@xdblarg{\LT@c@ption\@firstofone}}}
+\def\LT@makecaption#1#2#3{%
+ \LT@mcol\LT@cols c{\hbox to\z@{\hss\parbox[t]\LTcapwidth{%
+ \sbox\@tempboxa{#1{#2: }#3}%
+ \ifdim\wd\@tempboxa>\hsize
+ #1{#2: }#3%
+ \else
+ \hbox to\hsize{\hfil\box\@tempboxa\hfil}%
+ \fi
+ \endgraf\vskip\baselineskip}%
+ \hss}}}
+\def\LT@output{%
+ \ifnum\outputpenalty <-\@Mi
+ \ifnum\outputpenalty > -\LT@end@pen
+ \LT@err{floats and marginpars not allowed in a longtable}\@ehc
+ \else
+ \setbox\z@\vbox{\unvbox\@cclv}%
+ \ifdim \ht\LT@lastfoot>\ht\LT@foot
+ \dimen@\pagegoal
+ \advance\dimen@-\ht\LT@lastfoot
+ \ifdim\dimen@<\ht\z@
+ \setbox\@cclv\vbox{\unvbox\z@\copy\LT@foot\vss}%
+ \@makecol
+ \@outputpage
+ \setbox\z@\vbox{\box\LT@head}%
+ \fi
+ \fi
+ \global\@colroom\@colht
+ \global\vsize\@colht
+ \vbox
+ {\unvbox\z@\box\ifvoid\LT@lastfoot\LT@foot\else\LT@lastfoot\fi}%
+ \fi
+ \else
+ \setbox\@cclv\vbox{\unvbox\@cclv\copy\LT@foot\vss}%
+ \@makecol
+ \@outputpage
+ \global\vsize\@colroom
+ \copy\LT@head\nobreak
+ \fi}
+\def\LT@end@hd@ft#1{%
+ \LT@echunk
+ \ifx\LT@start\endgraf
+ \LT@err
+ {Longtable head or foot not at start of table}%
+ {Increase LTchunksize}%
+ \fi
+ \setbox#1\box\z@
+ \LT@get@widths
+ \LT@bchunk}
+\def\endfirsthead{\LT@end@hd@ft\LT@firsthead}
+\def\endhead{\LT@end@hd@ft\LT@head}
+\def\endfoot{\LT@end@hd@ft\LT@foot}
+\def\endlastfoot{\LT@end@hd@ft\LT@lastfoot}
+\def\LT@startpbox#1{%
+ \bgroup
+ \let\@footnotetext\LT@p@ftntext
+ \setlength\hsize{#1}%
+ \@arrayparboxrestore
+ \vrule \@height \ht\@arstrutbox \@width \z@}
+\def\LT@endpbox{%
+ \@finalstrut\@arstrutbox
+ \egroup
+ \the\LT@p@ftn
+ \global\LT@p@ftn{}%
+ \hfil}
+\def\LT@p@ftntext#1{%
+ \edef\@tempa{\the\LT@p@ftn\noexpand\footnotetext[\the\c@footnote]}%
+ \global\LT@p@ftn\expandafter{\@tempa{#1}}}%
+\endinput
+%%
+%% End of file `longtable.sty'.
diff --git a/puthesis.cls b/puthesis.cls
new file mode 100644
index 0000000..45969cd
--- /dev/null
+++ b/puthesis.cls
@@ -0,0 +1,1561 @@
+%
+% puthesis.cls 2011-11-17 Mark Senn http://engineering.purdue.edu/~mark
+%
+% INDEX: Purdue University thesis document class
+%
+% DESCRIPTION:
+%
+% This is a LaTeX document class for Purdue University theses.
+%
+% USAGE:
+%
+% See http://engineering.purdue.edu/~mark/puthesis for more information.
+%
+
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesClass{puthesis}[2011/11/17 Purdue thesis class]
+\usepackage{ifthen}
+\newcommand{\ifthen}[2]{\ifthenelse{#1}{#2}{}}
+\usepackage{endnotes}
+\usepackage{pulongtable}
+\usepackage{rotating}
+\usepackage{lscape}
+
+\makeatletter
+
+\def\@@number{\string##}
+
+\newcount{\@@i}
+\newcounter{@@tcount}
+\newcounter{@@volume}
+\newcounter{last@@volume}
+\newcounter{save@@page}
+\newlength{\@@captionwidth}
+\newlength{\@@parindent} \setlength{\@@parindent}{\parindent}
+\newlength{\@@padding}
+\newlength{\@@tlength}
+\newlength{\@@ulength}
+\newcommand{\@@dept}{unknown}
+\newcommand{\set@@dept}[1]{\renewcommand{\@@dept}{#1}}
+
+\newcommand{\@@Repeat}[2]{%
+ \@@i=0
+ \loop
+ \ifnum\@@i<#2
+ #1
+ \advance\@@i by 1
+ \repeat
+}
+
+\newcommand{\@@blankpage}{%
+ \clearpage
+ \mbox{}\clearpage
+}
+
+\newcommand{\articlepages}[1]{%
+ \@@Repeat{\@@blankpage}{#1}
+}
+
+\newif\if@@more
+\@@moretrue
+\newcommand{\articleinclude}[1]{%
+ \def\@@t{#1}
+ \@@i=0
+ \loop
+ \advance\@@i by 1
+ \def\@@u{\@@t\the\@@i.eps}
+ \immediate\write16{\@@u}
+ \IfFileExists{\@@u}{\noindent\includegraphics[width=\textwidth]{\@@u}\newpage}{\@@morefalse}
+ \if@@more\repeat
+}
+
+\DeclareOption{iupuiece}{\set@@dept{iupuiece}}
+\DeclareOption{aae}{\set@@dept{aae}}
+\DeclareOption{agecon}{\set@@dept{agecon}}
+\DeclareOption{agry}{\set@@dept{agry}}
+\DeclareOption{ansc}{\set@@dept{ansc}}
+\DeclareOption{che}{\set@@dept{che}}
+\DeclareOption{ce}{\set@@dept{ce}}
+\DeclareOption{edci}{\set@@dept{edci}}
+\DeclareOption{ece}{\set@@dept{ece}}
+\DeclareOption{hsci}{\set@@dept{hsci}}
+\DeclareOption{ie}{\set@@dept{ie}}
+\DeclareOption{mgmt}{\set@@dept{mgmt}}
+\DeclareOption{mse}{\set@@dept{mse}}
+\DeclareOption{me}{\set@@dept{me}}
+\DeclareOption{ne}{\set@@dept{ne}}
+\DeclareOption{chem}{\set@@dept{chem}}
+\DeclareOption{cs}{\set@@dept{cs}}
+\DeclareOption{eas}{\set@@dept{eas}}
+\DeclareOption{math}{\set@@dept{math}}
+\DeclareOption{phys}{\set@@dept{phys}}
+\DeclareOption{stat}{\set@@dept{stat}}
+\newcommand{\pendnotes}{}
+\DeclareOption{endnotes}{%
+ \let\footnote=\endnote
+ \renewcommand{\pendnotes}{%
+ \newpage
+ \begingroup
+%% ! \renewcommand{\baselinestretch}{0.9}
+ \setlength{\parindent}{0pt}
+ \setlength{\parskip}{1.5ex}
+ \renewcommand{\enotesize}{\normalsize}
+ \theendnotes
+ \endgroup
+ }
+}
+\newcommand{\@@type}{unknown}
+\newcommand{\set@@type}[1]{\renewcommand{\@@type}{#1}}
+\DeclareOption{bypass}{\set@@type{bypass}}
+\DeclareOption{dissertation}{\set@@type{dissertation}}
+\DeclareOption{prelim}{\set@@type{prelim}}
+\DeclareOption{thesis}{\set@@type{thesis}}
+\newboolean{@@uglyheadings}
+\setboolean{@@uglyheadings}{false}
+\DeclareOption{uglyheadings}{\setboolean{@@uglyheadings}{true}}
+\newboolean{@@unset}
+\newcommand{\@@optionbibstyle}{}
+\newcommand{\set@@optionbibstyle}[1]{\renewcommand{\@@optionbibstyle}{#1}\addtocounter{@@tcount}{1}}
+\DeclareOption{abbrvnat}{\set@@optionbibstyle{abbrvnat}}
+\DeclareOption{aer}{\set@@optionbibstyle{aer}}
+\DeclareOption{agsm}{\set@@optionbibstyle{agsm}}
+\DeclareOption{aip}{\set@@optionbibstyle{aip}}
+\DeclareOption{alpha}{\set@@optionbibstyle{alpha}}
+\DeclareOption{ama}{\set@@optionbibstyle{ama}}
+\DeclareOption{apacite}{\set@@optionbibstyle{apacite}}
+\DeclareOption{apalike}{\set@@optionbibstyle{apalike}}
+\DeclareOption{astron}{\set@@optionbibstyle{astron}}
+\DeclareOption{chicago}{\set@@optionbibstyle{chicago}}
+\DeclareOption{ieee}{\set@@optionbibstyle{ieee}}
+\DeclareOption{ieeetr}{\set@@optionbibstyle{ieeetr}}
+\DeclareOption{jfm}{\set@@optionbibstyle{jfm}}
+\DeclareOption{jfm2}{\set@@optionbibstyle{jfm2}}
+\DeclareOption{kluwer}{\set@@optionbibstyle{kluwer}}
+\DeclareOption{plain}{\set@@optionbibstyle{plain}}
+\DeclareOption{plainnat}{\set@@optionbibstyle{plainnat}}
+\DeclareOption{unsrt}{\set@@optionbibstyle{unsrt}}
+\DeclareOption{unsrtnat}{\set@@optionbibstyle{unsrtnat}}
+\newif\if@openbib
+\@openbibfalse
+%
+\newboolean{@@nonchapterblankpages}
+\setboolean{@@nonchapterblankpages}{false}
+\newcommand{\nonchapterblankpages}{\setboolean{@@nonchapterblankpages}{true}}
