Open AccessSensitivity of predictive species distribution models to change in grain size
Author(s) -
Guisan Antoine,
Graham Catherine H.,
Elith Jane,
Huettmann Falk
Publication year - 2007
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/j.1472-4642.2007.00342.x
Subject(s) - grain size , species distribution , biodiversity , sample size determination , ranking (information retrieval) , ecology , sensitivity (control systems) , statistics , biological system , environmental science , econometrics , mathematics , biology , habitat , computer science , materials science , machine learning , metallurgy , electronic engineering , engineering
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.