A data-driven geospatial workflow to improve mapping species distributions and assessing extinction risk under the IUCN Red List

Abstract

32Species distribution maps are essential for assessing extinction risk and guiding conservation efforts. Here, 33we developed a data-driven, reproducible geospatial workflow to map species distributions and evaluate 34 their conservation status consistent with the guidelines and criteria of the IUCN Red List. Our workflow 35 follows five automated steps to refine the distribution of a species starting from its Extent of Occurrence 36(EOO) to Area of Habitat (AOH) within the species range. The ranges are produced with an Inverse 37 Distance Weighted (IDW) interpolation procedure, using presence and absence points derived from primary 38 biodiversity data. As a case-study, we mapped the distribution of 2,273 bird species in the Americas, 55% 39 of all terrestrial birds found in the region. We then compared our produced species ranges to the expert-40 drawn IUCN/BirdLife range maps and conducted a preliminary IUCN extinction risk assessment based on 41 criterion B (Geographic Range). We found that our workflow generated ranges with fewer errors of 42 omission, commission, and a better overall accuracy within each species EOO. The spatial overlap between 43 both datasets was low (28%) and the expert-drawn range maps were consistently larger due to errors of 44 commission. Their estimated Area of Habitat (AOH) was also larger for a subset of 741 forest-dependent 45 birds. We also found that incorporating geospatial data increased the number of threatened species by 52% 46 in comparison to the 2019 IUCN Red List, and 103 species could be placed in threatened categories (VU, 47 EN, CR) pending further assessment. The implementation of our geospatial workflow provides a valuable 48 alternative to increase the transparency and reliability of species risk assessments and improve mapping 49 species distributions for conservation planning and decision-making.50 51

Regional Assessment of the Conservation Status of Snubfin Dolphins (Orcaella heinsohni) in the Kimberley Region, Western Australia

“…However, both the EOO and AOO can be calculated in multiple ways, including via cartographic (i.e., grid-based) and areographic (i.e., buffer-based) methods, as well as their individual variants (Breiner and Bergamini, 2018) and combinations (Hernández and Navarro, 2007). Different methods will often yield disparate estimates of threat (Gaston and Fuller, 2009), an issue which is exacerbated by the continuing emergence of alternative metrics (e.g., Area of Habitat, AOH; Ocampo-Peñuela et al, 2016;Brooks et al, 2019;Palacio et al, 2020), and by lingering confusion surrounding how the metrics recommended by the IUCN should be implemented in practice (Breiner and Bergamini, 2018). Without a clear consensus on which approach is most appropriate, comparisons of conservation status across areas are likely to be blurred by methodological decisions that remain largely subjective.…”

Priority conservation areas and a global population estimate for the Critically Endangered Philippine Eagle derived from modelled range metrics using remote sensing habitat characteristics

“…Various spatial workflows have been proposed and implemented for calculating AOH, which overlay and clip elevational and landcover preferences within the range of species presence points (Brooks et al 2019). Deductive methods using clipped environmental layers with expert-drawn maps (Harris & Pimm 2008), or inductive modelling methods using inverse distance weighted interpolation (Palacio et al 2021), and logistic regression (Dahal et al 2021;Lumbierres et al 2021), have been successful in estimating AOH but rely on a spatially homogenous sample of presence points. For many rare species in remote, hard to survey areas presence data is either insufficient, or may be heavily biased towards a well-sampled region but lacking elsewhere (Syfert et al 2014;Dahal et al 2021).…”

“…2019). Deductive methods using clipped environmental layers with expert-drawn maps (Harris & Pimm 2008), or inductive modelling methods using inverse distance weighted interpolation (Palacio et al . 2021), and logistic regression (Dahal et al .…”

Local Gazetteers Reveal Contrasting Patterns of Historical Distribution Changes between Apex Predators and Mesopredators in Eastern China

“…We used the Inverse Distance Weighted (IDW) tool to reconstruct the distribution based on the valid records using ArcGIS Desktop 10.3. This tool has been widely applied to species distribution reconstruction (Roberts et al 2004; Hijmans 2012; Takahashi et al 2014; Barbosa 2015; Areias-Guerreiro et al 2016; Palacio et al 2020). The interpolation value in each grid was generated by the the nearest 12(default value) records, including presence records or absence records.…”

“…The main assumption is that species are more likely to be found closer to presence points and farther from absences (i.e. spatial auto-correlation), and the local influence of points (weighted average) diminishes with distance (Palacio et al 2020). The result value means the probability of animal occurrence, in [0,1].…”