+\newcommand{\nononchapterblankpages}{\setboolean{@@nonchapterblankpages}{false}}
+\DeclareOption{nonchapterblankpages}{\nonchapterblankpages}
+\DeclareOption{nononchapterblankpages}{\nononchapterblankpages}
+%
+\newboolean{@@chapterblankpages}
+\setboolean{@@chapterblankpages}{true}
+\newcommand{\chapterblankpages}{\setboolean{@@chapterblankpages}{true}}
+\newcommand{\nochapterblankpages}{\setboolean{@@chapterblankpages}{false}}
+\DeclareOption{chapterblankpages}{\chapterblankpages}
+\DeclareOption{nochapterblankpages}{\nochapterblankpages}
+%
+\newboolean{@@coversheets}
+\setboolean{@@coversheets}{true}
+\newcommand{\coversheets}{\setboolean{@@coversheets}{true}}
+\newcommand{\nocoversheets}{\setboolean{@@coversheets}{false}}
+\DeclareOption{coversheets}{\coversheets}
+\DeclareOption{nocoversheets}{\nocoversheets}
+%
+\DeclareOption{miser}{
+ \nononchapterblankpages
+ \nochapterblankpages
+ \nocoversheets
+}
+\DeclareOption{nomiser}{
+ \nonchapterblankpages
+ \chapterblankpages
+ \coversheets
+}
+
+\newboolean{number@@all@@volumes}
+\setboolean{number@@all@@volumes}{false}
+\DeclareOption{numberallvolumes}{\setboolean{number@@all@@volumes}{true}}
+\DeclareOption{nonumberallvolumes}{\setboolean{number@@all@@volumes}{false}}
+
+%\DeclareOptions*(\PassOptionsToClass{\CurrentOption}{report}}
+ % Used to count number of citation/bibliography styles used.
+\setcounter{@@tcount}{0}
+\ProcessOptions
+\newcommand{\IW}[1]{\immediate\write16{#1}}
+{
+ \catcode`\+=13
+ \def+{\space}
+ \ifthen{\equal{\@@dept}{unknown}}
+ {
+ \IW{}
+ \IW{You must specify an option for your school or department, for example,}
+ \IW{++++\string\documentclass[aae]{puthesis}}
+ \IW{The available school and department options are defined at}
+ \IW{++++http://engineering.purdue.edu/\string~mark/puthesis/\@@number Options}
+ \IW{ABORTING...}
+ \IW{}
+ \stop
+ }
+ \ifthen{\equal{\@@type}{unknown}}
+ {
+ \IW{}
+ \IW{You must specify an option for your type of document, for example,}
+ \IW{++++\string\documentclass[aae,dissertation]{puthesis}}
+ \IW{The available document types are}
+ \IW{++++bypass+++++++++Master's Bypass Report}
+ \IW{++++dissertation+++Ph.D. dissertation}
+ \IW{++++prelim+++++++++Ph.D. Preliminary Report}
+ \IW{++++thesis+++++++++Master's Thesis}
+ \IW{ABORTING...}
+ \IW{}
+ \stop
+ }
+ \ifnum\value{@@tcount}>1
+ \IW{}
+ \IW{You may specify only one citation/bibliography style from the below list:}
+ \IW{++++abbrvnat}
+ \IW{++++agsm}
+ \IW{++++aip}
+ \IW{++++alpha}
+ \IW{++++ama}
+ \IW{++++apacite}
+ \IW{++++apalike}
+ \IW{++++astron}
+ \IW{++++chicago}
+ \IW{++++ieee}
+ \IW{++++ieeetr}
+ \IW{++++jfm}
+ \IW{++++jfm2}
+ \IW{++++kluwer}
+ \IW{++++plain}
+ \IW{++++plainnat}
+ \IW{++++unsrt}
+ \IW{++++unsrtnat}
+ \IW{See}
+ \IW{++++http://engineering.purdue.edu/\string~mark/puthesis/\@@number Options}
+ \IW{for more details.}
+ \IW{ABORTING...}
+ \IW{}
+ \stop
+ \fi
+}
+\LoadClass[12pt,twoside]{report}[2004/02/16]
+
+% Got these lines using "grep dotted report.cls" and changed
+% as needed for new \@dottedtocline \vskip parameter.
+\renewcommand*\l@section{\@dottedtocline{1}{\smalltocskip}{1.5em}{2.3em}}
+\renewcommand*\l@subsection{\@dottedtocline{2}{\smalltocskip}{3.8em}{3.2em}}
+\renewcommand*\l@subsubsection{\@dottedtocline{3}{\smalltocskip}{7.0em}{4.1em}}
+\renewcommand*\l@paragraph{\@dottedtocline{4}{\smalltocskip}{10em}{5em}}
+\renewcommand*\l@subparagraph{\@dottedtocline{5}{\smalltocskip}{12em}{6em}}
+\renewcommand*\l@figure{\@dottedtocline{1}{\bigtocskip}{0em}{2.3em}}
+\renewcommand*\l@table{\@dottedtocline{1}{\bigtocskip}{0em}{2.3em}}
+
+%{\obeyspaces
+% \gdef\@@Identification{{
+% \IW{}
+% \IW{ ||}
+% \IW{ see || below}
+% \IW{ ||}
+% \IW{ \string\\ || //}
+% \IW{ \string\\||//}
+% \IW{ \string\\//}
+% \IW{+----------------------------------------------------------------------------+}
+% \IW{| This document is using the 2011/11/17 version of puthesis.cls. It was |}
+% \IW{| probably read in because you used \string\documentclass[...]{puthesis}. |}
+% \IW{| |}
+% \IW{| Only the latest version of this file available from |}
+% \IW{| http://engineering.purdue.edu/\string~mark/puthesis |}
+% \IW{| is supported. Put it in the same subdirectory as your thesis. |}
+% \IW{| |}
+% \IW{| This file may be being read from your thesis directory or somewhere else. |}
+% \IW{| To find out which is being used search for ``puthesis.cls'' near the |}
+% \IW{| beginning of this log. If it has ``(puthesis.cls'' or ``(./puthesis.cls'' |}
+% \IW{| it's from your thesis directory, otherwise it is being read form somewhere |}
+% \IW{| else. |}
+% \IW{+----------------------------------------------------------------------------+}
+% \IW{ //\string\\}
+% \IW{ //||\string\\}
+% \IW{ // || \string\\}
+% \IW{ ||}
+% \IW{ see || above}
+% \IW{ ||}
+% \IW{}
+%}}
+%}
+
+\def\AppendixFigure{\relax}
+\def\AppendixTable{\relax}
+\def\@@bibname{LIST OF REFERENCES}
+\def\@@deptbibstyle{unsrt}
+\def\@@coversheetspace{\vfill}
+\def\@@evenfoot{}
+\def\@@evenhead{\hfil\textrm{\thepage}}
+\newboolean{@@figurecaptions}
+ \setboolean{@@figurecaptions}{false}
+\def\@@oddfoot{}
+\def\@@oddhead{\hfil\textrm{\thepage}}
+\def\@@sectionbaselinestretch{\relax}
+\def\@@sectionseries{\bfseries}
+\def\@@startthebibliography{\coversheet{\@@bibname}}
+\def\@@startvita{\coversheet{\@@vitaname}}
+\def\@@subsectionbaselinestretch{\relax}
+\def\@@subsectionseries{\bfseries}
+\def\@@subsubsectionbaselinestretch{\relax}
+\def\@@subsubsectionseries{\bfseries}
+\def\@@t{\relax}
+\def\@@thebibliographyparsep{\relax}
+
+\ifthen{\equal{iupuiece}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{
+ \ifthenelse{\boolean{@@inappendix}}
+ {\large\bf APPENDIX \thechapter\\\uppercase{#1}}
+ {\large\bf\thechapter. \uppercase{#1}}
+ }
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@startvita{\relax}
+ }
+\ifthen{\equal{aae}{\@@dept}}
+ {
+ \def\fnum@table{\tablename~\thetable.~}
+ \setboolean{@@figurecaptions}{true}
+ \def\@@makechapterhead#1{\large\bf\thechapter. #1}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ }
+\ifthen{\equal{agecon}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\rm\uppercase{Chapter \thechapter. #1}}
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}}
+ \def\@@makechapterheadspaceb{}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ }
+%
+% The agry option is being built to be most like the plain Purdue standards.
