Cardador, L; Díaz-Luque, JA; Hiraldo, F; Gilardi, JD; Tella, JL; (2018) The effects of spatial survey bias and habitat suitability on predicting the distribution of threatened species living in remote areas. Bird Conservation International , 28 (4) pp. 581-592. 10.1017/S0959270917000144.

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Abstract

Knowledge of a species’ potential distribution and the suitability of available habitat are fundamental for effective conservation planning and management. However, the quality of information on the distribution of species and their required habitats is highly variable in terms of accuracy and availability across taxa and regions, particularly in tropical landscapes where accessibility is especially challenging. Species distribution models (SDMs) provide predictive tools for addressing gaps for poorly surveyed species, but they rarely consider biases in geographical distribution of records and their consequences. We applied SDMs and variation partitioning analyses to investigate the relative importance of habitat characteristics, human accessibility, and their joint effects in the global distribution of the Critically Endangered Blue-throated Macaw Ara glaucogularis, a species endemic to the Amazonian flooded savannas of Bolivia. The probability of occurrence was skewed towards more accessible areas, mostly secondary roads. Variability in observed occurrence patterns was mostly accounted for by the pure effect of habitat characteristics (76.2%), indicating that bias in the geographical distribution of occurrences does not invalidate species-habitat relationships derived from niche models. However, observed spatial covariation between land-use at a landscape scale and accessibility (joint contribution: 22.3%) may confound the independent role of land-use in the species distribution. New surveys should prioritise collecting data in more remote (less accessible) areas better distributed with respect to land-use composition at a landscape scale. Our results encourage wider application of partitioning methods to quantify the extent of sampling bias in datasets used in habitat modelling for a better understanding of species-habitat relationships, and add insights into the potential distribution of our study species and opportunities for its conservation.

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