Geology topics

Jesse S. Lewis

Publications and source records attributed to Jesse S. Lewis.

6 recordsLinked to original sources

USGS2025

When the wild things are: Defining mammalian diel activity and plasticity

Circadian rhythms are a mechanism by which species adapt to environmental variability and fundamental to understanding species behavior. However, we lack data and a standardized framework to accurately assess and compare temporal activity for species during rapid ecological change. Through a global network representing 38 countries, we leveraged 8.9 million mammalian observations to create a library of 14,587 standardized diel activity estimates for 445 species. We found that less than half the species’ estimates were in agreement with diel classifications from the reference literature and that species commonly used more than one diel classification. Species diel activity was highly plastic when exposed to anthropogenic change. Furthermore, body size and distributional extent were strongly associated with whether a species is diurnal or nocturnal. Our findings provide essential knowledge of species behavior in an era of rapid global change and suggest the need for a new, quantitative framework that defines diel activity logically and consistently while capturing species plasticity.

Science Advances↗

USGS2024

Climate, food and humans predict communities of mammals in the United States

Aim The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities. Location Conterminous United States. Time Period 2010–2021. Major Taxa Studied Twenty-five species of mammals. Methods We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities. Results Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west. Main Conclusions Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.

Conterminous United States↗

USGS2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

Ecology and Evolution↗

USGS2019

Predicting functional responses in agro-ecosystems from animal movement data to improve management of invasive pests

Functional responses describe how changing resource availability affects consumer resource use, thus providing a mechanistic approach to prediction of the invasibility and potential damage of invasive alien species (IAS). However, functional responses can be context dependent, varying with resource characteristics and availability, consumer attributes, and environmental variables. Identifying context dependencies can allow invasion and damage risk to be predicted across different ecoregions. Understanding how ecological factors shape the functional response in agro‐ecosystems can improve predictions of hotspots of highest impact and inform strategies to mitigate damage across locations with varying crop types and availability. We linked heterogeneous movement data across different agro‐ecosystems to predict ecologically driven variability in the functional responses. We applied our approach to wild pigs ( Sus scrofa ), one of the most successful and detrimental IAS worldwide where agricultural resource depredation is an important driver of spread and establishment. We used continental‐scale movement data within agro‐ecosystems to quantify the functional response of agricultural resources relative to availability of crops and natural forage. We hypothesized that wild pigs would selectively use crops more often when natural forage resources were low. We also examined how individual attributes such as sex, crop type, and resource stimulus such as distance to crops altered the magnitude of the functional response. There was a strong agricultural functional response where crop use was an accelerating function of crop availability at low density (Type III) and was highly context dependent. As hypothesized, there was a reduced response of crop use with increasing crop availability when non‐agricultural resources were more available, emphasizing that crop damage levels are likely to be highly heterogeneous depending on surrounding natural resources and temporal availability of crops. We found significant effects of crop type and sex, with males spending 20% more time and visiting crops 58% more often than females, and both sexes showing different functional responses depending on crop type. Our application demonstrates how commonly collected animal movement data can be used to understand context dependencies in resource use to improve our understanding of pest foraging behavior, with implications for prioritizing spatiotemporal hotspots of potential economic loss in agro‐ecosystems.

California. Florida, Georgia, Louisiana, Missouri,↗

USGS2016

Inferring invasive species abundance using removal data from management actions

Evaluation of the progress of management programs for invasive species is crucial for demonstrating impacts to stakeholders and strategic planning of resource allocation. Estimates of abundance before and after management activities can serve as a useful metric of population management programs. However, many methods of estimating population size are too labor intensive and costly to implement, posing restrictive levels of burden on operational programs. Removal models are a reliable method for estimating abundance before and after management using data from the removal activities exclusively, thus requiring no work in addition to management. We developed a Bayesian hierarchical model to estimate abundance from removal data accounting for varying levels of effort, and used simulations to assess the conditions under which reliable population estimates are obtained. We applied this model to estimate site-specific abundance of an invasive species, feral swine ( Sus scrofa ), using removal data from aerial gunning in 59 site/time-frame combinations (480–19,600 acres) throughout Oklahoma and Texas, USA. Simulations showed that abundance estimates were generally accurate when effective removal rates (removal rate accounting for total effort) were above 0.40. However, when abundances were small (<50) the effective removal rate needed to accurately estimates abundances was considerably higher (0.70). Based on our post-validation method, 78% of our site/time frame estimates were accurate. To use this modeling framework it is important to have multiple removals (more than three) within a time frame during which demographic changes are minimized (i.e., a closed population; ≤3 months for feral swine). Our results show that the probability of accurately estimating abundance from this model improves with increased sampling effort (8+ flight hours across the 3-month window is best) and increased removal rate. Based on the inverse relationship between inaccurate abundances and inaccurate removal rates, we suggest auxiliary information that could be collected and included in the model as covariates (e.g., habitat effects, differences between pilots) to improve accuracy of removal rates and hence abundance estimates.

Ecological Applications↗

USGS2015

Pathogen exposure varies widely among sympatric populations of wild and domestic felids across the United States

Understanding how landscape, host, and pathogen traits contribute to disease exposure requires systematic evaluations of pathogens within and among host species and geographic regions. The relative importance of these attributes is critical for management of wildlife and mitigating domestic animal and human disease, particularly given rapid ecological changes, such as urbanization. We screened >1,000 samples from sympatric populations of puma (Puma concolor), bobcat (Lynx rufus) and domestic cat (Felis catus) across urban gradients in six sites, representing three regions, in North America for exposure to a representative suite of bacterial, protozoal and viral pathogens (Bartonella sp., Toxoplasma gondii, feline herpesvirus-1, feline panleukopenea virus, feline calicivirus, feline immunodeficiency virus). We evaluated prevalence within each species, and examined host trait and land cover determinants of exposure-providing an unprecedented analysis of factors relating to potential for infections in domesticated and wild felids. Prevalence differed among host species (highest for puma and lowest for domestic cat) and was greater for indirectly transmitted pathogens. Sex was inconsistently predictive of exposure to directly transmitted pathogens only, and age infrequently predictive of both direct and indirectly transmitted pathogens. Determinants of pathogen exposure were widely divergent between the wild felid species. For puma, suburban landuse predicted increased exposure to Bartonella sp. in southern California, and FHV-1 exposure increased near urban edges in Florida. This may suggest inter-specific transmission with domestic cats via flea vectors (California) and direct contact (Florida) around urban boundaries. Bobcats captured near urban areas had increased exposure to T. gondii in Florida, suggesting an urban source of prey. Bobcats captured near urban areas in Colorado and Florida had higher FIV exposure, possibly suggesting increased intra-specific interactions through pile-up of home ranges. Beyond these regional and pathogen specific relationships, proximity to the wildland urban interface did not generally increase the probability of disease exposure in wild or domestic felids, emphasizing the importance of local ecological determinants. Indeed, pathogen exposure was often negatively associated with the wildland urban interface for all felids. Our analyses suggest cross-species pathogen transmission events around this interface may be infrequent, but followed by self-sustaining propagation within the new host species.

California, Colorado,Florida↗