Abstract Plant–animal interactions result in many ecosystem services, such as pollination. In the western hemisphere, and especially the Neotropics, hummingbirds are important pollinators for thousands of plant species, yet there is much to be learned about the spatial and temporal variation in mechanisms influencing these interactions. We collected an extensive dataset from 39 sites across elevation gradients spanning 4.7–3522 m above sea level in three regions: the Pacific slope of the Talamanca Mountain Range in central‐southern Costa Rica, the Chocó‐Andean region of northwestern Pichincha province in Ecuador, and the Atlantic Forest in southeastern Brazil. Because these regions have very different biogeographic histories, comparing them allows us to identify general patterns of interactions. Data on flower abundance and plant–hummingbird interactions were gathered monthly for 1.5–2 years along 1.5‐km transects situated along an elevation gradient in each country during 2017 and 2024. Trait data for flowers were also measured. This effort resulted in 95,824 pairwise interactions between 715 plants and 79 hummingbird species, and also 873 interactions with another 12 bird species in Ecuador. These data are being used to develop a predictive framework on species interactions necessary to understand and conserve biodiversity and ecosystem services. These data also provide detailed information on plants important for hummingbird conservation which have, for example, been used to guide restoration projects. The data are available for reuse under CC BY 4.0 licensing. Please cite the data release on Zenodo and this data paper when reusing the dataset.
Abstract Managing and conserving species in the face of the current biodiversity crisis requires consistent information on the status of wild animal populations across large spatiotemporal scales. Budgetary constraints, differences in national legislation, and the challenges of coordinating logistics transnationally often limit survey efforts to spatial extents that rarely encompass the full distribution of many species, and temporal extents that rarely exceed a few years. These limitations are particularly relevant in regions like Europe, where multiple countries often share and manage populations of the same wild species across national borders and under different legislative frameworks. Cooperation and collaboration can help overcome some of these challenges. Here, we present data from the first 2 years of Snapshot Europe, a collaborative, coordinated, and standardized European‐wide mammal survey conducted by camera‐trap experts working across the continent. Between mid‐August and mid‐November of 2021 and 2022, more than 160 researchers simultaneously collected camera‐trap data at 1198 and 967 locations across 81 and 55 arrays and 22 and 23 countries, respectively, for a total of 90,230 camera‐trap days (49,033 and 41,197 in 2021 and 2022, respectively), with 91% of the data collected in September and October. Thirty (28%) out of 106 arrays across the 2 years were sampled both in 2021 and 2022, allowing for comparisons of estimates over time. All data were managed and processed in Wildlife Insights ( https://www.wildlifeinsights.org/ ); each record was reviewed by at least one participant who confirmed or edited the initial classifications provided by the AI model integrated into the platform. The final dataset contains a total of 94,441 observations of 63 species of mammals and 5024 detections of 78 species of birds. These data capture a subsample of the European wildlife community and can be used to explore ecological questions related to drivers of distribution, abundance, and activity across the continent. We envision repeating this effort annually for the foreseeable future. Data for Snapshot Europe 2023, 2024, and 2025 are currently being revised and will be made publicly available soon. Collated, all the annual datasets will enable estimates of trends over time. Additionally, four sister initiatives, Snapshot USA (2019‐ongoing), Snapshot Japan (2023‐ongoing), Snapshot Brazil and Snapshot Chile (both started in 2025), annually collect data based on the same sampling protocol, enabling a new era of global‐scale comparisons. The images associated with the data accompanying this publication are available in Wildlife Insights. The data are available for reuse under CC BY‐SA 4.0 licensing. Please cite this data paper when reusing all or part of this dataset.
Abstract Ecologists use meta‐analysis models to propagate uncertainty from statistical models of independent datasets into a higher‐level analysis. Meta‐analysis can be used to combine independent studies or as a two‐stage method for hierarchical modeling of data components from a large‐scale or long‐term study that are impractical to analyze in a single statistical model. While linear meta‐regression against study‐level covariates is common, ecological patterns are frequently nonlinear. For such cases, meta‐analysis methodology lacks tools to estimate semiparametric smooth meta‐regression models. We propose new spline meta‐regression methods by melding meta‐analysis with additive mixed models, resulting in the class of additive mixed meta‐analysis (AMMA) models. In a simulation study, when first‐stage SEs are heterogeneous, meta‐splines perform better than naive approaches such as simple generalized additive models (GAMs). In a case study to investigate 43‐year patterns of seasonally averaged phytoplankton levels in San Francisco Bay, California, USA, meta‐splines give notably different results than naive methods in such heterogeneous cases. Seasonal averages and SEs are estimated annually, then combined with meta‐splines to investigate long‐term, nonlinear changes in the season‐scale averages. The meta‐analysis approach is practical, fully propagates the statistical information and uncertainties, and provides advantages for visualization. The methods are available in the R package splinemixmeta .
