Search

Exchanges of mass and energy between a subalpine coniferous forest and the atmosphere have been continuously monitored since few decades at Renon (Bolzano, Italy) applying the eddy covariance (EC) technique. The station is part of the Integrated Carbon Observation System (ICOS) EU Research Infrastructure. The area surrounding the site is characterized by complex topography, with a mean slope angle of about 11° and a Southward aspect. In this study, we focused on the analysis of turbulent energy fluxes (sensible and latent heat) and the energy budget closure of the forest during a period of about three months (August-October 2021), when a below-canopy EC system was additionally deployed to better understand the dynamics of turbulent exchanges. The energy balance closure was assessed for periods characterized by distinct wind circulation patterns (thermally-driven slope winds vs. synoptic winds) and turbulent energy fluxes were processed applying different coordinate rotation methods (double rotation and planar fit). We found significant differences in the magnitude of sensible and latent heat fluxes computed with double rotation or planar fit. These differences were more marked during periods characterized by slope winds, suggesting a connection with local advection performed by these winds.

BackgroundClimate change is one of the main factors shaping the distribution and biodiversity of organisms, among others by greatly altering water availability, thus exposing species and ecosystems to harsh desiccation conditions. Insects are especially threatened by these challenging dry environments, because of their small size and thus large surface area to volume ratio. Integrating transcriptomics and physiology is key to advancing our knowledge on how species cope with desiccation stress, and these studies are still best accomplished in model organisms.ResultsHere, we characterized the natural variation of European D. melanogaster populations across climate zones and found that strains from arid regions were similar or more tolerant to desiccation compared with strains from temperate regions. Tolerant and sensitive strains differed not only in their transcriptomic response to stress but also in their basal expression levels. We further showed that gene expression changes in tolerant strains correlated with their physiological response to desiccation stress and with their cuticular hydrocarbon composition. Transposable elements, which are known to influence stress response across organisms, were not found to be enriched nearby differentially expressed genes. Finally, we identified several tRNA-derived small RNA fragments that differentially targeted genes in response to desiccation stress.ConclusionsOur results showed that by integrating transcriptomics with physiological trait analysis we can pinpoint the genetic basis of the differences in tolerance to desiccation stress found in natural D. melanogaster populations. Moreover, we showed that, beyond starvation and aging, tRNA-derived small RNA fragments (tRFs) appear to be relevant post-transcriptional gene regulators in response to desiccation stress.

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness., Proceedings of the National Academy of Sciences of the United States of America, 119 (6), ISSN:0027-8424, ISSN:1091-6490

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.

Atmospheric nitrogen (N) depositions have increased sharply since the industrial revolution. Numerous authors forecasted severe impacts on forest ecosystem services and functioning when new N input exceeds both biotic and abiotic holding capacity, in what is known as the N saturation process. However, most of the studies performed so far to quantify these impacts have largely neglected the potential role of the canopy and associated biota in biogeochemical processes. The present review collects and summarizes the existing literature about all the interactions of atmospheric N deposition and the canopy, evidencing different processes that may interfere with them. By summarizing the research of the last three decades, we found that a share of both wet and dry N deposition can be retained within the canopy, partially absorbed by the leaves or other aerial parts and utilized for plant metabolism, substantially changing the fate of N in the ecosystem. Focusing on canopy-associated biota, we analyzed how this compartment can represent a highly reactive layer and have an active role in different processes involved in the N cycle such as nitrification, fixation and volatilization. It emerges clearly that the canopy hosts a complex network of processes; however, despite this, most of the manipulative experiments failed to include the canopy into the studies. The documented manipulative experiments which included the canopy are mainly short-term and represent very few forest types. Moreover, we highlighted a lack of research concerning the interaction between the complex network of canopy processes, atmospheric N deposition and other global change factors, such as increasing atmospheric CO2 concentration, increasing temperature and drought events.

Abstract. Forests ability to store carbon is strongly connected with the amount of nitrogen (N) that forest ecosystems can retain; N is indeed considered the most limiting nutrient for terrestrial ecosystem's net primary productivity. Since the industrial revolution, human activities have more than doubled the rate of N input into the nitrogen cycle and this could alleviate N limitation thus stimulating plant growth. However, it has been suggested that when N availability exceeds biotic demand and abiotic sinks, additional N can trigger a negative cascade effect: nutrient imbalance, reduced productivity, increased losses of N, eutrophication and acidification of soil and water, leading toward forest decline and net greenhouse gases emissions. The consequences of increased N deposition on forest depend in large share on the fate of N in the ecosystem, which can be simulated and quantified by a fertilization at a known isotopic signature. Nevertheless, most of the tracer experiments performed so far added the fertilizer directly to the forest floor, neglecting the potential role of N uptake by the forest canopy. In the Italian Alps, we are conducting an experiment where both types of N additions (above and below the canopy layer) are performed in two different forest stands, to understand if canopy fertilization better simulates ecological consequences of increased atmospheric N deposition. These field-scale manipulation experiments are willing to test two different hypotheses: i) the N uptake by trees in the above-canopy N addition experimental sites is higher than under-canopy N addition ii) forest growth rate varies with the type of treatment. To describe the fate of the applied N, stable isotope techniques have been adopted: the forest sites, fertilized with NH4NO3 at a known isotopic signature, are sampled for all the ecosystem components (plant, soil and water) periodically to determine the total N content and its isotopic signature. The δ15N values permit to calculate the recovery of N-fertilizer in tree tissues, soil and leaching-water, allowing us to understand how N allocation varies under these two fertilization strategies and how this affects C sequestration potential. Results regarding the short-term effects over the first 6 years of data collection will be presented.

