Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using multiple taxa

Abstract

Background: Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion. Results: Focal taxon (butterflies, reptiles, and mammals), sampling bias, connectivity and the choice of SDM algorithm were the most sensitive parameters; collectively these reflect data quality issues. In contrast, cell removal rule and species weights contributed much less to overall variability. Using currently available species data, we found the current effectiveness of Egypt's protected areas for conserving fauna was low. Conclusions: For SCP to be useful, there is a lower limit on data quality, requiring data-poor countries to improve sampling strategies and data quality to obtain unbiased data for as many taxa as possible. Since our sensitivity analysis may not generalise, conservation planners should use sensitivity analyses more routinely, particularly relying on more than one combination of SDM algorithm and surrogate group, consider correction for sampling bias, and compare the spatial patterns of predicted priority sites using a variety of settings. The sensitivity of SCP to connectivity parameters means that the responses of each species to habitat loss are important knowledge gaps.

Causal inference and large-scale expert validation shed light on the drivers of SDM accuracy and variance

“…Once this comparison has been made, the SDM can be used to predict habitat suitability at any geographic location and point in time for which the relevant environmental data are available. This feature of SDMs makes them extremely useful for such applications as predicting the spread of invasive species (Montalva et al, 2017) and disease vectors (Simons et al, 2019), predicting future shifts in species' distributions in response to climate change (Stewart et al, 2022), and spatial conservation planning (El-Gabbas et al, 2020). If SDMs are to be applied in such settings, however, it is important that they perform well in terms of predicting habitat suitability.…”

Fin whale (Balaenoptera physalus) distribution modeling on their Nordic and Barents Seas feeding grounds

“…Sampling bias has been shown to affect model performance and interpretation and should be carefully considered when signs of spatial bias exist (El‐Gabbas & Dormann, 2018a; Fithian et al, 2015; Fourcade et al, 2014; Phillips et al, 2009; Warton et al, 2013). Failure to eliminate sampling bias, particularly in environmental space, can lead to the suboptimal use of SDMs for conservation prioritization and inefficient use of limited conservation resources (El‐Gabbas et al, 2020; Grand et al, 2007). In this study, we considered four methods for spatial sampling bias correction.…”

“…Despite the valued use of presence‐only SDMs to support conservation decision making in data‐poor situations (El‐Gabbas et al, 2020; Smith et al, 2021), these data are usually opportunistic and come without information on the group size, sampling design and efforts, which can lead to spatial, temporal, or environmental biases (El‐Gabbas & Dormann, 2018a; Fourcade et al, 2013). This sampling bias can highly affect SDMs performance and inference and thus needs to be corrected for (El‐Gabbas & Dormann, 2018a; Phillips et al, 2009; Warton et al, 2013).…”

Four key challenges in the open‐data revolution

“…Ultimately however, greater international efforts are needed to increase the coverage of global biodiversity data in under‐represented countries. In this regard, the application of conservation prioritisation in data‐poor countries to expedite ecological data collection is a promising avenue of progress (El‐Gabbas et al., 2020 ; Kujala et al., 2018 ). Furthermore, the development of lasting partnerships between researchers in high‐income and low‐income countries to build capacity is required to even biases in ecological data archiving (Donhauser & Shaw, 2019 ).…”