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Patterns and Processes in Forest Insect Population Dynamics by Josie Patterns and processes in forest insect population dynamics by Josie Hughes A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Ecology and Evolution University of Toronto Copyright © 2012 by Josie Hughes Abstract Patterns and processes in forest insect population dynamics Josie Hughes Doctor of Philosophy Graduate Department of Ecology and Evolution University of Toronto 2012 This dissertation is concerned with effects dispersal and forest structure on forest insect population dynamics, and with identifying generating processes by comparing observed patterns to model predictions. In chapter 2, we investigated effects of changing forest landscape patterns on integro-difference models of host-parasitoid population dynamics. We demonstrated that removing habitat can increase herbivore density when herbivores don't disperse far, and parasitoids disperse further, due to differences in dispersal success between trophic levels. This is a novel potential explanation for why forest fragmentation increases the duration of forest tent caterpillar outbreaks. To better understand spatial model behaviour, we proposed a new local variation of the dispersal success approxima- tion. The approximation successfully predicts effects of habitat loss and fragmentation on realistically complex landscapes, except when outbreak cycle amplitude is very large. Lo- cal dispersal success is useful in part because parameters can be estimated from widely available habitat data. In chapter 3, we investigated how well a discretized integro- difference model of mountain pine beetle population dynamics predicted the occurrence of new infestations in British Columbia. We found that a model with a large dispersal kernel, and high emigration from new, low severity infestations yielded the best pre- dictions. However, we do not believe this to be convincing evidence that many beetles disperse from new, low severity infestations. Rather, we argued that differences in habitat quality, detection errors, and Moran effects can all confound dispersal patterns, making it difficult to infer dispersal parameters from observed infestation patterns. Nonetheless, predicting infestation risk is useful, and large kernels improve predictions. In chapter 4, we used generalized linear mixed models to characterize spatial and temporal variation in the propensity of jack pine trees to produce pollen cones, and account for confounding ef- fects on the relationship between pollen cone production and previous defoliation by jack pine budworm. We found effects of stand age, and synchronous variation in pollen cone ii production among years. Accounting for background patterns in pollen cone production clarified that pollen cone production declines in with previous defoliation, as expected. iii Dedication To my family, and those who persevere. iv Acknowledgements Thanks to Marie-Jos´ee Fortin and members of the landscape ecology lab for support of all kinds. Thanks to David Pritchard, Heather Coiner, and all my friends and family for patience, encouragement, and companionship along the way. Thanks to Jacques R´egni`ere,Don Jackson, and Helene Wagner for serving on my supervisory committee. Thanks to Greg Dwyer, Christina Cobbold, Andrew Liebhold, Barry Cooke, Mark Lewis, Barbara Bentz, Anthony Ives, Mario Pineda-Krch, Sharon Bewick and other members of the Forest Insects Working Group at the National Institute for Mathematical and Biological Synthesis for encouragement, insight, and helpful discussions. Thanks to Vince Nealis (Canadian Forest Service) for providing the data in chapter 4, and for other contributions to chapters 3 and 4. Thanks to Marvin Eng, Adrian Walton, Tim Ebata (British Columbia Ministry of Forests) and Gurp Thandi (Canadian Forest Service) for access to and help with British Columbia mountain pine beetle and forest cover data. Thanks to Andrew Fall and Greg Wilson (http://software-carpentry.org/) for teaching practical programming. Thanks to Kosta Zabashta for help implementing a very early version of the dispersal model. Thanks to David Pritchard for opportune assistance with the more mystifying features of open source software, and for pointing out useful tools. Thanks to all contributors to open source software projects (R, Python, LaTeX, Subversion, GDAL, PythonXY, RStudio, Cygwin, TortoiseSVN), and to users who share their knowledge. Thanks to Jordan Pleet for assistance with Mathematica (chapter 2). Computing in chapter 2 was supported by an allocation of advanced computing re- sources provided by the National Science Foundation. The computations were performed on Kraken, Athena, or Nautilus at the National Institute for Computational Sciences (http://www.nics.tennessee.edu/). Thanks also to Eric Carr (NIMBIOS) for help with computing in chapter 2. Computing resources for chapter 3 were provided in part by Canada Foundation for Innovation (CFI #12301 and CFI #203383) grants to Igor Ju- risica, and IBM. Funding for this work