N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools.

Several plant species emit isoprene, a compound able to protect plants against high temperatures and oxidative stresses, and to affect the oxidative capacity of the atmosphere. The emission of isoprene in monocots is much less investigated than in dicots. We explored the emission of isoprene among members of the Poaceae tribe Arundineae, and its potential impact on plant performance. Our results confirm, also in monocots, the existence of a significant correlation between photosynthesis and isoprene emission and further suggest that isoprene inversely correlates to habitat xericity in unstressed Arundineae. Isoprene emission capacity developed rapidly in unfolding leaves, implying some developmental differences in the control of this biosynthetic pathway with respect to dicots. Among Arundineae, marked species-specific differences in several parameters related to plant productivity (photosynthesis, carbon lost as isoprene, water use efficiency, biomass and chloroplast ulrastructure) were observed. Isoprene presence improved structural organization of thylakoid membrane system and decreased the heat dissipation at physiological temperature. Our results demonstrate that, as in dicots, also in monocots isoprene could contribute to improve photosynthesis performance, although this was not necessarily reflected in higher yields, at least in unstressed conditions.

In mountainous areas of Europe, the abandonment of grasslands followed by forest expansion is the dominant land-use change. Labile (i.e. easily decomposable) litter represents the major source for soil microbial products, which promote soil aggregation and long-term C stabilization. Our objective was to investigate changes in the content and origin of soil C components involved into aggregate stabilization (i.e. carbohydrates) following forest expansion on abandoned grassland in the Alps, where only few studies have been conducted. Changes in carbohydrates and thermally labile C were assessed along a land-use gradient in the Southern Alps (Italy) following analysis of carbohydrate monomers and thermal analysis of mineral soil and physical soil fractions. The land-use gradient comprised managed grassland, two transitional phases in which grassland abandonment led to colonization by Picea abies (L.) Karst., and an old forest dominated by Fagus sylvatica L. and P. abies. Grassland abandoned for 10 years tended to have higher levels of carbohydrate and thermally labile soil C than managed grassland and old forest, presumably caused by differences in the quality and amount of litter input. Carbohydrates and thermally labile C showed similar patterns in bulk soil, suggesting that thermal analysis can be used to complement chemical analysis although a straightforward relationship could not be established. Following forest expansion on abandoned grassland, ratios of microbially to plant-derived carbohydrates and thermally labile to resistant components decreased in bulk soil and soil fractions. Forest expansion entailed decreasing amounts of microbially derived compounds known to be important for aggregate stability, and corresponded to decreased soil C allocation to stable aggregates. The combination of carbohydrate and thermal analyses revealed a lower abundance of microbially derived C components after forest colonization on abandoned grasslands, thus resulting in lower physical protection of soil C considering that carbohydrates of microbial origin actively promote soil aggregation.

The release of organic compounds from roots is a key process influencing soil carbon (C) dynamics and nutrient availability in terrestrial ecosystems. Through this process, plants stimulate microbial activity and soil organic matter (SOM) mineralization thus releasing nitrogen (N) that sustains gross and net primary production (GPP and NPP, respectively). Root inputs also contribute to SOM formation. In this study, we quantified the annual net root-derived C input to soil (Net-Croot) across six high fertility forests using an in-growth core isotope technique. On the basis of Net-Croot, wood and coarse root biomass changes, and eddy covariance data, we quantified net belowground C sequestration. Belowground C accumulation and GPP were inversely related to soil C:N, but not to climate or stand age. Soil C content and C:N were also related to soil texture. At these high fertility sites, biomass growth did not change with soil C:N; however, biomass growth-to-GPP ratio significantly increased with increasing soil C:N. This was true for both our six forest sites and for another 23 high fertility sites selected at a global scale. We suggest that, at high fertility sites, plant N demand interacts with soil C:N stoichiometry and microbial activity, resulting in higher allocation of C to above ground tree biomass with increasing soil C:N ratio. When C:N is high, microbes have a low C use efficiency, respire more of the fresh C inputs by roots and prime SOM decomposition, thereby increasing N availability for tree uptake. Soil C sequestration would therefore decrease, whereas the extra N released during SOM decomposition can promote tree growth and ecosystem C sink allocation in aboveground biomass. Conversely, C is sequestered in soil when low soil C:N promotes microbial C use efficiency and new SOM formation and stabilization on clay particles.

