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Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe’s known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985–2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests., Measurement(s)Geographic DistributionTechnology Type(s)digital curationFactor Type(s)countrySample Characteristic - Environmentprimary forestSample Characteristic - LocationEurope Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14540625

Background Over the last 25 years, the prominent forest certification system established by the Forest Stewardship Council (FSC) has used by many companies worldwide for claiming responsible forest management. The objectives of the Russian National FSC standard to decrease the size of clearcuts and the retention of forest elements such as residual seed trees need on-site validation to proof the effectiveness of FSC. To assess the ecological impacts of harvesting practices and benefits of FSC certification, we geospatially compared logging activities with and without FSC certification. Within a sample area covering approximately 3,000 km2 in the east of the Russian Arkhangelsk Region, we used available data on tree cover loss and satellite images to assess secondary impacts of clearcuttings on adjacent remnant forests and to quantify the logging intensity. Additionally, the size and structure as well as the density of skidding trails of ten specific clearcuttings located within the sample area were surveyed using satellite images and in the field observation to delineate the boundaries of clearcuts and forested remnants within the clearcuts. Results We found a significant increase of small-scale tree cover loss in the proximity of the clearcuts. Patchy dieback is possibly linked to the scale and intensity of logging in the surroundings. On the investigated clearcuts, FSC failed to reduce the size, to increase the retention of forest remnants including seed trees on logged areas, and to maintain larger tracts of undisturbed ground and soil compared to clearcuts that were logged before they received FSC-certification. Conclusions Trees and forest remnants remaining inside an increasingly stressed forest ecosystem matrix may not resist further harvesting-related and climate change-induced stresses and disturbances. Large-scale clearcuttings seem to have negative impacts even in adjacent forests and undermine the ecological effectiveness of FSC certification in the study area. The Russian FSC standard is not clearly setting effective guidelines that induce a change in clearcutting practices in order to reduce ecological risks.

Expectations of what forests and woodlands should provide vary among locations, stakeholder groups, and over time. Developing multifunctional forests requires understanding of the dynamic roles of traditions and cultural legacies in social-ecological systems at multiple levels and scales. Implementing policies about multifunctional forests requires a landscape and social-ecological perspective, and recognition of both spatial and temporal features at multiple scales. This study explores the dissemination of even-aged silviculture in central, eastern and northern Europe, and the consequences of choosing different vantage points in social-ecological systems for mapping of barriers, and to identify levers, towards multifunctional forest landscapes. Using a narrative approach, we first summarise the development of even-aged silviculture in four European regions. Next, we focus on Sweden as a keen adopter of even-aged silviculture, and identify levers at three groups of vantage points. They were (1) biosphere with biodiversity as short-hand for composition, structure and function of ecosystems, which support human well-being at multiple scales; (2) society in terms of different levels of stakeholder interactions from local to global, and (3) economy represented by value chain hierarchies and currencies. The emergence of even-aged silviculture >200 years ago formed an expanding frontier from central to northern Europe. Sustained yield wood production and biodiversity conservation encompass different portfolios of ecosystem aspects and spatio-temporal scales. Ignorance and lack of knowledge about these differences enforce their mutual rivalry. An exploratory review of six groups of stakeholders at multiple levels in the traditional industrial forest value chain highlights inequalities in terms of distribution of income and power across different levels of governance. This effectively marginalises other than powerful industrial actors. The distribution of financial results along the value chain is dynamic in space and time, and not all benefits of forest ecosystems can be measured using monetary valuation. There are also other currencies and incentives. A discussion of cultural trajectories in central and eastern European, Russian and Swedish forest management illustrates that forest history patterns repeat themselves. Longitudinal case studies of countries and regions can help foster holistic multi-dimensional and multi-level systems thinking. Application of deep levers of change is likely to require external drivers. A key challenge is to handle the manufacturing of doubt and decay of truth, i.e., the appearance of alternative facts, and the diminishing role of evidence and systems analyses in political and civic discourses. This transition is fuelled by new and rapidly evolving digital arenas.

