Suchergebnisse

The Earth's carbon and hydrologic cycles are intimately coupled by gas exchange through plant stomata1, 2, 3. However, uncertainties in the magnitude4, 5, 6 and consequences7, 8 of the physiological responses9, 10 of plants to elevated CO2 in natural environments hinders modelling of terrestrial water cycling and carbon storage11. Here we use annually resolved long-term δ13C tree-ring measurements across a European forest network to reconstruct the physiologically driven response of intercellular CO2 (Ci) caused by atmospheric CO2 (Ca) trends. When removing meteorological signals from the δ13C measurements, we find that trees across Europe regulated gas exchange so that for one ppmv atmospheric CO2 increase, Ci increased by ~0.76 ppmv, most consistent with moderate control towards a constant Ci/Ca ratio. This response corresponds to twentieth-century intrinsic water-use efficiency (iWUE) increases of 14 ± 10 and 22 ± 6% at broadleaf and coniferous sites, respectively. An ensemble of process-based global vegetation models shows similar CO2 effects on iWUE trends. Yet, when operating these models with climate drivers reintroduced, despite decreased stomatal opening, 5% increases in European forest transpiration are calculated over the twentieth century. This counterintuitive result arises from lengthened growing seasons, enhanced evaporative demand in a warming climate, and increased leaf area, which together oppose effects of CO2-induced stomatal closure. Our study questions changes to the hydrological cycle, such as reductions in transpiration and air humidity, hypothesized to result from plant responses to anthropogenic emissions. ; European Union projects ISONET (EVK2-2001-00237); Carbo-Extreme (226701); Millennium (017008); Swiss National Science Foundation (iTREE CRSII3_136295); N.J.L. the UK NERC (NE/B501504)

Demand for models in biodiversity assessments is rising, but which models are adequate for the task? We propose a set of best-practice standards and detailed guidelines enabling scoring of studies based on species distribution models for use in biodiversity assessments. We reviewed and scored 400 modeling studies over the past 20 years using the proposed standards and guidelines. We detected low model adequacy overall, but with a marked tendency of improvement over time in model building and, to a lesser degree, in biological data and model evaluation. We argue that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments. We encourage broad community participation toward the expansion and ongoing development of the proposed standards and guidelines. ; publishedVersion ; Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Adapting the management of forest resources to climate change involves addressing several crucial aspects to provide a valid basis for decision making. These include the knowledge and belief of decision makers, the mapping of management options for the current as well as anticipated future bioclimatic and socioeconomic conditions, and the ways decisions are evaluated and made. We investigate the adaptive management process and develop a framework including these three aspects, thus providing a structured way to analyze the challenges and opportunities of managing forests in the face of climate change. We apply the framework for a range of case studies that differ in the way climate and its impacts are projected to change, the available management options, and how decision makers develop, update, and use their beliefs about climate change scenarios to select among adaptation options, each being optimal for a certain climate change scenario. We describe four stylized types of decision-making processes that differ in how they (1) take into account uncertainty and new information on the state and development of the climate and (2) evaluate alternative management decisions: the "no-change," the "reactive," the "trend-adaptive," and the "forward-looking adaptive" decision-making types. Accordingly, we evaluate the experiences with alternative management strategies and recent publications on using Bayesian optimization methods that account for different simulated learning schemes based on varying knowledge, belief, and information. Finally, our proposed framework for identifying adaptation strategies provides solutions for enhancing forest structure and diversity, biomass and timber production, and reducing climate change-induced damages. They are spatially heterogeneous, reflecting the diversity in growing conditions and socioeconomic settings within Europe. ; This work was supported by the Seventh Framework Program of the EC Grant Agreement No. 226544. We thank the MINECO "Ramón y Cajal" (Ref. RYC-2013-14262) for funding the research contract of JGG. KK was funded by the project Resilient Forests (KB-29-009-003) of the Dutch Ministry of Economic Affairs. BJT and JBJ acknowledge support from the Danish National Research Foundation (DNRF Grant No 96). This research has received also funding from the European Union's H2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 691149 (SuFoRun).