Sensitivity of predictive species distribution models to change in grain size

Details

Serval ID
serval:BIB_268067532C9A
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Sensitivity of predictive species distribution models to change in grain size
Journal
Diversity and Distributions
Author(s)
Guisan A., Graham C. H., Elith J., Huettmann F.
ISSN
1366-9516
Publication state
Published
Issued date
2007
Peer-reviewed
Oui
Volume
13
Number
3
Pages
332-340
Language
english
Notes
Workshop on Predictive Modelling of Species Distributions, New Tools for the XXI Century
Baeza, SPAIN, NOV 02-04, 2005
Univ Int Andalucia
Abstract
Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.
Keywords
environmental grain, niche-based modelling, natural history collections, presence-only data, resolution, spatial scale, sample size, species distribution modelling, model comparison, predictive performance
Web of science
Create date
24/01/2008 19:06
Last modification date
20/08/2019 13:05
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