+% It was originally based on the CS format.
+\ifthen{\equal{agry}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\thechapter\quad\uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@sectionseries{}
+ \def\@@subsectionseries{}
+ \def\@@subsubsectionseries{}
+ }
+\ifthen{\equal{ansc}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{
+ \ifthenelse{\boolean{@@inappendix}}
+ {\large\bf APPENDIX \thechapter\\\uppercase{#1}}
+ {\large\bf\thechapter. \uppercase{#1}}
+ }
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@startvita{\relax}
+ }
+\ifthen{\equal{che}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\thechapter. \uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.65625truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.65625truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@sectionbaselinestretch{\renewcommand{\baselinestretch}{1}}
+ \def\@@sectionseries{}
+ \def\@@subsectionbaselinestretch{\renewcommand{\baselinestretch}{1}}
+ \def\@@subsectionseries{\relax}
+ \def\@@subsubsectionbaselinestretch{\renewcommand{\baselinestretch}{1}}
+ \def\@@subsubsectionseries{\relax}
+ }
+\ifthen{\equal{ce}{\@@dept}}
+ {
+ \def\fnum@table{\tablename~\thetable.~}
+ \setboolean{@@figurecaptions}{true}
+ \def\@@makechapterhead#1{\large\rm\thechapter. \uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@subsectionseries{\normalfont}
+ \def\@@subsubsectionseries{\normalfont}
+ }
+\ifthen{\equal{edci}{\@@dept}}
+ {
+ \def\@@deptbibstyle{apalike}
+ \def\@@makechapterhead#1{\large\bf\thechapter. #1}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@startthebibliography{}
+ \def\@@startvita{\relax}
+ }
+\ifthen{\equal{ece}{\@@dept}}
+ {
+ \def\@@deptbibstyle{ieeetr}
+ \renewcommand*\l@section{\@dottedtocline{1}{\bigtocskip}{1.5em}{2.3em}}
+ \renewcommand*\l@subsection{\@dottedtocline{2}{\bigtocskip}{3.8em}{3.2em}}
+ \renewcommand*\l@subsubsection{\@dottedtocline{3}{\bigtocskip}{7.0em}{4.1em}}
+ \renewcommand*\l@paragraph{\@dottedtocline{4}{\bigtocskip}{10em}{5em}}
+ \renewcommand*\l@subparagraph{\@dottedtocline{5}{\bigtocskip}{12em}{6em}}
+ \renewcommand*\l@figure{\@dottedtocline{1}{\bigtocskip}{1.5em}{2.3em}}
+ \def\@@makechapterhead#1{{\large\bf\thechapter. \uppercase{#1}}}
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterhead#1{{\large\bf\uppercase{#1}}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \nochapterblankpages
+ }
+\ifthen{\equal{hsci}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{
+ \ifthenelse{\boolean{@@inappendix}}
+ {\large\bf APPENDIX \thechapter\\\uppercase{#1}}
+ {\large\bf\thechapter. \uppercase{#1}}
+ }
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \nochapterblankpages
+ }
+\ifthen{\equal{ie}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{
+ \ifthenelse{\boolean{@@inappendix}}
+ {\large\bf APPENDIX \thechapter\\\uppercase{#1}}
+ {\large\bf\thechapter. \uppercase{#1}}
+ }
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \nochapterblankpages
+ }
+\ifthen{\equal{mgmt}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\large\bf\thechapter. #1}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ }
+\ifthen{\equal{mse}{\@@dept}}
+ {
+ \def\AppendixFigure{\addtocontents{lof}{Appendix Figure\hfil}}
+ \def\AppendixTable{\addtocontents{lot}{Appendix Table\hfil}}
+ \def\@@coversheetspace{\vspace*{0.53125truein}}
+ \def\@@makechapterhead#1{
+ \rm
+ \ifthen{\boolean{@@inappendix}}
+ {APPENDIX }
+ \thechapter. \uppercase{#1}
+ }
+ \def\@@makechapterheadspacea{\vspace*{0.65625truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.65625truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@sectionbaselinestretch{\renewcommand*{\baselinestretch}{1.0}}
+ \def\@@subsectionbaselinestretch{\renewcommand*{\baselinestretch}{1.0}}
+ \def\@@subsectionseries{\normalfont}
+ \def\@@subsubsectionseries{\normalfont}
+ \def\@@thebibliographyparsep{\parsep 6pt}
+ }
+\ifthen{\equal{me}{\@@dept}}
+ {
+ \ifthenelse{\equal{\@@optionbibstyle}{jfm}}
+ {\def\@@deptbibstyle{jfm}}
+ {\ifthenelse{\equal{\@@optionbibstyle}{jfm2}}
+ {\def\@@deptbibstyle{jfm2}}
+ {\def\@@deptbibstyle{pumeunsrt}}
+ }
+ \def\fnum@table{\tablename~\thetable.~}
+ \setboolean{@@figurecaptions}{true}
+ \def\@@makechapterhead#1{\rm\thechapter. \uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \nochapterblankpages
+ }
+\ifthen{\equal{ne}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{
+ \ifthenelse{\boolean{@@inappendix}}
+ {\large\bf APPENDIX \thechapter\\\uppercase{#1}}
+ {\large\bf\thechapter. \uppercase{#1}}
+ }
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \nochapterblankpages
+ }
+\ifthen{\equal{chem}{\@@dept}}
+ {
+ % The vertical spacing between floats appearing either at the
+ % top or at the bottom of a page for double column floats in
+ % two-column page format.
+ \setlength{\dblfloatsep}{36pt}
+ % The vertical spacing between floats and text, for both top
+ % and bottom floats for double column floats in two-column page
+ % format.
+ \setlength{\dbltextfloatsep}{36pt}
+ % The vertical spacing between floats appearing either at the
+ % top or at the bottom of a page.
+ \setlength{\floatsep}{36pt}
+ % The vertical spacing above and below a float that appears in
+ % the middle of a text page with the h placement argument.
+ \setlength{\intextsep}{36pt}
+ \def\@@makechapterhead#1{\thechapter. #1}
+ \def\@@makechapterheadspacea{\vspace*{0.7truein}}
+ \def\@@makechapterheadspaceb{\vspace*{36pt}}
+ \def\@@makeschapterheadspacea{\vspace*{0.7truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{36pt}}
+ % The vertical spacing between floats and text, for both top
+ % and bottom floats.