Grassland abandonment followed by forest succession is the dominant land-use change in the European Alps. We studied the impact of current forest expansion on mountain grassland on changes in physical soil organic carbon (SOC) fractions along a land-use and management gradient, focusing on changes in aggregate stability and particulate organic matter (POM). Four successional stages were investigated: managed grassland, two transitional phases in which grassland abandonment led to colonization by Picea abies (L.) Karst., and old mixed forest dominated by Fagus sylvatica L. and P. abies. Soil samples collected from the mineral soil (0–5 cm, 5–10 cm, 10–20 cm) were fractionated following two procedures: 1) aggregate size fractionation, separating aggregates based on their dimension, and 2) size-density fractionation, separating stable aggregates from non-occluded POM. The dimension of aggregates assessed by aggregate size fractionation tended to increase, whereas SOC allocation to stable aggregates assessed by size-density fractionation decreased following conversion of grassland to forest (e.g. from 81 to 59 % in the 0–5 cm layer). The amount of SOC stored in POM increased by 3.8 Mg ha−1 in the integrated 0–20 cm layer from managed grassland to old forest. The combination of two physical SOC fractionation procedures revealed that natural forest succession on abandoned grasslands led to a decline in physical SOC stability in the mineral soil, suggesting that SOC can become more susceptible to management and environmental change.

The dominant land-use change in the Southern Alps is grassland abandonment, followed by forest expansion. Forest expansion is commonly thought to increase ecosystem C sequestration, but contradictory results have been reported. Our objective was to investigate soil organic carbon (SOC) and nitrogen (N) changes after forest expansion on abandoned grasslands. A land use and management gradient was identified in a subalpine area of Trentino (Italy) comprising four successional stages: (i) managed grassland (manG), mown and manured annually for more than 100 years; (ii) grassland abandoned 10 years ago (abanG), with shrubs and Picea abies saplings; (iii) early-stage forest (earlyF), dominated by P. abies established on grassland abandoned around 1970; and (iv) old forest (oldF), dominated by Fagus sylvatica and P. abies, representing a reference for long-term forest land use. Organic and mineral soil layers were collected down to 30 cm at eight points within three plots for each successional stage. Significantly more SOC was stored in organic layers of oldF compared with other successional stages and the organic layer N stocks were higher in oldF compared with manG and abanG. The SOC and N concentrations were higher in the top 10 cm mineral soil of grassland plots compared with forest. The earlyF and oldF stored, on average, 28–35% less SOC in top 10 cm mineral soil compared with manG and abanG when stocks were calculated using equivalent soil mass (ESM) and equivalent soil depth (ESD) approaches respectively. SOC stocks in mineral soil (0–30 cm) calculated with ESD decreased from manG to oldF; when accounting for total stone content, SOC stocks in earlyF and oldF were 40% and 50% lower than in manG, respectively. The inclusion of organic layers offset mineral SOC stock differences when only stones Our study revealed a decrease in total SOC stocks after forest expansion on abandoned subalpine grasslands. Tree biomass development contributed to increasing C storage, especially in a long-term forest land use. Forest management should consider that tree biomass and organic layer development require a minimum time period following grassland abandonment to compensate for the decrease in mineral SOC stocks and contribute positively to climate change mitigation.

Forests are covering a substantial part of European mountains. The elongation of the growing season due to climate change and warmer summers are increasing the rate of soil respiration. However, the effect is at some sites partially compensated by droughts. Temperate mountain forests ecosystems are mostly releasing carbon dioxide whereas nitrogen oxides are of lesser importance. Societal changes are also affecting the greenhouse gas emissions from forests. The structural change in agriculture causes an increase of the forest area. The change in land use from grassland to forest, leads to the formation of an organic litter layer on the soil surface but to a reduction of the carbon input by decaying roots to the mineral layers. Moreover, the effect of the increased carbon sequestration in the biomass of more productive forests is partially offset by losses of carbon dioxide from the soil. Climate change also calls for an adaptation of the strategy of forest management. The economical feasibility of timber production in remote high-elevation forests will not be significantly increased. Nevertheless, even marginally productive mountain forests need to be managed in order to ensure the provision of ecosystem services such as protection against natural hazards. Mountain forests with a stable stand structure can better cope with disturbances such as storms and biotic pressures, thereby reducing the risk of carbon losses to the atmosphere.

