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How do species interactions determine species ranges?

Project Description

Location, University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE .

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP). The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners: British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see

Project details

It is clear that environmental conditions alone cannot explain species geographic ranges. Biotic interactions (BIs) are widely suggested as alternative explanations, but it is unknown how often, in what situations, and in what ways BIs predominantly affect species ranges. Addressing this challenge is one of the most central aims of ecology, and is a major ‘missing piece’ in efforts to understand global biodiversity patterns and forecast biodiversity responses to environmental change.

This studentship will take a highly novel approach, investigating how abiotic conditions alter two key components of BI strength: frequency and intensity1,2. These two components vary across geographic space and with environmental conditions, leading to predictable, but unstudied, effects on species’ ranges1. Much research in this area is hamstrung by limited data on BI strength and species’ ranges, which are taxonomically and geographically biased and cannot infer causality 3.The studentship will therefore take a theoretical approach to develop general principles that can be tested with empirical data.

Project Aims and Methods

a) Investigate the ecological and evolutionary parameters and environmental scenarios that permit BIs to structure species’ ranges. Use Lotka-Volterra (LV) models of two interacting species in which the strength of the interaction depends on species-specific parameters drawn from literature-informed expectations1,4, . LV models will be solved for specified birth and death parameters to identify a feasible range of interaction strengths.

b) Measure the potential magnitude of BI effects on species’ ranges. Use an Integro Difference equation (IDE) approach, in which IDEs include biotic and abiotic effects on population growth rates, drawing parameter values from a) and employing realistic dispersal capacities5. IDEs for multiple species will be simulated simultaneously in landscapes based on real topographic and vegetation maps.

c) Predict taxa and ecosystems where BI effects on species’ ranges could be stronger than environmental effects. Look for taxa with ecological traits and geographic regions with environmental gradients that correspond to parameters identified in a).

d) Test predictions from c) with empirical data. Use joint Species Distribution Models6, meta-analysis1, and existing demographic datasets(e.g. 7) to search for the predicted BI effects on species’ ranges.


• Regan Early - Joint Species Distribution Modelling, collating large datasets, advanced frequentist statistics, coding in R and Python, GIS, meta-analysis, biodiversity concepts, science presentation.

• Bram Kuijper – Analysis of species interaction models (e.g., Lotka Volterra models), evolutionary game theory, simulation models in C++, eco-evolutionary models, evolutionary concepts.

• Steven White – Analysis of population and spread models, advanced computational techniques, mathematical analysis, coding in multiple languages, population dynamics, IDE modelling

• External training - Bayesian statistics, phylogenetic analysis, IDE Modelling.

The student will attend FABio, the lead supervisor’s biweekly research group. FABio is a ‘community of practice’, where members from undergraduate to academic share literature, field and computational analytical skills, and present their research developments to each other.

The student will also attend international, inter-disciplinary, working groups, via the lead supervisor’s current commitments and long-term collaborations, in Spain and the USA. The student will learn how to integrate data from a wide range of taxa and disciplines (e.g. including agricultural ecology, disease, and palaeoecology), and learn how to jointly develop conceptual as well as empirical manuscripts.

Funding Notes

“NERC GW4+ funded studentship available for September 2019 entry. For eligible students, the studentship will provide funding of fees and a stipend which is currently £14,777 per annum for 2018-19.


Students from EU countries who do not meet the residency requirements may still be eligible for a fees-only award but no stipend. Applicants who are classed as International for tuition fee purposes are not eligible for funding.”


1. Early R & Keith S. (2019) Geographically variable biotic interactions and implications for species ranges. Global Ecol Biogeogr, In press.
2. Louthan AM, et al. (2015) Where and When do Species Interactions Set Range Limits? Trends in Ecology & Evolution; 30(12), 780 – 792.
3. Dormann CF, et al. (2018) Biotic interactions in species distribution modelling: 10 questions to guide interpretation and avoid false conclusions. Global Ecol Biogeogr, 00:1 –13.
4. Emmerson M, et al. (2004) How does global change affect the strength of trophic interactions? Basic and Applied Ecology, 5(6): 505-514
5. Morrison, L., et al. (2018). "Species traits suggest European mammals facing the greatest climate change are also least able to colonize new locations." Diversity and Distributions 24(9): 1321-1332.
6. Pollock LJ, et al. (2014), Understanding co‐occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM). Methods Ecol Evol, 5: 397-406. doi:10.1111/2041-210X.12180
7. Hemrová, L., et al. (2017). "Drivers of plant species’ potential to spread: the importance of demography versus seed dispersal." Oikos 126(10): 1493-1500.

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