The importance of locally sourced data in identifying population trends: Insights from Iberian vertebrates

Understanding species population trends is key for assessing their conservation status and proposing measures to ensure their future persistence amid recent biodiversity loss. However, studies are reporting contrasting biodiversity trends over time. These discrepancies can be partly attributed to biases in global datasets, which might not capture the representativeness of local processes. Here, we aimed to address this gap of knowledge by complementing data included in the Living Planet Database (LPD), one of the largest repositories of population time-series, with locally sourced data from the Iberian Peninsula. The study aim: (i) to assess the state of wildlife Iberian vertebrates using population time-series across taxonomic groups and (ii) to determine differences between locally sourced data and LPD (evaluating also the differences between data sources). To supplement LPD, we conducted a review, analysing over 6000 peer-reviewed manuscripts and grey literature documents. We obtained 999 population time-series for 294 vertebrate species compiled in an Iberian Vertebrate (IbeV) database, two times the number of populations as the LPD includes. Our results indicate contrasting population trends across taxonomic groups, with freshwater amphibians and bony fishes showing steep declines. Moreover, the LPD shows a positive trend and IbeV indicates no net change over time. Threatened species did not exhibit net changes in population trends, while non-threatened species showed positive trends. We showed that local databases can provide distinct population trends compared to global databases. This approach highlights the need to bridge the gap between global and local datasets, to support context-specific management and conservation programmes.

This study was funded by SIBECOL grant “Projects for Early Career Researchers 2020”. RCRC was supported by the European Union-Next Generation EU in the Maria Zambrano Program (ZAMBRANO 21-26). ZMR was supported by a postdoctoral contract funded by the Junta de Andalucía (POSTDOC_21_00353). RAR was supported by “Margarita Salas” postdoctoral grant from the Ministerio de Universidades del Gobierno de España and the Next Generation EU – Recovery, Transformation and Resilience Plan. EA was supported by Generalitat Valenciana and European Social Fund (APOSTD/2021), and MCIN/AEI/grant number FJC2021-047885-I. EAs was supported by a ·Vicenç Mut· postdoctoral grant funded by the Department of European Funds, University and Culture of the Govern de les Illes Balears (PD/041/2021). JB acknowledges the support received by the Spanish Ministry of Science and Innovation under the RyC fellowship (RYC2021-033650-I). ACC was founded by the Research Council of Norway (RCN-project nr. 315051). CGC was supported by a Junior Leader Fellowship contract (LCF/BQ/PR22/11920005) funded by “la Caixa” Foundation (ID 100010434). RPR was funded by FJC2020-045938-I/MCIN/AEI/10.13039/501100011033 and European Union “NextGenerationEU”/“PRTR”. IS was supported by European Union-Next Generation EU (MARSALAS21-30).