Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents

Papeş, M. and Gaubert, P. (2007), Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Diversity and Distributions, 13: 890–902. doi:10.1111/j.1472-4642.2007.00392.x


In order for a species to occupy their ecological niche that abiotic and biotic conditions need to be favorable in addition to being geographically accessible. These niches are most often modeled with the most common data – present records, but this data has plenty of issues including unknown sampling holes, linking time of collection with abiotic factors, biased geographical sampling, and geo-referencing museum specimens. Poorly studied species have the additional challenge of low sample size, which exacerbates the previous issues and may also biased sampling of environmental space. Previous studies have shown ENM with small sample sizes performance are dependent on model and variable choice, machine learning does better. The authors use this discrepancy and model performance to motivate the comparison of GARP to the (at the time) newer modeling approach of MaxEnt.

Models were compared for 12 species. The current state of was collected from museums specimens, which were geo-referenced. All 19 Bioclim variables were used at the 4.5 km resolution. The default values were used for MaxEnt and along with linear features. In the case of N>10 quadratic features were also used. GARP, a machine learning methods, used 50% of the data to produce 200 to 500 models. The remaining 50% of the data was used to test model performance; the 10 models with the lowest false-negative rate were kept. Outputs of each modeling approach were compared using zonal statistics. The ecological niche models were combined with land-use and current reservation/conservation status.

MaxEnt and GARP models had general positive association – but not a strong trend (Figure 1). In other words, they had similar distributions but very different means. MaxEnt predictions’ were broader than GARP, the reverse of expected (Figure 2 and 3).