Consequences of spatial autocorrelation for niche‐based models

By Pedro Segurado, Miguel B. Araújo, W. Kunin. 2006

362 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/576928c424c7840a070b7892a866da275a3bb7d8

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

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): “…in species distribution models results from the lack of relevant explanatory variables such as biotic interactions (Austin & Meyers 1996; Fielding & Bell 1997; Parra et al. 2004; Peterson et al. 1999; Raxworthy et al. 2003), or the role of spatial autocorrelation (Segurado et al. 2006).”
Habitat mapping as a tool for conservation and sustainable use of marine resources: Some perspectives from the MAREANO Programme, Norway (2015, background): “The spatial structure in species distributions can be caused bybiotic processes (e.g., predation and competition), the spatial variation of environmental drivers such as sediment composition, temperature, and historical events (Legendre and Fortin, 1989; Segurado et al., 2006).”
Tools for integrating range change, extinction risk and climate change information into conservation management (2013, methodology): “An alternative approach is space-for-time substitution, whereby models are developed in one region and projections are tested against data from another region (Randin et al. 2006, Segurado et al. 2006).”
Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, background): “, 2010), although a few studies have used spatially coarse biogeographical data for native taxa (e.g. Diniz-Filho et al., 2003; Segurado et al., 2006).”
Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. (2012, background): “Key words: AUC; Bioclim; cross-validation; MaxEnt; model evaluation; niche model; pairwise distance sampling; spatial autocorrelation; spatial sorting bias; species distribution model.”
Modelling and predicting fungal distribution patterns using herbarium data (2008, background): “Factors such as small sample sizes, errors in species occurrence data, biased sampling (including spatial bias), non-independence, inappropriate choice of scale, absence of relevant predictors and overfitting can all reduce the reliability of the GLM and Maxent models (Graham et al., 2004, 2008; Her”
Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “Such review can be found in Keitt et al. (2002), Segurado et al. (2006), Wintle and Bardos (2006) and Dormann et al. (2007).”
Focusing Conservation Efforts for the Critically Endangered Brown-headed Spider Monkey (Ateles fusciceps) Using Remote Sensing, Modeling, and Playback Survey Methods (2011, background): “Further, we deleted points if they clustered within a 5-km radius of each other to minimize impacts of spatial autocorrelation (Segurado et al. 2006).”
SAM: a comprehensive application for Spatial Analysis in Macroecology (2010, background): “…model uses the information on the relative position of the species occurrence to generate a spatial weighting covariate, and aims to improve the model predictive power by accounting for stochastic processes driving species distribution, such as species’ dispersal capacity (Segurado et al. 2006).”
Waterbird Populations and Pressures in the Baltic Sea (2011, background): “Line transect data usually display a high degree of spatial autocorrelation and if strong spartial autocorrelation remains in the model residuals the significance values might be inflated and hence lead to unreliable explanatory and predictive power (Segurado et al. 2006).”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, neutral): “Gurevitch (2002) and Segurado et al. (2006) which are”
Using district-level occurrences in MaxEnt for predicting the invasion potential of an exotic insect pest in India (2014, background): “Performance of different ENM/SDM also depends on species characteristics, spatial resolution and extent of the study area, and choice of predictor variables (Evangelista et al., 2008; Guisan et al., 2007a,b).”
Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation (2009, methodology): “GARP implements four types of rules: atomic, logistic regression, bioclimatic envelope, and negated bioclimatic envelope rules.”
Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long‐term monitoring programs (2007, background): “Here also, further investigation of the role that the spatial component of species distributions plays in the development of habitat models is needed (Segurado et al., 2006).”
Making better biogeographical predictions of species’ distributions (2006, background): “From a different perspective, spatial structures in the data can also generate bias in predictive models by inflating the significance level, and various solutions have been proposed to overcome the problem (Segurado et al. 2006).”