title: Climate impacts on species’ distributions in space and time
Creators: name: Lovell, Rebecca S. L.
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: space-for-time substitution
subject: climate change
subject: species distribution models
subject: local adaptation

description: Anthropogenic climate change is having major impacts on biodiversity, including driving shifts in species’ geographic distributions (occupancy and abundance). As a result, a major focus of contemporary eco-evolutionary research is on understanding how species’ distributions are changing, and predicting how they may continue to change in the future. To generate projections of species’ distributions under future climate scenarios, researchers often identify relationships between climate variables and species’ occupancy or abundance over geographic space and then project these spatial relationships over time. This ‘space-for-time substitution’ (SFTS) approach is very widely applied, but rests on key assumptions. In this thesis, I first conducted an extensive review of the use of the SFTS approach for predicting eco-evolutionary responses to climate change. I then used both empirical and simulated spatiotemporal abundance data to test the reliability of SFTS for projecting changes in species’ distributions under future climate change, as well as exploring the insights that can be gleaned from spatiotemporal abundance data. After setting out the broad context of climate change and the potential utility of SFTS (Chapter 1), I then reviewed how SFTS are used in climate change ecology and evolution (Chapter 2), identifying how the approach is applied and its limitations. This review revealed that SFTS are widely used across different subfields (focusing on genotypes, phenotypes, species’ distributions and ecological communities) and that, while distinct disciplines appear to have progressed largely in isolation, they have arrived at broadly comparable methods. I also found that across all subfields, the SFTS approach relies on common but undertested assumptions relating to the identification of causal climate-distribution relationships in space and the transferability of these relationships over time (i.e. whether relationships are equivalent in space and time). Therefore, I arrived at the recommendations that there should be a focus on testing these key assumptions and that greater communication between different subfields would be of great benefit for the SFTS approach. Having identified that there is a limited understanding of when spatial climate-distribution relationships can be reliably transferred over time, I then tested this using long-term UK-wide butterfly abundance data from the UK Butterfly Monitoring Scheme. First, in Chapter 3, I used the Orange-tip butterfly as a case study to develop an approach for comparing spatial and temporal climate-distribution relationships. Specifically, I first identified the seasonal periods over which temperature and precipitation variables best explained variation in Orange-tip abundance. Then, using these climate predictors, I decomposed climate variation into its spatial and temporal components, which allowed comparison of the spatial and temporal effects of temperature and precipitation variables on the Orange-tip’s occupancy and abundance across the species’ UK range. The aim of this work was to identify whether the temporal relationships between climate variables and each of abundance and occupancy were consistent with causality (i.e. non-zero and in the same direction) and equivalent to those observed over space, as is assumed when making SFTS projections. This revealed complex patterns in the level of equivalence between the spatial and temporal effects of temperature on the Orange-tip butterfly’s distribution, with variation both across the species’ range and between occupancy versus abundance. Conversely, precipitation appeared to not have a causal effect on the Orange-tip butterfly’s distribution, with little effect on occupancy or abundance over time. This emphasises the importance of identifying causal relationships before making projections; if the spatial precipitation-distribution relationships were assumed to be causal, they would generate misleading projections over time. Overall, this chapter demonstrates that the reliability of SFTS projections can be highly context-dependent, even for a single species, and this highlights the need for further exploration of when spatial relationships can be transferred over time. In Chapter 4 I then extended this approach to consider seven univoltine butterfly species in the UK, with the aim of exploring the extent of variation in level of equivalence between spatial and temporal temperature-distribution relationships, both among and within species. Applying the methodology from Chapter 3 to multiple species revealed that both the causality of temperature-distribution relationships and the equivalence between relationships in space and time was highly variable across species, distribution metrics (occupancy versus abundance), and within a species’ climate range. The finding that temperature-distribution relationships for many species are unlikely to be causal – even when considering the seasonal periods of temperature that exhibited the strongest correlations with each species’ abundance – suggests that SFTS projections of species’ distributions under climate change may often be using non-causal relationships. In addition, even where relationships were consistent with causality, the slopes of the temporal temperature-distribution relationships were generally shallower than the local spatial slopes. This suggests that, over shorter timescales, species’ occupancies and abundances could not respond rapidly enough to track the temperature-distribution relationships observed over space and so SFTS projections may overestimate the magnitude of change in the near-term. Overall, these results add to Chapter 3 in emphasising the need to generate an understanding of the scenarios, including timescales, under which SFTS projections will be most reliable. Finally, in Chapter 5, I aimed to address the dearth of theory relating to the application of SFTS that was revealed in my review of the field (Chapter 2). To achieve this, I used individual-based simulations, incorporating adaptation and gene flow in a stepping-stone model, to examine the patterns of spatial and temporal temperature-abundance relationships that are expected under a range of climate change, evolutionary and ecological scenarios. Spatial relationships between temperature and abundance were found to be humped in the presence of temperature-determined carrying capacities and humped with a plateaued peak when carrying capacities were constant across sites and years. In general, the slopes of the temporal temperature-abundance relationships were shallower than the local spatial slopes, with spatial and temporal slopes most similar under a weaker local adaptation scenario. This suggests that SFTS may often overestimate the magnitude of population responses to temperature changes over time and highlights the importance of considering the rates of processes operating in space and time. Additionally, where local adaptation was stronger, temperature-abundance relationships over time were found to be humped, suggesting that local adaptation leaves a discernible imprint on spatiotemporal abundance data and potentially offering an alternative to logistically challenging experimental approaches for detecting local adaptation. These results also suggest that local adaptation may undermine space-for-time projections of abundance changes in the short term, before populations have had time to adapt to new conditions. A key next step is to build on this work and test the utility of this approach in real world, multi-driver settings. Taken together, my thesis demonstrates the risks of relying on SFTS for projecting species’ responses to climate change, and emphasises the need for further validation across different study systems and timescales. Specifically, I demonstrated how both the causality of climate-distribution relationships and the equivalence between spatial and temporal relationships can vary both across species and within an individual species’ range. Additionally, I found that temporal climate-distribution relationships were generally shallower than those seen in space, suggesting that responses in time may often lag behind climate change. This emphasises the importance of understanding the speed of processes operating over space and time, and how this affects the timescales over which SFTS projections are most reliable. Importantly, my empirical and simulation work also identified that spatiotemporal abundance data has been largely underutilised for testing the key assumptions of SFTS, with my simulations (Chapter 5) demonstrating that there may be unrealised potential for this type of data to detect local adaptation of populations to specific environmental drivers. Overall, my thesis demonstrated that considering spatial and temporal environmental effects together provides an opportunity to gain insights into the processes driving abundance trends and to improve the robustness of SFTS projections.