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The wader package provides functions to download and generate summaries for the count, nesting, indicator, and weather data from the Wading Bird Project. The Wading Bird Project is a long-term (and ongoing) monitoring site in the Everglades water conservation areas. The raw data files can be found at https://github.com/weecology/evergladeswadingbird. 

The edenR package provides functions to retrieve, process and summarize the EDEN water depth data. The data begin in 1991 and are continuously updated. 

Abstract The challenges of monitoring wildlife often limit the scales and intensity of the data that can be collected. New technologies—such as remote sensing using unoccupied aircraft systems (UASs)—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 developed 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 review of the same imagery shows that our primary approach identifies nests with comparable performance to human review, 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 review 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 showed that automating nest detection, a key first step toward 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. 

Abstract Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long‐term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts. 

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. 

Abstract Regional long‐term monitoring can enhance the detection of biodiversity declines associated with climate change, improving future projections by reducing reliance on space‐for‐time substitution and increasing scalability. Rodents are diverse and important consumers in drylands, regions defined by the scarcity of water that cover 45% of Earth's land surface and face increasingly drier and more variable climates. We analyzed abundance data for 22 rodent species across grassland, shrubland, ecotone, and woodland ecosystems in the southwestern USA. Two time series (1995–2006 and 2004–2013) coincided with phases of the Pacific Decadal Oscillation (PDO), which influences drought in southwestern North America. Regionally, rodent species diversity declined 20%–35%, with greater losses during the later time period. Abundance also declined regionally, but only during 2004–2013, with losses of 5% of animals captured. During the first time series (wetter climate), plant productivity outranked climate variables as the best regional predictor of rodent abundance for 70% of taxa, whereas during the second period (drier climate), climate best explained variation in abundance for 60% of taxa. Temporal dynamics in diversity and abundance differed spatially among ecosystems, with the largest declines in woodlands and shrublands of central New Mexico and Colorado. Which species were winners or losers under increasing drought and amplified interannual variability in drought depended on ecosystem type and the phase of the PDO. Fewer taxa were significant winners (18%) than losers (30%) under drought, but the identities of winners and losers differed among ecosystems for 70% of taxa. Our results suggest that the sensitivities of rodent species to climate contributed to regional declines in diversity and abundance during 1995–2013. Whether these changes portend future declines in drought‐sensitive consumers in the southwestern USA will depend on the climate during the next major PDO cycle.