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Conservation planning with uncertain climate change projections - PubMed

Conservation planning with uncertain climate change projections

Heini Kujala et al. PLoS One. 2013.

Abstract

Climate change is affecting biodiversity worldwide, but conservation responses are constrained by considerable uncertainty regarding the magnitude, rate and ecological consequences of expected climate change. Here we propose a framework to account for several sources of uncertainty in conservation prioritization. Within this framework we account for uncertainties arising from (i) species distributions that shift following climate change, (ii) basic connectivity requirements of species, (iii) alternative climate change scenarios and their impacts, (iv) in the modelling of species distributions, and (v) different levels of confidence about present and future. When future impacts of climate change are uncertain, robustness of decision-making can be improved by quantifying the risks and trade-offs associated with climate scenarios. Sensible prioritization that accounts simultaneously for the present and potential future distributions of species is achievable without overly jeopardising present-day conservation values. Doing so requires systematic treatment of uncertainties and testing of the sensitivity of results to assumptions about climate. We illustrate the proposed framework by identifying priority areas for amphibians and reptiles in Europe.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. A flow chart showing how species distribution maps for baseline and future are created across the different modelling techniques m, discounting the mean values (denoted by an asterix) with inter-model variability (standard deviation, see text).

Connectivity maps CBj and CFj are created based on the discounted baseline and future layers, resulting in four different input maps per species j and per scenario s. Prioritization is done separately for each scenario, producing multiple results per scenario where weights given to future distributions and connectivities are varied. Trade-off curves (Fig. 5) and comparisons between scenarios (Fig. 3) are done by focusing on the top 10% priorities of each Zonation result.

Figure 2
Figure 2. Areas of disagreement between models and across species, for scenarios A1, A2, B1, and B2, all for 2080.

Uncertainty is illustrated as the per grid average across species of the coefficient of variation (ratio between the standard deviation and the mean) of predicted suitabilities by the four bioclimatic models. Orange areas indicate cells for which on average the standard deviation equals the mean.

Figure 3
Figure 3. Top 10% conservation priorities for European amphibian and reptiles, including baseline distributions, projected future distributions and connectivity between them, when equal weight is given to present and future.

A) Overlap of priorities across the four SRESS scenarios. Red indicates areas identified as top 10% priorities with all four scenarios; blue areas are identified by only one scenario B) Classification of the top 10% priorities into their relative importance as baseline cores, future cores, sources and stepping stones. Baseline and future cores were identified as the areas from the top ranked cells that according to habitat quality would be most important for species within their present and future distributions. Sources indicate areas that are most important for dispersal from present to future areas as climate changes. Similarly, stepping stones facilitate species migration to future core areas. They are parts of the predicted future distribution best connected to the present distribution.

Figure 4
Figure 4. Relative losses and gains for a combination of planning and evaluation scenarios, indicated by negative (loss) and positive (gain) values in the y axis.

Circles are species and circle size reflects the sum of baseline climatic suitability across cells. Panels A–D correspond to evaluation scenarios A1, A2, B1, and B2 respectively. Graphs within these panels correspond to the planning scenarios. Species are distributed along the x axis according to the expected change in future climatic suitability according to the evaluation scenario. Negative values in the x axis indicate species expected to experience a decrease in future climatic suitability.

Figure 5
Figure 5. Trade-off curves for reductions in baseline conservation level (blue) and gains in future conservation level (red), as higher weight is given to the future, for the 4 SRESS scenarios.

Each point corresponds to a different set of spatial priorities, selected with a different combination of weights for baseline and future layers. Baseline always receives a weight of one, while weight for the future is varied from zero to one. The difference between gain and loss curves is indicated with a grey line.

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Grants and funding

For financial support HK and AM thank the Academy of Finland Centre of Excellence Programme 2006–2011, grants 213457 and 129636 (URL: http://www.aka.fi/en-GB/A/Centres-of-Excellence-/). HK was also funded by a Luonnonvaraisten eliöiden kestävän käytön ja suojelun tutkijakoulu Graduate School fellowship (URL: http://www.helsinki.fi/luova) and by Finnish Cultural Foundation, grant SKR-00070832 (URL: http://www.skr.fi). AM acknowledges the support by the ERC-StG (European Research Council Starting Grant, URL: http://erc.europa.eu/starting-grants) project Global Environmental Decision Analysis (GEDA), grant 260393. MC and MBA were funded by the European Commission Seventh Framework Program project European RESPONSES to climate change (grant agreement number 244092, URL: http://www.responsesproject.eu/). MBA also acknowledges the Spanish Research Council (URL: http://www.csic.es/web/guest/home), the ‘Rui Nabeiro’ Biodiversity Chair (URL: http://www.catedra.uevora.pt/rui-nabeiro/), and the Danish National Research Foundation (URL: http://www.dg.dk/en/) for support of his research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.