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Jörg Sommer, Pierre L. Ibisch und Achim Brunnengräber: Ökologie und Heimat – Gutes Leben für alle oder die Rückkehr der braunen Naturschützer? Jahrbuch Ökologie 2021. Hirzel Verlag, Stuttgart 2020. 247 Seiten. Kartoniert 19,80 €. ISBN 978-3-7776-2864-6

Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe’s known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985–2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests.

A whole systems thinking approach to conservation has spawned new approaches in adaptive management planning that require a crucial understanding of what is essential for the functionality of ecosystems and the biodiversity they embrace. In this context, the key ecological attributes (KEA) have been introduced as aspects of a conservation target's biology or ecology that, if missing or altered, would lead to the loss of that target over time. Ecological stresses describe the impaired status of KEAs. Whilst for threats, the drivers of stresses, a systematic classification has been suggested and adopted by IUCN, all existing proposals for stresses and KEAs are preliminary. In order to fill the gap and provide conservation analysts and practitioners with a standard terminology supporting adaptive management planning we suggest a first hierarchical framework and comprehensive classification of key ecological attributes and corresponding stresses to biodiversity. Analyzing 22 vulnerability assessments in 13 countries, spread across 5 continents, as well as an extensive literature review, we identified 144 specific KEAs and stresses. These are differentiated and classified according to three hierarchical levels, 11 KEA and stress classes and 42 general KEAs and stresses. Our classification may help with describing and understanding both the natural functionality and also impaired functioning of biodiversity targets, as well as assist with the development of appropriate conservation strategies. The classification of key ecological attributes is presented as a list but it is important to recognize that the diverse array of KEAs and stresses are systemically interrelated across scales.

Forest management influences a variety of ecosystem structures and processes relevant to meso- and microclimatic regulation, but little research has been done on how forest management can mitigate the negative effects of climate change on forest ecosystems. We studied the temperature regulation capacity during the two Central European extreme summers in 2018 and 2019 in Scots pine plantations and European beech forests with different management-related structural characteristics. We found that the maximum temperature was higher when more trees were cut and canopy was more open. Logging 100 trees per hectare increased maximum temperature by 0.21–0.34 K at ground level and by 0.09–0.17 K in 1.3 m above ground. Opening the forest canopy by 10% significantly increased T max, measured 1.3 m above ground by 0.46 K (including pine and beech stands) and 0.35 K (only pine stands). At ground level, T max increased by 0.53 K for the model including pine and beech stands and by 0.41 K in pure pine stands. Relative temperature cooling capacity decreased with increasing wood harvest activities, with below average values in 2018 (and 2019) when more than 656 (and 867) trees per hectare were felled. In the pine forests studied, the relative temperature buffering capacity 1.3 m above ground was lower than average values for all sample plots when canopy cover was below 82%. In both study years, mean maximum temperature measured at ground level and in 1.3 m was highest in a pine-dominated sample plots with relatively low stand volume (177 m 3 ha −1) and 9 K lower in a sample plot with relatively high stock volumes of Fagus sylvatica (>565 m 3 ha −1). During the hottest day in 2019, the difference in temperature peaks was more than 13 K for pine-dominated sample plots with relatively dense (72%) and low (46%) canopy cover. Structural forest characteristics influenced by forest management significantly affect microclimatic conditions and therefore ecosystem vulnerability to climate change. We advocate keeping the canopy as dense as possible (at least 80%) by maintaining sufficient overgrowth and by supporting deciduous trees that provide effective shade.