The partitioning of the total soil CO2 efflux into its two main components: respiration from roots (and root-associated organisms) and microbial respiration (by means of soil organic matter (SOM) and litter decomposition), is a major need in soil carbon dynamics studies in order to understand if a soil is a net sink or source of carbon. The heterotrophic component of the CO2 efflux was estimated for 11 forest sites as the ratio between the carbon stocks of different SOM pools and previously published (Δ14C derived) turnover times. The autotrophic component, including root and root-associated respiration, was calculated by subtracting the heterotrophic component from total soil chamber measured CO2 efflux. Results suggested that, on average, 50.4 % of total soil CO2 efflux was derived from the respiration of the living roots, 42.4 % from decomposition of the litter layers and less than 10 % from decomposition of belowground SOM. The Δ14C method proved to be an efficient tool by which to partition soil CO2 efflux and quantify the contribution of the different components of soil respiration. However the average calculated heterotrophic respiration was statistically lower compared with two previous studies dealing with soil CO2 efflux partitioning (one performed in the same study area; the other a meta-analysis of soil respiration partitioning). These differences were probably due to the heterogeneity of the SOM fraction and to a sub-optimal choice of the litter sampling period.

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863-881 nm) and the H18 (745-751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.

Ground reference data collection represents an important element in the prediction of stem volume with LiDAR-derived variables, and at present it is the most expensive part of such analyses. In this paper two aspects of ground reference data collection were analyzed: (1) the positioning error of the ground plots; and (2) the optimal number of training plots. A system for the prediction of stem volume at area-based level was adopted. LiDAR data were preprocessed and 13 variables describing both height and coverage were extracted. Models were defined using a stepwise ordinary least square (OLS) regression. Three experiments were conducted: (i) the role of the plots positioning error on prediction accuracy; (ii) the influence of random downsampling of plot numbers on prediction accuracy; and (iii) the influence of a stratified downsampling of plot numbers on prediction accuracy based on LiDAR-derived variables. A dataset comprising 799 ground plots was used. They were distributed throughout a mountainous area in the Southern Alps, where the presence of a complex landscape increases the uncertainty of the Global Positioning System (GPS) accuracy, and where a large variety of tree forest species and climatic environments make it necessary to have a large number of sample plots for accurate characterization of the study area. All the experiments provided important indications for LiDAR based forest inventories: the GPS error did not significantly influence the prediction accuracy and it was possible to reduce the number of training samples without compromising the generalization ability of the prediction model. Leading on from these findings, a new ground sampling protocol based on genetic algorithms was proposed. The new protocol allowed us to obtain promising results for the considered dataset: using only 53 training plots, instead of 534 in the original dataset, we obtained the same results for the validation set. These results, obtained in a complex mountainous area, are representative of Alpine environments and allow us to infer that similar (or better) results could also be obtained within non mountainous areas.

In this study we examined ecosystem respiration (R-ECO) data from 104 sites belonging to FLUXNET, the global network of eddy covariance flux measurements. The goal was to identify the main factors involved in the variability of R-ECO: temporally and between sites as affected by climate, vegetation structure and plant functional type (PFT) (evergreen needleleaf, grasslands, etc.). We demonstrated that a model using only climate drivers as predictors of R-ECO failed to describe part of the temporal variability in the data and that the dependency on gross primary production (GPP) needed to be included as an additional driver of R-ECO. The maximum seasonal leaf area index (LAI(MAX)) had an additional effect that explained the spatial variability of reference respiration (the respiration at reference temperature T-ref=15 degrees C, without stimulation introduced by photosynthetic activity and without water limitations), with a statistically significant linear relationship (r2=0.52, P 70% of the variance for most vegetation types. Exceptions include tropical and Mediterranean broadleaf forests and deciduous broadleaf forests. Part of the variability in respiration that could not be described by our model may be attributed to a series of factors, including phenology in deciduous broadleaf forests and management practices in grasslands and croplands.

In this paper, we present a study on the efficiency of multi-return LIDAR (Light Detection Ranging) data in the estimation of forest stem volume over a multi-layered forest area in the Italian Alps. The goals of this paper are (1) to verify the usefulness of multi-return LIDAR data compared to single-return data in forest volume estimation and (2) to define the optimal resolution of a stem volume distribution raster map over the investigated area. To achieve these goals, raw data were segmented into a net, and different cell dimensions were investigated to maximize the relationship between the LIDAR data and the ground-truth information. Twenty predicting variables (e.g., mean height, coefficient of variation) have been extracted from multi-return LIDAR data, and a multiple linear regression analysis has been used for predicting tree stem volume. Experimental results found that the optimal resolutions of the net square cells were 40 m. The analysis indicated that in a mixed multi-layered forest, characterized by a complex vertical structure, the correct selection of the map spatial resolution and the inclusion of the secondary-return data were important factors for improving the effectiveness of the laser scanning approach in forest inventories. The experimental tests showed that the chosen model is effective for the estimation of stem volume over the analyzed area, providing good results on all the three considered validation methods.

