Collaborative Research: LTREB Renewal: Experimental determination of trophic dynamics and energy flows in a semiarid habitat

This long-term program seeks to understand how food webs respond to environmental variability in semi-arid ecosystems, where rainfall pulses drive dramatic but short-lived bursts of plant growth. Drawing on more than 37 years of continuous experimental research, the team will investigate how small mammal populations respond to rainfall pulses and how their foraging behaviors shape the structure and function of the ecosystem. Better understanding how droughts, extreme rainfall events, and shifting resource availability shape populations and communities will enable informed responses to critical challenges related to desertification and ecosystem management. The project will generate broadly applicable insights into how pulsed resources shape consumer behavior and organismal interactions across arid ecosystems worldwide. In addition, the project supports education and workforce development through international collaborations between U.S. and Chilean institutions, hands-on training opportunities for students and early-career researchers, and outreach programs that engage audiences across career stages. These efforts will help prepare a skilled workforce equipped to excel across multiple career paths in STEM with the use of advanced data analysis using machine learning and hands-on experiences in metabarcoding and high-thoughtput analysis, thereby advancing the national interest in science, ecosystem management, and societal well-being. This project advances NSF’s priorities in Biotechnology and Artificial Intelligence The project will integrate long-term field data with complementary analytical approaches, including stable isotope analysis and dietary DNA metabarcoding, to quantify how small mammals utilize distinct “fast” (forb-based) and “slow” (shrub-based) energy channels across both short-term seasonal cycles and longer-term interannual climate variability associated with El Niño-Southern Oscillation (ENSO). By combining isotopic and genetic methods, the research will generate detailed, individual-level dietary profiles that reveal patterns of trophic niche partitioning, dietary specialization, and temporal shifts in resource use across multiple trophic levels. These fine-grained empirical data will be used to parameterize and test dynamic, process-based models that link resource pulses, consumer population dynamics, and foraging behavior to emergent patterns of food-web stability and temporal variability. The project will also incorporate advanced data science approaches, including machine learning techniques, to identify nonlinear relationships and improve predictive capacity in this highly variable system. Methodological innovations in biotechnology, such as high-throughput sequencing and isotope-enabled analytics, coupled with open-access data libraries that are required to support both taxonomic and functional interpretations, will provide unprecedented resolution of the structure of food webs. Associated training programs will include hands-on course-based undergraduate research experiences in dietary metabarcoding, stable isotopes, bioinformatics, and reproducible practices in data science. Together, these approaches will allow the team to disentangle the relative roles of abiotic forcing and biotic interactions in structuring ecological communities. The resulting models and datasets will provide a robust framework for predicting how ecosystems respond to increasing climatic variability and will contribute broadly to ecological theory, long-term ecological research, and the development of transferable tools for analyzing complex, data-rich environmental systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2541064 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Seth Newsome | Institution: University of New Mexico, ALBUQUERQUE, NM | Award Amount: $370,309 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2541064 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2541064.html