Identifying sites with high biodiversity value using filtered species records from a biodiversity information facility

Abstract

AbstractAimEfficient mitigation of the biodiversity crisis requires targeted conservation actions in locations with high species richness, the presence of endangered species and unique species communities. However, prioritising sites remains challenging because of sparse knowledge on biodiversity, limiting the possibility of communicating efficiently with local decision makers. We examine easy‐to‐replicate, yet robust, methods to identify areas with high conservation values on large spatial scales using data filtering and complementary biodiversity indicators based on species records from a biodiversity information facility.LocationFinland, Europe.MethodsWe illustrate the protocol by focusing on Lepidoptera in Finnish municipal districts. We mobilised over 3 million species records on 878 native Lepidoptera (2001–2020) from the Finnish Biodiversity Information Facility. We estimated the richness of overall and endangered species using species accumulation curves, as well as the uniqueness of species communities, using measures of local contribution to beta diversity (LCBD). After testing for multiple thresholds and their effect on indicator accuracy, 97 districts with >5000 records were included in the analyses.ResultsEstimated overall species richness was highest on the southern coast and significantly decreased in the North, following a known pattern with Lepidoptera in Finland. Species richness was not the highest in the districts with the greatest number of records and the ranking differed from the raw data, demonstrating the importance of correcting for sampling intensity. The estimated number of endangered species correlated with overall species richness, except in northernmost districts, where the proportion of endangered species was exceptionally high. High LCBD replacement (i.e. unique species communities) was concentrated in the Southwest (hemi‐boreal) and North (northern boreal) of the country.Main ConclusionsWe provided an example and interpretations of how scalable biodiversity indicators based on accumulation curves and LCBD analyses, and careful data filtering (thresholds) can be used to identify sites with conservation priorities from multi‐sourced species records.