Six Italian research sites, representative of Mediterranean and mountain forests and equipped with eddy covariance towers, were used in this study to test the performance of the CENTURY 4.5 model in predicting the dynamics of soil organic carbon (SOC) changes during the commitment periods (CP) of the Kyoto Protocol (2008-2012; 2013-2017). We show that changes in SOC stocks over short periods of time are difficult to detect, and explore the potential for models to be used for reporting SOC changes for forests that will remain forests, under Article 3.4 of the Kyoto Protocol. As the eddy covariance flux sites have been active for 10 yr on average, being initiated over the period between 1996 and 1998, the model was evaluated by comparing the modelled SOC stocks with those directly measured at each site in different years. Since long term series of observed values for soil carbon were not available, the validation of other model outputs such as net primary production (NPP) and soil nitrogen stocks, gives some confidence in long term simulations. Once the model performance was evaluated, two climate change scenarios, A1F1 (world markets-fossil fuel intensive) and B2 (local sustainability), were considered for prediction of C stock changes during the commitment periods of the Kyoto Protocol. In general, despite the need to consider the uncertainties in the direct measurements, at each site model fit with measured SOC stocks was good, with the simulated values within the standard deviation of the measurements. In this regard, the similarity between the SOC measured in 2008 and that predicted for the two forthcoming commitment periods points out the difficulty of detecting carbon stock changes by direct measurements, given the closeness in time to the present of the commitment periods. In any case, all sites show positive variations that are possibly related to the fertilization effects of increasing CO 2 and to longer growing seasons, since no change in management occurred. Compared with the SOC measured in 2008, at the end of the second commitment period, the modelled SOC variations were smaller than 2% in the Mediterranean forests and comprised between 2% and 7% in the mountain forests. These variations, although small, indicate it might be possible to statistically detect differences after 10 yr in mountain forests with a reasonable number of samples. In conclusion, this work shows that since SOC stock changes are minimal within both CP, models can be effective tools for estimating future changes in SOC amounts, as an alternative to, or in support of, direct measurements when a short period of time is considered.

Soil respiration is the second largest flux of carbon between terrestrial ecosystems and the atmosphere and is affecting climate sensitivity and vulnerability of the terrestrial carbon stock. Monitoring soil carbon dioxide efflux is a complex task, due to the high spatial and temporal variability of the fluxes. For this reason, more than 30 sampling points are required to attain reliable estimates of ecosystem soil respiration. However, the number of sampled points is often limited by labour, time and budget constraints. Stratified sampling is an alternative to random sampling as a method to reduce the number of sampling points when an effective proxy variable is available for the definition of the strata. In order to evaluate different sampling strategies we tested, with a Monte Carlo simulation, the effectiveness of random and stratified samplings, using experimental data collected in three alpine ecosystems (two forests and one grassland). We evaluated an innovative method for defining the strata to be sampled. The method is based on an initial sampling of soil respiration from a large number of candidate points in order to account for the spatial variability. The minimum number of sampling points required to adequately represent soil respiration for the entire area were then selected using stratified statistical sampling. We show that this method is unbiased and that it reduces considerably the uncertainty in the sampling process compared to random sampling. The method was highly effective in the two forest ecosystems, characterized by a high spatial variability in soil respiration and by a high temporal correlation of the fluxes. On the contrary the method was not so effective in the grassland site, where fluxes have lower spatial variability and temporal correlation. However, the stratified sampling offered a consistent reduction of the error (%) of the estimated annual soil CO 2 efflux in all the ecosystems. At the grassland ecosystem the average reduction of the error (%) of the annual CO 2 efflux was about 12%, while at the forest ecosystems the average reductions were 55% and 57%, respectively.

Belowground carbon inputs, in particular rhizodeposition, are a key component of the global carbon cycle and yet their accurate quantification remains a major challenge. In the present paper, the in-growth soil cores- 13 C method was used to quantify net root carbon input (root-derived C). Four different ecosystem types (forest, alpine grassland, apple orchard and vineyard) in northern Italy, characterized by C 3 vegetation with a broad range of aboveground net primary production (ANPP; 155–770 gC m − 2 y − 1 ) were investigated. Cores, filled with soil of a known C 4 isotopic signature were inserted at each site for twelve months. After extraction, root-derived C was quantified by applying a mass balance equation. Gross primary production (GPP) was determined by eddy covariance whereas ANPP was quantified using a biometric approach. NPP partitioning among sites differed, with fruit production dominating at agricultural sites. At these sites, belowground C inputs were dominated by rhizodeposits, likely due to relatively high root turnover. In natural ecosystems (forest and grassland) fine root production dominated belowground net primary production (BNPP) likely due to higher root growth determined by low phosphorus availability. Root derived C represented a significant contribution to BNPP varying from 40 to 60%. Our results underline the fact that failure to account for rhizodeposits may lead to a significant underestimation of BNPP.

