Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes

By Christopher K. Wikle. 2003

441 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation

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Citations in the corpus

The phenology of plant invasions: a community ecology perspective (2011, unknown): “Advances in uncertainty theory (Wikle 2003) allow fitting basic models that estimate errors surrounding such dates (Dose and Menzel 2004) Organization- Large-scale citizen-science Project Budburst Generally cover vast Citizen-collected data often based plant projects that provide data…”
Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant (2010, methodology): “A Bayesian framework, which has already been applied to the problem of invasive spread (e.g. Wikle, 2003), might be especially useful in this respect.”
Estimating the wood density of species for carbon stock assessments (2011, methodology): “We use a Bayesian framework to make estimates based on phylogenetic relatedness, because it enables the incorporation of prior knowledge and phyloge- netic dependence, and also readily models missing data from actual observations (Banerjee et al. 2003; Wikle 2003; Clark et al. 2005).”
High‐dimensional coexistence based on individual variation: a synthesis of evidence (2010, background): “As described by Berliner (1996, see also Ver Hoef and Frost 2003, Wikle 2003, Clark 2005, Wikle and Berliner 2005, Clark and Gelfand 2006, Royle and Dorazio 2008, Cressie et al. 2009), data at different scales can be coherently assimilated in hierarchical models.”
Observing Changing Ecological Diversity in the Anthropocene (2013, background): “…already begun to change (Parmesan 2006, Root and Weckstein 1994, Wake et al 2009) and data on species distributions collected in the future will be less and less likely to be reflective of adjustment to climate and more and more likely to exhibit dynamic and disequilibrium responses (Wikle 2003).”
Modelling occurrence and abundance of species when detection is imperfect (2005, background): “The probability model g(N; u) for the unobservable abundance parameters, {Ni}, in conjunction with the sampling model Eq. 3 yields what is often referred to as a hierarchical model (Link et al. 2002, Clark 2003, Wikle 2003 ).”
A Bayesian Random Effects Discrete-Choice Model for Resource Selection: Population-Level Selection Inference (2006, background): “404 The Journal of Wildlife Management 70(2) and White 2002, Brooks et al. 2000), modeling spatial distributions of species (Penttinen et al. 2003) and predicting invasive species population growth (Wikle 2003).”
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi (2013, background): “Examples are numerous, but include marine biogeochemistry (Dowd 2006; Jones, Parslow, and Murray 2010; Dowd 2011; Parslow, Cressie, Campbell, Jones, and Murray 2013), ecological population dynamics (Wikle 2003; Newman, Fernández, Thomas, and Buckland 2009; Peters, Hosack, and Hayes 2011; Hosack, Pet”
Statistical solutions for error and bias in global citizen science datasets (2014, methodology): “455 Hierarchical Bayesian approaches to spatial and temporal modeling have also 456 been used to explicitly model sampling in relation to a latent underlying 457 process (Wikle, 2003).”
Statistical inference for stochastic simulation models--theory and application. (2011, methodology): “…for example, by means of data augmentation (Dempster et al. 1977), state-space models (Patterson et al. 2008), hierarchical Bayesian models (Wikle 2003; Clark & Gelfand 2006) or diffusion approximations (Holmes 2004), our ability to calculate likelihoods for complex stochastic systems is…”