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Open AccessJournal ArticleDOI

Effects of species’ ecology on the accuracy of distribution models

Jana M. McPherson, +1 more
- 01 Feb 2007 - 
- Vol. 30, Iss: 1, pp 135-151
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TLDR
None of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra-specific niche differentiation, and these analyses provide conservation scientists and resource managers with a rule of thumb that helps distinguish between species whose occurrence is reliably or less reliably predicted by distribution models.
Abstract
In the face of accelerating biodiversity loss and limited data, species distribution models - which statistically capture and predict species' occurrences based on environmental correlates - are increasingly used to inform conservation strategies. Additionally, distribution models and their fit provide insights on the broad-scale environmental niche of species. To investigate whether the performance of such models varies with species' ecological characteristics, we examined distribution models for 1329 bird species in southern and eastern Africa. The models were constructed at two spatial resolutions with both logistic and autologistic regression. Satellite-derived environmental indices served as predictors, and model accuracy was assessed with three metrics: sensitivity, specificity and the area under the curve (AUC) of receiver operating characteristics plots. We then determined the relationship between each measure of accuracy and ten ecological species characteristics using generalised linear models. Among the ecological traits tested, species' range size, migratory status, affinity for wetlands and endemism proved most influential on the performance of distribution models. The number of habitat types frequented (habitat tolerance), trophic rank, body mass, preferred habitat structure and association with sub-resolution habitats also showed some effect. In contrast, conservation status made no significant impact. These findings did not differ from one spatial resolution to the next. Our analyses thus provide conservation scientists and resource managers with a rule of thumb that helps distinguish, on the basis of ecological traits, between species whose occurrence is reliably or less reliably predicted by distribution models. Reasonably accurate distribution models should, however, be attainable for most species, because the influence ecological traits bore on model performance was only limited. These results suggest that none of the ecological traits tested provides an obvious correlate for environmental niche breadth or intra-specific niche differentiation.

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Citations
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Journal ArticleDOI

Methods to account for spatial autocorrelation in the analysis of species distributional data : a review

TL;DR: In this paper, the authors describe six different statistical approaches to infer correlates of species distributions, for both presence/absence (binary response) and species abundance data (poisson or normally distributed response), while accounting for spatial autocorrelation in model residuals: autocovariate regression; spatial eigenvector mapping; generalised least squares; (conditional and simultaneous) autoregressive models and generalised estimating equations.
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AUC: a misleading measure of the performance of predictive distribution models

TL;DR: The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models as discussed by the authors.
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Where is positional uncertainty a problem for species distribution modelling

TL;DR: It is proposed that local spatial association is a way to identify the species occurrence records that require treatment for positional uncertainty and developed and presented a tool in the R environment to target observations that are likely to create error in the output from SDMs as a result of positional uncertainty.
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Not as good as they seem: the importance of concepts in species distribution modelling

TL;DR: This work discusses three important topics that must be kept in mind when modelling species distributions, namely the distinction between potential and realized distribution, the effect of the relative occurrence area of the species on the results of the evaluation of model performance, and the general inaccuracy of the predictions of the realized distribution provided by species distribution modelling methods.
References
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Journal ArticleDOI

Predictive habitat distribution models in ecology

TL;DR: A review of predictive habitat distribution modeling is presented, which shows that a wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management.
Journal ArticleDOI

A review of methods for the assessment of prediction errors in conservation presence/absence models

TL;DR: Thirteen recommendations are made to enable the objective selection of an error assessment technique for ecological presence/absence models and a new approach to estimating prediction error, which is based on the spatial characteristics of the errors, is proposed.
Journal ArticleDOI

Predicting species distribution: offering more than simple habitat models.

TL;DR: An overview of recent advances in species distribution models, and new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales are suggested.
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