Empirical evidence that diversionary feeding increases productivity in ground-nesting birds

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

Abstract The recovery of predator populations can negatively impact other species conservation concern, leading to conflicts. Evidence-based solutions are needed resolve such conflicts without sacrificing hard-won gains for recovering species. Well-designed, large-scale field experiments provide the most rigorous evidence justify new forms intervention, but they notoriously hard implement. Further, monitoring scarce negative impacts is challenging, calling indirect and non-invasive methods. Uncertainties remain about whether observational adequately reflects true processes interest. Having conducted a well-designed, large-scale, diversionary feeding experiment that reduced artificial nest depredation, we evaluated this translated capercaillie productivity in same area. Using camera traps aimed at dust baths, non-invasively monitored hen over 3 years 30 1km 2 grid cells under randomised control (un-fed) treatment (fed) design. Diversionary significantly increased probability detected would have brood. did not change brooding season, indicating hens brood had failed due depredation rather than predation chicks. detecting with was 0.85 (0.65-0.94) fed locations, more double unfed which 0.37 (CI 0.2-0.57). average size time, differ between sites. This line natural mortality occurring independently feeding. Importantly, chance having areas predicted leads substantial increase overall – expected number chicks per end sampling season. just 0.82 (0.35 1.29) sites 1.90 (1.24 2.55) study provides compelling empirical positively affects productivity, offering an effective non-lethal solution increasingly common conflict where both prey afforded protection.

Language: Английский

Improving the integration of artificial intelligence into existing ecological inference workflows

Methods in Ecology and Evolution, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 26, 2024

Abstract Artificial intelligence (AI) has revolutionised the process of identifying species and individuals in audio recordings camera trap images. However, despite developments sensor technology, machine learning statistical methods, a general AI‐assisted data‐to‐inference pipeline yet to emerge. We argue that this is, part, due lack clarity around several decisions existing workflows, including: choice classifier used (e.g. semi‐ vs. fully automated); how confidence scores are interpreted; availability selection appropriate methods for drawing ecological inferences. Here, we attempt conceptualise workflow associated with automated tools ecology. motivate perspective using our experiences occupancy modelling monitoring data collected through passive acoustic trapping, priority areas future developments. offer an accessible guide support community navigating capitalising on rapid technological methodological advances. describe different error types arise from both sensor‐based classifiers themselves; handled at each stage workflow; finally, implications opportunities deciding step pipeline. recommend ‘black box’ like neural network classification algorithms should be embraced ecology, but widespread uptake requires more formal integration AI into inference workflows. Like broadly, however, successful development new pipelines is multidisciplinary endeavour input everyone invested collecting, processing, analysing data.

Language: Английский