As a result of ongoing climate change and more frequent heat events, the regulating services of land cover in terms of moderating and mitigating local temperatures are increasingly important. While the reduced temperatures found in forests and wetlands are recognized, their wider contribution to regional landscape cooling remains largely uncharacterized and unquantified. Herein, we propose and test a new method that estimates the temperature response and inertia of landscapes in high temperatures, based on land cover share. In order to achieve this goal, we combined the MODIS daytime land surface temperature (henceforth LST) time series and CORINE land cover data. We classified the time series in two ways, i.e. by stepwise temperature range (−10/−5 °C to +35/+40 °C) and by the occurrence of hot days (days with a mean LST ≥ 30 °C). As an explanatory variable, we developed and used a greenest pixel composite of the MODIS normalized difference vegetation index (NDVI) time series. In our study area, covering parts of northeastern Germany and western Poland, the fragmented landscape has heterogeneous temperature patterns, including urban heat islands, warm agricultural areas, cool forests and cold wetlands. We found that at high temperature ranges only forests and wetlands remained comparably cool, with LSTs up to 20.8 °C lower than the maximum LST in the study area. The analysis of land cover shares and LSTs revealed the substantial cooling effect of forests and wetlands in line with increasing land cover share in higher temperature ranges, as well as on hot days. The relation between LST and the NDVI indicated vegetation cover as the cause. We propose the corresponding metrics to quantify landscape-level temperature regulation. Equally, we advocate for management to identify these ecosystem services and their current and potential contributions, along with implications for sustaining and increasing, both tree cover and wetlands and thereby adapting landscapes to climate change.

The Forest Stewardship Council (FSC) is a voluntary sustainability standard with global reach that has been developed to encourage responsible and sustainable forest management. Despite its broad appeal, there is little scientific assessment to substantiate the effectiveness of FSC in the boreal zone. In this study, an ecosystem-based and participatory approach was applied to a case study in the Arkhangelsk Region of the Russia Federation to assess the potential influence of the principles, criteria and indicators of the Russian FSC standard. An ECOSEFFECT theoretical plausibility analysis was conducted to evaluate the potential effectiveness of FSC in safeguarding the ecological integrity of the ecosystem. Besides spatial analysis and a field visitation, core elements of the methodological procedure were workshops with experts and stakeholders who directly contributed to knowledge mapping and analysis. The results of the study suggest FSC can potentially influence and improve forest management including monitoring and evaluation, foster the institutional capacity, and enhance knowledge on the impacts of forest management. Theoretically, FSC has a certain potential to reduce a range of anthropogenic threats to the ecosystem, such as large-scale deforestation and forest degradation, logging of High Conservation Value Forests, large size of clear-cuts, excessive annual allowable cuts, damage to trees during forest operations, and hydrological changes. However, human-induced fire is the only ecological stress that was assumed to be effectively tackled through a strong and positive influence of FSC. The results of the theoretical analysis with a semi-quantitative evaluation revealed the potential for FSC to generate much more effective outcomes for biodiversity by prudently targeting key ecological problems. The biggest problem is the large-scale clear-cutting practice, especially within IFL. These devastating practices are not promoted by, but are compliant with the current Russian FSC standard. This feeds doubts about the consistency of FSC practice and its credibility.

International audience

A small plot study of Chalara infestation was carried out in a recently established stand of natural secondary woodland on an abandoned field in Suffolk. The aim was to determine the pattern of disease spread in relation to the structure of young ash stands. Wind-dispersed colonisation by ash was typically patchy but clustered along the eastern edge of mature ancient semi-natural woodland. All the trees were small with an average diameter of 1.25 ± .614 cm and average height of 1.63 ± 1.54 metres. Symptoms of dieback were recorded in 51% of the 220 quadrats surveyed but was most prevalent in areas where tree density was highest. Of the 1,343 trees sampled 33% were infected and showing advanced stages of dieback in areas where the disease was most concentrated. The observed positive correlation between tree height and the prevalence of dieback may be due to chance effects emerging from larger patterns of wind-born dispersal in both host and pathogen. The results of the study point to indiscriminate modes o...

