Modelling invasion for a habitat generalist and a specialist plant species

By Paul H. Evangelista, Sunil Kumar, Thomas J. Stohlgren, Catherine S. Jarnevich, Alycia Crall, J. Norman, D. Barnett. 2008

251 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/29fa6ce36dc451f15442dca1ef7991c3fb329ee4

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Improving and integrating data on invasive species collected by citizen scientists (2010, background): “The addition of opportunistic samples helps define the range of environmental conditions that exist at each species location (i.e., environmental envelope; Jarnevich et al. 2006; Evangelista et al. 2008).”
Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US (2009, methodology): “We conclude that it is advisable to compare modeling approaches, particularly because techniques that are successful for one species may not be successful for others, as has been shown for habitat specialists and generalists by Evangelista et al. (2008).”
Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (2014, methodology): “We chose MaxEnt because: (1) it is a presence-only modeling algorithm (no absence data are needed), (2) it has performed relatively better than other modeling methods (Elith et al., 2006; Evangelista et al., 2008; Kumar et al., 2009), and (3) it is relatively robust to small sample sizes (Pearson et”
The area under the precision‐recall curve as a performance metric for rare binary events (2019, result): “Our simulations found that AUC- ROC was higher for rarer species, in line with previous findings (Luoto et al., 2005; Elith et al., 2006; Evangelista et al., 2008; Jimenez- Valverde et al., 2008), and also generally confirmed increases in AUC- ROC with an increasing geographic extent (Lobo et al.,…”
Benefits of hyperspectral remote sensing for tracking plant invasions (2011, methodology): “Typically, these predictive models include generalized additive models, logistic regression, classification and regression tree model, random forest, maximum entropy and bioclimatic envelope models (Peterson, 2003; Thuiller et al., 2005; Elith et al., 2006; Evangelista et al., 2008; Stohlgren et al.”
Knowing the past to predict the future: land‐use change and the distribution of invasive bullfrogs (2010, methodology): “Habitat suitability models are often used for this task, with applications in risk assessment or in control strategies (e.g., Ward, 2007; Evangelista et al., 2008; Nielsen et al., 2008; Ficetola et al., 2009).”
Challenges of predicting the potential distribution of a slow-spreading invader: a habitat suitability map for an invasive riparian tree (2010, unknown): “It is userfriendly, produces robust metrics to evaluate model fit and has proven effective in predicting habitatspecific species such as saltcedar (Tamarix sp.) at small spatial extents (Evangelista et al. 2008).”