Variation in niche and distribution model performance: The need for a priori assessment of key causal factors

Saupe, Erin E.; Barve, Vijay; Myers, Christopher W.; Soberón, Jorge; Barve, Narayani; Hensz, Christopher Michael; Peterson, A. Townsend; Owens, Hannah L.; Lira‐Noriega, Andrés

doi:10.1016/j.ecolmodel.2012.04.001

Cited by 201 publications

(255 citation statements)

References 49 publications

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“…en el impacto potencial del cambio climático, en las proyecciones de ocurrencia geográfi ca de las especies invasoras y en la identifi cación de zonas y regiones geográfi cas que requieren ser exploradas (Guisan y Zimmermann, 2000;Hansen et al, 2001;Anderson et al, 2003;Elith y Leathwick, 2009;Peterson et al, 2011;Saupe et al, 2012). El modelado del nicho ecológico proporciona una aproximación de los factores ambientales más importantes que intervienen la distribución de las especies.…”

Section: José Antonio López-sandoval Et Alunclassified

Modelado de los factores ambientales que determinan la distribución de especies sinantrópicas de <em>Physalis</em>

et al. 2015

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5Autor para la correspondencia. lauro@colpos.mxResumen: El modelado de la distribución de especies sinantrópicas (malezas) y los factores ambientales que determinan dicha distribución han sido poco estudiados. Physalis tiene 90 especies distribuidas en las Américas, y algunas especies en el Viejo Mundo. México alberga cerca de 70 especies y aproximadamente 35 son endémicas. La sección Angulatae incluye diez especies, todas sinantrópicas en mayor o menor grado. Las especies se concentran en la Sierra Madre Occidental, la Sierra Madre del Sur y en la Faja Volcánica Transmexicana. El objetivo de este trabajo fue modelar e identifi car las variables ambientales que determinan la distribución potencial de las diez especies de Physalis sección Angulatae. Se emplearon 524 registros revisados por especialistas en la taxonomía del grupo y 20 variables ambientales; de éstas 12 fueron climáticas, tres edáfi cas, dos de cobertura de la vegetación y tres de atributos topográfi cos. Los modelos se calcularon con el algoritmo Maxent. Los resultados del modelado mostraron que el hábitat más adecuado para la persistencia de ocho especies se defi nió por el índice normalizado diferencial de vegetación en los meses secos del año, la materia orgánica del suelo, la altitud y el aspecto, las cuales en conjunto explicaron entre el 73 y el 91 % de la variación en su distribución. Otros factores como la precipitación total anual y la isotermalidad determinaron la distribución de P. crassifolia y de P. glabra, respectivamente. El índice normalizado diferencial de la vegetación y las propiedades de los suelos, son predictores determinantes en la distribución potencial de las especies de la sección Angulatae. Palabras clave: máxima entropía, predictores ambientales, sección Angulatae, tomatillos. Abstract:The modeling of the distribution of synanthropic (weedy) species and the environmental factors that determine their distribution is not well studied. Physalis has 90 species distributed in the Americas, and several in the Old World. Mexico harbors about 70 species and approximately 35 are endemic. The section Angulatae includes 10 species, all synanthropic to some degree. The species tended to concentrate in the Sierra Madre Occidental, the Sierra Madre del Sur, and the Transmexican Volcanic Belt. The aim of this work was to model and identify the environmental variables that determine the potential distribution of the ten species of Physalis sect. Angulatae. A total of 524 records that had been verifi ed by the taxonomic experts of the group and 20 environmental variables were used; 12 were climatic, and the other eight were novel and of different types: three soil properties, two normalized differential vegetation indexes and three topographic attributes. The models were obtained with the Maxent algorithm. The results of the modelling showed that the most suitable habitat for the persistence of eight species was delimited by the normalized differential vegetation index during the dry months of the year, the soil organic matter, the elev...

