Models for Ecological Data: An Introduction

By Daniel T. Haydon. 2008

153 citations recorded by the source corpus.

Subfields: Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/583d0d0a7da0a45b8e97bf00980e4e76b24c827d

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In Nature and Landscape Conservation

Citations in the corpus

Interspecific variation in primary seed dispersal in a tropical forest (2008, methodology): “We accommodated interannual variation in population-level seed production by fitting hierarchical models for each species incorporating lognormal variation in the fecundity parameter among years (Clark 2007).”
Measuring long‐distance seed dispersal in complex natural environments: an evaluation and integration of classical and genetic methods (2008, methodology): “We used hierarchical Bayesian models to fit the models to the empirical data set (Gelman et al. 1995; Clark 2007).”
Beyond description: the active and effective way to infer processes from spatial patterns. (2009, methodology): “…the process directly or building experiments (e.g., Jolles et al. 2002, Fajardo and McIntire 2007) because they are not practical (Schurr et al. 2004, Clark 2007), prohibitively expensive, unethical, or overly time consuming, leaving them with this pattern–process approach as a sole option.”
Statistical solutions for error and bias in global citizen science datasets (2014, background): “Hierarchical models are increasingly popular in 333 ecological analyses, and books covering the topic have been published 334 recently, from frequentist (Clark, 2007) and Bayesian perspectives (Royle and 335 Dorazio, 2008).”
A guide to Bayesian model selection for ecologists (2015, background): “Gotelli and Ellison (2012) and Bolker (2008) provide excellent background on contemporary ecological statistics, and from a Bayesian perspective see Clark (2007), Royle and Dorazio (2008), Link and Barker (2010), and Hobbs and Hooten (in press).”
Ecological non-linear state space model selection via adaptive particle Markov chain Monte Carlo (AdPMCMC) (2010, methodology): “risation has important implications for the choice of static parameter priors in the population models used here. In this paper we have followed a common practice in the ecological literature, e.g., [Clark, 2007], wherein parameters such as the intrinsic rate of growth r and carrying capacity K are ”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, background): “As a consequence, the completeness and biases of the input data are rarely assessed and the uncertainty associated with the mapping process is rarely taken into account (see Clark, 2007).”
Stacking species distribution models and adjusting bias by linking them to macroecological models (2014, methodology): “…are estimated with uncertainty, and ignoring this uncertainty would result in misleadingly narrow confidence intervals on Sj. Error propagation techniques (e.g. Chapter 5 in Clark, 2007) could be used in combination with Eqn 3 to fully account for uncertainty in the site-level richness prediction.”
Understanding movement data and movement processes: current and emerging directions. (2008, background): “Hierarchical Bayes for movement data and movement processes Advances in hierarchical Bayes (HB) have opened up new opportunities for inference on biological processes (Gelfand & Smith 1990; Carlin & Louis 2000; Wikle 2003; Clark 2005, 2007).”
Dynamic interactions of life and its landscape: feedbacks at the interface of geomorphology and ecology (2010, methodology): “This is where statistical tools such as structural equations modelling (SEM) (Shipley, 2000; Grace, 2006), Bayesian hierarchical modelling (Gelman and Hill, 2007) and related methodologies are opening up new opportunities (Clark, 2007).”