Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

By S. Menke, D. Holway, Robert N. Fisher, Walter Jetz. 2009

101 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Genetics

View Paper: https://www.semanticscholar.org/paper/8fb5bd0409d7108bad4257c2ee7efab318413737

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

1. Jane Elith
2. A. Townsend Peterson
3. Steven J. Phillips
4. Walter Jetz
5. Wilfried Thuiller
6. Michael R. Kearney
7. Morgane Barbet-Massin
8. Miguel B. Araújo

In Ecology, Evolution, Behavior and Systematics

1. Jane Elith
2. Alexander D.T. Vierod
3. Morgane Barbet-Massin
4. Maria João Santos
5. Michael R. Kearney
6. Adam B. Smith
7. Andrew J. Davies
8. Steven J. Phillips

In Genetics

1. Jane Elith
2. Morgane Barbet-Massin
3. Maria João Santos
4. Michael R. Kearney
5. Adam B. Smith
6. Steven J. Phillips
7. Walter Jetz
8. A. Townsend Peterson

Citations in the corpus

• The art of modelling range‐shifting species (2010, background): “2008; Elith & Leathwick 2009), but range-shifting species create two main problems for them: (1) the species records no longer reflect stable relationships with environment, and (2) environmental combinations in future scenarios will not have been adequately sampled (Menke et al. 2009).”
• Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “…data sets containing sample selection bias (Araújo & Guisan, 2006; Phillips et al., 2009; Cabral and Schurr, 2010), autocorrelation (Dormann, 2007a; Legendre, 1993; Segurado et al., 2006) and imperfect measurement (Barry and Elith, 2006; Clark, 2005; McInerny and Purves, 2011; Menke et al., 2009).”
• Testing the ability of species distribution models to infer variable importance (2019, background): “Although the effect of these factors on variable importance has received some attention (e.g., Menke et al. 2009; Franklin et al. 2013; Pradervand et al. 2013; Mertes & Jetz 2017; Acevedo et al. 2017), studies using real species are often complicated by the fact that the true importance of…”
• A 40‐year, continent‐wide, multispecies assessment of relevant climate predictors for species distribution modelling (2014, background): “Identifying the most appropriate variables is a crucial step to maximize the performance of species distribution modelling and its projection in space or time (Austin, 2002; Ara ujo & Guisan, 2006; Elith & Leathwick, 2009; Menke et al., 2009; Ara ujo & Peterson, 2012).”