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Section: José Antonio López-sandoval Et Alunclassified

Modelado de los factores ambientales que determinan la distribución de especies sinantrópicas de <em>Physalis</em>

et al. 2015

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“…Therefore, a priori selection of a set of less correlated variables (e.g., R 2 < 0.75) is often necessary, wherein the putatively biologically most relevant predictors should be selected (e.g., Saupe et al 2012). As long as the intercorrelation structure among selected and omitted predictors is stable, this information reduction is appropriate -but not when it changes, since in this case ENM projections may become unreliable.…”

Section: Introductionmentioning

confidence: 99%

“…This extra flexibility has been shown to increase the accuracy of predictions computed with these methods, frequently outperforming more conventional approaches (Elith et al 2006, Hernandez et al 2006, Wisz et al 2008. However, appropriate selection of absence or pseudo-absence data requires special attention here since the choice may strongly influence the reliability and interpretation of the results (Saupe et al 2012). Various authors have suggested that the most appropriate background data should reflect the environmental space that is potentially colonizable by the target species (e.g., Anderson and Raza 2010, Barve et al 2011, Saupe et al 2012.…”

Section: Introductionmentioning

confidence: 99%

“…However, appropriate selection of absence or pseudo-absence data requires special attention here since the choice may strongly influence the reliability and interpretation of the results (Saupe et al 2012). Various authors have suggested that the most appropriate background data should reflect the environmental space that is potentially colonizable by the target species (e.g., Anderson and Raza 2010, Barve et al 2011, Saupe et al 2012. Although this restriction of the training area of an ENM may result in a more specific model, it is at the same time commonly less generalizable across space and time.…”

Section: Introductionmentioning

confidence: 99%

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Disentangling Interpolation and Extrapolation Uncertainties in Species Distribution Models: A Novel Visualization Technique for the Spatial Variation of Predictor Variable Colinearity

Rödder

Engler

2012

Biodiv. Inf.

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Abstract. -Environmental niche models (ENMs) are increasingly used in many scientific fields, with most studies requiring the application of the ENM to predict the likelihood of occurrence and/or environmental suitability in locations and time periods outside the range of the data set used to fit the model. Uncertainty in the quality of ENM predictions caused by errors of interpolation and extrapolation has been acknowledged for a long time, but the explicit consideration of the magnitude of such errors is, as yet, uncommon. Among other issues, the spatial variation in the colinearity of the environmental predictor variables used in the development of ENMs may cause misleading predictions when applying ENMs to novel locations and time periods. In this paper, we provide a framework for the spatially explicit identification of areas prone to errors caused by changes in the inter-correlation structure (i.e. their colinearity) of environmental predictors used for ENM development. The proposed method is compatible with all ENM algorithms currently employed, and expands the available toolbox for assessing the uncertainties rising from ENM predictions. We provide an implementation of the analysis as a script for the R statistical platform in an online appendix.

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“…48 000 km 2 ). Further, bioclimatic modelling choices may add additional sources of uncertainty [56,57]. Here, we have used an ensemble modelling approach to account for differences generated from modelling approaches, and thresholds based on current best practices.…”

Section: (B) Step 2: Demographic Model Parameterizationsmentioning

confidence: 99%

Integrating multiple lines of evidence into historical biogeography hypothesis testing: aBison bisoncase study

et al. 2014

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One of the grand goals of historical biogeography is to understand how and why species' population sizes and distributions change over time. Multiple types of data drawn from disparate fields, combined into a single modelling framework, are necessary to document changes in a species's demography and distribution, and to determine the drivers responsible for change. Yet truly integrated approaches are challenging and rarely performed. Here, we discuss a modelling framework that integrates spatio-temporal fossil data, ancient DNA, palaeoclimatological reconstructions, bioclimatic envelope modelling and coalescence models in order to statistically test alternative hypotheses of demographic and potential distributional changes for the iconic American bison (Bison bison). Using different assumptions about the evolution of the bioclimatic niche, we generate hypothetical distributional and demographic histories of the species. We then test these demographic models by comparing the genetic signature predicted by serial coalescence against sequence data derived from subfossils and modern populations. Our results supported demographic models that include both climate and human-associated drivers of population declines. This synthetic approach, integrating palaeoclimatology, bioclimatic envelopes, serial coalescence, spatio-temporal fossil data and heterochronous DNA sequences, improves understanding of species' historical biogeography by allowing consideration of both abiotic and biotic interactions at the population level.

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Variation in niche and distribution model performance: The need for a priori assessment of key causal factors

Cited by 201 publications

References 49 publications

Modelado de los factores ambientales que determinan la distribución de especies sinantrópicas de <em>Physalis</em>

Modelado de los factores ambientales que determinan la distribución de especies sinantrópicas de <em>Physalis</em>

Disentangling Interpolation and Extrapolation Uncertainties in Species Distribution Models: A Novel Visualization Technique for the Spatial Variation of Predictor Variable Colinearity

Integrating multiple lines of evidence into historical biogeography hypothesis testing: aBison bisoncase study

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