Rewilding: How does the cessation of livestock grazing affect upland food-webs?

Extensive ecosystem restoration is increasingly seen as being central to conserving biodiversity [1] and stabilizing the climate of the Earth [2]. Rewilding, defined as “the reorganisation of biota and ecosystem processes to set an identified social–ecological system on a preferred trajectory, leading to the self‐sustaining provision of ecosystem services with minimal ongoing management” [3], is increasingly being considered as an environmental management option in many British upland areas. For the past decades, policy and practice in the British uplands has primarily focused on food production (mainly livestock grazing) and forestry, with secondary goals of supporting biodiversity and providing additional ecosystem services. However, exiting the EU and the likely associated changes in subsidy regimes, combined with the UK government’s stated policy of “public money for public goods”, has made understanding the consequences of management decisions about the future of the uplands urgent [4]. Yet the scientific evidence required to make such decisions, in particular how rewilding will affect upland biodiversity (especially species of conservation concern), is lacking.

The Glen Finglas livestock grazing experiment was established in 2002 to examine how changes in sheep and cattle stocking densities affects upland bird and animal communities. Detailed monitoring of plants, invertebrates, mammals and birds is carried out regularly, providing a unique long-term dataset for examining how upland food-webs change as a result of reduced livestock grazing pressure, or when it is completely removed. After 15 years of experimental grazing treatments, significant changes in plant and animal communities became evident, with species such as bog asphodel, bracken and blaeberry benefitting from the removal of grazing [5] and a significant increase in bird species richness [6]. Thus, vegetation structural changes as a result of livestock removal is likely to affect other trophic levels within the food-web, but this is yet to be examined using the long-term Glen Finglas dataset.

This project will maximise the long-term Glen Finglas dataset by constructing highly-resolved species-interaction networks that incorporate plants, arthropods, mammals and birds using state-of-the-art techniques that account for uncertainty in the complex food-webs. Ecological networks not only describe the interactions between species, but also provide a way of examining the underlying structure of communities and the function and stability of ecosystems. Uniquely, it will investigate how livestock grazing treatments affect the structure, complexity and robustness (i.e., the attack tolerance of the network to species extinction) of upland food-webs, providing policy-relevant evidence on how changes in upland management affects ecosystem resilience. Furthermore, the project will incorporate much-needed temporal dynamics into the study of food-webs under different levels of perturbation, allowing novel insights into how biological networks ‘rewire’, which can then be used to predict restoration outcomes in adaptive network models [7].

The project has three interlinked objectives:
OBJ. 1: Construct upland food-webs consisting of plants, arthropods, mammals and birds based on observed and inferred species-interactions over a 20 year period.
OBJ. 2: Compare the structure, complexity and robustness of upland food-webs under different experimental grazing treatments.
OBJ. 3: Predict long-term management outcomes for the uplands using adaptive network models that incorporate long-term data

There will be opportunities for fieldwork at Glen Finglas with trained staff to improve network construction methods and/or test specific hypotheses devised by the student and supervisory team.

Impact summary: The Glen Finglas experiment is hosted by Woodland Trust Scotland and the results are used to guide management decisions across their estate (as well as other NGOs). Outputs from the experiment to date have informed Government agencies, in particular regarding livestock management for biodiversity. In the current context of rewilding, the project will provide a much-needed evidence base for decision making after the UK has exited the EU.

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Image Captions

plant-animal interactions at Glen Finagles,Aifionn Evans with snipe chick,Bird ringing at Glen Finglas,Meadow pipit -colour ringed,Glen Finglas


A replicated, randomized-block experiment consisting of six replicates of four treatments was initiated in 2003 at Glen Finglas, in central Scotland, with baseline data collected in 2002. Plots are each approximately 3.3 ha in size with altitudes ranging from 200–500 m above sea level. Treatments of [I] 9 ewes per plot (2.72 ewes ha-1), [II] 3 ewes per plot (0.91 ewes ha-1), [III] 2 ewes per plot (0.61 ewes ha-1), and [IV] ungrazed, were randomly allocated to plots within each block. Within each plot, plants, small mammals (field voles) and breeding birds are surveyed each year, with more intensive fieldwork (e.g., foliar arthropods) carried at regular periods, but not annually. Other data (e.g. foxes, nocturnal moths, carabid beetles) are available from more focussed studies that have occurred over the past 20 years.