Growing levels of uncertainty and vulnerability generated by land use conversion and climate change set demands on local communities and national institutions to build synergies between the diverse array of knowledge systems in order to provide policy makers and practitioners with the best available information to decide what urgent actions must be taken. Science policy arenas and agreements such as the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) and the Convention on Biological Diversity (CBD) recognize the importance of different types of knowledge and the need for broad stakeholder involvement, yet the use of indigenous and local knowledge (ILK) in environmental decision-making processes is still underdeveloped. This study involved working with local stakeholders, using the MARISCO method (adaptive MAnagement of vulnerability and RISks at COnservation sites) to carry out a systematic situation analysis of the existing socioenvironmental conditions. The assessments were conducted in the Kavango East Region in northern Namibia with the participation of inhabitants of the Khaudum North Complex, a protected area network covering wooded savannahs belonging to the Northern Kalahari sandveld. General outcomes of the assessments and evaluations made by the local stakeholders concerning the most critical drivers of degradation of the ecosystems appeared to support existing scientific knowledge of the study area, demonstrating that community-based assessments can provide valuable information about socioecological systems where scientific data are scarce. The findings of this study also highlight the importance of power dynamics for the implementation of participatory processes and the interpretation of their outcomes.

Targeting the maintenance of functional ecosystems that provide the significant basis for human well‐being is an integral part of an ecosystem‐based sustainable development. Underlying causes of ecosystem degradation such as global climate change and ever‐growing human demands that rapidly shift socioeconomic and political baselines are often unmanageable at a local scale and require a new approach to planning and action in ecosystem management. The framework conditions that challenge sustainable development are shaped by increasing Volatility, Uncertainty, Complexity, and Ambiguity (VUCA concept). Using the MARISCO method (adaptive management of vulnerability and risks at conservation sites), we analyzed 22 conservation sites, covering 26 protected areas and six administrative areas on four different continents and involving 524 participants. VUCA conditions were present across cultures and biomes, yet the responses in planning and management varied among conservation sites. The findings of both the qualitative and quantitative analyses confirm that participants understand how far human well‐being heavily depends on the functionality of ecosystems that were seen to suffer from a wide range of stresses and threats of varying criticality. Worldwide, local stakeholders and experts rated impacts of global climate change as most critical. In attempts to achieve ecosystem‐based sustainable development, most management teams strive for more risk‐robust and adaptive strategies by advocating for active risk management. A common factor identified among all case studies was the need for cooperative management between smaller conservation sites in order to address large‐scale challenges.

Protected areas are arguably the most important instrument of biodiversity conservation. To keep them fit under climate change, their management needs to be adapted to address related direct and indirect changes. In our study we focus on the adaptation of conservation management planning, evaluating management plans of 60 protected areas throughout Germany with regard to their climate change-robustness. First, climate change-robust conservation management was defined using 11 principles and 44 criteria, which followed an approach similar to sustainability standards. We then evaluated the performance of individual management plans concerning the climate change-robustness framework. We found that climate change-robustness of protected areas hardly exceeded 50 percent of the potential performance, with most plans ranking in the lower quarter. Most Natura 2000 protected areas, established under conservation legislation of the European Union, belong to the sites with especially poor performance, with lower values in smaller areas. In general, the individual principles showed very different rates of accordance with our principles, but similarly low intensity. Principles with generally higher performance values included holistic knowledge management, public accountability and acceptance as well as systemic and strategic coherence. Deficiencies were connected to dealing with the future and uncertainty. Lastly, we recommended the presented principles and criteria as essential guideposts that can be used as a checklist for working towards more climate change-robust planning.

