Knowledge gaps and thesis aims

Despite a wealth of studies on species’ responses to climate change, there are still many gaps in our understanding, and the availability of national distribution and abundance data for butterflies, as well as detailed knowledge about species’ life-histories, allows a range of approaches to be taken to address knowledge gaps using butterflies as study species.

Quantifying variation in species’ responses to climate change is an important step in understanding how different species respond to climate change. As already detailed, inter-specific variation in distribution changes in response to climate change has been well documented (e.g. Parmesan et al., 1999), however it is not known whether intra-specific variation in responses to climate change also exists. Throughout this thesis, intra-specific variation refers specifically to variation within species over time, in other words, temporal variation in the rates of distribution and/or abundance change within species. Temporal variation in species responses to climate change has received little attention so far, but is an important issue for understanding whether the drivers of species’

distribution change vary over time, and also for informing projections of distribution change into the future. I therefore quantified both inter- and intra-specific variation in the distribution and

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the responses of habitat generalist species differed from those of habitat specialists, and whether these differences were temporally consistent. The comparison of generalist and specialist species’ responses should contribute to our understanding of how communities are expected to change as the climate changes, in particular whether generalists consistently show greater distribution

expansion than specialists, and therefore the homogenisation of communities under climate change should be expected (Menéndez et al., 2006).

Quantifying intra-specific variation in responses to climate change is a novel study in itself, but also paves the way for addressing potential determinants of rates of species’ distribution change. Previous studies have shown that explaining variation in rates of responses to climate change is challenging, and there were only weak relationships between distribution change and a range of life- history variables (e.g.Angert et al., 2011). There is therefore a lack of understanding of the

determinants of rates of distribution change. I tackled this question by drawing together variables that have previously been shown to affect distribution change in separate studies, and analysing them simultaneously. Thus I determined how important species-specific habitat availability,

dispersal ability and abundance changes were for explaining variation in rates of distribution change in southerly-distributed British butterflies. The potential explanatory variables examined were carefully chosen to reflect the interaction been species’ life history traits and the landscape across which they were expanding (or failing to expand) their distribution. For example, while species’ habitat specificity reflects the breadth of habitats a species can utilise, species’ habitat availability reflects both the species’ habitat associations and the amount of suitable habitat available in the landscape across which it has expanded its distribution. This study therefore holds novelty in the approach taken, with the aim of advancing upon previous studies by considering the potentially important interactions between species’ life history and the landscape.

Such empirical analyses are essential for understanding species’ responses to climate change, however they are inevitably limited by the spatial and temporal extent and the quality of available data. Consequently, models are widely used to project species’ distribution change, as these allow a range of questions to be tackled. Much detail was given above on the advances made in predictive distribution modelling, and I highlighted that integrated ‘hybrid’ models are likely to be of great utility in understanding how species’ distributions are likely to change in response to climate change (Huntley et al., 2010). The development of flexible predictive models that can be applied to a range of species and landscapes is therefore an important progression, and so I tested the ability of a spatially-explicit individual-based model, SPEED, to project the distribution change of a southerly- distributed butterfly species (Pararge aegeria) in Britain. The model projects species’ distribution

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change in relation to climate suitability, habitat availability, dispersal ability and population growth rate. The spatially explicit climate suitability and habitat availability components of the model are species-specific empirical components, while the dispersal ability and population growth rate components are mechanistic. The model is thus a hybrid which improves upon bioclimate-envelope modelling by considering the effects on distribution change of both species’ life history and

environmental variables (Huntley et al., 2010). Hybrid models are an emerging field, and other examples exist of predictive models which couple species’ distribution models (bioclimate

envelopes) with population dynamic models. For example, hybrid models have been used to predict species’ persistence under climate change in plants (Fordham et al., 2012) and birds (Zurell et al., 2012), and to assess the potential impact of assisted-colonisation on species’ persistence (Regan et

al., 2012). In contrast to these examples, the model presented in this thesis was developed to be

highly flexible and hold the potential to be applied to a wide range of species and landscapes, and this flexibility in itself is an advance in the field.

I used the model to further explore the effects of habitat, dispersal and population growth rate on rates of species’ distribution change, which allowed a similar question to that posed above using empirical data, to be tested using a modelling framework. Specifically, I aimed to ascertain how population growth rate affects the relative importance of climate, habitat and dispersal for species’ distribution change, as previous studies have suggested that population growth is likely to be an important determinant of distribution expansion (Willis et al., 2009b). I applied the model to twenty- eight species of southerly-distributed butterflies in Britain, using species-specific climate suitability and habitat availability data, and varying the dispersal ability and population growth rate within each species. I therefore aimed to advance our understanding of the determinants of distribution

expansion by modelling species’ distribution change in relation to key lift-history traits and

environmental variables. The application of a hybrid model allowed our mechanistic understanding of distribution change to be tested using spatially realistic habitat and climate data. Moreover, the flexibility of the model structure and the large amount of butterfly distribution and life-history data available meant that a large number of species’ distributions could be projected (N = 28), and so it was possible to use relatively detailed species-specific data without being restricted to studying a small number of species. This highlights the advantage of hybrid models, which incorporate both empirical and mechanistic components and can therefore be adapted given different data availability and study aims.

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