Integrating multi-taxa biodiversity conservation into upland ecosystem service land-use models

This studentship will incorporate evidence for biodiversity outcomes across the fullest suite of priority (threatened, rare) taxa, into spatially-explicit land-use models, to optimise strategic management for multiple ecosystem services (ES). This addresses a key shortcoming of existing ES decision-support tools, that rely on inappropriate proxies without efficacy for biodiversity enhancement.

Results will directly support land-use policy by addressing how upland habitats in the UK can be best managed to protect biodiversity and mitigate climate change.

Current conservation has failed to reverse biodiversity declines. To resolve this crisis, decision makers must optimally target interventions using robust evidence of which management actions to undertake, where. Conservation policy does not effectively use existing large bodies of information on the distribution and requirements of the full complement of biodiversity (typically 1000s of species).

Land management strategies must balance multiple ES objectives including food, carbon sequestration, flood mitigation and biodiversity. Multiple initiatives attempt to spatially reconcile these outcomes, but previous models lacked comprehensive understanding of biodiversity distribution, requirements, and management outcomes.

This studentship will address the crucial question of how to best manage landscapes, developing novel analytical approaches to inform spatial prioritisation for competing land-uses. The student will analyse species distribution and trait databases across plants and invertebrates, building on biodiversity auditing methodologies previously developed at UEA, and develop stacked environmental species distribution models (SDMs) in R.

Multi-SDMs will be integrated with future land-use models recently developed by RSPB scientists, that quantify other ES. This will evaluate the optimality of plausible land-use transitions, examining trade-offs and additionalities across forestry, agriculture, moorland management and rewilding.

The student will receive one-to-one training from the supervisory team in Big Data, spatial modelling, study design and hypothesis testing, scientific writing, data visualisation and science communication. Working with RSPB nature delivery teams and private and public land managers in upland regions, will give in-depth understanding of land management policy. You will be encouraged to develop your own research ideas alongside the core project aims.

The successful applicant will have a degree in ecology, geography, biology or related discipline, demonstrable experience of R and GIS, and an enthusiasm for nature conservation.