+ \setlength{\textfloatsep}{36pt}
+ }
+\ifthen{\equal{cs}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\thechapter\quad\uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.6truein}} % by trial and error
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@sectionseries{}
+ \def\@@subsectionseries{}
+ \def\@@subsubsectionseries{}
+ }
+\ifthen{\equal{eas}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\thechapter\quad\uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \def\@@sectionseries{}
+ }
+\ifthen{\equal{math}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\large\bf\thechapter. #1}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ }
+\ifthen{\equal{phys}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\large\bf\thechapter. #1}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ }
+\ifthen{\equal{stat}{\@@dept}}
+ {
+ \def\@@makechapterhead#1{\large\rm\thechapter. \uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ }
+\AtBeginDocument{
+ \usepackage{notoccite}
+}
+\AtEndDocument{
+%% \@@Identification
+%% \write\@auxout{\string\newcounter{\string\csname@@@appendix\endcsname}}
+%% \write\@auxout{\string\setcounter{\string\csname@@@appendix\endcsname}{\arabic{@@appendix}}}
+}
+\newenvironment{cland}
+ {\begin{landscape}\hbox\bgroup\hss\vbox\bgroup}
+ {\egroup\hss\egroup\end{landscape}}
+
+\newenvironment{lquotation}
+ {\begin{quotation}\renewcommand{\baselinestretch}{1}\reset@font}
+ {\end{quotation}}
+
+\newcommand{\coversheet}[1]{
+ {
+ \ifthen{\boolean{@@coversheets}}
+ {
+ \ifthenelse{\boolean{@@chapterblankpages}}
+ {\clearpage} %% ! {\cleardoublepage}
+ {\clearpage}
+ \pagestyle{empty}
+ \mbox{}
+ \@@coversheetspace
+ \begin{center}
+ #1
+ \end{center}
+ \vfill
+ \newpage
+ \addtocounter{page}{-1}
+ }
+ \@@NotTableOfContents
+ }
+}
+\setlength{\paperheight}{11truein}
+\setlength{\paperwidth}{8.5truein}
+\renewcommand*{\baselinestretch}{1.5}
+\setlength{\evensidemargin}{0.5truein}
+\setlength{\oddsidemargin}{0.5truein}
+\setlength{\textheight}{8.75truein}
+\setlength{\textwidth}{6truein}
+\clubpenalty=10000
+\widowpenalty=10000
+\displaywidowpenalty=10000
+\ifthen{\equal{mse}{\@@dept}}
+ {\setlength{\parindent}{0.40625truein}}
+%\setlength\parskip{0\p@ \@plus \p@}
+\pagenumbering{roman}
+\newcommand{\@@NotTableOfContents}{%
+ \renewcommand*{\@evenhead}{\@@evenhead}
+ \renewcommand*{\@oddhead}{\@@oddhead}
+ \renewcommand*{\@evenfoot}{\@@evenfoot}
+ \renewcommand*{\@oddfoot}{\@@oddfoot}
+}
+\@@NotTableOfContents
+\setlength{\topmargin}{-0.5truein}
+\addtolength{\topmargin}{0.04875truein}
+\settoheight{\headheight}{- ivx1 -}
+\setlength{\headsep}{0.5truein}
+\addtolength{\headsep}{-\headheight}
+\addtolength{\headsep}{-0.0625truein}
+\newcommand*{\@@TitleAuthor}{\relax}
+\newcommand*{\@@AbstractAuthor}{\relax}
+\renewcommand*{\author}[2]{%
+ \renewcommand*{\@@TitleAuthor}{#1}%
+ \renewcommand*{\@@AbstractAuthor}{#2}%
+}
+\newcommand*{\@@Campus}{\relax}
+\newcommand*{\@@Campus@Input}{}
+\newcommand*{\campus}[1]{%
+ \renewcommand\@@Campus@Input{#1}
+ \renewcommand{\@@t}{Fort Wayne} \ifthen{\equal{#1}{\@@t}}{\edef\@@Campus{\@@t}}
+ \renewcommand{\@@t}{Hammond} \ifthen{\equal{#1}{\@@t}}{\edef\@@Campus{\@@t}}
+ \renewcommand{\@@t}{Indianapolis} \ifthen{\equal{#1}{\@@t}}{\edef\@@Campus{\@@t}}
+ \renewcommand{\@@t}{West Lafayette} \ifthen{\equal{#1}{\@@t}}{\edef\@@Campus{\@@t}}
+ \renewcommand{\@@t}{Westville} \ifthen{\equal{#1}{\@@t}}{\edef\@@Campus{\@@t}}
+}
+
+
+\newcommand*{\@@TitleDegree}{\relax}
+\newcommand*{\@@AbstractDegree}{\relax}
+\newcommand*{\@@DegreeMonth}{\relax}
+\newcommand*{\@@DegreeYear}{\relax}
+\newcommand*{\pudegree}[4]{%
+ \renewcommand*{\@@TitleDegree}{#1}%
+ \renewcommand*{\@@AbstractDegree}{#2}%
+ \renewcommand*{\@@DegreeMonth}{#3}%
+ \renewcommand*{\@@DegreeYear}{#4}%
+}
+\newcommand*{\@@MajorProf}{\relax}
+\newcommand*{\majorprof}[1]{\renewcommand*{\@@MajorProf}{Major Professor: #1}}
+\newcommand*{\majorprofs}[1]{\renewcommand*{\@@MajorProf}{Major Professors: #1}}
+\newcommand*{\@@Title}{\relax}
+\renewcommand*{\title}[1]{\renewcommand*{\@@Title}{#1}}
+\renewcommand*{\maketitle}
+ {
+ \ifthen{\equal{\@@Campus@Input}{}}
+ {
+ \IW{You must specify which campus your degree is from, for example,}
+ \IW{\space\space\space\space\string\campus{West Lafayette}}
+ \IW{See}
+ \IW{\space\space\space\space http://engineering.purdue.edu/\string~mark/puthesis/\@@number campus}
+ \IW{for the valid choices.}
+ \IW{ABORTING...}
+ \IW{}
+ \stop
+ }
+ \ifthen{\equal{\@@Campus}{\relax}}
+ {
+ \IW{The campus specified in your}
+ \IW{\space\space\space\space\string\campus{\@@Campus@Input}}
+ \IW{command is not valid.}
+ \IW{See}
+ \IW{\space\space\space\space http://engineering.purdue.edu/\string~mark/puthesis/\@@number campus}
+ \IW{for the valid choices.}
+ \IW{ABORTING...}
+ \IW{}
+ \stop
+ }
+ {
+ \renewcommand*{\baselinestretch}{2} \reset@font
+ \setcounter{save@@page}{\value{page}}
+ \begin{titlepage}
+ \mbox{}
+ \vfil
+ \vfil
+ \begin{center}
+ \uppercase\expandafter{\@@Title}
+ \end{center}
+ \ifthen{\(\boolean{number@@all@@volumes} \and \value{last@@volume}>1\) \or \value{@@volume}>1}
+ {
+ \begin{center}
+ VOLUME \the@@volume
+ \end{center}
+ }
+ \vfil
+ \begin{center}
+ \ifthen{\equal{bypass}{\@@type}}{A Master's Bypass Report\\}
+ \ifthen{\equal{dissertation}{\@@type}}{A Dissertation\\}
+ \ifthen{\equal{prelim}{\@@type}}{A Preliminary Report\\}
+ \ifthen{\equal{thesis}{\@@type}}{A Thesis\\}
+ Submitted to the Faculty\\
+ of\\
+ Purdue University\\
+ by\\
+ \@@TitleAuthor
+ \end{center}
+ \vfil
+ \begin{center}
+ In Partial Fulfillment of the\\
+ Requirements for the Degree\\
+ of\\
+ \@@TitleDegree
+ \end{center}
+ \ifthenelse{\equal{mse}{\@@dept}}
+ {}
+ {\vfil}
+ \begin{center}
+ \@@DegreeMonth\ \@@DegreeYear\\
+ Purdue University\\
+ \@@Campus, Indiana
+ \end{center}
+ \ifthenelse{\equal{mse}{\@@dept}}
+ {}
+ {\vfil\vfil}
+ \end{titlepage}
+ \setcounter{page}{\value{save@@page}}
+ \ifthen{\value{@@volume}=0 \or \value{@@volume}=1}
+ {\setcounter{page}{2}}
+ }
+ }
+\newenvironment{dedication}%
+ {%
+ \newpage
+ \mbox{}
+ \vfil
+ \begin{center}%
+ }%
+ {%
+ \end{center}%
+ \vfil
+ \eject
+ \@@NotTableOfContents
+ }
+\newboolean{@@inother}
+\setboolean{@@inother}{false}
+ % #1 "next" or "odd": start on next or next odd page?
