Predictive habitat distribution models in ecology

By Antoine Guisan, N. E. Zimmermann. 2000

7,652 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2138bc4fab4c6d9fbf78b2970be65f6770676da9

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Influence of bioclimatic variables on tree‐line conifer distribution in the Greater Yellowstone Ecosystem: implications for species of conservation concern (2008, background): “The capacity of the model to distinguish correctly between occupied and unoccupied sites is another important aspect of model evaluation (Guisan & Zimmerman, 2000).”
Global legume diversity assessment: Concepts, key indicators, and strategies (2013, methodology): “…to print version). been used since the first stages of research in taxonomy and biogeography and now the sophisticated methodology of distribution modeling (Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005; Elith & al., 2006; Franklin, 2009) can be used to draw maps of biodiversity indicators.”
Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia (2009, background): “of species distribution modeling methods are available to predict potential suitable habitat for a species (Guisan and Zimmermann, 2000; Guisan and Thuiller, 2005; Elith et al., 2006; Guisan et al., 2007a,b; Wisz et al., 2008).”
Invasive and flexible: niche shift in the drosophilid Zaprionus indianus (Insecta, Diptera) (2010, background): “, the range is determined by the current niche and not strongly affected by stochastic processes, especially dispersal and other historical processes—see Araújo and Pearson 2005) and, under these circumstances, they allow accurate predictions of future distributions following introduction to a new a”
GIS-based modeling of Javan Hawk-Eagle distribution using logistic and autologistic regression models (2008, background): “These advances have led to the development and use of numerous statistical techniques to predict species’ potential distribution by relating known species’ distributions to the spatial distribution of environmental variables (Guisan and Zimmermann, 2000; Zaniewski et al., 2002).”
Error and uncertainty in habitat models (2006, methodology): “A wide variety of approaches is currently used for the prediction of species’ distributions ( Guisan & Zimmerman 2000; Phillips, Anderson & Schapire 2006).”
Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters (2008, background): “A wide range of techniques is now available to model habitat requirements and these have been extensively reviewed (e.g. Guisan & Zimmermann, 2000; see also Redfern et al., 2006 for a particular review on marine species).”
IDENTIFYING BREEDING HABITAT FOR THE IBERIAN LYNX: INFERENCES FROM A FINE‐SCALE SPATIAL ANALYSIS (2003, methodology): “Once the best approximating model was selected, a model evaluation with a different data set was desirable (Guisan and Zimmermann 2000).”
The BIOKLIM Project: Biodiversity Research between Climate Change and Wilding in a temperate montane forest - The conceptual framework (2009, background): “This equates the p/10 rule of thumb with p = min[Σ(presences, Σ(absences)] (guisan & zimmermann 2000).”
A comparative evaluation of presence‐only methods for modelling species distribution (2007, methodology): “The number of methods available for modelling patterns of species distribution is immense (Guisan & Zimmermann, 2000; Scott et al ., 2002; Guisan & Thuiller, 2005), and evaluating the relative performance of different methods remains a continuous challenge in ecology and conservation biology…”
Potential species distribution modeling and the use of principal component analysis as predictor variables (2014, background): “This approach is especially important to produce basic information for such disciplines as biogeography, conservation biology, ecology, evolutionary biology, and others (Guisan and Zimmermann, 2000; Guisan and Thuiller, 2005; Elith and Leathwick, 2009b; Peterson et al., 2011).”
Focusing Conservation Efforts for the Critically Endangered Brown-headed Spider Monkey (Ateles fusciceps) Using Remote Sensing, Modeling, and Playback Survey Methods (2011, methodology): “It is an empirical modeling approach, based on indirect and direct variables (Guisan and Zimmermann 2000).”
The effect of species response form on species distribution model prediction and inference (2009, background): “Guisan and Zimmermann (2000) presented a comprehensive review and classified the methods into two categories: (1) regression-based methods; and (2) environmental envelope methods.”