Estimates of leaf gas-exchange characteristics using standard clamp-on leaf chambers are prone to errors because of diffusion leaks. While some consideration has been given to CO(2) diffusion leaks, potential water vapour diffusion leaks through chamber gaskets have been neglected. We estimated diffusion leaks of two clamp-on Li-Cor LI-6400 (Li-Cor, Inc., Lincoln, NE, USA) leaf chambers with polymer foam gaskets and enclosing either 2 or 6 cm(2) leaf area, and conducted a sensitivity analysis of the diffusion leak effects on Farquhar et al. photosynthesis model parameters - the maximum carboxylase activity of ribulose 1 x 5-bisphosphate carboxylase/oxygenase (Rubisco) (V(cmax)), capacity for photosynthetic electron transport (J(max)) and non-photorespiratory respiration rate in light (R(d)). In addition, net assimilation rate (A(n)) versus intercellular CO(2) (C(i)) responses were measured in leaves of Mediterranean evergreen species Quercus ilex L. enclosing the whole leaf chamber in a polyvinyl fluoride bag flushed with the exhaust air of leaf chamber, thereby effectively reducing the CO(2) and water vapour gradients between ambient air and leaf chamber. For the empty chambers, average diffusion leak for CO(2), K(CO2), (molar flow rate corresponding to unit CO(2) mole fraction difference) was ca. 0.40 micromol s(-1). K(CO2) increased ca. 50% if a dead leaf was clamped between the leaf chamber. Average diffusion leak for H(2)O was ca. 5- to 10-fold larger than the diffusion leak for CO(2). Sensitivity analyses demonstrated that the consequence of a CO(2) diffusion leak was apparent enhancement of A(n) at high CO(2) mole fraction and reduction at lower CO(2) mole fraction, and overall compression of C(i) range. As the result of these modifications, Farquhar et al. model parameters were overestimated. The degree of overestimation increased in the order of V(cmax) < J(max) < R(d), and was larger for smaller chambers and for leaves with lower photosynthetic capacity, leading to overestimation of all three parameters by 70-290% for 2 cm(2), and by 10-60% for 6 cm(2) chamber. Significant diffusion corrections (5-36%) were even required for leaves with high photosynthetic capacity measured in largest chamber. Water vapour diffusion leaks further enhanced the overestimation of model parameters. For small chambers and low photosynthetic capacities, apparent C(i) was simulated to decrease with increasing A(n) because of simultaneous CO(2) and H(2)O diffusion leaks. Measurements in low photosynthetic capacity Quercus ilex leaves enclosed in 2 cm(2) leaf chamber exhibited negative apparent C(i) values at highest A(n). For the same leaves measured with the entire leaf chamber enclosed in the polyvinyl fluoride bag, C(i) and A(n) increased monotonically. While the measurements without the bag could be corrected for diffusion leaks, the required correction in A(n) and transpiration rates was 100-500%, and there was large uncertainty in Farquhar et al. model parameters derived from 'corrected'A(n)/C(i) response curves because of uncertainties in true diffusion leaks. These data demonstrate that both CO(2) and water vapour diffusion leaks need consideration in measurements with clamp-on leaf cuvettes. As plants in natural environments are often characterized by low photosynthetic capacities, cuvette designs need to be improved for reliable measurements in such species.

A simple estimation of heterotrophic respiration can be obtained analytically as the y-intercept of the linear regression between soil-surface CO2 efflux and root biomass. In the present study, a development of this indirect methodology is presented by taking into consideration both the temporal variation and the spatial heterogeneity of heterotrophic respiration. For this purpose, soil CO2 efflux, soil carbon content and main stand characteristics were estimated in seven evergreen forest ecosystems along an elevation gradient ranging from 250 to 1740 m. For each site and for each sampling date the measured soil CO2 efflux (R S) was predicted with the model R S = a × S C + b × R D ± e, where S C is soil carbon content per unit area to a depth of 30 cm and R D is the root density of the 2–5 mm root class. Regressions with statistically significant a and b coefficients allowed the indirect separation of the two components of soil CO2 efflux. Considering that the different sampling dates were characterized by different soil temperature, it was possible to investigate the temporal and thermal dependency of autotrophic and heterotrophic respiration. It was estimated that annual autotrophic respiration accounts for 16–58% of total soil CO2 efflux in the seven different evergreen ecosystems. In addition, our observations show a decrease of annual autotrophic respiration at increasing availability of soil nitrogen.

Leaf age-dependent changes in structure, nitrogen content, internal mesophyll diffusion conductance ( g m ), the capacity for photosynthetic electron transport ( J max ) and the maximum carboxylase activity of Rubisco ( V cmax ) were investigated in mature non-senescent leaves of Laurus nobilis L., Olea europea L. and Quercus ilex L. to test the hypothesis that the relative significance of biochemical and diffusion limitations of photosynthesis changes with leaf age. The leaf life-span was up to 3 years in L. nobilis and O. europea and 6 years in Q. ilex . Increases in leaf age resulted in enhanced leaf dry mass per unit area ( M A ), larger leaf dry to fresh mass ratio, and lower nitrogen contents per dry mass ( N M ) in all species, and lower nitrogen contents per area ( N A ) in L. nobilis and Q. ilex . Older leaves had lower g m , J max and V cmax . Due to the age-dependent increase in M A , mass-based g m , J max and V cmax declined more strongly (7- to 10-fold) with age than area-based (5- to 7-fold) characteristics. Diffusion conductance was positively associated with foliage photosynthetic potentials. However, this correlation was curvilinear, leading to lower ratio of chloroplastic to internal CO 2 concentration ( C c / C i ) and larger drawdown of CO 2 from leaf internal air space to chloroplasts ( D C ) in older leaves with lower g m . Overall the agedependent decreases in photosynthetic potentials were associated with decreases in N M and in the fraction of N in photosynthetic proteins, whereas decreases in g m were associated with increases in M A and the fraction of cell walls. These age-dependent modifications altered the functional scaling of foliage photosynthetic potentials with M A , N M , and N A . The species primarily differed in the rate of agedependent modifications in foliage structural and functional characteristics, but also in the degree of agedependent changes in various variables. Stomatal openness was weakly associated with leaf age, but due to species differences in stomatal openness, the distribution of total diffusion limitation between stomata and mesophyll varied among species. These data collectively demonstrate that in Mediterranean evergreens, structural limitations of photosynthesis strongly interact with biochemical limitations. Age-dependent changes in g m and photosynthetic capacities do not occur in a co-ordinated manner in these species such that mesophyll diffusion constraints curb photosynthesis more in older than in younger leaves.