+ % #2 what to print at top of page
+ % #3 "y" or "n": put in table of contents?
+ % #4 amount of extra space to put after heading at top of page
+\newcommand{\@@nonchapter}[4]{{%
+ \@@NotTableOfContents
+ \bgroup
+ \setboolean{@@inother}{true}
+ \renewcommand{\large}{}%
+ \renewcommand{\bf}{}%
+ \ifthenelse{\equal{ce}{\@@dept}}
+ {\chapter*{\uppercase{#2}}}
+ {\chapter*{#2}}
+ \ifthen{\equal{y}{#3}}
+ {
+ \ifthenelse{\equal{ce}{\@@dept}}
+ {\addcontentsline{toc}{chapter}{\uppercase{#2}}}
+ {\addcontentsline{toc}{chapter}{#2}}
+ }
+ \egroup
+ \vspace{#4}
+}}
+\newenvironment{acknowledgments}%
+ {\@@nonchapter{next}{ACKNOWLEDGMENTS}{n}{0pt}}%
+ {}
+\newenvironment{preface}%
+ {\@@nonchapter{next}{PREFACE}{n}{0pt}}%
+ {}
+\renewcommand*{\tableofcontents}{
+ \@@nonchapter{odd}{TABLE OF CONTENTS}{n}{0pt}
+ {\leftskip=0pt \noindent\hbox to\textwidth{\hfil Page}\par}
+ {%
+% \renewcommand*{\@oddhead}{%
+% \hfil\textrm{\hfil oddhead a \thepage}%
+% \renewcommand*{\@oddhead}{\hfil oddhead b \thepage}%
+% }
+% \renewcommand*{\@evenhead}{%
+% \hfil\textrm{\hfil evenhead a \thepage}%
+% \renewcommand*{\@evenhead}{\hfil evenhead b \thepage}%
+% }
+ \output={
+ \let \par \@@par
+ \ifnum \outputpenalty<-\@M
+ \@specialoutput
+ \else
+ \@makecol
+ \@opcol
+ \@startcolumn
+ \@whilesw \if@fcolmade \fi
+ {%
+ \@opcol\@startcolumn}%
+ \fi
+ \ifnum \outputpenalty>-\@Miv
+ \ifdim \@colroom<1.5\baselineskip
+ \ifdim \@colroom<\textheight
+ \@latex@warning@no@line{Text page \thepage\space
+ contains only floats}%
+ \@emptycol
+ \else
+ \global \vsize \@colroom
+ \fi
+ \else
+ \global \vsize \@colroom
+ \fi
+ \else
+ \global \vsize \maxdimen
+ \fi
+ {\leftskip=0pt \noindent\hbox to\textwidth{\hfil Page}\par}
+ }
+ \renewcommand{\baselinestretch}{1}\reset@font
+ \@starttoc{toc}
+ }
+}
+\ifthen{\equal{che}{\@@dept}\or \equal{ece}{\@@dept}\or \equal{hsci}{\@@dept}\or \equal{me}{\@@dept}}
+ {%
+ \def\numberline#1{\hb@xt@\@tempdima{#1\hfil}}
+ \renewcommand*\l@figure{\@dottedtocline{1}{\bigtocskip}{0em}{2.3em}}
+ \renewcommand*\l@table{\@dottedtocline{1}{\bigtocskip}{0em}{2.3em}}
+ }
+\def\bigtocskip{0.5\baselineskip plus.2\p@}
+\def\smalltocskip{0pt}
+\def\@dottedtocline#1#2#3#4#5#6{%
+ \ifnum #1>\c@tocdepth
+ \else
+ \vskip #2
+ {%
+ \leftskip #3
+ \rightskip \@tocrmarg
+ \parfillskip -\rightskip
+ \parindent #3
+ \@afterindenttrue
+ \interlinepenalty\@M
+ \leavevmode
+ \@tempdima #4
+ \advance\@tempdima \@@padding
+ \advance\leftskip \@tempdima
+ \hbox{}\hskip -\leftskip
+ #5\nobreak
+ \leaders\hbox{$\m@th \mkern \@dotsep mu.\mkern \@dotsep mu$}\hfill
+ \nobreak
+ \renewcommand{\@pnumwidth}{1.55em}
+ \setlength{\@@tlength}{\@pnumwidth}
+ \settowidth{\@@ulength}{\reset@font \rm #6}
+ \ifdim \@@ulength>\@@tlength
+ \hbox to\@@ulength{\hfil\reset@font \rm #6}\par
+ \else
+ \hbox to\@@tlength{\hfil\reset@font \rm #6}\par
+ \fi
+ }%
+ \fi
+}
+\renewcommand*{\listoftables}{
+ \@@nonchapter{next}{LIST OF TABLES}{y}{0pt}
+ {\leftskip=0pt \noindent\hbox to\textwidth{Table\hfil Page}\par}
+ {%
+ \output={
+ \let \par \@@par
+ \ifnum \outputpenalty<-\@M
+ \@specialoutput
+ \else
+ \@makecol
+ \@opcol
+ \@startcolumn
+ \@whilesw \if@fcolmade \fi
+ {%
+ \@opcol\@startcolumn}%
+ \fi
+ \ifnum \outputpenalty>-\@Miv
+ \ifdim \@colroom<1.5\baselineskip
+ \ifdim \@colroom<\textheight
+ \@latex@warning@no@line{Text page \thepage\space
+ contains only floats}%
+ \@emptycol
+ \else
+ \global \vsize \@colroom
+ \fi
+ \else
+ \global \vsize \@colroom
+ \fi
+ \else
+ \global \vsize \maxdimen
+ \fi
+ {\leftskip=0pt \noindent\hbox to\textwidth{Table\hfil Page}\par}
+ }
+ \renewcommand{\baselinestretch}{1}\reset@font
+ \@starttoc{lot}
+ }
+}
+\renewcommand{\listoffigures}{
+ \@@nonchapter{next}{LIST OF FIGURES}{y}{0pt}
+ {\leftskip=0pt \noindent\hbox to\textwidth{Figure\hfil Page}\par}
+ {%
+ \output={
+ \let \par \@@par
+ \ifnum \outputpenalty<-\@M
+ \@specialoutput
+ \else
+ \@makecol
+ \@opcol
+ \@startcolumn
+ \@whilesw \if@fcolmade \fi
+ {%
+ \@opcol\@startcolumn}%
+ \fi
+ \ifnum \outputpenalty>-\@Miv
+ \ifdim \@colroom<1.5\baselineskip
+ \ifdim \@colroom<\textheight
+ \@latex@warning@no@line{Text page \thepage\space
+ contains only floats}%
+ \@emptycol
+ \else
+ \global \vsize \@colroom
+ \fi
+ \else
+ \global \vsize \@colroom
+ \fi
+ \else
+ \global \vsize \maxdimen
+ \fi
+ {\leftskip=0pt \noindent\hbox to\textwidth{Figure\hfil Page}\par}
+ }
+ \renewcommand{\baselinestretch}{1}\reset@font
+ \@starttoc{lof}
+ }
+}
+\newcommand{\@@startlist}[1]{
+ \@@nonchapter{odd}{#1}{y}{0pt}%
+ \setlength{\LTleft}{\parindent}%
+ \setlength{\LTright}{0truein}%
+ %% ! change to \setlength[\@@tlength}{\textwidth - \LTleft - \LTright - 2*\tabcolsep - 1truein} later
+ \setlength{\@@tlength}{\textwidth}
+ \addtolength{\@@tlength}{-\LTleft}
+ \addtolength{\@@tlength}{-\LTright}
+ \addtolength{\@@tlength}{-\tabcolsep}
+ \addtolength{\@@tlength}{-\tabcolsep}
+ \addtolength{\@@tlength}{-1truein}
+ %% ! Get real width of first column.
