In species distribution modeling (SDM), the environmental representativeness effect hinders the comparison and generalization of discrimination statistics, as their values are context-dependent and may vary without reflecting actual differences in model accuracy. To address this issue, a harmonization approach based on the uniform distribution of suitability values has been proposed, demonstrating effectiveness when true absence data are available. However, in most cases, models solely rely on presence records and use background points instead of true absences, posing additional validation challenges. This study simulates habitat suitability and presence-absence data, and evaluates the robustness of several validation indices: background-based AUC (AUCb), its harmonized version (uAUCb), and two variations of the Boyce index. The results show that the Boyce index remains unaffected by the representativeness effect, whereas AUCb varies across scenarios, confirming the influence of the representativeness effect on SDM results with background data. Harmonization through uAUCb successfully makes values comparable, but its reliability depends on sample size, requiring at least 100 presences and 10000 background points.
The global biodiversity crisis, driven by habitat loss, climate change, and overexploitation, demands efficient data management and accessibility to inform conservation decisions. While open-access biodiversity data have increased, a substantial portion, termed "dark data," remains unpublished and inaccessible. We explored the mobilization of dark data existing in Environmental Assessment (EA) reports in Spain to improve the Digital Accessible Knowledge for 43 field-recorded threatened species. Based on the IUCN Red List criteria, we evaluated the impact of EA-related dark data on two required metrics: the extent of occurrence (EOO) and area of occupancy (AOO). Our results show that integrating dark data increased EOO and AOO for 23% and 93% of the field-recorded threatened species, respectively, including endangered species like Aquila adalberti. We highlight the importance of mobilizing data collected during the EA processes and ensuring that data are made findable, accessible, interoperable, and reusable (FAIR) to inform conservation decisions. Mobilizing EA-related dark data offers a cost-effective approach by which to strengthen biodiversity management, facilitate decision-making, and justify biodiversity conservation.
The niche centrality hypothesis predicts that individuals near the niche centroid have higher fitness due to more favorable environmental conditions. In this study, we evaluated the effect of the niche structure of the Antioquia Wren on 20 different traits grouped into four datasets: morphology, genetic diversity, coloration and acoustics. We tested the relationship with distinct niche configurations using minimum volume ellipsoids, and evaluated the relationship between the distance to the niche centroid and trait values through ordinary linear and generalized linear squared regression models. We found positive relationships for variables associated with beak morphology and crown feathers hue. Conversely, we found a negative relationship with birdsong frequency and the distance between longest primary and first secondary flight feathers. All effects of the niche structure on traits were weak (<0.2). We found no consistency in the relationship between niche structure and the remaining 13 traits. We identify potential mechanisms underlying both positive relationships and the absence of trait-niche relationships. Our findings emphasize that factors such as biotic interactions, climatic heterogeneity, range size, niche breadth, centroid position, and intrinsic trait variability are likely to shape how species conform to the niche centrality hypothesis.
A discussion is presented of the use of citizen science data to study trends in population abundance and on numbers of species. The discussion is framed around two widely accepted models of indices of biological signal per unit of effort. When the trend is number of species per unit of effort, the model is the MichaelisMenten equation, that has an asymptote. When the trend is in abundance per unit effort, the model is the simple linear "catch/effort" model of fisheries. It is found that number of species per unit of effort has an artifactual tendency to negative trends, while abundance per unit effort is more likely to be less influenced by artifacts of the metrics. A calculation shows that, under certain assumptions, the number of records in iNaturalist is a good approximation to number of different individuals sighted. These results are checked by simulation and exemplified using data for three families of butterflies of Mexico.
Downloading images of preserved specimens in bulk is becoming increasingly important for many research projects, especially those connected with machine learning , image analysis. A useful source of images is the standard biodiversity aggregator, the Global Biodiversity Information Facility (GBIF). Here we identify four major issues connected to GBIF image downloads, distinct from those associated with text downloads. These are specialIntscript license considerations, specialIntscript citation issues, specialIntscript restricting to specific providers for project reasons or cybersecurity concerns, and, finally, specialIntscript attempting to use links that are no longer functioning (often referred to as "link rot" or "data rot"). We suggest an incremental approach to downloading and suggest techniques for improved image download. We provide an implementation of our suggestions in Python (gbif- image-downloader).
