New measures for assessing model equilibrium and prediction mismatch in species distribution models

Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accura...

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Bibliographic Details
Published in:Diversity & distributions 2013-10, Vol.19 (10), p.1333-1338
Main Authors: Márcia Barbosa, A., Real, Raimundo, Muñoz, A.-Román, Brown, Jennifer A.
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Applied ecology
Biodiversity
Biodiversity conservation
BIODIVERSITY LETTER
Biogeography
Biological and medical sciences
Climate change
Commission
Conservation biology
Disease models
distribution equilibrium
Ecological modeling
Environmental conservation
Fundamental and applied biological sciences. Psychology
General aspects
General aspects. Techniques
Invasive species
Methods and techniques (sampling, tagging, trapping, modelling...)
model evaluation
Modeling
omission
over-prediction
prediction error
Predictive modeling
predictive performance
Species
Synecology
under-prediction
Wildlife conservation
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Summary:Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species' distributions under climate and land-use change.
ISSN:1366-9516
1472-4642
DOI:10.1111/ddi.12100