The effects of spatial survey bias and habitat suitability on predicting the distribution of threatened species living in remote areas

Abstract

SummaryKnowledge 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.

First systematic sampling approach to estimating the global population size of the Critically Endangered Blue-throated MacawAra glaucogularis

“…To obtain local Blue-throated Macaw density estimates within each subpopulation, we sampled three to four survey areas (11 in total; Figure 1, Table 1), which were chosen based on prior knowledge of the local occurrence of the species and logistical considerations such as accessibility and cooperation of landowners (ranchers). Four survey areas (two in the north-western and one each in the north-eastern and southern subpopulation) were the most inaccessible and located >10 km from the nearest secondary road, thereby avoiding spatial survey bias towards more accessible areas as documented by Cardador et al (2018). Areas of both high and low Blue-throated Macaw abundance or frequency of occurrence as determined by prior qualitative surveys, anecdotal and incidental observations obtained between 2005 and 2014 were included.…”

“…Obtaining reliable population size estimates for globally threatened bird species is of vital importance for conservation status assessments, the implementation of appropriate conservation actions and effective management. A number of survey (sampling) and census methods for estimating (Herzog et al 2012), whereas the extent of suitable habitat is thought to range between 9,236 km 2 (Herzog et al 2012) and 19,249 km 2 (Cardador et al 2018). The species' range is divided into two or three disjunct, potentially isolated subpopulations, and it is patchily distributed even within each subpopulation's small range extent (Yamashita andMachado de Barros 1997, Berkunsky et al 2016).…”

Quantifying the impact of vegetation‐based metrics on species persistence when choosing offsets for habitat destruction

“…1). We used the species-specific maximum training sensitivity plus specificity (Cardador et al 2018) as our threshold, which was extracted from the MaxEnt model outputs (Supporting Information). We derived species-specific dispersal and demographic parameters from the literature and tested them through sensitivity analyses (Supporting Information).…”

Importance and effectiveness of correction methods for spatial sampling bias in species with sex‐specific habitat preference

“…The systematic sampling design allowed a better estimation of the parameters with or without corrections for potential spatial bias, as it covers a larger part of the environmental variation in the landscape and forces the observer to also visit areas with expected lower habitat quality, thus reducing possible spatial bias (Cardador et al, ). The use of real observer trajectories to sample a virtual species confirmed that a non‐negligible sampling bias can occur in the absence of a predefined sampling design, in coherence with our hypothesis H1.…”

“…However, when the targeted background points are generated over a too restricted area, it can also reduce model accuracy and results in lower prediction performances (Fourcade, Engler, Rödder, & Secondi, 2014;Thuiller, Brotons, Araújo, & Lavorel, 2004;Warton et al, 2013). As an alternative, Cardador, Diaz-luque, Hiraldo, Gilardy, and Tella (2017) recently proposed to include the potential causes of bias as predictor variables in the models, in combination with a random background sampling.…”