Comparing species abundance models

By Joanne Potts, Jane Elith. 2006

398 citations recorded by the source corpus.

Subfields: Ecological Modeling, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/13e162eea8935d1082bd4e7e6baeab85be986be9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Economics and Econometrics

In Nature and Landscape Conservation

Citations in the corpus

A unified approach to model selection using the likelihood ratio test (2011, background): “These models are all designed for unrestricted count data: see Martin et al. 2005; and Potts & Elith 2006, for specific details of these models and of their application to ecological data (and the Appendix S1 contains a brief comparison between hurdle and non-hurdle models for Poisson data).”
The consequences of checking for zero‐inflation and overdispersion in the analysis of count data (2019, background): “(Note that when selecting between the QP and NB models, the best choice is not always straightforward; see Ver Hoef and Boveng (2007), also see Potts and Elith (2006).)”
Waterbird Populations and Pressures in the Baltic Sea (2011, methodology): “The final combined model was evaluated by measuring Spearman’s rank correlation between observed and predicted values (Potts & Elith 2006).”
Species distribution modelling of marine benthos: a North Sea case study (2011, methodology): “Thus, Bayesian and other modelling ap - proaches will be necessary tools in future prediction of quantitative characteristics of species distribution (Pearce & Boyce 2006, Potts & Elith 2006, Anadón et al. 2010, Chakraborty et al. 2010).”
A quantitative review of abundance-based species distribution models (2021, methodology): “Current abundance model evaluations examine only a limited set of statistical frameworks and the best options may be overlooked (Pearce and Ferrier 2001, Potts and Elith 2006, Oppel et al. 2012, Bahn and McGill 2013).”
Mixed Effects Models and Extensions in Ecology with R (2009, methodology): “For investigations in deeper waters, Priede et al. (2006) developed a free-fall vehicle with a downward looking high sensitivity ISIT (Intensified Silicon Intensified Target) video camera focussed on a 0.”
Comparison of five modelling techniques to predict the spatial distribution and abundance of seabirds (2012, methodology): “…to fit models in a hierarchical fashion (e.g., a ‘hurdle model’), including a component that estimates the occurrence probability, and a subsequent component that estimates the number of individuals given that the species is present (Millar, 2009; Potts and Elith, 2006; Wenger and Freeman, 2008).”
Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics (2011, methodology): “Rather than integrative mixture models (Martin et al., 2005), a two-stage modelling process (Potts & Elith, 2006) was selected because (i) results are easier to interpret, (ii) results from both types of models will be of interest as they reflect different stages of invasion and (iii) the models…”
Space—time zero‐inflated count models of Harbor seals (2007, methodology): “Hurdle and ZIP models are reviewed in Ridout et al. (1998), with a recent comparison of hurdle and ZIP models in Potts and Elith (2006).”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, background): “…based on abundance responses (Austin et al., 2006; Austin, 2007) into patterns of species presences and absences (He & Gaston, 2003; Hui et al., 2006), especially if occurrence and abundance patterns respond by different mechanisms to the same environmental controls (Potts & Elith, 2006).”
Is there a correlation between abundance and environmental suitability derived from ecological niche modelling? A meta‐analysis (2017, background): “Species abundance datasets are rare because collecting such data is money and time consuming (Potts and Elith 2006).”
Predicting distributions of invasive species (2013, methodology): “These include CLIMEX and generalised regression methods that can model count data (e.g. Poisson regression; Fithian & Hastie, 2013; Potts & Elith, 2006).”
Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? (2007, methodology): “Potts and Elith (2006) found that a zero-inflated model was better than either quasi-Poisson or negative binomial for modeling abundance of a rare plant species, but they point out that zero-inflation is a special type of overdispersion that may be most appropriate when occurrence is rare (a…”
Effects of spatial autocorrelation and imperfect detection on species distribution models (2018, methodology): “This model is widely used in the literature to make abundance predictions (Potts & Elith, 2006; Zuur, Ieno, Walker, Saveliev, & Smith, 2009).”