The main determinants of soil respiration were investigated in 11 forest types distributed along an altitudinal and thermal gradient in the southern Italian Alps (altitudinal range 1520m, range in mean annual temperature 7.81C). Soil respiration, soil carbon content and principal stand characteristics were measured with standardized methods. Soil CO2 fluxes were measured at each site every 15‐20 days with a closed dynamic system (LI-COR 6400) using soil collars from spring 2000 to spring 2002. At the same time, soil temperature at a depth of 10cm and soil water content (m 3 m � 3 ) were measured at each collar. Soil samples were collected to a depth of 30cm and stones, root content and bulk density were determined in order to obtain reliable estimates of carbon content per unit area (kgCm � 2 ). Soil respiration and temperature data were fitted with a simple logistic model separately for each site, so that base respiration rates and mean annual soil respiration were estimated. Then the same regression model was applied to all sites simultaneously, with each model parameter being expressed as a linear function of site variables. The general model explained about 86% of the intersite variability of soil respiration. In particular, soil mean annual temperature explained the most of the variance of the model (0.41), followed by soil temperature interquartlile range (0.24), soil carbon content (0.16) and soil water content (0.05).

The aim of this chapter was to quantify the soil organic carbon (SOC) stock in the top 30 cm of mineral soil for the whole Italian territory, according to the different land use types of the Intergovernmental Panel on Climate Change (IPCC) cropland category (arable land, agroforestry, vineyards, olive groves, orchards and rice fields), as a basis for future land use scenarios and to address mitigation policy at country level. Besides, two independent studies addressing the current status and the future trends of SOC for the whole cropland category at regional level were reported. The subdivision of the cropland category into classes is functional to assess the impacts on the SOC stock due to land use changes from and to agricultural uses, providing the starting or ending point scenario. The differences emphasized for the soils of a subcategory under the different types of climate can be possibly used for future-oriented agricultural practices. The comparison of the total mean values of the different cropland subcategories shows significant differences in the SOC stock. Considering the year 2000 and applying to each subcategory area the specific average SOC stock value found in this study, the total amount of C stored in the upper 30 cm of the whole cropland category results to be 516.3 ± 156.9 Tg C. This amount represents about 17 % of the total SOC estimated for the top 50 cm of soils of total surface of Italy, which reports about 2,900 Tg C, thus indicating the importance to preserve the large amount of SOC stored in cropland category. In conclusion, given the few estimates available at European level, repeated SOC inventories aimed to define the SOC content in cropland soils are important for future stock change evaluation.

Increasing human demands on soil-derived ecosystemservices requires reliable data on global soil resources for sustainable development. The soil organic carbon (SOC) pool is a key indicator of soil quality as it affects essential biological, chemical and physical soil functions such as nutrient cycling, pesticide and water retention, and soil structure maintenance. However, information on the SOC pool, and its temporal and spatial dynamics is unbalanced. Even in well-studied regions with a pronounced interest in environmental issues information on soil carbon (C) is inconsistent. Several activities for the compilation of global soil C data are under way. However, different approaches for soil sampling and chemical analyses make even regional comparisons highly uncertain. Often, the procedures used so far have not allowed the reliable estimation of the total SOC pool, partly because the available knowledge is focused on not clearly defined upper soil horizons and the contribution of subsoil to SOC stocks has been less considered. Even more difficult is quantifying SOC pool changes over time. SOC consists of variable amounts of labile and recalcitrant molecules of plant, and microbial and animal origin that are often operationally defined. A comprehensively active soil expert community needs to agree on protocols of soil surveying and lab procedures towards reliable SOC pool estimates. Already established long-term ecological research sites, where SOC changes are quantified and the underlying mechanisms are investigated, are potentially the backbones for regional, national, and international SOC monitoring programs., JRC.H.5-Land Resources Management

