Acevedo, M. A. | bioRxiv Science

bioRxiv2025

MONITORING POPULATION EXTINCTION RISK WITH COMMUNITY SCIENCE DATA

AbstractThe robust estimation of local extinction risk is central to inform management and conservation efforts. Still, estimating this key demographic parameter requires standardized monitoring data that are lacking for most species and systems. The analysis of community science data is emerging as a promising alternative. These expansive datasets leverage observations from multiple volunteers that provide higher temporal and spatial resolution. Nevertheless, the proper analysis of community science data is challenging because it requires accounting for additional complexities in the intrinsic ecological and observational processes. To address this issue, we describe and test a quantitative approach that fits continuous state-space models iteratively to eBird data with the ultimate goal of estimating local persistence probability through time. We evaluated model accuracy by comparing estimates and trends from eBird with those from the endangered Everglades snail kite long-term, standardized monitoring project. We also performed two separate sensitivity analyses (temporal and sampling thinning) to assess how robust the persistence estimates are to a reduction in the number of eBird observations available. Our results showed that the temporal trend trajectory of local population persistence estimated from eBird closely matched that from standardized monitoring. Moreover, the trend remained similar even when reducing the amount of eBird data available to 5% of the original data set - a reduction from 258 to 13 weeks or from 7,714 to 385 lists of observations across 5 years of monitoring. Synthesis and applicationsOur modeling framework provides a robust, computationally efficient, and easy-to-apply tool for monitoring local persistence probability that can support global conservation efforts. This will complement the monitoring of species population viability in places where standardized monitoring is still lacking, but community science observations are common.

ecology↗

bioRxiv2020

A Tunable, Simplified Model for Biological Latch Mediated Spring Actuated Systems

We develop a model of latch-mediated spring actuated (LaMSA) systems relevant to comparative biomechanics and bioinspired design. The model contains five components: two motors (muscles), a spring, a latch, and a load mass. One motor loads the spring to store elastic energy and the second motor subsequently removes the latch, which releases the spring and causes movement of the load mass. We develop open-source software to accompany the model, which provides an extensible framework for simulating LaMSA systems. Output from the simulation includes information from the loading and release phases of motion, which can be used to calculate kinematic performance metrics that are important for biomechanical function. In parallel, we simulate a comparable, directly actuated system that uses the same motor and mass combinations as the LaMSA simulations. By rapidly iterating through biologically relevant input parameters to the model, simulated kinematic performance differences between LaMSA and directly actuated systems can be used to explore the evolutionary dynamics of biological LaMSA systems and uncover design principles for bioinspired LaMSA systems. As proof of principle of this concept, we compare a LaMSA simulation to a directly actuated simulation that includes a either Hill-type force-velocity trade-off or muscle activation dynamics, or both. For the biologically-relevant range of parameters explored, we find that the muscle force-velocity trade-off and muscle activation have similar effects on directly actuated performance. Including both of these dynamic muscle properties increases the accelerated mass range where a LaMSA system outperforms a directly actuated one.

biophysics↗