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Overcoming limitations of modelling rare species by using ensembles of small models
Author(s) -
Breiner Frank T.,
Guisan Antoine,
Bergamini Ariel,
Nobis Michael P.
Publication year - 2015
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12403
Subject(s) - overfitting , rare species , transferability , computer science , environmental niche modelling , ensemble learning , ensemble forecasting , species distribution , rare events , threatened species , predictive modelling , machine learning , statistics , ecology , econometrics , biology , mathematics , artificial neural network , logit , ecological niche , habitat
Summary Species distribution models ( SDM s) have become a standard tool in ecology and applied conservation biology. Modelling rare and threatened species is particularly important for conservation purposes. However, modelling rare species is difficult because the combination of few occurrences and many predictor variables easily leads to model overfitting. A new strategy using ensembles of small models was recently developed in an attempt to overcome this limitation of rare species modelling and has been tested successfully for only a single species so far. Here, we aim to test the approach more comprehensively on a large number of species including a transferability assessment. For each species, numerous small (here bivariate) models were calibrated, evaluated and averaged to an ensemble weighted by AUC scores. These ‘ensembles of small models’ ( ESM s) were compared to standard SDM s using three commonly used modelling techniques ( GLM , GBM and Maxent) and their ensemble prediction. We tested 107 rare and under‐sampled plant species of conservation concern in Switzerland. We show that ESM s performed significantly better than standard SDM s. The rarer the species, the more pronounced the effects were. ESM s were also superior to standard SDM s and their ensemble when they were evaluated using a transferability assessment. By averaging simple small models to an ensemble, ESM s avoid overfitting without losing explanatory power through reducing the number of predictor variables. They further improve the reliability of species distribution models, especially for rare species, and thus help to overcome limitations of modelling rare species.