+ %% ! \setcounter{\@@tcount}{\c@LT@tables}
+ %% ! \addtocounter(\@@tcount){1}
+ %% ! \def\stuff{\csname LT@\romannumeral\@@tcount\endcsname}
+ %% ! \showthe\stuff
+ %% ! \addtolength{\@@tlength}{-?}
+}
+\newenvironment{symbols}%
+ {%
+ \@@startlist{SYMBOLS}
+ \begin{longtable}{lp{\@@tlength}}%
+ }
+ {\end{longtable}}
+\newenvironment{abbreviations}
+ {
+ \@@startlist{ABBREVIATIONS}
+ \begin{longtable}{lp{\@@tlength}}%
+ }
+ {\end{longtable}}
+\newenvironment{nomenclature}
+ {
+ \@@startlist{NOMENCLATURE}
+ \begin{longtable}{lp{\@@tlength}}%
+ }
+ {\end{longtable}}
+\renewenvironment{glossary}
+ {
+ \@@startlist{GLOSSARY}
+ \begin{longtable}{lp{\@@tlength}}%
+ }
+ {\end{longtable}}
+\renewenvironment{abstract}%
+ {%
+ \@@nonchapter{next}{ABSTRACT}{y}{0pt}
+ {%
+ \renewcommand*{\baselinestretch}{1.0}\reset@font
+ \vspace*{\baselineskip}
+ \vbox{
+ \noindent
+ \@@AbstractAuthor~\@@AbstractDegree,
+ Purdue University,
+ \@@DegreeMonth\ %
+ \@@DegreeYear.
+ {%
+ \renewcommand*{\\}{}%
+ \@@Title.
+ }
+ \@@MajorProf\ifthen{\equal{me}{\@@dept}}{, School of Mechanical Engineering}.
+ }
+ \vspace*{\baselineskip}
+ \par
+ }
+ }%
+ {\par}
+\renewcommand{\chapter}{%
+ \ifthenelse{
+ \( \boolean{@@inother} \and \boolean{@@nonchapterblankpages} \)
+ \or \( \not \boolean{@@inother} \and \boolean{@@chapterblankpages} \)
+ }
+ {\cleardoublepage}
+ {\clearpage}
+ \@@NotTableOfContents
+ \global\@topnum\z@
+ \@afterindentfalse
+ \secdef \@chapter \@schapter
+}
+\newboolean{@@inchapters}
+\setboolean{@@inchapters}{false}
+\def\@chapter[#1]#2{%
+ \ifnum \c@secnumdepth >\m@ne
+ \refstepcounter{chapter}%
+ \typeout{\@chapapp\space\thechapter.}%
+ \setboolean{@@unset}{true}
+ \ifthen{\equal{agecon}{\@@dept}}
+ {\setboolean{@@unset}{false}\addcontentsline{toc}{chapter}{\protect\uppercase{\@chapapp\ \thechapter. #1}}}
+ \ifthen{\equal{ce}{\@@dept}}
+ {\setboolean{@@unset}{false}\addcontentsline{toc}{chapter}{\protect\numberline{\thechapter}\uppercase{#1}}}
+ \ifthen{\equal{mse}{\@@dept}}
+ {\setboolean{@@unset}{false}\addcontentsline{toc}{chapter}{\protect\uppercase{\@chapapp\ \thechapter. #1}}}
+ \ifthen{\boolean{@@unset}}
+ {\addcontentsline{toc}{chapter}{\protect\numberline{\thechapter}#1}}
+ \else
+ \ifthenelse{\equal{ce}{\@@dept}}
+ {\addcontentsline{toc}{chapter}{\uppercase{#1}}}
+ {\addcontentsline{toc}{chapter}{#1}}
+ \fi
+ \chaptermark{#1}%
+% \addtocontents{lof}{\protect\addvspace{10\p@}}%
+% \addtocontents{lot}{\protect\addvspace{10\p@}}%
+ \ifthenelse{\equal{ce}{\@@dept}}
+ {\@makechapterhead{\uppercase{#2}}}
+ {\@makechapterhead{#2}}
+ \@afterheading
+ \ifthen{\not \boolean{@@inchapters}}
+ {
+ \pagenumbering{arabic}%
+ \@@inchapterstrue
+ }
+}
+\def\@makechapterhead#1{%
+ \@@makechapterheadspacea
+ {\centering
+ \@@makechapterhead{#1}
+ \endgraf
+ }
+ \@@makechapterheadspaceb
+}
+\def\@makeschapterhead#1{%
+ \@@makeschapterheadspacea
+ {\centering
+ \ifthenelse{\equal{ce}{\@@dept}}
+ {\large\bf\uppercase{#1}}
+ {\large\bf #1}
+ \endgraf
+ }
+ \@@makeschapterheadspaceb
+}
+\def\@startsection#1#2#3#4#5#6{%
+ \if@noskipsec
+ \leavevmode
+ \fi
+ \par
+ \@tempskipa #4\relax
+ \@afterindenttrue
+ \ifdim \@tempskipa <\z@
+ \@tempskipa -\@tempskipa
+ \fi
+ \if@nobreak
+ \everypar{}%
+ \else
+ \addpenalty{\@secpenalty}%
+ \addvspace{\@tempskipa}%
+ \fi
+ \@ifstar
+ {\@ssect{#3}{#4}{#5}{#6}}%
+ {\@dblarg{\@sect{#1}{#2}{#3}{#4}{#5}{#6}}}%
+}
+\newenvironment{cabstract}{\begin{quote}\textbf{Abstract}\quad}{\end{quote}}
+\newtheorem{definition}{Definition}[section]
+\newtheorem{observation}{Observation}[section]
+\newsavebox{\proofbox}
+\sbox{\proofbox}{\rule{7pt}{7pt}}
+\newtheorem{Proof}{Proof}
+\renewcommand{\theProof}{}
+\newenvironment{proof}{\begin{Proof}\rm}{\hfill \usebox{\proofbox} \end{Proof}}
+\newtheorem{proposition}{Proposition}[section]
+\newtheorem{theorem}{Theorem}[section]
+\renewcommand{\section}{%
+ \@startsection
+ {section}%
+ {1}%
+ {\z@}%
+ {24pt}%
+ {12pt}%
+ {\@@sectionbaselinestretch\reset@font\@@sectionseries}%
+}
+\ifthen{\boolean{@@uglyheadings}}
+ {
+ \def\undertext#1{$\underline{\smash{\hbox{#1}}}$}
+ \def\section{
+ \vspace{36truept}
+ \secdef{\@@section}{\@@ssection}
+ }
+ \def\@@section[#1]#2{
+ \refstepcounter{section}
+ \sectionmark{#1}
+ \hbox to \textwidth{\hss\undertext{\thesection\ #2}\hss}\nopagebreak
+ \addcontentsline{toc}{section}{\protect\numberline{\csname thesection\endcsname}#1}
+ \nopagebreak
+ }
+ \def\@@ssection#1{
+ \sectionmark{#1}
+ \hbox to \textwidth{\hss\undertext{#1}\hss}\nopagebreak
+ \nopagebreak
+ }
+ }
+\renewcommand{\subsection}{%
+ \@startsection
+ {subsection}%
+ {2}%
+ {\z@}%
+ {24pt}% \@plus -1ex \@minus -.2ex}%
+ {12pt}% 1.5ex \@plus .2ex}%
+ {\@@subsectionbaselinestretch\reset@font\@@subsectionseries}%
+}
+\ifthen{\boolean{@@uglyheadings}}
+ {
+ \renewcommand{\subsection}[1]{
+ \refstepcounter{subsection}
+ \vspace{36truept}
+ \subsectionmark{#1}
+ \hbox to \textwidth{\hss\thesubsection\ #1\hss}\nopagebreak
+ \addcontentsline{toc}{subsection}{\protect\numberline{\csname thesubsection\endcsname}#1}
+ \nopagebreak
+ }
+ }
+\renewcommand{\subsubsection}{
+ \@startsection
+ {subsubsection}%
+ {3}%
+ {\z@}%
+ {24pt}% \@plus -1ex \@minus -.2ex}%
+ {12pt}% 1.5ex \@plus .2ex}%
+ {\@@subsubsectionbaselinestretch\reset@font\@@subsubsectionseries}%
+}
+\ifthen{\equal{mse}{\@@dept}}
+ {
+ \def\undertext#1{$\underline{\hbox{#1}}$}
+ \newbox{\@@Strut}
