Host Trap Data - mice and chipmunk

These data are part of a larger, long-term project to understand function in a complex forest network of linked and interacting taxa. The broad approach has been to quantify the strength of interactions between pairs of taxa and to embed those pairwise interactions in a more comprehensive interaction web, which itself might be affected by ongoing shifts in temperature and precipitation patterns. Specifically, this approach allows us to examine both top-down and bottom-up forces impacting small mammals, their tick parasites and associated pathogens. This particular dataset reflects numbers and estimated population abundances for white-footed mice and eastern chipmunks trapped and tagged at six Cary Institute grid plots during August and September across 31 years.File list:host.TrapData 1991-2022.csvLTREB_hostAbundance_README - contains full metadata including definitions for variables in the data file, host.TrapData 1991-2022.csv</p

Data associated with: Beyond AI for X: A Synergistic Future for AI and Ecology

The file is associated with: B.A. Han, K.R. Varshney, S. LaDeau, A. Subramanian, K.C. Weathers, J. Zwart. Submitted. Beyond ‘AI for X’: A Synergistic Future for AI and Ecology. Abstract:  Research in both ecology and artificial intelligence (AI) strives for predictive understanding of complex systems, where nonlinearities arise from multidimensional interactions and feedbacks across multiple scales. After a century of advances built on a staggered cycle of computational development and ecological adaptation, we foresee a critical need for intentional synergy to meet current societal challenges against the backdrop of global change.  The unpredictability of systems-level phenomena and associated challenges in understanding resilience dynamics are critical challenges on a rapidly changing planet. Here, we spotlight both the promise and the urgency of a synergistic convergence research paradigm between ecology and AI. The systems studied in ecology are a challenge to fully and holistically model, even using the most prominent AI technique today: deep neural networks. Moreover, ecological systems have emergent and resilient behavior that should inspire new, robust AI architectures and methodologies. We share several examples of how challenges in ecological systems modeling will require advances in AI techniques that are themselves inspired by the systems they seek to model.  Both fields have inspired each other, albeit indirectly, in an evolution toward this convergence. Here we emphasize the need for more purposeful synergy to accelerate understanding of ecological resilience whilst building the resilience currently lacking in modern AI. There are persistent epistemic barriers that require attention in both disciplines, yet the implications of a successful convergence go beyond advancing ecological disciplines or achieving an artificial general intelligence -- they are critical for both persisting and thriving in an uncertain future.  File list: AIandML_results_SHARE.csv - contains literature search results from Clarivate Web of Science.</p