To mix or not to mix: comparing the predictive performance of mixture models vs. separate species distribution models

Species distribution models (SDMs) are an important tool for studying the patterns of species across environmental and geographic space. For community data, a common approach involves fitting an SDM to each species separately, although the large number of models makes interpretation difficult and fa...

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Bibliographic Details
Published in:Ecology (Durham) 2013-09, Vol.94 (9), p.1913-1919
Main Authors: Hui, Francis K. C, Warton, David I, Foster, Scott D, Dunstan, Piers K
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Animals
Applied ecology
Archetypes
Biogeography
Biological and medical sciences
community level modeling
Comparative analysis
Computer Simulation
cross validation
Data processing
Datasets
Demography
Ecological modeling
Ecosystems
Environmental conservation
Fundamental and applied biological sciences. Psychology
General aspects
General aspects. Techniques
generalized linear models
Marine ecology
Mathematical models
Methods and techniques (sampling, tagging, trapping, modelling...)
mixture models
Modeling
Models, Biological
Population ecology
Species
species archetypes
species distribution models
Species Specificity
Synecology
Temperature
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Summary:Species distribution models (SDMs) are an important tool for studying the patterns of species across environmental and geographic space. For community data, a common approach involves fitting an SDM to each species separately, although the large number of models makes interpretation difficult and fails to exploit any similarities between individual species responses. A recently proposed alternative that can potentially overcome these difficulties is species archetype models (SAMs), a model-based approach that clusters species based on their environmental response. In this paper, we compare the predictive performance of SAMs against separate SDMs using a number of multi-species data sets. Results show that SAMs improve model accuracy and discriminatory capacity compared to separate SDMs. This is achieved by borrowing strength from common species having higher information content. Moreover, the improvement increases as the species become rarer.
ISSN:0012-9658
1939-9170
DOI:10.1890/12-1322.1