+ \renewcommand{\subsubsection}[1]{
+ \refstepcounter{subsubsection}
+ \vspace{6truept}
+ \subsubsectionmark{#1}
+ \noindent \undertext{\vphantom{gjpqy}\thesubsubsection. #1}\newline\nopagebreak
+ \addcontentsline{toc}{subsubsection}{\protect\numberline{\csname thesubsubsection\endcsname}#1}%
+ \nopagebreak\indent
+ }
+ }
+\ifthen{\boolean{@@uglyheadings}}
+ {
+ \renewcommand{\subsubsection}[1]{
+ \refstepcounter{subsubsection}
+ \vspace{36truept}
+ \subsubsectionmark{#1}
+ \noindent \undertext{\thesubsubsection. #1}\newline\nopagebreak
+ \addcontentsline{toc}{subsubsection}{\protect\numberline{\csname thesubsubsection\endcsname}#1}
+ \nopagebreak
+ }
+ }
+\ifthen{\boolean{@@uglyheadings}}
+ {
+ \setcounter{tocdepth}{4}
+ \newcommand*{\l@subsubsubsection}{\@dottedtocline{4}{\smalltocskip}{11.2em}{5.0em}}
+ \def\subsubsubsectionmark#1{}
+ \newcounter{subsubsubsection}[subsubsection]
+ \renewcommand{\thesubsubsubsection}{\thesubsubsection.\arabic{subsubsubsection}}
+ \newcommand{\subsubsubsection}[1]{
+ \refstepcounter{subsubsubsection}
+ \vspace{36truept}
+ \subsubsubsectionmark{#1}
+ \noindent \thesubsubsubsection. #1\newline\nopagebreak
+ \addcontentsline{toc}{subsubsubsection}{\protect\numberline{\csname thesubsubsubsection\endcsname}#1}
+ \nopagebreak
+ }
+ }
+\def\@sect#1#2#3#4#5#6[#7]#8{%
+ \ifnum #2>\c@secnumdepth
+ \let\@svsec\@empty
+ \else
+ \refstepcounter{#1}%
+ \edef\@svsec{\csname the#1\endcsname\hskip 1em}%
+ \fi
+ \ifthen{\equal{che}{\@@dept}}
+ {%
+ \ifnum #2=3
+ \refstepcounter{#1}%
+ \edef\@svsec{\csname the#1\endcsname\hskip 1em}%
+ \fi
+ }%
+ \@tempskipa #5\relax
+ \ifdim \@tempskipa>\z@
+ \begingroup
+ #6\relax
+ \@hangfrom{\hskip #3\relax\@svsec}%
+ {\interlinepenalty \@M #8\par}%
+ \endgroup
+ \csname #1mark\endcsname{#7}%
+ \addcontentsline{toc}{#1}{%
+ \ifnum #2>\c@secnumdepth
+ \else
+ \protect\numberline{\csname the#1\endcsname}%
+ \fi
+ #7%
+ }%
+ \else
+ \def\@svsechd{%
+ #6%
+ \hskip #3\relax
+ \@svsec #8\csname #1mark\endcsname
+ {#7}%
+ \addcontentsline{toc}{#1}{%
+ \ifnum #2>\c@secnumdepth
+ \else
+ \protect\numberline{\csname the#1\endcsname}
+ \fi
+ #7%
+ }%
+ }%
+ \fi
+ \@xsect{#5}%
+}
+\renewcommand*{\l@chapter}{\@dottedtocline{0}{\bigtocskip}{0em}{1.4em}}
+\ifthen{\equal{ece}{\@@dept}\or \equal{hsci}{\@@dept}}
+ {\renewcommand{\figurename}{Fig.}}
+\renewcommand{\fnum@figure}{\figurename~\thefigure.}
+\renewcommand{\fnum@table}{\tablename~\thetable}
+\newboolean{@@centercaption}
+\renewcommand{\caption}{%
+ \setboolean{@@centercaption}{true}
+ \refstepcounter\@captype \@dblarg{\@caption\@captype}%
+}
+\newcommand\bcaption{%
+ \setboolean{@@centercaption}{true}
+ \refstepcounter\@captype \@dblarg{\@caption\@captype}%
+}
+\long\def\@makecaption#1#2{%
+ \vspace*{\abovecaptionskip}
+ \ifthenelse{\boolean{@@centercaption}} % center caption
+ {
+ \setlength{\@@captionwidth}{\textwidth}
+ \addtolength{\@@captionwidth}{-4\@@parindent}
+ \ifthenelse{\equal{figure}{\@captype} \or \boolean{@@figurecaptions}}
+ {
+ \sbox\@tempboxa{#1 #2}%
+ \renewcommand{\baselinestretch}{1.0}\reset@font
+ \ifdim \wd\@tempboxa >\hsize
+ \centerline{\parbox[t]{\@@captionwidth}{#1 #2}}
+ \else
+ \centerline{#1 #2}%
+ \fi
+ }
+ {
+ \renewcommand{\baselinestretch}{1.0}\reset@font
+ \centerline{#1}
+ \sbox\@tempboxa{#2}%
+ \ifdim \wd\@tempboxa >\hsize
+ \centerline{\parbox[t]{\@@captionwidth}{#2}}
+ \else
+ \centerline{#2}%
+ \fi
+ }
+ \vspace*{\belowcaptionskip}
+ }
+ {
+ \setlength{\topsep}{0pt}
+ \setlength{\parskip}{0pt}
+ \setlength{\partopsep}{0pt}
+ \begin{quote}
+ \renewcommand{\\}{}
+ \renewcommand{\baselinestretch}{1.0}\reset@font
+ \ifthenelse{\equal{aae}{\@@dept}}
+ {#1. #2\par}
+ {
+ \ifthenelse{\equal{table}{\@captype}}
+ {\hfil\strut #1\hfil\break #2\par}
+ {#1 #2\par}
+ }
+ \end{quote}
+ }
+ \vskip\belowcaptionskip
+}
+\setlength\belowcaptionskip{5\p@}
+\long\def\@makefntext#1{%
+ \baselineskip=12pt
+ \noindent
+ \@makefnmark #1%
+}
+\ifthenelse{\equal{}{\@@optionbibstyle}}
+ {\newcommand{\@@bibstyle}{\@@deptbibstyle}}
+ {\newcommand{\@@bibstyle}{\@@optionbibstyle}}
+
+\ifthen{\equal{abbrvnat}{\@@bibstyle}}
+ {
+ \usepackage[sort]{natbib}
+ \renewcommand{\bibname}{{\normalsize\rm LIST OF REFERENCES}}
+ \bibliographystyle{abbrvnat}
+ }
+
+\ifthen{\equal{aer}{\@@bibstyle}}
+ {
+ \usepackage{harvard}
+ \bibliographystyle{aer}
+ }
+
+
+\ifthen{\equal{agsm}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \usepackage{harvard}
+ \bibliographystyle{agsm}
+ }
+
+\ifthen{\equal{aip}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \usepackage{revtex}
+ \renewcommand{\bibname}{{\normalsize\rm LIST OF REFERENCES}}
+ \bibliographystyle{aip}
+ }
+
+\ifthen{\equal{alpha}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \bibliographystyle{alpha}
+ }
+
+\ifthen{\equal{ama}{\@@bibstyle}}
+{
+ \usepackage{cite}
+ \bibliographystyle{ama}
+}
+
+\ifthen{\equal{apacite}{\@@bibstyle}}
+ {
+ \usepackage{apacite}
+ \bibliographystyle{apacite}
+ }
+
+\ifthen{\equal{apalike}{\@@bibstyle}}
+ {
+ \usepackage{apalike}
+ \bibliographystyle{apalike}
+ }
+
+\ifthen{\equal{astron}{\@@bibstyle}}
+ {
+ \usepackage{astron}
+ \bibliographystyle{astron}
+ }
+
+\ifthen{\equal{chicago}{\@@bibstyle}}
+ {
+ \usepackage{natbib}
+ \bibliographystyle{chicago}
+ }
+
+\ifthen{\equal{ieee}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \bibliographystyle{ieee}
+ }
+
