A virtual ecologist approach for improving methods in movement ecology


As human encroachment into natural systems continues, we must develop ways to reconcile human and animal needs while protecting biodiversity and human health. Studying animal movement provides incredible insights into their requirements [1, 2] as well as highlighting anthropogenic impacts [3, 4] and potential conflict areas [5, 6]. Insights from movement ecology are frequently incorporated into conservation plans [7]; therefore, it is imperative we fully grasp the generalisability and robustness of these findings.

Low sample sizes, localised studies, and a growing diversity of analytical approaches could all be limiting generalisability and replicability in movement ecology, but assessing the replicability of ecological studies is costly and disincentivised by the current publication system [8], despite a clear agreement on replication’s value. Movement ecology in particular, with its rapid growth and reliance on expensive telemetry devices, has yet to be subjected to rigorous replicability efforts.

To avoid further costs to both animals and researchers associated with repeat studies, we can leverage a virtual ecologist approach to explore a key component implicated in undermining replicability of results – researcher degrees of freedom [9]. Researcher degrees of freedom, or analytical flexibility, can be used to generate findings that better fit within the publishing incentive system, namely narratively-coherent significant results. However, these incentives can reward underpowered extreme results over robust, more reliable but less glamorous studies [10, 11]. A virtual ecology approach will reveal what options are available to researchers when assessing movement ecology data, and via the implementation of a multiverse analysis [12], identify what choices can markedly impact results from a known simulated truth.

Research Objectives

The foundation of this project is the development of an agent-based model to simulate animal movement, integrating several key components: spatial covariates (e.g., habitat and connectivity), central tendency (i.e., home range), behavioural states (e.g., resting, foraging, and dispersal), and interspecific & intraspecific relationships (e.g., attraction & avoidance). The model will then be used to explore how researcher choice impacts findings, and how these findings deviate from parameters used to characterise the simulated data. Once the impacts of analysis choice are known, the student will apply them to a real world scenario and illustrate the utility of multiverse analysis in displaying the robustness of results.

The project will have three main objectives.

1. Develop an openly available animal movement simulation package in R using C++
2. Use the developed simulation to run a multiverse of movement analyses to demonstrate researcher choice impacts.
3. Complete a meta-analysis informed by the multiverse approach with the goal of better illustrating the uncertainty surrounding a
particular conservation question.

The project will result in an open-source movement modelling R package that can be used in conjunction with a conceptual framework to help inform movement ecology study design, as well as retroactively explore the robustness of movement ecology findings. The R package will have applications beyond study design, allowing exploration of theoretical movement ecology processes and our ability to detect the effects of those processes in movement data.

The student should have a background or strong interest in computer programming (ideally in R, C, or C++), data analysis, and movement ecology. The student will benefit from supervisor expertise in theory, agent-based modelling, conservation biology and environmental sciences.


To address the research objectives of this project, the student will interact frequently with supervisors at the University of Stirling, Newcastle University, and the University of Glasgow. The student will joint the Evolving Organisms research group at the University of Stirling.

The student will build agent-based models by writing their own code, and will cultivate experience in the development of scientific software and the virtual ecologist approach. Coding will be done in collaboration with the lead supervisor, and all software developed will be published open access.

Finally, the student will present their research at national and international conferences and the University of Stirling’s BES student symposium, and be encouraged to publish their results in international peer-reviewed journals.

Project Timeline

Year 1

Exploration of existing movement modelling approaches, literature review, and modelling skills development (R, C++, git). Conceptual development of movement model framework.

Year 2

Software development, planning of multiverse approach.

Year 3

Apply multiverse approach to simulated data and an appropriate real world conservation question, presentation at national and international conferences.

Year 3.5

Writing and completion of thesis.

& Skills

During this project the student will learn two programming languages (R and C++) and associated software development skills, namely R package creation, software documentation, version control (git), and development of reproducible working environments.

Multiverse analysis will require the student to develop data management skills adequate for managing a large complex dataset, as well as a deep understanding of a diversity of movement data analysis methods. Later stages of the project will provide the student with opportunities to develop meta-analytical and evidence synthesis skills.

Meta-analysis stages will build collaborations between the student and movement data collectors, in addition to those already present between the collaborating institutions.

Finally, the student will present their research at national and international conferences and the University of Stirling’s BES student symposium, and be encouraged to publish their results in international peer-reviewed journals.

References & further reading

[1] Doherty, TS, et al. 2019. Animal movement varies with resource availability, landscape configuration and body size: A conceptual model and empirical example. Landscape Ecology, 34:603-614.[2] Noonan, MJ, et al. 2020. Effects of body size on estimation of mammalian area requirements. Conservation Biology, 34:1017-1028.[3] Tucker, MA, et al. 2019. Large birds travel farther in homogeneous environments. Global Ecology and Biogeography, 28:576-587.[4] Tucker, MA, et al. 2018. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science, 359:466-469.[5] Marshall, BM, et al. 2018. Hits close to home: Repeated persecution of king cobras (Ophiophagus hannah) in northeastern Thailand. Tropical Conservation Science, 11:194008291881840.[6] Valeix, M, et al. 2012. Behavioural adjustments of a large carnivore to access secondary prey in a human-dominated landscape: Wild prey, livestock and lion ecology. Journal of Applied Ecology, 49:73-81.[7] Fraser, KC. 2018. Tracking the conservation promise of movement ecology. Frontiers in Ecology and Evolution, 6:150.[8] Schnitzer, SA & WP Carson. 2016. Would Ecology Fail the Repeatability Test? BioScience, 66:98-99.[9] Zurell, D. 2010. The virtual ecologist approach: Simulating data and observers. Oikos, 119:622-635.[10] Barto, EK & MC Rillig. 2012. Dissemination biases in ecology: Effect sizes matter more than quality. Oikos, 121:228-235.[11] Brembs, B. 2019. Reliable novelty: New should not trump true. PLOS Biology, 17:e3000117.[12] Steegen, S. 2016. Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11:702-712.

Further Information

To apply, please email Dr Duthie (CC Dr Mill, Prof Matthiopoulos, and Prof Park), with (1) a one page cover letter indicating why you are interested in this project, (2) your CV with contact information for two references, and (3) full transcripts of previous qualifications obtained. The deadline for application is 8 JAN 2021 (17:00 GMT). For eligible candidates, funding is available to cover tuition fees, stipend and research costs. However, please note that this project is in competition with others for funding, and success will depend on the quality of applications received.

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