Modelling habitat‐suitability using museum collections: an example with three sympatric Apodemus species from the Alps

By B. A. Reutter, V. Helfer, A. Hirzel, Peter Vogel. 2003

111 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b693efc2a98dbd6bc14b6005227b5a4db710c52b

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In Ecological Modeling

In Ecology

Citations in the corpus

Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, background): “(Dennis & Hardy 1999; Dennis et al. 1999; Dennis & Thomas 2000; Dennis et al. 2006; Zaniewski et al. 2002; Anderson 2003; Reutter et al. 2003; Graham et al. 2004; Soberón & Peterson 2004; Martínez-Meyer 2005).”
Evaluating the ability of habitat suitability models to predict species presences (2006, background): “(2) A second approach is to assess how much the model predictions differ from random expectation (e.g. Boyce et al., 2002; Hirzel et al., 2002; Reutter et al., 2003).”
Assessing the effects of pseudo-absences on predictive distribution model performance (2008, background): “e c o l o g i c a l m o d e l l i n g 2 1 0 ( 2 0 0 8 ) 478–486 avai lab le at www.sc iencedi rec t .com journa l homepage: www.e lsev ier .com/ locate /eco lmodel Assessing the effects of pseudo-absences on predictive distribution model performance”
Five (or so) challenges for species distribution modelling (2006, background): “Currently, the vast majority of data available for species distribution modelling come from museum and other natural history collections (e.g. Graham, 2001; Ponder et al., 2001; Reutter et al., 2003; Stockman et al., 2006).”
More complex distribution models or more representative data (2008, background): “…a crude approach, may introduce an indeterminate number of false absences into models owing to the all-too-frequent sampling biases in biological information (see Dennis et al. 1999, Dennis & Thomas 2000, Zaniewski et al. 2002, Reutter et al. 2003, Graham et al. 2004, Martínez-Meyer 2005).”
Inferring habitat-suitability areas with ecological modelling techniques and GIS: A contribution to assess the conservation status of Vipera latastei (2006, methodology): “All rights reserved. os). tion range (Barbadillo et al., 1999; Brito et al., 2001; Fahd and”
Modelling distribution and abundance with presence‐only data (2006, methodology): “ uses factor analysis to quantify the environmental conditions of the presence sites by comparing them to the environmental conditions of the entire region of interest, and predictions are provided as a habitat suitability index (Hirzel et al. 2002; Dettki, Löfstrand & Edenius 2003; Reutter et a”
Habitat suitability modelling and niche theory (2008, background): “2003), or in the environmental space by measuring niche similarities (e.g. Reutter et al .”
Taxonomist survey biases and the unveiling of biodiversity patterns (2009, background): “For example, when species richness predictions for a whole territory are carried out using only the data from well-surveyed localities, the forecasted values tend to be higher in the most surveyed localities (Lobo and Martı́n-Piera, 2002; Romo et al., 2006; Sánchez-Fernández et al., 2008).”
Modelling and predicting fungal distribution patterns using herbarium data (2008, methodology): “Therefore, modifying existing statistical tools and developing new methods so that herbarium data, despite their shortcomings, can be used for modelling habitat suitability, is currently a growing field (e.g. Hirzel et al., 2002, 2006; Reutter et al., 2003; Engler et al., 2004).”