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Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?

Overview of attention for article published in PLOS ONE, March 2012
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Title
Do Stacked Species Distribution Models Reflect Altitudinal Diversity Patterns?
Published in
PLOS ONE, March 2012
DOI 10.1371/journal.pone.0032586
Pubmed ID
Authors

Rubén G. Mateo, Ángel M. Felicísimo, Julien Pottier, Antoine Guisan, Jesús Muñoz

Abstract

The objective of this study was to evaluate the performance of stacked species distribution models in predicting the alpha and gamma species diversity patterns of two important plant clades along elevation in the Andes. We modelled the distribution of the species in the Anthurium genus (53 species) and the Bromeliaceae family (89 species) using six modelling techniques. We combined all of the predictions for the same species in ensemble models based on two different criteria: the average of the rescaled predictions by all techniques and the average of the best techniques. The rescaled predictions were then reclassified into binary predictions (presence/absence). By stacking either the original predictions or binary predictions for both ensemble procedures, we obtained four different species richness models per taxa. The gamma and alpha diversity per elevation band (500 m) was also computed. To evaluate the prediction abilities for the four predictions of species richness and gamma diversity, the models were compared with the real data along an elevation gradient that was independently compiled by specialists. Finally, we also tested whether our richness models performed better than a null model of altitudinal changes of diversity based on the literature. Stacking of the ensemble prediction of the individual species models generated richness models that proved to be well correlated with the observed alpha diversity richness patterns along elevation and with the gamma diversity derived from the literature. Overall, these models tend to overpredict species richness. The use of the ensemble predictions from the species models built with different techniques seems very promising for modelling of species assemblages. Stacking of the binary models reduced the over-prediction, although more research is needed. The randomisation test proved to be a promising method for testing the performance of the stacked models, but other implementations may still be developed.

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Geographical breakdown

Country Count As %
Switzerland 4 1%
Germany 4 1%
Spain 3 1%
India 2 <1%
Colombia 2 <1%
United States 2 <1%
Greece 2 <1%
United Kingdom 2 <1%
Peru 1 <1%
Other 6 2%
Unknown 242 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 57 21%
Researcher 53 20%
Student > Master 39 14%
Student > Bachelor 21 8%
Student > Doctoral Student 16 6%
Other 47 17%
Unknown 37 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 134 50%
Environmental Science 67 25%
Earth and Planetary Sciences 12 4%
Biochemistry, Genetics and Molecular Biology 3 1%
Medicine and Dentistry 2 <1%
Other 11 4%
Unknown 41 15%