No abstract

As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to identify the nonlinearities and interactions emergent from such complexity, especially across broad swaths of parameter space. This limits understanding of the ecological mechanisms underlying model behavior. Machine learning approaches are a potential answer to this issue, given their predictive ability when applied to complex large datasets. While perceptions that machine learning is a “black box” linger, we seek to illuminate its interpretive potential in ecological modeling. To do so, we detail our process of applying random forests to complex model dynamics to produce both high predictive accuracy and elucidate the ecological mechanisms driving our predictions. Specifically, we employ an empirically rooted ontogenetically stage-structured consumer-resource simulation model. Using simulation parameters as feature inputs and simulation output as dependent variables in our random forests, we extended feature analyses into a simple graphical analysis from which we reduced model behavior to three core ecological mechanisms. These ecological mechanisms reveal the complex interactions between internal plant demography and trophic allocation driving community dynamics while preserving the predictive accuracy achieved by our random forests.

Bioenergetic approaches have been greatly influential for understanding community functioning and stability and predicting effects of environmental changes on biodiversity. These approaches use allometric relationships to establish species' trophic interactions and consumption rates, and have been most successfully applied to aquatic ecosystems. Terrestrial ecosystems, where body mass is less predictive of plant-consumer interactions, present inherent challenges that these models have yet to meet. Here, we review the literature on the processes governing terrestrial plant-consumer interactions and develop a bioenergetic framework integrating those processes.Our framework integrates for the first-time bioenergetics specific to terrestrial plants and their consumers within a food-web approach that also considers mutualistic interactions, advancing understanding of terrestrial food webs and predictions of their responses to environmental changes.

Actinobacteria that live mutualistically with leaf-cutter ants secrete antibiotics that may induce antibiotic resistance in nearby soil bacteria. We tested for the first time whether soil bacteria near and inside Atta cephalotes nests in Costa Rica show higher levels of antibiotic resistance than bacteria collected farther away. We collected soil samples 0 m to 50 m away from ant nests and grew bacteria from them on agar with paper discs treated with antibiotics of common veterinary use. As a proxy for antibiotic resistance, we measured the distance from the edge of each disc to the closest bacterial colonies. In general, resistance to oxytetracycline increased with proximity to leaf-cutter ant nests. Antibiotic resistance to oxytetracycline was also higher in samples collected inside the nest than in samples from the nest mound; not all antibiotics demonstrated the same trend. A preliminary exploratory morphological analysis suggests bacterial communities between 0 m and 50 m from ant nests were similar in diversity and abundance, indicating the pattern of antibiotic resistance described above may not be caused by differences in community composition. We conclude that actinobacteria living mutualistically with A. cephalotes drive natural antibiotic resistance to tetracycline in proximal bacterial communities.

Shared water facilities are widespread in resource-poor settings within low- and middle-income countries. Since gathering water is essential, shared water sites may act as an important COVID-19 transmission pathway, despite stay-at-home recommendations. This analysis explores conditions under which shared water facility utilization may influence COVID-19 transmission. We developed two SEIR transmission models to explore COVID-19 dynamics. The first describes an urban setting, where multiple water sites are shared within a community, and the second describes a rural setting, where a single water site is shared among communities. We explored COVID-19 mitigation strategies including social distancing and adding additional water sites. Increased water site availability and social distancing independently attenuate attack rate and peak outbreak size through density reduction. In combination, these conditions result in interactive risk reductions. When water sharing intensity is high, risks are high regardless of the degree of social distancing. Even moderate reductions in water sharing can enhance the effectiveness of social distancing. In rural contexts, we observe similar but weaker effects. Enforced social distancing and density reduction at shared water sites can be an effective and relatively inexpensive mitigation effort to reduce the risk of COVID-19 transmission. Building additional water sites is more expensive but can increase the effectiveness of social distancing efforts at the water sites. As respiratory pathogen outbreaks—and potentially novel pandemics—will continue, infrastructure planning should consider the health benefits associated with respiratory transmission reduction when prioritizing investments.