Established under the European Union (EU) Birds and Habitats Directives, Natura 2000 is one of the largest international networks of protected areas. With the spatial designation of sites by the EU member states almost finalized, the biggest challenge still lying ahead is the appropriate management of the sites. To evaluate the cross-scale functioning of Natura 2000 implementation, we analyzed 242 questionnaires completed by conservation scientists involved in the implementation of Natura 2000 in 24 EU member states. Respondents identified 7 key drivers of the quality of Natura 2000 implementation. Ordered in decreasing evaluation score, these drivers included: network design, use of external resources, legal frame, scientific input, procedural frame, social input, and national or local policy. Overall, conservation scientists were moderately satisfied with the implementation of Natura 2000. Tree modeling revealed that poor application of results of environmental impact assessments (EIA) was considered a major constraint. The main strengths of the network included the substantial increase of scientific knowledge of the sites, the contribution of nongovernmental organizations, the adequate network design in terms of area and representativeness, and the adequacy of the EU legal frame. The main weaknesses of Natura 2000 were the lack of political will from local and national governments toward effective implementation; the negative attitude of local stakeholders; the lack of background knowledge of local stakeholders, which prevented well-informed policy decisions; and the understaffing of Natura 2000 management authorities. Top suggestions to improve Natura 2000 implementation were increase public awareness, provide environmental education to local communities, involve high-quality conservation experts, strengthen quality control of EIA studies, and establish a specific Natura 2000 fund.

The Forest Stewardship Council (FSC) is widely recognized as certification standard for responsible forest management across all major forest biomes. Still, only few field studies assessed if FSC certification generates measurable positive impacts in a managed forest ecosystem. Consequently, unequivocal evidence on ecological effectiveness of FSC is scarce. Especially the Russian boreal forests, where FSC has shown a rapid growth of certified areas, have hardly been studied regarding ecologically positive achievements delivered by certification compared to conventional practices. In this case study, systematic research was carried out to assess the ecological effectiveness of FSC in the boreal forests of the Arkhangelsk Region, Russian Federation. At harvested sites belonging to two companies, managed with and without FSC-certification respectively, and at a primary reference forest, 95 variables related to five indicators (living tree biomass, deadwood, rejuvenation, vegetation, and microclimate regulation) were empirically assessed. With regard to the most ecologically relevant parameters chosen for this study, there were very few substantial differences of FSC-certified forestry operations to conventional ones. This can be explained with the equally high amounts of harvested timber (ca. 97% extracted) as well as the practice of large-scale clearcutting (up to >50 ha), generating significant structural and functional ecological changes in the forest ecosystem. The findings suggest that there is no real change in forest operations towards an ecologically responsible harvesting. Rather, FSC-certified forestry contributes to the ongoing loss of primary forests in the remaining Intact Forest Landscapes, and FSC does not seem to slow down the loss of ecosystem functionality. Actually, the large-scale clearcuts do not resemble any comparable natural disturbance regime and produce risky ecological changes related to micro- and mesoclimate. Our data show substantial heating of the large clearcuts and the loss of ecosystemic buffering capacity, being highly relevant in the context of vulnerability to climate change.

The island of New Guinea hosts the third largest expanse of tropical rainforest on the planet. Papua New Guinea-comprising the eastern half of the island-plans to nearly double its national road network (from 8,700 to 15,000 km) over the next three years, to spur economic growth. We assessed these plans using fine-scale biophysical and environmental data. We identified numerous environmental and socioeconomic risks associated with these projects, including the dissection of 54 critical biodiversity habitats and diminished forest connectivity across large expanses of the island. Key habitats of globally endangered species including Goodfellow's tree-kangaroo (Dendrolagus goodfellowi), Matchie's tree kangaroo (D. matschiei), and several birds of paradise would also be bisected by roads and opened up to logging, hunting, and habitat conversion. Many planned roads would traverse rainforests and carbon-rich peatlands, contradicting Papua New Guinea's international commitments to promote low-carbon development and forest conservation for climate-change mitigation. Planned roads would also create new deforestation hotspots via rapid expansion of logging, mining, and oil-palm plantations. Our study suggests that several planned road segments in steep and high-rainfall terrain would be extremely expensive in terms of construction and maintenance costs. This would create unanticipated economic challenges and public debt. The net environmental, social, and economic risks of several planned projects-such as the Epo-Kikori link, Madang-Baiyer link, Wau-Malalaua link, and some other planned projects in the Western and East Sepik Provinces-could easily outstrip their overall benefits. Such projects should be reconsidered under broader environmental, economic, and social grounds, rather than short-term economic considerations.