Project overview: Agricultural expansion and intensification are major drivers of habitat fragmentation and biodiversity loss. Yet agricultural landscapes are not necessarily biodiversity deserts. Trees retained or planted within farms (TIFs) may provide habitat, food resources and movement pathways for wildlife. However, we know surprisingly little about which characteristics of tree-based agricultural systems make them effective for wildlife movement, and whether their contribution to connectivity depends more on the amount of tree cover, its spatial configuration, tree characteristics or the wider landscape context. This PhD (3.5 years) will investigate the wildlife connectivity potential of tree-based agricultural systems, combining biodiversity surveys, animal movement data, landscape modelling and remote sensing to understand how trees within agricultural landscapes influence wildlife movement and landscape connectivity.
The research forms part of the ERC-funded TIF-CER project awarded to Prof Pfeifer: From Forests to Fields to Sustainable Rural Landscapes: A Multiscale Framework for Quantifying Biodiversity and Climate Interrelationships. TIF-CER is establishing an international network of field plots across contrasting agroclimatic zones in the UK, Italy, Philippines, Tanzania, Chile and Tunisia. The project will work with local partners and research teams.
The project will use standardised biodiversity monitoring across the TIF-CER field network to examine how wildlife responds to tree-based farming systems. Methods may include: camera trapping; bird point counts and acoustic monitoring; environmental DNA; other standardised wildlife surveys; collection of species-level ecological and functional trait information. Species will be characterised according to ecological traits, habitat requirements and functional roles. Particular attention will be given to species that depend on tree resources and that are functionally important within agricultural landscapes.
Research questions: The PhD will address questions about biodiversity interactions with TIFs, including: How do tree density, morphology, age and spatial configuration influence wildlife habitat and connectivity? Do different types of TIFs (e.g. scattered trees, tree belts, riparian systems, agroforestry lines) differ in their connectivity potential? How does the connectivity of TIFs vary for species and functional groups with different habitat requirements and movement strategies? How does the surrounding agricultural landscape influence the ability of wildlife to move through TIFs?
Field research: A major component of the PhD will involve intensive in-situ field measurements in selected sites of the TIF-CER field network and integration of data from across the field network. Detailed movement data will be collected from selected bird species in the Philippines and accompanied by genome sequencing to investigate population genetic structure and gene flow across the fragmented landscape. The candidate will contribute to monitoring systems with contrasting tree densities and tree-based farming systems. They will develop spatial models of wildlife connectivity using approaches such as circuit theory and resistance-surface modelling.
The PhD will contribute to a broader objective of TIF-CER: understanding how biodiversity functions and processes operating at the scale of farms translate into outcomes at the scale of landscapes. The PhD will be embedded within an international research programme involving researchers in ecology, hydrology, conservation science, landscape ecology, soil science, emote sensing, genomics and environmental modelling. The project will engage with land managers and stakeholders to explore how connectivity findings can inform practical landscape management and tree-based restoration.
Training: The project provides training across several complementary areas:
- Understanding habitat fragmentation, ecological networks and connectivity.
- Wildlife monitoring: Camera trapping, acoustic monitoring
- Animal movement ecology: GPS tracking, habitat selection
- Spatial explicit modelling
- Remote sensing: UAV and Sentinel satellite data
- Conservation genomics
Candidate profile
We are looking for an enthusiastic candidate with a strong interest in wildlife ecology, conservation biology, landscape ecology, spatial ecology or a related discipline.
Essential criteria:
- a good degree in a relevant subject
- experience in field-based research
- experience in quantitative analysis and data interpretation using R
Experience in one or more of the following would be advantageous:
- wildlife survey methods or camera trapping
- GIS and spatial analysis
- R or other programming languages
- remote sensing
- landscape connectivity modelling
You do not need to have experience in all of these areas. The project is deliberately interdisciplinary and training will be provided. The results will contribute to understanding how tree-based farming systems can be designed to support climate adaptation, agricultural production and biodiversity conservation.