• Energy Research

Impacts of climate change on natural and human systems will become increasingly severe as the magnitude of climate change increases. Climate change adaptation interventions to address current and projected impacts are thus paramount. Yet, evidence on their effectiveness remains limited, highlighting the need for appropriate ecological indicators to measure progress of climate change adaptation for the natural environment. We outline conceptual, analytical, and practical challenges in developing such indicators, before proposing a framework with three process-based and two results-based indicator types to track progress in adapting to climate change. We emphasize the importance of dynamic assessment and modification over time, as new adaptation targets are set and/or as intervention actions are monitored and evaluated. Our framework and proposed indicators are flexible and widely applicable across species, habitats, and monitoring programmes, and could be accommodated within existing national or international frameworks to enable the evaluation of both large-scale policy instruments and local management interventions. We conclude by suggesting further work required to develop these indicators fully, and hope this will stimulate the use of ecological indicators to evaluate the effectiveness of policy interventions for the adaptation of the natural environment across the globe.

This is the report from the Condatis project “Decision support for restoring ecological networks in rapidly developing, biodiverse countries” for the case study in Indonesia. We would like to thank our project partners and everyone who helped us with this. For more information please see our website www.condatis.org.uk and the web app created by this project www.webapp.condatis.org.uk. The authors also acknowledge funding from the UK Natural Environment Research Council grant number NE/R009597/1.

AbstractEstimating population spread rates across multiple species is vital for projecting biodiversity responses to climate change. A major challenge is to parameterise spread models for many species. We introduce an approach that addresses this challenge, coupling a trait‐based analysis with spatial population modelling to project spread rates for 15 000 virtual mammals with life histories that reflect those seen in the real world. Covariances among life‐history traits are estimated from an extensive terrestrial mammal data set using Bayesian inference. We elucidate the relative roles of different life‐history traits in driving modelled spread rates, demonstrating that any one alone will be a poor predictor. We also estimate that around 30% of mammal species have potential spread rates slower than the global mean velocity of climate change. This novel trait‐space‐demographic modelling approach has broad applicability for tackling many key ecological questions for which we have the models but are hindered by data availability.

AbstractAimClimate change has been predicted to facilitate poleward expansion of many early‐successional specialist invertebrates. The Grizzled Skipper, Pyrgus malvae, is a threatened butterfly in long‐term decline that has not met expectations of northern expansion in Britain, possibly indicating that climate change has not improved northern habitat suitability or that another driver (e.g. land use change) is masking its effects. Here, we explore the effect of climate on population size trends over four decades, and whether any regions show an improving population trend that may be a precursor to northern expansion. Examining detailed spatio‐temporal abundance data can reveal unexpected limitations to population growth that would not be detectable in widely used climate envelope models.LocationCentral and southern England.MethodsMixed models were used to investigate P. malvae population size in relation to time and monthly climate measures across its UK range since 1976, based on repeated transect walks.ResultsWe found that P. malvae population size declined more over time in the north and west of its UK range than in the south and east, and was negatively related to high December temperature and summer rainfall. However, the effect sizes of temperature and rainfall were minimal.Main ConclusionsThe last 40 years of climate change have not ameliorated climate suitability for P. malvae at its range edge, contrary to expectations from spatial‐only climate envelope models. The clear long‐term downward trends in population size are independent of climate change and we propose probably due to habitat deterioration. Our findings highlight potential hazards in predicting species range expansions from spatial models alone. Although some climate variables may be associated with a species’ distribution, other factors may be more dominant drivers of trends and therefore more useful predictors of range changes.

Many species appear to be undergoing shifts in phenology, arising from climate change. To predict the direction and magnitude of future changes requires an understanding of how phenology depends on climatic variation. Species show large-scale spatial variation in phenology (affected by differentiation among populations) as well as variation in phenology from year-to-year at the same site (affected predominantly by local plasticity). Teasing apart spatial and temporal variation in phenology should allow improved predictions of phenology under climate change. This study is the first to quantify large-scale spatial and temporal variation in the entire emergence pattern of species, and to test the relationships found by predicting future data. We use data from up to 33 years of permanent transect records of butterflies in the United Kingdom to fit and test models for 15 butterfly species. We use generalized additive models to model spatial and temporal variation in the distribution of adult butterflies over the season, allowing us to capture changes in the timing of emergence peaks, relative sizes of peaks and/or number of peaks in a single analysis. We develop these models using data for 1973–2000, and then use them to predict phenologies from 2001 to 2006. For six of our study species, a model with only spatial variation in phenology is the best predictor of the future, implying that these species have limited plasticity. For the remaining nine species, the best predictions come from a model with both spatial and temporal variation in phenology; for four of these, growing degree-days have similar effects over space and time, implying high levels of plasticity. The results show that statistical phenology models can be used to predict phenology shifts in a second time period, suggesting that it should be feasible to project phenologies under climate change scenarios, at least over modest time scales.

Dispersal is fundamental in determining biodiversity responses to rapid climate change, but recently acquired ecological and evolutionary knowledge is seldom accounted for in either predictive methods or conservation planning. We emphasise the accumulating evidence for direct and indirect impacts of climate change on dispersal. Additionally, evolutionary theory predicts increases in dispersal at expanding range margins, and this has been observed in a number of species. This multitude of ecological and evolutionary processes is likely to lead to complex responses of dispersal to climate change. As a result, improvement of models of species’ range changes will require greater realism in the representation of dispersal. Placing dispersal at the heart of our thinking will facilitate development of conservation strategies that are resilient to climate change, including landscape management and assisted colonisation.SynthesisThis article seeks synthesis across the fields of dispersal ecology and evolution, species distribution modelling and conservation biology. Increasing effort focuses on understanding how dispersal influences species' responses to climate change. Importantly, though perhaps not broadly widely‐recognised, species' dispersal characteristics are themselves likely to alter during rapid climate change. We compile evidence for direct and indirect influences that climate change may have on dispersal, some ecological and others evolutionary. We emphasise the need for predictive modelling to account for this dispersal realism and highlight the need for conservation to make better use of our existing knowledge related to dispersal.

Further information on Condatis methodology, and supplementary tables and figures.