Geology topics

Jillian R. Kilborn

Publications and source records attributed to Jillian R. Kilborn.

5 recordsLinked to original sources

USGS2025

DeepFaune New England: A species classification model for trail camera images in northeastern North America

The DeepFaune New England model classifies wildlife species in trail camera images, identifying 24 taxa from northeastern North America with high (97%) accuracy. The model was adapted from the DeepFaune model for identifying European wildlife, demonstrating the practicality of transfer learning across continents. The majority of training data is openly licensed, and the model itself is open source, enabling easy integration into camera trapping workflows. The open source software is available at ( https://code.usgs.gov/vtcfwru/deepfaune-new-england ), and has been further integrated into the PyTorch-Wildlife framework.

New England↗

USGS2024

Monitoring animal populations with cameras using open, multistate, N-mixture models

Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail-Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated four commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, and 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose ( Alces alces ) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy, and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental states are clearly identifiable. We discuss several avenues for future research and caveats for using multistate DM models for large-scale population monitoring.

New Hampshire, Vermont↗

USGS2023

A Great Escape: Resource availability and density-dependence shape population dynamics along trailing range edges

Populations along geographical range limits are often exposed to unsuitable climate and low resource availability relative to core populations. As such, there has been a renewed focus on understanding the factors that determine range limits to better predict how species will respond to global change. Using recent theory on range limits and classical understanding of density dependence, we evaluated the influence of resource availability on the snowshoe hare Lepus americanus along its trailing range edge. We estimated variation in population density, habitat use, survival, and parasite loads to test the Great Escape Hypothesis (GEH), i.e. that density dependence determines, in part, a species' persistence along trailing edges. We found that variability in resource availability affected density and population fluctuations and led to trade-offs in survival for snowshoe hare populations in the northeastern USA. Hares living in resource-limited environments had lower and less variable population density, yet higher survival and lower parasitism compared to populations living in resource-rich environments. We suggest that density-dependent dynamics, elicited by resource availability, provide hares a unique survival advantage and partly explain persistence along their trailing edge. We hypothesize that this low-density escape from predation and parasitism occurs for other prey species along trailing edges, but the extent to which it occurs is likely conditional on the quality of matrix habitat. Our work indicates that biotic factors play an important role in shaping species' trailing edges and more detailed examination of non-climatic factors is warranted to better inform conservation and management decisions.

New Hampshire, Vermont↗

USGS2021

Abiotic stress and biotic factors mediate range dynamics on opposing edges

Aim In the face of global change, understanding causes of range limits are one of the most pressing needs in biogeography and ecology. A prevailing hypothesis is that abiotic stress forms cold (upper latitude/altitude) limits, whereas biotic interactions create warm (lower) limits. A new framework – Interactive Range-Limit Theory (iRLT) – asserts that positive biotic factors such as food availability can ameliorate abiotic stress along cold edges, whereas abiotic stress can have a positive effect and mediate biotic interactions (e.g., competition) along warm limits. Location Northeastern United States Taxon Carnivora Methods We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a 6-year period (2014–2019) of six carnivore species across a broad latitudinal (42.8–45.3°N) and altitudinal (3–1451 m) gradient. Results We found that snow directly limits populations, but prey or habitat availability can influence range dynamics along cold edges. For example, bobcats ( Lynx rufus ) and coyotes ( Canis latrans ) were limited by deep snow and long winters, but the availability of prey had a strong positive effect. Conversely, snow had a strong positive effect on the warm limits of Canada lynx ( Lynx canadensis ), countering the negative effect of competition with the phylogenetically similar bobcat and with coyotes, highlighting how climate mediates competition between species. Main conclusions We used an integrated dataset that included competitors and prey species collected at the same spatial and temporal scale. As such, this design, along with a causal modeling framework (SEM), allowed us to evaluate community-wide hypotheses at macroecological scales and identify coarse-scale drivers of species' range limits. Our study supports iRLT and underscores the need to consider direct and indirect mechanisms for studying range dynamics and species' responses to global change.

New Hampshire, Vermont↗

USGS2018

Looking beyond wildlife: Using remote cameras to evaluate accuracy of gridded snow data

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS ) gridded predictions using data from three winters (2014–2016). Snow depth observations were correlated with SNODAS predictions for sites ( R 2 = 0.20) and regions ( R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer‐dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species’ responses to climate change.

New Hampshire, Vermont↗