Detecting the signature of environmental change in partially observed ecological networks

In the face of rapid and widespread environmental change, developing a thorough understanding of ecosystems and the species and interactions within them is vital to conservation and management. Characterising ecosystems through network structure and dynamics enables an understanding of how different components of ecosystems interact, how these interactions influence stability of communities, and how ecosystems might be conserved (Harvey et al., 2017). This project will use network analysis of a forest ecosystem in combination with a long-term Lepidopteran data set to develop an understanding of ecological dynamics in a temperate rainforest ecosystem.

Forest ecosystems are vulnerable to the effects of climate change, in addition to other pressures including logging and grazing. Scotland’s temperate rainforest is a rare ecosystem in the UK today, requiring further study and conservation efforts. Lepidoptera, particularly moths, are an important component of terrestrial ecosystems, playing vital roles as herbivores, prey, and pollinators, and potentially acting as indicators of ecosystem condition (Bachand et al., 2014). Changes in moth diversity and species composition over time have been documented in the UK, and could provide indications of wider ecological change (Coulthard et al., 2019). The study of trophic and pollination networks incorporating moths provides important insight into the structure and stability of forest ecosystem networks.

Long-term ecological monitoring is important to understand how ecosystems change over time, particularly in light of climate change. Combining long-term ecological monitoring with network-level analyses could provide a powerful method of detecting and predicting ecological change. The Rothamsted Insect Survey has had a light trap collecting macro moths at the Scottish Centre for Ecology and the Natural Environment (SCENE) since 1968, creating a valuable long term data set on moth abundance and diversity spanning more than 50 years. During this time, close to half a million moths across 369 species have been collected. Ecological data over such a long time-series is uncommon and provides a valuable opportunity to examine changes and fluctuations in an important ecosystem, the ancient oak woodland around Loch Lomond. Analysis of these data in 2003 found increasing species richness and changes in phenology of some species (Salama, Knowler & Adams, 2007). However, there has as yet been little opportunity to examine how these changes may have affected or been driven by the wider ecosystem.

This project will characterise the current ecological networks comprising trophic interactions, seed dispersal, and pollination relationships involving Lepidoptera and the organisms directly linked to them in the ecosystem, together with their dependencies on woodland vegetation phenology.

The project is organized around two primary objectives:

1) To characterise the trophic network of the temperate rainforest around Loch Lomond that is centred on Lepidoptera

2) To use historical data on one trophic level (moths) in combination with models of the structure of the current network to make inferences about the structure and dynamics of the network in the past, providing insight about how the wider ecosystem may have changed.


Moth herbivory interactions will be reconstructed from established records of preferred food plants of moth species, and contemporary field surveying. Moths collected by light trapping will be sampled to detect and identify pollen grains to construct a moth pollination network. Visitation and herbivory/frugivory networks will be constructed using focal foraging observations of birds visiting or foraging on plants. Diet analysis of faecal samples of birds and mammals with metabarcoding may be used to determine which moth or plant species they feed on, contributing to the construction of a trophic network model.

To further investigate the implications of changes in the moth community for the wider network, changes over time in abundance or presence of moths with different ecological roles or functional traits will be examined. Any relationships of these trends with changing environmental conditions, and the potential implications of such changes in the moth community for the wider network will be examined.

A range of statistical techniques will be applied to construct network models, potentially using Bayesian networks or Generalised Lotka-Volterra networks (Milns, Beale & Smith, 2010; Liao, Xavier & Zhu, 2020). These models will be used to examine contributions of individual species to network structure and complexity; effects of taxonomic diversity and species richness on trophic interaction diversity and network connectivity; and implications for network stability. Bayesian network analysis will be used to make inferences about partially observed historical networks using contemporary network models (Mitchell et al., 2021).

Project Timeline

Year 1

– Literature review
– Preliminary analysis of existing long-term moth data
– Study design and pilot study
– Data collection to construct models of trophic and pollination networks

Year 2

– Network modelling
– Further Data collection to construct models of trophic and pollination networks
– Refining techniques to use current network models to make inferences about past networks

Year 3

– Possible further data collection
– Network modelling
– Combine current network models with historical data to make inferences about past networks
– Writing thesis

Year 3.5

– Writing thesis

& Skills

The student will have the opportunity to develop skills in study design and scientific communication by contributing to the planning of their research and writing up results for publication. They will also develop the ability to use a range of statistical analysis methods to construct ecological network models and use these to make inferences about partially observed networks, including machine learning and artificial intelligence. The long-term moth data set will provide the opportunity to develop skills in analysing time series data. The project will require the student to develop skills in identification of Lepidoptera, pollen grains, and woodland birds and plants, in addition to a range of field surveying techniques. There will be opportunities to work alongside other research organisations, with possibilities for collaboration in data collection, sample processing, and analysis. The location of the study site in a SSSI and national park will provide opportunities to work with conservation and land management organisations, developing skills in interdisciplinary work and applying research to practical conservation.

References & further reading

achand, M., Pellerin, S., Côté, S.D., Moretti, M., et al. (2014) Species indicators of ecosystem recovery after reducing large herbivore density: Comparing taxa and testing species combinations. Ecological Indicators. 38, 12–19.
Coulthard, E., Norrey, J., Shortall, C. & Harris, W.E. (2019) Ecological traits predict population changes in moths. Biological Conservation. 233 (March), 213–219.
Harvey, E., Gounand, I., Ward, C.L. & Altermatt, F. (2017) Bridging ecology and conservation: from ecological networks to ecosystem function. Journal of Applied Ecology. 54, 371–379.
Liao, C., Xavier, J.B. & Zhu, Z. (2020) Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge. BMC Ecology. 20 (1), 1–15.
Milns, I., Beale, C.M. & Smith, V.A. (2010) Revealing ecological networks using Bayesian network inference algorithms. Ecology. 91 (7), 1892–1899.
Mitchell, E.G., Wallace, M.I., Smith, V.A., Wiesenthal, A.A., et al. (2021) Bayesian Network Analysis reveals resilience of the jellyfish Aurelia aurita to an Irish Sea regime shift. Scientific Reports. [Online] 11 (1), 1–14. Available from: doi:10.1038/s41598-021-82825-w.
Salama, N.K.G., Knowler, J.T. & Adams, C.E. (2007) Increasing abundance and diversity in the moth assemblage of east Loch Lomondside, Scotland over a 35 year period. Journal of Insect Conservation. 11 (2), 151–156.

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