The project will pool and process the existing plant and animal data that has been systematically collected over a 20 year period. Species-interactions will be determined based on observed interactions in the field (e.g., insect flower visitation, prey provisioned to nestlings), recorded interactions in public repositories (e.g., UKCEH Database of Insects and their Food Plants [DBIF]) and probabilistic models for species co-occurrence. Food-webs will be created for each plot and across years, following a similar methodology that was recently used to construct potential landscape-scale pollinator networks across Great Britain [8].

The project will test and a develop a range of contemporary stochastic and dynamic network analyses, especially Bayesian approaches, to examine the effects of livestock grazing pressure on network complexity, stability and robustness. It will evaluate the potential of Machine Learning to construct networks. The experimental design is well-suited for hierarchical modelling. The project will also evaluate the application of dynamic network models for predicting long-term management outcomes, with a particular focus on rewilding (i.e., when livestock are completely removed from upland habitats).

There are opportunities for fieldwork at Glen Finglas with staff from the James Hutton Institute (JHI), including a placement at JHI (Aberdeen) where the long-term data is curated and stored.

Project Timeline

Year 1

The student will be introduced to the 20-year dataset and receive training in food-web construction methods, Machine Learning and ecological network modelling. Some supplementary plant and animal sampling at Glen Finglas to fill identified ‘interactions gaps’ in the dataset.

Year 2

Validation of interactions based on species co-occurrence models. Modelling the impacts of livestock treatments on the upland food-web, focussing on network structure, complexity and robustness (contrasting qualitative and quantitative structures and using Frequentist and Bayesian approaches). Some hypothesis testing at Glen Finglas using a sub-component of the network. First publication.

Year 3

Training in adaptive network models, parameter testing and analysis using the long-term dataset. Forecast management outcomes using network predictively. Second publication. Communicate results to stakeholders, publics and academic community.

Year 3.5

Thesis writing and final publications

& Skills

The student will have full access to Newcastle’s SAgE Faculty Research Development Framework (RDF). This programme provides training in the four key domains of the Vitae RDF including: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence and impact.

Specifically, the student will benefit from working in a team consisting of applied ecologists (James Hutton Institute), network ecologists (Newcastle University) and ecological modellers (St. Andrews) on a novel and policy-relevant topic. The project will specifically provide skills in: 1) ecological census techniques; 2) network construction (including Machine Learning) and bioinformatics using Newcastle’s Rocket HPC; 3) bespoke network analysis and modelling; 4) science communication (for a range of audiences, including articles in The Conversation). Further training in statistical methods is available at JHI through Biomathematics and Statistics Scotland.

References & further reading

More information about the Glen Finglas experiment can be found here: https://www.hutton.ac.uk/research/departments/ecological%20sciences/research%20facilities/glen-finglas-grazing

References: [1] IPBES. 2019. IPBES Secretariat. [2] IPCC. 2019. World Meteorological Organization. [3] Pettorelli, N. et al. (2018). J. Appl. Ecol. 55: 1114– 1125. [4] Sandom C.J. et al. (2019), J. Appl. Ecol. 56: 266–73. [5] Pakeman, R. J. et al. (2019) J. Appl. Ecol. 56: 1794– 1805. [6] Malm et al. (2020). J. Appl. Ecol. 57: 1514-1523. [7] Raimundo, R.L. et al. (2018) Trends Ecol. Evol. 33, 664– 675. [8] Redhead, J.W. et al. (2018) Ecol. Lett. 21:1821-1832.

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