+\ifthen{\equal{ieeetr}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \bibliographystyle{ieeetr}
+ }
+
+\ifthen{\equal{jfm}{\@@bibstyle}}
+ {
+ \usepackage{natbib}
+ \bibliographystyle{jfm}
+ }
+
+\ifthen{\equal{jfm2}{\@@bibstyle}}
+ {
+ \usepackage{natbib}
+ \bibliographystyle{jfm2}
+ }
+
+\ifthen{\equal{kluwer}{\@@bibstyle}}
+ {
+ \usepackage{harvard}
+ \bibliographystyle{kluwer}
+ }
+
+\ifthen{\equal{pumeunsrt}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \bibliographystyle{pumeunsrt}
+ }
+
+\ifthen{\equal{plain}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \bibliographystyle{plain}
+ }
+
+\ifthen{\equal{plainnat}{\@@bibstyle}}
+ {
+ \usepackage{natbib}
+ \renewcommand{\bibname}{{\normalsize\rm LIST OF REFERENCES}}
+ \bibliographystyle{plainnat}
+ }
+
+\ifthen{\equal{unsrt}{\@@bibstyle}}
+ {
+ \usepackage{cite}
+ \bibliographystyle{unsrt}
+ }
+
+\ifthen{\equal{unsrtnat}{\@@bibstyle}}
+ {
+ \usepackage{natbib}
+ \renewcommand{\bibname}{{\normalsize\rm LIST OF REFERENCES}}
+ \bibliographystyle{unsrtnat}
+ }
+
+\renewenvironment{thebibliography}[1]
+ {
+ \@@startthebibliography
+ \@@nonchapter{odd}{\@@bibname}{y}{24pt}
+ \list
+ {\@biblabel{\arabic{enumiv}}}%
+ {%
+ \settowidth\labelwidth{\@biblabel{#1}}%
+ \leftmargin\labelwidth
+ \advance\leftmargin\labelsep
+ \if@openbib
+ \advance\leftmargin\bibindent
+ \itemindent -\bibindent
+ \listparindent \itemindent
+ \parsep \z@
+ \fi
+ \@@thebibliographyparsep
+ \usecounter{enumiv}%
+ \let\p@enumiv\@empty
+ \renewcommand{\theenumiv}{\arabic{enumiv}}%
+ \baselineskip=12pt
+ }%
+ \if@openbib
+ \renewcommand{\newblock}{\par}
+ \else
+ \renewcommand{\newblock}{\hskip .11em \@plus.33em \@minus.07em}%
+ \fi
+ \sloppy\clubpenalty4000\widowpenalty4000%
+ \sfcode`\.=\@m
+ }
+ {}
+\newboolean{@@inappendix}
+\newcommand{\@@appendixname}{Appendix}
+\renewcommand{\appendix}{\par
+ \setboolean{@@inappendix}{true}
+ \setcounter{chapter}{0}%
+ \setcounter{section}{0}%
+ \coversheet{\uppercase\expandafter{\@@appendixname}}
+ \renewcommand{\@chapapp}{\appendixname}%
+ \renewcommand{\thechapter}{\Alph{chapter}}
+}
+\newcommand{\@@appendicesname}{Appendices}
+\newcommand{\appendices}{\par
+ \setboolean{@@inappendix}{true}
+ \setcounter{chapter}{0}%
+ \setcounter{section}{0}%
+ \coversheet{\uppercase\expandafter{\@@appendicesname}}
+ \renewcommand{\@chapapp}{\appendixname}%
+ \renewcommand{\thechapter}{\Alph{chapter}}
+}
+\newcommand{\@@vitaname}{VITA}
+\newenvironment{vita}
+ {
+ \@@startvita
+ \@@nonchapter{odd}{\@@vitaname}{y}{0pt}%
+ }
+ {}
+\def\@starttoc#1{%
+ \begingroup
+ \@input{\jobname.#1}%
+ \if@filesw \expandafter\newwrite\csname tf@#1\endcsname
+ \immediate\openout \csname tf@#1\endcsname \jobname.#1\relax
+ \fi
+ \global\@nobreakfalse
+ \endgroup
+}
+\newcommand{\num}[1]{\ensuremath{#1}}
+\newcommand{\ten}[1]{\ensuremath{{}\cdot 10^{#1}}}
+
+\setcounter{topnumber}{10}
+\renewcommand{\topfraction}{1}
+\setcounter{bottomnumber}{10}
+\renewcommand{\bottomfraction}{1}
+\setcounter{totalnumber}{10}
+\renewcommand{\textfraction}{0}
+\renewcommand{\floatpagefraction}{0}
+
+\setlength{\floatsep}{18pt plus 3pt minus 3pt}
+\setlength{\textfloatsep}{30pt plus 3pt minus 6pt}
+\setlength{\intextsep}{18pt plus 3pt minus 3pt}
+\setlength{\dblfloatsep}{18pt plus 3pt minus 3pt}
+\setlength{\dbltextfloatsep}{30pt plus 3pt minus 6pt}
+
+\newcommand{\Baselinestretch}[1]{\renewcommand{\baselinestretch}{#1}\reset@font}
+
+\def\volume{
+ \addtocounter{@@volume}{1}
+ \write\@auxout{\string\setcounter{last@@volume}{\the@@volume}}
+ \ifthen{\(\boolean{number@@all@@volumes} \and \value{last@@volume}>1\) \or \value{@@volume}>1}
+ {
+ \addtocontents{toc}{\string\vskip\string\baselineskip \noindent VOLUME \the@@volume}
+ }
+ \maketitle
+}
+
+\ifthen{\equal{me}{\@@dept}}
+ {
+ \ifthenelse{\equal{\@@optionbibstyle}{jfm}}
+ {\def\@@deptbibstyle{jfm}}
+ {\ifthenelse{\equal{\@@optionbibstyle}{jfm2}}
+ {\def\@@deptbibstyle{jfm2}}
+ {\def\@@deptbibstyle{pumeunsrt}}
+ }
+ \def\fnum@figure{\figurename~\thefigure.~}
+ \def\fnum@table{\tablename~\thetable.~}
+ \setboolean{@@figurecaptions}{true}
+ \def\@@makechapterhead#1{\rm\thechapter. \uppercase{#1}}
+ \def\@@makechapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makechapterheadspaceb{\vspace{0.5\baselineskip}}
+ \def\@@makeschapterheadspacea{\vspace*{0.5truein}}
+ \def\@@makeschapterheadspaceb{\vspace*{0.5\baselineskip}}
+ \nochapterblankpages
+ }
+
+\makeatother
+
+\raggedbottom
diff --git a/thesis.tex b/thesis.tex
new file mode 100644
index 0000000..689ac9c
--- /dev/null
+++ b/thesis.tex
@@ -0,0 +1,46 @@
+%
+% Thesis: Kenneth F. Kellner, Spring 2012
+%
+\documentclass[ece,thesis,chicago]{puthesis}
+\usepackage{amsmath}
+\usepackage{textcomp}
+\usepackage{graphics}
+\usepackage{booktabs}
+\usepackage{courier}
+\usepackage{setspace}
+\usepackage{listings}
+\lstset{language=R, basicstyle=\ttfamily\singlespacing, keywordstyle=\ttfamily}
+
+\title{
+ Temporal Dynamics of Mast and Small Mammals:\\
+ Short-term Responses to Silviculture%
+}
+\author{Kenneth F. Kellner}{Kellner, Kenneth F.}
+\majorprof{Robert K. Swihart}
+\pudegree{Master of Science}{M.S.}{May}{2012}
+\campus{West Lafayette}
+
+
+\begin{document}
+
+\volume
+
+\include{front}
+
+\include{introduction}
+
+\include{chapter2}
+
+\include{chapter3}
+
+\include{chapter4}
+
+\include{conclusion}
+
+\nocite{*}
+
+\bibliography{allcites}
+
+\include{appendices}
+
+\end{document}