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Guardians of the Crops: Harnessing Insectivorous Birds for Sustainable Farming and Food Security
Project Description
Insect-eating birds play a vital role in suppressing crop pests, reducing the need for chemical inputs, and supporting long-term food security. Yet in many rural communities across the Global South, these vital allies are often hunted, in part because their ecological and economic benefits are underappreciated. This PhD project seeks to demonstrate that insect-eating birds do far more good than harm, ultimately benefiting farmers and boosting crop yields. Set in rural Zambia, the study spans intensive and non-intensive farms and their adjacent habitats to: (i) characterise the diversity and structure of the insectivorous bird communities; (ii) identify major crop pests and measure their impact on yield; (iii) quantify how effectively birds suppress pests, and translate this into economic value; (iv) assess any ecosystem disservices associated to insectivorous birds; and (iv) document traditional farming practices, capturing how local knowledge define pathways or barriers to biodiversity-friendly practices. We will use focal observations, camera traps, and bioacoustic recordings analysed with AI-assisted methods to map out the complexity of crop-pest-insectivorous bird multilayer networks and their variation across farms and adjacent habitats (landscape ecology). Interviews with local communities will uncover perception, traditional knowledge, and barriers to biodiversity-friendly practice using natural language processing (NLP). Through a series of open, participatory workshops and community meetings, we aim to engage in a two-way dialogue to co-design best practices that promote sustainable farming, ensuring benefits for both people and nature.
Research themes
Project Specific Training
Field survey: Camera traps and sound recording methods – one-to-one instruction by supervisory team.
Data analyses: of a 4-5-day course on data analyses of images and sounds using AI-assisted methods.
Network analyses: one-to-one instruction by the supervisory team and 4-5 days course to program in R, if needed
NLP methods: 4-5 days course
Advance statistics using R or Python: one-to-one instruction by the supervisory team and 4-5 days course, if needed.
Potential Career Trajectory
This project equips you with field ecological expertise, policy-relevant insights, and AI/advanced data analysis skills using R or Python — a rare and valuable combination. That makes you competitive not only for academic roles but also for careers in conservation, agricultural development, government, NGOs, and tech-for-environment sectors.Examples of potential career pathways include:
(i) Academic pathways: Research and Teaching in Ecology and agroecology, sustainable agriculture, conservation biology. Combining field ecology + AI applications + Advanced quantitative skills would position you well for interdisciplinary departments. This combination of expertise will position you well to apply for competitive postdoctoral positions.
(ii) Agricultural and Development Organisations (FAO): designing sustainable food systems, technical advisors on nature-based pest management, agroecological practices, and sustainable development goals (SDG), including programs in Africa, and the Global South.
(iii) Government and Policy roles: Advising ministries of agriculture or environment on integrating biodiversity into farming policies. Evidence-based policymaking around pesticide reduction, ecosystem services, and food security.
(iv) Technology & Data Science Applications: for example applying AI skills (bioacoustics, NLP, computer vision) to environmental monitoring. Roles in tech companies, AI-for-good startups, or environmental data consultancies. Building decision-support tools for farmers, NGOs, or governments.
(IV) International Development & Community Engagement: Program officer or sustainability consultant in NGOs focused on rural livelihoods, climate resilience, or sustainable farming. Expertise in participatory approaches and co-design strengthens relevance for community-based work.
Project supervisor/s
Cristina Garcia
Department of Biological Sciences
Royal Holloway University of London
Cristina.Garcia@rhul.ac.uk
Laszlo Bogre
Department of Biological Sciences
Royal Holloway University of London
l.bogre@rhul.ac.uk