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Songbird reproductive success can decline from consuming mercury-contaminated aquatic insects, but assessments of hydrologic conditions influencing songbird mercury exposure are lacking. We monitored breast feather total mercury (THg) concentrations and reproductive success in the U.S. federally listed endangered Cape Sable Seaside Sparrow (CSSS: Ammospiza maritima mirabilis) over three breeding seasons in the Florida Everglades. We used model comparison to explore the influence of annual hydrologic variation on adult CSSS THg concentrations, and tested mercury effects on individual reproductive success (individuals’ mate status, apparent nest success, and total productivity) that were scaled to estimates on population productivity using a demographic model. We identified four hydrologic models that explained annual variation in adult THg concentrations, with the top model showing a negative association between THg concentrations and drought length of the previous breeding season and a positive association between THg concentrations and dry-season water recession rate (model adjusted R2 = 0.82). Adult male mating probability declined by 63% across the range of THg concentrations observed. We found no mercury effect on CSSS nest success or total productivity. However, demographic modeling suggested the reduced mating could produce a 60% decrease in population productivity compared to a scenario with no THg impact. Our results suggest that CSSS mercury exposure is influenced by local hydrologic conditions that can increase early breeding failure (lack of breeding initiation) and potentially limit population productivity. This study is the first to describe CSSS mercury exposure and its potential reproductive costs at the individual and population levels. 

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.