Geology topics

David W. Wolfson

Publications and source records attributed to David W. Wolfson.

6 recordsLinked to original sources

USGS2026

Waterfowl move less in heterogeneous and human-populated landscapes, with implications for spread of avian influenza viruses

Animal movements contribute to the spread of infectious diseases and are driven in part by environmental conditions. We investigated the links among the environment, animal movement, and infectious disease dynamics in waterfowl, which are among the primary wildlife hosts of avian influenza viruses. By combining telemetry data on 4606 individuals from 26 waterfowl species with data on land cover, weather, and vegetation, we found that waterfowl moved less in areas of higher land cover heterogeneity and higher human population density. Moreover, predicted waterfowl movement distances were weakly but positively correlated with distances between detections of H5N1 highly pathogenic avian influenza in wild waterfowl, suggesting that environmental conditions might contribute to the spread of this disease via their effects on bird movements. By considering wildlife movements alongside other drivers of infectious disease dynamics, such as livestock production and human mobility, we move closer to predicting outbreaks and informing interventions.

Ecology Letters↗

USGS2026

Interacting effects of human presence and landscape modification on birds and mammals

Sustainable human–wildlife coexistence requires a mechanistic understanding of the many ways that humans affect animals. However, progress is hampered by the lack of accessible data measuring the dynamic presence of people. Here, we leverage mobile-device data to disentangle how human presence and landscape modification differentially influence the use of geographic and environmental space for 37 mammal and bird species across the United States. Human presence affected more than 65% of species, with substantial variation across species. For ~60% of species that responded to human activities, the effects were interdependent—animals tended to react more strongly to human presence in less modified habitats. Our results demonstrate that human presence and landscape modification have complex combined effects on wildlife, which need to be considered for effective management.

Science↗

USGS2024

High variability of migration strategies in a re-established Cygnus buccinator (Trumpeter Swan) population

The Interior Population (IP) of Cygnus buccinator (Trumpeter Swan), formerly extirpated by market hunting, was re-established in eastern North America by releasing individuals from both migratory and non-migratory populations. Their current annual movement patterns are largely unknown. We deployed 113 GPS-GSM transmitters on IP C. buccinator in 6 U.S. states and 1 Canadian province across the current IP breeding range. Using data from 252 “swan-years”, we estimated migration phenology using piecewise regression models fit to each yearly time-series of displacement from the breeding site. We fit a latent-state model to characterize population-level associations between breeding latitude and maximum extent of migration, and linear mixed models to quantify associations between individual characteristics (e.g., breeding status, sex) and migration phenology. At the individual level, 59% of swans moved to distant nonbreeding-period areas (long-distance migration, defined as moving >100 km from the breeding site), 16% exhibited regional migration (25–100 km from breeding site), 19% exhibited non-migratory but local movements (<25 km from breeding site), and 6% exhibited multiple migration strategies. Swans breeding at more-northern latitudes departed their territories earlier in autumn, returned later in the spring, and migrated farther from their breeding territories than those breeding at more southern latitudes. Although the population-level association between migration extent and breeding latitude was positive, some individuals remained close to the location of their breeding site during the nonbreeding period. Breeding swans departed later in the autumn than non-breeders, but breeding status did not have a strong association with arrival in the spring. IP C. buccinator are partial migrants, with a continuum of strategies that vary latitudinally, from local movements to long-distance migration. Much of the variability in movement patterns related to factors tied to natural history demands (e.g., breeding status) and response to environmental conditions (e.g., through associations with breeding latitude).

Ornithology↗

USGS2022

Using piecewise regression to identify biological phenomena in biotelemetry datasets

1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status. 2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval. 3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces ; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan. 4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures.

Journal of Animal Ecology↗

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

Juvenile Sandhill Cranes exhibit wider ranging and more exploratory movements than adults during the breeding season

Sandhill Cranes Antigone canadensis exhibit delayed sexual maturity and breeding, and therefore juvenile Cranes searching for suitable territories to occupy have different ecological constraints on movements than adults, which must defend a territory and raise young. We used fine-scale GPS telemetry data to characterize and compare movements of adult and juvenile Cranes near the boundary between two populations in Minnesota, USA, from arrival on natal areas in the spring until staging prior to autumn migration. Juvenile and adult Cranes had marked differences in movement patterns throughout the breeding season. Juveniles were more likely than adults to display long-distance movements: they revisited areas less frequently throughout the breeding season and they had lower average residence times.

Minnesota↗