Can models of presence‐absence be used to scale abundance? Two case studies considering extremes in life history

By Scott E. Nielsen, Chris J. Johnson, D. Heard, M. Boyce. 2005

220 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/e426d9e71fb4d2fb9bf97eefb7dcdb94fb870625

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Presence and Absence of Bats Across Habitat Scales in the Upper Coastal Plain of South Carolina (2006, background): “…components are greater might show stronger links to landscape-level variables, as well as to forest patch–level characteristics, as older forested areas become more isolated and rare on the landscape (Wickramasinghe et al. 2003, Evelyn et al. 2004, Gehrt and Chelsvig 2004, Nielsen et al. 2005).”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, background): “Species with low prevalence impose a particular challenge due to the usually limited number of occurrence observations available, and the lower performance of most models when faced with small sample sizes (Bourg et al., 2005; Nielsen et al., 2005).”
Comparative interpretation of count, presence–absence and point methods for species distribution models (2012, background): “Indeed, several empirical studies have demonstrated a lack of correlation between occupancy and local abundance (Pearce & Ferrier 2001; Nielsen et al. 2005; Jimenez-Valverde et al. 2009).”
Improving species distribution models: the value of data on abundance (2014, background): “The relationship between environmental suitabil- ity and abundance has been widely discussed (Pearce & Ferrier 2001; Nielsen et al. 2005).”
Dynamic wildlife habitat models: Seasonal foods and mortality risk predict occupancy-abundance and habitat selection in grizzly bears (2010, background): “Wildlife habitat models, such as RSFs (Manly et al., 2002), have increased dramatically in popularity over the past decade, despite few if any demonstrated linkages to demographic processes or direct measures of regulating factors of populations (Nielsen et al., 2005; Johnson and Seip, 2008).”
Modelling distribution and abundance with presence‐only data (2006, background): “Alternatively, it may be possible to modify zero-inflated Poisson or negative binomial (ZIP or ZINB) regression models (Welsh et al. 1996; Barry & Welsh 2002; Dirnböck & Dullinger 2004; Nielsen et al. 2005) to model abundance given availability, where available locations are assigned a value of zero”