Research on the impacts of climate change on soil organic carbon (SOC) stocks has focused on the effects of changes in average climate, but the potential effects of increased climate variability, including more frequent extreme events, remain under-examined. In this study, set in a semiarid agricultural landscape in southeastern Australia, we used the Rothamsted carbon (RothC) model to isolate the effects of interannual rainfall variability on SOC stocks over a 50 yr period. We modelled SOC trends in response to 3 scenarios that had the same 50 yr average climate but different interannual rainfall distributions: non-changing average climate, historic variability (H), and increased variability due to more frequent extreme rainfall years (XH). Relative to the non-changing average climate, RothC simulations predicted net decreases in mean SOC stocks to 50 yr of 11% under the H scenario and 13% under the XH scenario. These decreases were the result of predicted SOC decreases (and increased CO2 emissions) in extreme wet years (ca. 0.26 Mg ha(-1) yr(-1)) that were not counterbalanced by SOC increases in extreme dry years (ca. 0.11 Mg ha(-1) yr(-1)). No significant difference in mean SOC stocks at 50 yr between the H and XH scenarios was likely due to an increase in both extreme wet and counterbalancing extreme dry years in the latter. Strong negative correlations were found between annual changes in SOC stocks and rainfall. Our modelled predictions indicate the potential for extreme rainfall years to influence SOC gains and losses in semiarid environments and highlight the importance of maintaining plant inputs in these environments, particularly during extreme wet years.

QuestionPlant communities are not necessarily spatially exclusive; a point in space can exhibit properties of multiple communities. Such variation can be described using floristically defined 'fuzzy' units, however these may not be easily delineated using standard remote sensing methods. Is there value in considering communities as fuzzy? Can species distribution modelling methods be used to represent fuzzy communities spatially?LocationWestern Victoria, Australia.MethodsFuzzy communities were objectively identified from vegetation census quadrats with a cluster analysis of ordinated species data. Boosted regression trees were used to create models that defined relationships between the sampled communities and environmental predictor variables. These were applied to the mapped predictors to create maps of estimated fuzzy community membership for the entire study area.ResultsFour separate fuzzy communities were identified from the sampled vegetation data. Models were created for each community and these were effectively used to generate maps of fuzzy community membership. Individual fuzzy community maps illustrated vegetation variation that could not be discerned on a discretely classified map.ConclusionsFuzzy communities were found to represent a greater proportion of species variation than discretely classified units. Species distribution modelling methods were effective in creating independent spatial maps of each floristically defined fuzzy community; however the interpretation of these maps is more complex than with a single discrete community map.

Adaptation of forest management to climate change requires an understanding of the effects of climate on forests, industries and communities; prediction of how these effects might change over time; and incorporation of this knowledge into management decisions. This requires multiple forms of knowledge and new approaches to forest management decisions. Partnerships that integrate researchers from multiple disciplines with forest managers and local actors can build a shared understanding of future challenges and facilitate improved decision making in the face of climate change.Climate change presents significant potential risks to forests and challenges for forest managers. Adaptation to climate change involves monitoring and anticipating change and undertaking actions to avoid the negative consequences and to take advantage of potential benefits of those changes.This paper aimed to review recent research on climate change impacts and management options for adaptation to climate change and to identify key themes for researchers and for forest managers.The study is based on a review of literature on climate change impacts on forests and adaptation options for forest management identified in the Web of Science database, focusing on papers and reports published between 1945 and 2013.One thousand one hundred seventy-two papers were identified in the search, with the vast majority of papers published from 1986 to 2013. Seventy-six percent of papers involved assessment of climate change impacts or the sensitivity or vulnerability of forests to climate change and 11 % (130) considered adaptation. Important themes from the analysis included (i) predicting species and ecosystem responses to future climate, (ii) adaptation actions in forest management, (iii) new approaches and tools for decision making under uncertainty and stronger partnerships between researchers and practitioners and (iv) policy arrangements for adaptation in forest management.Research to support adaptation to climate change is still heavily focused on assessing impacts and vulnerability. However, more refined impact assessments are not necessarily leading to better management decisions. Multi-disciplinary research approaches are emerging that integrate traditional forest ecosystem sciences with social, economic and behavioural sciences to improve decision making. Implementing adaptation options is best achieved by building a shared understanding of future challenges among different institutions, agencies, forest owners and stakeholders. Research-policy-practice partnerships that recognise local management needs and indigenous knowledge and integrate these with climate and ecosystem science can facilitate improved decision making.

This book reflects a critical review and synthesis of economic and related literature pertaining to forestry and forest management by a long-time forester, teacher of forest economics, UN administrator, and forest policy advisor and consultant to many governments, agencies and companies. To quote the author 'After playing around in the field of forest economics for the best part of sixty years, I wanted to sort out my ideas on the subject'.

The extraordinary drought during the summer of 2003 in Central Europe allowed to examine responses of adult beech trees (Fagus sylvatica) to co-occurring stress by soil moisture deficit and elevated O3 levels under forest conditions in southern Germany. The study comprised tree exposure to the ambient O3 regime at the site and to a twice-ambient O3 regime as released into the canopy through a free-air O3 fumigation system. Annual courses of photosynthesis (Amax), stomatal conductance (gs), electron transport rate (ETR) and chlorophyll levels were compared between 2003 and 2004, the latter year representing the humid long-term climate at the site. ETR, Amax and gs were lowered during 2003 by drought rather than ozone, whereas chlorophyll levels did not differ between the years. Radial stem increment was reduced in 2003 by drought but fully recovered during the subsequent, humid year. Comparison of AOT40, an O3 exposure-based risk index of O3 stress, and cumulative ozone uptake (COU) yielded a linear relationship throughout humid growth conditions, but a changing slope during 2003. Our findings support the hypothesis that drought protects plants from O3 injury by stomatal closure, which restricts O3 influx into leaves and decouples COU from high external ozone levels. High AOT40 erroneously suggested high O3 risk under drought. Enhanced ozone levels did not aggravate drought effects in leaves and stem.

Abstract not available due to copyright.

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow-acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short-lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions.