Biology subjects

Ernest, S. M.

Publications and source records attributed to Ernest, S. M..

4 recordsLinked to original sources

bioRxiv2025

Using time-series remote sensing to identify and track individual bird nests at large scales

The challenges of monitoring wildlife often limits the scales and intensity of the data that can be collected. New technologies - such as remote sensing using unoccupied aircraft systems (UAS) - can collect information more quickly, over larger areas, and more frequently than is feasible using ground-based methods. While airborne imaging is increasingly used to produce data on the location and counts of individuals, its ability to produce individual-based demographic information is less explored. Repeat airborne imagery to generate an imagery time-series provides the potential to track individuals over time to collect information beyond one-off counts, but doing so necessitates automated approaches to handle the resulting high-frequency large-spatial scale imagery. We develop an automated time-series remote sensing approach to identifying wading bird nests in the Everglades ecosystem of Florida, USA to explore the feasibility and challenges of conducting time-series based remote sensing on mobile animals at large spatial scales. We combine a computer vision model for detecting birds in weekly UAS imagery of colonies with biology-informed algorithmic rules to generate an automated approach that identifies likely nests. Comparing the performance of these automated approaches to human assessment of the same imagery shows that our primary approach identifies nests with comparable performance to human photo assessment, and that a secondary approach designed to find quick-fail nests resulted in high false positive rates. We also assessed the ability of both human photo assessment and our primary algorithm to find ground-verified nests in UAS imagery and again found comparable performance, with the exception of nests that fail quickly. Our results show that automating nest detection, a key first step towards estimating nest success, is possible in complex environments like the Everglades and we discuss a number of challenges and possible uses for these types of approaches.

ecology↗

bioRxiv2024

Near real-time monitoring of long legged wading birds using uncrewed aircraft systems and computer vision

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real-time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end-to-end workflow for conducting near real-time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual level species data using a Retinanet-50 object detector, post-processed, archived, and presented on a web-based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species-level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post-survey processing, data on the populations of these species is available in near real-time (< 1 week from the date of the survey) providing information at the time-scales needed for ecological forecasting and active management.

ecology↗

bioRxiv2019

Temporal changes in species composition affect a ubiquitous species views of patch quality

Metacommunity theory, particularly the patch dynamics archetype, suggests that an organisms perspective of patch quality can depend solely on the local competitive environment. Across landscapes, however, shifts in species composition often co-occur with shifts in habitat, making it difficult to disentangle the role of competitors and environment on assessments of patch quality. Using 26 years of rodent community time-series data, we show that perception of patch quality by a small, ubiquitous granivore (Chaetodipus penicillatus) shifted with both spatial and temporal changes in species composition. In the mid-1990s, C. baileyi, a novel competitor, colonized and the study site. C. penicillatus patch preference shifted with increasing abundance of C. baileyi, including corresponding changes in estimated survival, probability of movement between patches, and the arrival of new individuals in patches. Changes in energy use on patches due to the establishment of C. baileyi point to a potential mechanism for the differences in patch quality perceived by C. penicillatus. These results demonstrate that experimental time-series data can be used to examine how changes in species composition and, specifically, changes in the competitive landscape, can affect perception of patch quality and patch preference.

ecology↗

bioRxiv2018

The Portal Project: a long-term study of a Chihuahuan desert ecosystem

This is a data paper for the Portal Project, a long-term ecological study of rodents, plants, and ants located in southeastern Arizona, U.S.A. This paper contains an overview of methods and information about the structure of the data files and the relational structure among the files. This is a living data paper and will be updated with new information as major changes or additions are made to the data. All data - along with more detailed data collection protocols and site information - is archived at: https://doi.org/10.5281/zenodo.1215988.

ecology↗