Laura Pollock

Edward Vendrow, Julia Chae, Rupa Kurinchi-Vendhan et al.

Large community science platforms such as iNaturalist contain hundreds of millions of biodiversity images that often capture ecological context on behaviors, interactions, phenology, and habitat. Yet most ecological workflows rely on metadata filtering or manual inspection, leaving this secondary information inaccessible at scale. We introduce INQUIRE-Search, an open-source system that enables scientists to rapidly and interactively search within an ecological image database for specific concepts using natural language, verify and export relevant observations, and utilize this discovered data for novel scientific analysis. Compared to traditional methods, INQUIRE-Search takes a fraction of the time, opening up new possibilities for scientific questions that can be explored. Through five case studies, we show the diversity of scientific applications that a tool like INQUIRE-Search can support, from seasonal variation in behavior across species to forest regrowth after wildfires. These examples demonstrate a new paradigm for interactive, efficient, and scalable scientific discovery that can begin to unlock previously inaccessible scientific value in large-scale biodiversity datasets. Finally, we emphasize using such AI-enabled discovery tools for science call for experts to reframe the priorities of the scientific process and develop novel methods for experiment design, data collection, survey effort, and uncertainty analysis.

Hager Radi Abdelwahed, Mélisande Teng, Robin Zbinden et al.

Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typically, SDMs relate species occurrences to environmental variables representing abiotic factors, such as temperature, precipitation, and soil properties. However, species distributions are also strongly influenced by biotic interactions with other species, which are often overlooked. While some methods partially address this limitation by incorporating biotic interactions, they often assume symmetrical pairwise relationships between species and require consistent co-occurrence data. In practice, species observations are sparse, and the availability of information about the presence or absence of other species varies significantly across locations. To address these challenges, we propose CISO, a deep learning-based method for species distribution modeling Conditioned on Incomplete Species Observations. CISO enables predictions to be conditioned on a flexible number of species observations alongside environmental variables, accommodating the variability and incompleteness of available biotic data. We demonstrate our approach using three datasets representing different species groups: sPlotOpen for plants, SatBird for birds, and a new dataset, SatButterfly, for butterflies. Our results show that including partial biotic information improves predictive performance on spatially separate test sets. When conditioned on a subset of species within the same dataset, CISO outperforms alternative methods in predicting the distribution of the remaining species. Furthermore, we show that combining observations from multiple datasets can improve performance. CISO is a promising ecological tool, capable of incorporating incomplete biotic information and identifying potential interactions between species from disparate taxa.