was provided by an NSERC-CGS award to J. Hughes, an NSERC Discovery grant to Marie-Jos´eeFortin, and the Department of Ecology and Evo- lutionary Biology at the University of Toronto (Frederick P. Ide Graduate Awards in Ecology and Evolutionary Biology, Archibald. G. Huntsman Graduate Award in Zool- ogy, and Edna Margaret Robertson Scholarships to J. Hughes). The Canadian Forest Service (J. R´egni`ere)funded an extended stay at the Laurentian Forestry Centre in May and June 2009. The Australian Centre of Excellence for Risk Analysis (ACERA) funded attendance at a workshop on Spatial Models for Non-Equilibrium Systems (Oc- tober 2007). Brian Sturtevant funded a spruce budworm field trip to Minnesota, and v attendance at a LANDIS workshop (October 2007). Mark Lewis funded a visit to the Centre for Mathematical Biology at the University of Alberta (January 2010). Work on chapter 2 was conducted as a part of the Forest Insects Working Group at the Na- tional Institute for Mathematical and Biological Synthesis, sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Depart- ment of Agriculture through NSF Award #EF-0832858, with additional support from The University of Tennessee, Knoxville. vi Contents 1 Introduction 1 2 The effects of forest spatial structure on insect outbreak dynamics: insights from host-parasitoid models 6 2.1 Abstract . .6 2.2 Introduction . .7 2.3 Spatial population models . 10 2.3.1 Model structure . 10 2.3.2 Local population dynamics . 11 2.3.3 Dispersal Kernel . 16 2.3.4 Landscape configuration . 16 2.3.5 Parameter values . 18 2.3.6 Effects of landscape configuration on herbivore density . 20 2.4 Dispersal success approximations . 25 2.4.1 Local dispersal success approximation . 25 2.4.2 Average dispersal success approximation . 27 2.4.3 Mean-field approximation . 28 2.4.4 Accuracy of the local dispersal success approximation . 29 2.4.5 Effects of landscape configuration on dispersal success approxima- tion of mean herbivore density . 33 2.5 Comparison to data . 37 2.6 Discussion . 39 2.6.1 Effect of landscape configuration on herbivore population density 39 2.6.2 The effects of fragmentation on forest tent caterpillar outbreaks . 40 2.6.3 Estimating dispersal parameters from data . 42 2.6.4 The local dispersal success approximation . 43 vii 3 Does damage-dependent long-distance dispersal explain mountain pine beetle spread? 46 3.1 Abstract . 46 3.2 Introduction . 47 3.3 Methods . 49 3.3.1 Estimating the proportion of trees killed by beetles and defining the study area . 49 3.3.2 Model of mountain pine beetle population dynamics and dispersal 54 3.3.3 Dispersal pressure model parameters . 61 3.3.4 Measuring effects of dispersal pressure model parameters on pre- diction accuracy . 61 3.4 Results . 67 3.4.1 Effects of region and dispersal pressure parameters on prediction accuracy . 67 3.4.2 Variation in prediction accuracy over time and space . 71 3.5 Discussion . 74 3.5.1 Does damage-dependent dispersal explain mountain pine beetle in- festation spread? . 74 3.5.2 How far do mountain pine beetle infestations spread? . 75 3.5.3 Pattern-process analysis of mountain pine beetle population dy- namics . 78 4 The effects of jack pine budworm defoliation on jack pine pollen cone production: untangling reciprocal interactions in space-time data 83 4.1 Abstract . 83 4.2 Introduction . 84 4.3 Methods . 86 4.4 Selecting the response and potential predictors: spatial scale, collinearity and overspecification . 86 4.5 Building a generalized linear mixed model . 94 4.5.1 The initial GLMM . 94 4.5.2 Model validation . 95 4.5.3 Model selection . 95 4.6 Results . 96 4.6.1 Identification of a valid full GLMM . 96 4.6.2 Model selection . 100 viii 4.6.3 Background patterns in pollen cone production . 100 4.6.4 Effects of defoliation and forest attributes on pollen cone production103 4.7 Discussion . 108 4.7.1 Background patterns in pollen cone production . 108 4.7.2 Effects of defoliation on pollen cone production . 110 4.7.3 The importance of data exploration and model validation . 110 4.7.4 Using old data to build new models . 111 5 Conclusion 113 5.1 Insights from qualitative comparison of model predictions and observed patterns . 113 5.2 Pattern-process analysis of dispersal . 114 5.3 The local dispersal success approximation . 117 5.4 Effects of trees on forest insect outbreaks . 119 5.5 Pattern-process analysis of forest insect population dynamics . 120 Bibliography 121 A Chapter 2 supplementary material 154 A.1 Conditions for host and parasitoid viability . 154 A.2 Effects of landscape configuration on local outbreak duration in the FTC model ..................................... 157 A.3 Effects of landscape configuration on herbivore density in the LBM model 162 A.4 Summarizing the effect of landscape configuration on mean herbivore density167 A.4.1 Summarizing the effect of habitat removal . 167 A.4.2 Summarizing the effect of habitat arrangement . 171 A.5 Other measures of population dynamics . 174 A.5.1 Redundancy among measures of FTC model dynamics .
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