The influence of data source and species distribution modelling method on spatial conservation priorities

Aim Species distribution models are an important conservation tool; however, performance can vary with factors including data inputs and modelling method. Model outputs are often under-evaluated for explanatory and predictive capacity. Our aim was to evaluate the capacity of existing data for seven small mammal species to provide useful inferences for management planning. Location Bathurst and Melville (collectively the Tiwi) Islands, Northern Territory, Australia. Methods We developed species distribution models (SDMs) with generalized linear models (GLMs) and boosted regression trees (BRTs) using survey data (351 sites) of small mammals, with two sets of environmental predictors: (a) field-study measurements and (b) available remotely sensed rasters. Predictive capacity of models was evaluated using percentage of deviance explained (%DE) and area under the receiver operating characteristic curve (AUC). We used Marxan to evaluate the influence of different model and data types as input for identifying spatial priorities. Results Field-informed SDMs performed well across both modelling methods, with relatively high test AUC values (mean = 0.82, range = 0.64-0.97) and test %DE (mean = 22.5%, range = 3.5%-65.8%). Remotely sensed models performed relatively poorly, with lower test AUC values (mean = 0.7, range = 0.56-0.86) and lower test %DE (mean = 8.9%, range = 0.03%-24.9%). A notable exception was remotely sensed models for Melomys burtoni (AUC = 0.85 & 0.86, %DE = 23.3% & 24.9%, Bathurst and Melville respectively). Marxan site irreplaceability rankings demonstrated low to marginal agreement using field-informed and remotely sensed inputs (Pearson correlation coefficient = 0.3), and similarly, using GLM and BRT model inputs (0.29). Main conclusions The occurrence of small mammals on the Tiwi Islands can be reasonably explained with field-informed variables, but not with remotely sensed alternatives. Different models lead to different conservation priorities. Our work emphasizes the importance of thoroughly testing SDMs prior to decision-making.