Biodiversity impacts of future climate and societal change scenarios

Overview

Predicting how biodiversity may be impacted by human actions in the future has focussed on climate change scenarios. But climate is not the only driver of species’ abundances and distributions. Many aspects of human society and activities, including severity of resource extraction and use, population size, land use and its intensity, food and diets, built infrastructure and transport networks, recycling and pollution, and social attitudes to sustainability and conservation, have major impacts on ecosystem degradation and biodiversity loss. Changes in human society will interact with climate change to profoundly affect the landscapes species live within, but this has not been accounted for in predictions of biodiversity impacts. This project will use our recent projections of UK climate and societal change under different scenarios that explore variation in greenhouse gas emissions and ways in which society might change in terms of resource use and social structures (Pedde et al 2020). This has produced UK maps of climate, land cover, and intensity of land use, and how these would change over the next decades under different scenarios. These modelled data and maps are at high spatial and temporal resolution, and present a unique opportunity to model biodiversity change under a wide range of future drivers, which this studentship will address.

The idea is to model how different types of species will increase, contract, or re-distribute their UK ranges under the different scenarios. For example, under the scenario of a future with low rates of climate change, nature conservation a priority, and strong global links, we might expect many natives to expand their ranges, but introductions of new species and invasives may also be facilitated. Under a scenario of rapid climate change and a society that exploits resources while reducing trade links, we might expect native species to decline as the climate and landscape becomes inhospitable, but few introductions of pest and diseases. Exploring these scenarios allows us to understand the range of outcomes for biodiversity and the key threats. One can model range shifts and contraction using, e.g., metapopulation or population spread models (Chapman et al. 2016, Bullock et al 2020). Different species types can be modelled, including natives, range edge species, invasives, and pests, and we have pioneered approaches to model new invasions (Chapman et al 2017). The student will have the opportunity to work with a team experienced in modelling spatial dynamics (Bullock, Chapman), climate data and modelling (Robinson), and societal and land use change (Dunford). The student will be able to significantly advance understanding of future biodiversity in a changing world.

Methodology

The project will focus on spatial modelling of species, using input data on changes to climate, land use, and other aspects of human activities, including resource use, nature protection and built infrastructure. Ultimately the student will develop a complex modelling framework for assessing species dynamics under a range of climate and societal drivers. This will involve the following steps.
1) Assessing the state of knowledge of main drivers of species’ range expansion and contraction, and spatial dynamics, for native species, invasives and pests.
2) Compiling available data on species population dynamics (e.g. the COMADRE and COMPADRE databases) and dispersal abilities, and developing approaches to gap-fill for missing data (e.g. virtual species).
3) Developing simple models to use (initially synthetic) data on climate, land use and other key drivers on species range shifting, to investigate interactions among drivers and their form and complexity.
4) Using these models explore how species’ ecology – dispersal ability, habitat associations, demographic rates – affect responses to multiple drivers and ability to spread vs extinction risk.
5) Use the findings from the simple model to develop a complex model that efficiently uses the fine-resolution climate and land cover data, and data on other important aspects of societal change (e.g. resource use intensity) to explore how different types of species will respond to the different climate + societal scenarios developed by UKCEH, including the scenario trajectories to 2080.
6) Develop a statistical approach to explore how species type and multiple drivers interact to affect spatial dynamics – to provide greater generality concerning extinction probabilities and characteristics that facilitate persistence.

Project Timeline

Year 1

Literature review of species’ spatial dynamics in response to climate change and other anthropogenic drivers, and interactions among these. A likely review paper from this.
Review of and training in modelling approaches to species range shifting and spatial dynamics.
Training in development of climate and societal change scenarios, understanding the outputs and assessment of their use for modelling species dynamics.
Development of a modelling plan, including the types of species (natives, invasives, etc), the modelling approach, computing requirements and software to be used. Determine what types of input data are needed.

Year 2

Create a simple, model to explore species spatial dynamics under multiple drivers
Use the model to explore interactions among drivers and impacts on different species types (varying in dispersal abilities, thermal tolerances, habitat type, etc). Write paper on the impact of interacting drivers.
Develop a complex model that uses fine resolution climate and land cover/use data for the UK and models species dynamics explicitly – using High Performance Computing to handle large data volumes. Possibly use ‘virtual species’ to fill data gaps (Santini et al 2019).

Year 3

Model spatial dynamics using the complex model for a range of species, contrasting the range of drivers and a variety of species types.
Explore the importance of different drivers and their interactions using the model and statistical analysis.
Write 2-3 papers on modelled range shifts, invasions and spatial re-distributions of different types of species, including extinction probabilities and characteristics that facilitate persistence or spread.

Year 3.5

Finalise papers and their publication
Complete thesis

Training
& Skills

The student can be resident at either UKCEH or Stirling, and will spend significant periods of time at the non-resident institution. This will enable them to receive training in species spatial modelling, species population and movement ecology, climate modelling, and land use and ecosystem modelling. This will provide the student with training for careers in academia, conservation, and in data or modelling for the private sector.
1) Handling, collating and compiling large datasets, including working with GIS and database software.
2) Use of R, Python and/or C++ for modelling and high performance computing clusters.
3) Advanced statistics in R to analyse model outputs, create virtual species, and assess non-linear interactions.
4) Interdisciplinary working, including bringing together approaches from climate modelling, socio-economics, population ecology, and spatial data.
5) Developing and bug-checking models, and enhancing to improve efficiency, including use of HPC.
6) Presenting complex models and results to audiences of academic and users (e.g. conservationists).

References & further reading

Bullock et al. (2020) Human-mediated dispersal and disturbance shape the metapopulation dynamics of a long-lived herb. Ecology https://doi.org/10.1002/ecy.3087
Chapman et al. (2016) Modelling the introduction and spread of non-native species: international trade and climate change drive ragweed invasion. Global Change Biology https://doi.org/10.1111/gcb.13220
Chapman et al. (2017) Global trade networks determine the distribution of invasive non-native species. Global Ecology and Biogeography https://doi.org/10.1111/geb.12599
Pedde, et al. (2021). Enriching the Shared Socioeconomic Pathways to co-create consistent multi-sector scenarios for the UK. Science of the Total Environment https://doi.org/10.1016/j.scitotenv.2020.143172
Santini et al (2016) A trait-based approach for predicting species responses to environmental change from sparse data: how well might terrestrial mammals track climate change? Global Change Biology https://doi.org/10.1111/gcb.13271

Further Information

James Bullock, jmbul@ceh.ac.uk, 07824 460866

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