Developing a predictive modelling capacity for a climate change-vulnerable blanket bog habitat: Assessing 1961-1990 baseline relationships

Aim: Understanding the spatial distribution of high priority habitats and developing predictive models using climate and environmental variables to replicate these distributions are desirable conservation goals. The aim of this study was to model and elucidate the contributions of climate and topography to the distribution of a priority blanket bog habitat in Ireland, and to examine how this might inform the development of a climate change predictive capacity for peat-lands in Ireland. Methods: Ten climatic and two topographic variables were recorded for grid cells with a spatial resolution of 1010 km, covering 87% of the mainland land surface of Ireland. Presence-absence data were matched to these variables and generalised linear models (GLMs) fitted to identify the main climatic and terrain predictor variables for occurrence of the habitat. Candidate predictor variables were screened for collinearity, and the accuracy of the final fitted GLM was evaluated using fourfold cross-validation based on the area under the curve (AUC) derived from a receiver operating characteristic (ROC) plot. The GLM predicted habitat occurrence probability maps were mapped against the actual distributions using GIS techniques. Results: Despite the apparent parsimony of the initial GLM using only climatic variables, further testing indicated collinearity among temperature and precipitation variables for example. Subsequent elimination of the collinear variables and inclusion of elevation data produced an excellent performance based on the AUC scores of the final GLM. Mean annual temperature and total mean annual precipitation in combination with elevation range were the most powerful explanatory variable group among those explored for the presence of blanket bog habitat. Main conclusions: The results confirm that this habitat distribution in general can be modelled well using the non-collinear climatic and terrain variables tested at the grid resolution used. Mapping the GLM-predicted distribution to the observed distribution produced useful results in replicating the projected occurrence of the habitat distribution over an extensive area. The methods developed will usefully inform future climate change predictive modelling for Irelan

Synthesising plausible futures for biodiversity and ecosystem services in Europe and Central Asia using scenario archetypes

Scenarios are a useful tool to explore possible futures of social-ecological systems. The number of scenarios has increased dramatically over recent decades, with a large diversity in temporal and spatial scales, purposes, themes, development methods, and content. Scenario archetypes generically describe future developments and can be useful in meaningfully classifying scenarios, structuring and summarizing the overwhelming amount of information, and enabling scientific outputs to more effectively interface with decision-making frameworks. The Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES) faced this challenge and used scenario archetypes in its assessment of future interactions between nature and society. We describe the use of scenario archetypes in the IPBES Regional Assessment of Europe and Central Asia. Six scenario archetypes for the region are described in terms of their driver assumptions and impacts on nature (including biodiversity) and its contributions to people (including ecosystem services): business-as-usual, economic optimism, regional competition, regional sustainability, global sustainable development, and inequality. The analysis shows that trade-offs between nature’s contributions to people are projected under different scenario archetypes. However, the means of resolving these trade-offs depend on differing political and societal value judgements within each scenario archetype. Scenarios that include proactive decision making on environmental issues, environmental management approaches that support multifunctionality, and mainstreaming environmental issues across sectors, are generally more successful in mitigating trade-offs than isolated environmental policies. Furthermore, those scenario archetypes that focus on achieving a balanced supply of nature’s contributions to people and that incorporate a diversity of values are estimated to achieve more policy goals and targets, such as the UN Sustainable Development Goals and the Convention on Biological Diversity Aichi targets. The scenario archetypes approach is shown to be helpful in supporting science-policy dialogue for proactive decision making that anticipates change, mitigates undesirable trade-offs, and fosters societal transformation in pursuit of sustainable development