Associate Professor Université de Montréal, Quebec, Canada
Background/Question/Methods Open data hold an underexploited potential to improve ecological indicators used in conservation research. Indicators such as ecological uniqueness, which can help identify areas with exceptional species composition and potential conservation targets, are often limited to local or regional scales because of sampling limitations for single studies. Yet, these measures could also be highly informative if applied over large spatial scales and using different data types. This presentation will show how open data allows measuring community uniqueness over broad spatial extents and extending uniqueness assessments to species interactions. We first used species distribution modelling to predict community composition across North America based on eBird community science data. We then measured ecological uniqueness using the local contributions to beta diversity (LCBD) and explored their spatial variation across different regions and scales. We finally combined community predictions with an open interaction metaweb to produce localized ecological networks, then verified if interaction and community uniqueness show a similar spatial distribution.
Results/Conclusions The use of open data revealed two novel insights regarding the distribution of ecological uniqueness across broad spatial extents. First, our community predictions showed that the relationship between uniqueness and species richness changes according to the area under study. The form and variance of the uniqueness-richness relationship were affected by the region's extent size, richness profile and proportion of rare species. As a result, sites identified as unique may vary according to regional characteristics, which should be considered in conservation recommendations. Second, our interaction uniqueness results showed a different spatial distribution from community uniqueness. Sites and areas may be unique in one community aspect and not the other (e.g. unique communities without unique interaction networks), highlighting the importance of considering different community components before recommending sites as conservation targets. Meanwhile, species distribution models generated broad-scale uniqueness predictions highly correlated with observation data. Therefore, our results show that open data can be helpful to extend uniqueness assessments and identify potential conservation targets over broad spatial scales.
