Case Applications of Statistical Models in Ecology and Evolution

Youhua Chen, PhD
University of Alberta

Series: Environmental Science, Engineering and Technology
BISAC: SCI027000

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Since the 20th century, there have been many exciting achievements in the field of ecology and evolution, many of which were formulated using statistical and mathematical functions. In particular, statistical and mathematical models are a necessity for developing some sub-disciplines and theories like population genetics and ecology, neutral theory of molecular evolution and biodiversity, and machine-learning techniques for species distribution modeling.

This book serves as an elementary guide to showcase some statistical and mathematical models that have been applied and used in contemporary ecological or evolutionary research. Some models may be old-fashioned, whilst some others have been further extended or developed so as to better address special research questions presented in each chapter of the book. Technical aspects of each statistical model are presented in detail to allow readers to follow and apply their own research and practices. (Imprint: Nova)

Preface

Part 1: Case Applications

Chapter 1. Permutation-free Fisher’s Z-transform Method for Significance Testing of Mantel’s Statistic

Chapter 2. Random Species/Population Change and Multiple-site Beta Diversity Indices: A Comparison of Contradictory Scenarios from Simulation- and Empirical BCI Tree-derived Data Sets

Chapter 3. On the Relationship between Community-level Beta Diversity and Partitioning Variation for Space and Environment: BCI Tree Community as a Case Study

Chapter 4. Distinguishing Niche and Neutral Processes: Issues in Variation Partitioning Statistical Methods and Further Perspectives

Chapter 5. Estimating Ancestral Ranges in Historical Biogeography: On the Development of Alternative Range Evolution Models for Maximum Likelihood Inference

Chapter 6. On the Relationships of Clade Age, Species Richness, and Phylogenetic Diversity of Global Mammals

Chapter 7. Spatial Point Pattern Analysis of County Locations of Mainland China: The Relative Roles of Anthropogenic Factors, Climatic Fluctuation and Water Availability

Chapter 8. Modeling Species-area Relationship with Measurement Uncertainty

Chapter 9. Estimating Population Change of Species Under Climate Change Using Occupancy-abundance Relationship

Chapter 10. ‘Coexist’: An R Package for Performing Species Coexistence Modeling and Analysis

Chapter 11. Fitness Equivalence Promotes Species Coexistence under Fluctuating Environments with Simultaneous Coupling of Dispersal and Competition

Part 2: Appendices of R Codes used for Computation and Simulation

Appendix 1. R code for Fisher Transform and Simulation

Appendix 2. R code for Simulating Asymmetric Dispersal and Coexistence

Author's Contact Information

Index

Chapter 1

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Chapter 2

Almeida-Neto M, Frensel D, Ulrich W (2012) Rethinking the relationship between nestedness and beta diversity: a comment on Baselga (2010). Glob Ecol Biogeogr 21:772–777.
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Baselga A (2010) Partitioning the turnover and nestedness components of beta diversity. Glob Ecol Biogeogr 19:134–143. doi: 10.1111/j.1466-8238.2009.00490.x.
Baselga, Jimenez-Valverde A, Niccolini G (2007) A multiple-site similarity measure independent of richness. Biol Lett 3:642–645.
Carvalho J, Cardoso P, Borges P, et al. (2012a) Measuring fractions of beta diversity and their relationships to nestedness: a theoretical and empirical comparison of novel approaches. Oikos Doi: 10.1111/j.1600–0706.2012.20980.x. doi: 10.1111/j.1600-0706.2012.20980.x.
Carvalho J, Cardoso P, Gomes P (2012b) Determining the relative roles of species replacement and species richness differences in generating beta-diversity patterns. Glob Ecol Biogeogr 21:760–771.
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Podani J, Shmera D (2012) A comparative evaluation of pairwise nestedness measures. Ecography (Cop). doi: 10.1111/j.1600-0587.2011.07319.x.
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Schmera D, Podani J (2011) Comments on separating components of beta diversity. Community Ecol 12:153–160.
Ulrich W, Gotelli N (2007) Null model analysis of species nestedness patterns. Ecology 88:1824–1831.

