A Framework for Quantifying Urban Predator–Prey Dynamics in Birds

Thesis Abstract

Urban landscapes increasingly reshape interactions between avian predators and prey, altering access to resources, migration cues, and breeding success. This study addresses the gap in integrative frameworks that quantify predator–prey dynamics for birds within anthropogenic environments, where conventional ecological models often fail to capture rapid habitat modification, artificial lighting, and human disturbance. The aim is to develop a transferable framework that quantifies spatially explicit predator-prey interactions in metropolitan avifauna, linking behavioural responses to demographic outcomes and informing urban wildlife management. Specific objectives are (1) to identify key avian predator guilds (e.g., peregrine falcon Falco peregrinus, corvids Corvus spp.) and prey assemblages (passerines, granivores) across urban–rural gradients; (2) to quantify encounter rates, predation pressure, and prey survival using integrated multi-source data; (3) to model how habitat structure, food availability, and human activity modulate predator–prey dynamics; (4) to evaluate the predictive performance of a spatially explicit dynamic framework against independent datasets; and (5) to derive management-relevant indicators for urban biodiversity conservation. The methodology adopts a mixed-methods, multi-site design across four mid-sized cities with varying urban density and green-space configuration. The population of interest comprises breeding and foraging birds and their primary predators within defined urban boundaries. A stratified random sampling approach yields 40 urban plots per city, with at least 200 focal observations per plot seasonally, over two annual cycles. Data collection employs (a) standardized point-count surveys and motion-activated camera traps to estimate predator density, prey abundance, and successful predation events; (b) GPS telemetry on a subset of 30 predators and 60 prey individuals to derive movement corridors, encounter probability, and habitat preference; (c) citizen-science supplementary records to extend temporal coverage; and (d) habitat metrics derived from high-resolution aerial imagery and geographic information systems (GIS) to quantify building density, green-space fragmentation, noise levels, artificial lighting, and food subsidies (e.g., bird feeders). Instruments include calibrated sound recorders for acoustic harassment metrics and predator call catalogs for species-specific interactions. Data analysis proceeds through a hierarchical Bayesian framework that integrates encounter data, survival rates, and habitat covariates, complemented by generalized linear mixed models (GLMMs) to test fixed effects of habitat features on predation pressure. A spatially explicit dynamical model is specified to simulate predator–prey interactions, using state-space formulations to account for observation error and process variability. Model selection relies on Deviance Information Criterion (DIC) and cross-validation with holdout years. The theoretical foundation draws on optimal foraging theory, the predator–prey arms race paradigm, and landscape ecology principles, with explicit incorporation of urban-specific factors such as light-attraction effects and artificial refuge loss. Validity and reliability are ensured via pilot testing of instruments, inter-observer calibration, and triangulation across camera, telemetry, and survey data. Ethical considerations include adherence to wildlife handling guidelines, minimization of disturbance during tagging, and data privacy for citizen-science contributions. Expected findings indicate that predator density and predation rates exhibit non-linear responses to green-space connectivity, with urban cores showing elevated encounter rates but reduced prey survival due to altered prey behavior and microhabitat use. Telemetry is anticipated to reveal corridor-based movement that increases encounter probability during crepuscular periods, while acoustic disturbance is predicted to dampen prey vigilance. The framework is expected to outperform non-integrated models in predicting short- and medium-term prey survival across sites, providing transferable indicators such as habitat suitability indices, encounter rate metrics, and predicted prey population trajectories under varying urban development scenarios. This study contributes to knowledge by offering a robust, transferable framework that integrates behavioral ecology, landscape metrics, and demographic outcomes to quantify urban predator–prey dynamics in birds. It informs urban biodiversity management by delivering decision-support tools that optimize green-space design, reduce predation risk where necessary, and enhance urban bird conservation. Conclusions emphasize the need for adaptive urban planning that preserves habitat connectivity while mitigating disturbance, and recommendations include targeted habitat restoration, moderated artificial lighting, and community-based monitoring to sustain balanced urban avifauna populations.