The effect of sample size and species characteristics on performance of different species distribution modeling methods

Abstract

Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.

Optimizing resiliency of reserve networks to climate change: multispecies conservation planning in the Pacific Northwest, USA

“…MAXENT was designed for use with presence-only data, and performed well in comparison with 15 alternate methods on a wide variety of taxa in diverse regions . It has shown especially robust performance in comparison to alternate methods at small sample sizes (e.g., 10 records as here; Hernandez et al, 2006;Pearson et al, 2007;Wisz et al, 2008). MAXENT uses a 'maximum entropy' approach that compares presence locations to a random subset of $ 10 000 background or available locations (Phillips et al, 2006).…”

Ecology and Geography of Human Monkeypox Case Occurrences Across Africa

“…Maxent results are more commonly presented as cumulative values (i.e., each cell receives a value equal to its assigned probability plus the sum of all lower probabilities), wherein a value of 100 indicates highest suitability and values close to zero would be unsuitable (Phillips et al, 2004;Peterson et al, 2007). To avoid overfitting, Maxent employs a smoothing feature called regularization to constrain estimated distributions, such that the average value for a given predictor remains within the empirical error boundaries and close to the empirical average (Phillips et al, 2004;Hernandez et al, 2006). Maxent output is in the form of floating-point ASCII raster grids which are then imported into GIS programs and reclassified into integer grids for analysis.…”

Predicting fish species richness and assemblages with climatic, geographic and morphometric factors: A broad-scale study in Chinese lakes

“…In fact, this phenomenon is in accordance with some previews studies (Manel et al, 2001;Liu et al, 2005;Gevery et al, 2009). The species' environmental range could also be one of the main factors which drive the uncertainty of the prediction of the three species in this study, as numerous studies validate that species with a smaller range can be better predicted than species with a larger environmental range (Hernandez et al, 2006;Grenouillet et al, 2011).…”