Chapter 3

Almeida-Neto M, Frensel D, Ulrich W (2012) Rethinking the relationship between nestedness and beta diversity: a comment on Baselga (2010). Glob Ecol Biogeogr 21:772–777.
Baselga A (2010) Partitioning the turnover and nestedness components of beta diversity. Glob Ecol Biogeogr 19:134–143. doi: 10.1111/j.1466-8238.2009.00490.x.
Baselga, Jimenez-Valverde A, Niccolini G (2007) A multiple-site similarity measure independent of richness. Biol Lett 3:642–645.
Borcard D, Legendre P, Drapeau P (1992) Partialling out the Spatial Component of Ecological Variation. Ecology 73:1045.
Carvalho J, Cardoso P, Borges P, et al. (2012a) Measuring fractions of beta diversity and their relationships to nestedness: a theoretical and empirical comparison of novel approaches. Oikos Doi: 10.1111/j.1600–0706.2012.20980.x. doi: 10.1111/j.1600-0706.2012.20980.x.
Carvalho J, Cardoso P, Gomes P (2012b) Determining the relative roles of species replacement and species richness differences in generating beta-diversity patterns. Glob Ecol Biogeogr 21:760–771.
Condit R, Pitman N, Leigh EG, et al. (2002) Beta-diversity in tropical forest trees. Science (80-) 295:666–9.
Dimitriadis C, Koutsoubas D (2011) Functional diversity and species turnover of benthic invertebrates along a local environmental gradient induced by an aquaculture unit: the contribution of species dispersal ability and rarity. Hydrobiologia 670:307–315.
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Legendre P, Borcard D, Peres-Neto P (2008) Analyzing or explaining beta diversity : Comment. Ecology 3238–3244.
Legendre P, Borcard D, Peres-Neto P (2005) Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol Monogr 75:435–450.
Legendre P, Legendre L (1998) Numerical ecology. Elsevier Science BV, Amsterdam.
Legendre P, Mi X, Ren H, et al. (2009) Partitioning beta diversity in a subtropical broad-leaved forest of China. Ecology 90:663–674.
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Peres-Neto P, Legendre P, Dray S, Borcard D (2006) Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87:2614–2625.
Podani J, Shmera D (2012) A comparative evaluation of pairwise nestedness measures. Ecography (Cop). doi: 10.1111/j.1600-0587.2011.07319.x.
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Schmera D, Podani J (2011) Comments on separating components of beta diversity. Community Ecol 12:153–160.
Smith TW, Lundholm JT (2010) Variation partitioning as a tool to distinguish between niche and neutral processes. Ecography (Cop) 33:648–655. doi: 10.1111/j.1600-0587.2009.06105.x.
Tuomisto H, Ruokolainen K (2006) Analyzing or explaining beta diversity? Understanding the targets of different methods of analysis. Ecology 87:2697–708.
Tuomisto H, Ruokolainen L, Ruokolainen K (2012) Modelling niche and neutral dynamics: on the ecological interpretation of variation partitioning results. Ecography (Cop) no–no. doi: 10.1111/j.1600-0587.2012.07339.x.
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Chapter 4

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Diniz-Filho J, Siqueira T, Padial A, et al. (2012) Spatial atucorrelation analysis allows disentangling the balance between neutral and niche processes in metacommunities. Oikos 121:201–210.
Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecol Modell 196:483–493.
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Legendre P, Borcard D, Peres-Neto P (2008) Analyzing or explaining beta diversity : Comment. Ecology 3238–3244.
Legendre P, Borcard D, Peres-Neto P (2005) Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecol Monogr 75:435–450.
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Chapter 5

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Chapter 6

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Chapter 7

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Chapter 8

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