Effectively and accurately mapping global biodiversity patterns for different regions and taxa

Abstract

Understanding patterns of biodiversity is crucial for developing appropriate conservation and management plans. The IUCN RedList is looked upon as a source of globally-consistent assessment of species extinction risk, including range maps as part of the extinction risk assessment. Species ranges are a central criterion in determining extinction vulnerability, and consequently apportioning conservation and research efforts. Thus, the accuracy of these maps is crucial to the effective conservation of global biodiversity. Given difficulties in acquiring sufficient, reliable point data and the need for species or diversity maps within many studies, countless papers rely on these centralized expert range maps. However, such efforts are vulnerable to errors if not carefully checked, and the drive to assess as many species as possible rather than to ensure meaningful quality assessment may drive high error rates, with huge implications for species conservation. Recent efforts to account for the over-generalization of species ranges by trimming species ranges with landcover and elevation also makes a number of assumptions on the consistency and accuracy of global data, the lack of politically-driven biases. Here, we analyse the biases present in 50768 animal IUCN and BirdLife maps and provide suggestions on how such analyses could be improved, and flag spatial and taxonomic inconsistencies to enable analysis to acknowledge the limitations of data in further analysis based on these maps. We also discuss effective ways to overcome these biases, the limits of such applications and explore alternative means of mapping diversity patterns.

Sampling biases shape our view of the natural world

“…Unfortunately, the resources presently available are not yet fit for the purpose of understanding and protecting global biodiversity, although many researchers attempt to do this. In common practice, current views of the natural world are limited either to ‘expert opinion' IUCN maps, where inherent knowledge gaps have huge consequences of data use, including persistent administrative‐area biases (Hughes et al 2021), or a coverage completeness for < 6.74% of the planet, based on terrestrial, temperate lowlands near roads and cities in developed countries and their coastal regions. Neither data set is currently sufficient for truly global analyses, and efforts must be made to better integrate these data, such that they may help alleviate the weaknesses and biases of each other.…”

Developing global vulnerabilities and conservation priorities for cave-dwelling bats

“…The inclusion of these measures enables prioritisation of cave ecosystems with rare and higher functional diversity attributes 67,68 complimenting the metrics based on geopolitical endemism and conservation status from IUCN [68][69][70] which are commonly used within prioritisation schemes. While the expert-based Redlist developed by the IUCN is the most comprehensive basis for conservation and species protection, it is not free from biases [71][72][73] , especially for bats, in which a large proportion of species are either taxonomically and spatially under-sampled or disproportionately studied particularly in most megadiverse and developing countries 74 . Overall, our observed patterns are consistent with previous global studies comparing the value of broad-and fine-scale analyses in identifying priorities.…”

A spatial fingerprint of land-water linkage of biodiversity uncovered by remote sensing and environmental DNA

“…Understanding the spatial distribution of biodiversity and its linkage across ecosystem types is essential, especially in an era of increasing human modifications of natural landscapes 1,2 . It is well-established that species and ecosystem functional diversity are unevenly distributed across landscapes, with pronounced diversity hot and cold spots 3,4 . Extensive work has also demonstrated how ecosystems more diverse in species are more productive and stable [5][6][7] .…”