Speaker
Description
As pressures on biodiversity intensify, a coordinated monitoring effort across Europe is urgently needed to track spatial and temporal trends and inform policy responses. Designing effective biodiversity observation networks requires capturing a broad range of species and habitats while enabling attribution of trends to underlying environmental drivers. Using Europe as a case study, we evaluated how different sampling strategies and network sizes influence the detection of biodiversity patterns and their attribution to five key anthropogenic drivers: climate change, land-use change, nitrogen deposition, biological invasions, and protection status.
We tested four spatial sampling designs—random, systematic, stratified, and proportionally stratified—across network sizes ranging from 128 to 131,072 2×2 km² sites (0.01% to 13% of the study area). Networks were assessed based on their coverage of over 1,300 species and 200 habitat types, as well as their representation across gradients of driver intensity.
Our findings show that network size is the dominant factor in capturing common species and habitats, while spatial design plays a greater role for rare entities. Stratified sampling, ensuring equal representation across environmental strata, outperformed other designs, particularly for rare species. This held true also for trend attribution, where the stratified sampling yielded higher representativeness across driver gradients, particularly for rare species.
Habitats were generally sampled more effectively than species, regardless of strategy or scale, underscoring the challenge of capturing the full spatial heterogeneity in species distribution. These insights lay the groundwork for a scalable, adaptive monitoring framework capable of supporting robust biodiversity trend assessments and policy-relevant attribution.
| Status Group | Doctoral Researcher |
|---|---|
| Poster Presentation Option | Yes, I’m willing to present as a poster. |