[1] Variations in the Dole Effect, which have been used to infer past changes in biospheric productivity, are strongly affected by isotopic discrimination in soil respiration. Respiration through the alternative oxidase (AOX) pathway is associated with a higher discrimination than the one associated with the “normal” dark respiration pathway (the cytochrome pathway, COX). However, observations of O2 discrimination and AOX activity in undisturbed natural environments are scarce. In the current study we measured the O2 concentration and stable isotopes in the root zone of tundra, boreal forest and alpine forest soils. To estimate the discrimination from this data, we have performed O2diffusion experiments in gamma-sterilized soil columns, with varying soil clay content. The discrimination found in the diffusion experiments was independent of clay content, and the value found, 14 ± 2‰, is the same as the one for binary diffusion of O2 in N2, indicating no interaction between the O2 and clay particles. Based on the field and laboratory results, the respiratory discrimination in the soils studied is 15–31‰, with the higher values associated with colder soils. The high discrimination found for cold (

Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from similar to 3 degrees S to similar to 70 degrees N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC-LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr (-1), with the highest respiration rate over tropical forests and the lowest value in dry and high-latitude areas.

article i nfo Remote sensing can be considered a key instrument for studies related to forests and their dynamics. At present, the increasing availability of multisensor acquisitions over the same areas, offers the possibility to combine data from different sensors (e.g., optical, RADAR, LiDAR). This paper presents an analysis on the fusion of airborne LiDAR and satellite multispectral data (IRS 1C LISS III), for the prediction of forest stem volume at plot level in a complex mountain area (Province of Trento, Southern Italian Alps), characterized by different tree species, complex morphology (i.e. altitude ranges from 65 m to 3700 m above sea level), and a range of different climates (from the sub-Mediterranean to Alpine type). 799 sample plots were randomly distributed over the 3000 km 2 of the forested areas of the Trento Province. From each plot, a set of variables were extracted from both LiDAR and multispectral data. A regression analysis was carried out considering two data sources (LiDAR and multispectral) and their combination, and dividing the plot areas into groups according to their species composition, altitude and slope. Experimental results show that the combination of LiDAR and IRS 1C LISS III data, for the estimation of stem volume, is effective in all the experiments considered. The best developed models comprise variables extracted from both of these data sources. The RMSE% on an independent validation set for the stem volume estimation models ranges between 17.2% and 26.5%, considering macro sets of tree species (deciduous, evergreen and mixed), between 17.5% and 29.0%, considering dominant species plots, and between 15.5% and 21.3% considering altitude and slope sets.

The aim of this inventory (acronym: INFOCARB) was to measure the organic carbon stored in the forest ecosystems of the Trento region (Provincia Autonoma di Trento, Northern Italy) in both above- and belowground pools, according to the Kyoto protocol and IPCC requirements. A total of 150 forest sampling points were selected on the entire regional area (6206 km2) with a statistical sampling approach, based on the timber volume as a proxy variable for a stratified sampling. Each sampling point was located with a GPS receiver and a 600 m2 circular plot was delimited around each point. Inside the plots, the biomass of trees, shrubs and herbaceous vegetation was measured, while litter was collected in systematically placed subplots. Topsoil (down to 30 cmdepth) was sampled with the excavationmethod on three systematically located pits, to determine the organic carbon content, the bulk density and the volume occupied by stones and roots. The inventory estimated the regional total carbon content of the forests as 71.9 5.2 Tg C, with an average carbon density of 207.01 14.5 Mg C ha1. The aboveground biomass and the soil had a similar carbon content, 43.2% and 44.6% of the total ecosystem carbon, respectively, whereas the root systems and the litter accounted for 9.6% and 2.6%, respectively. Due to the high inter-site variability, only weak statistical relationships were found between the soil carbon content and main ecosystem and climatic variables. However, when dividing the plots into different species-dominated forests, the beech sites differed significantly from the conifer sites in the carbon stock and the C/N ratio in the soil organic layers., JRC.H.2-Air and Climate

Soil respiration constitutes the second largest flux of carbon (C) between terrestrial ecosystems and the atmosphere. This study provides a synthesis of soil respiration (R s) in 20 European grasslands across a climatic transect, including ten meadows, eight pastures and two unmanaged grasslands. Maximum rates of R s ( $$ R_{{{\text{s}}_{{{\text{max}}}} }} $$ ), R s at a reference soil temperature (10°C; $$ R_{{{\text{s}}_{{{\text{10}}}} }} $$ ) and annual R s (estimated for 13 sites) ranged from 1.9 to 15.9 μmol CO2 m−2 s−1, 0.3 to 5.5 μmol CO2 m−2 s−1 and 58 to 1988 g C m−2 y−1, respectively. Values obtained for Central European mountain meadows are amongst the highest so far reported for any type of ecosystem. Across all sites $$ R_{{{\text{s}}_{{{\text{max}}}} }} $$ was closely related to $$ R_{{{\text{s}}_{{{\text{10}}}} }} $$ . Assimilate supply affected R s at timescales from daily (but not necessarily diurnal) to annual. Reductions of assimilate supply by removal of aboveground biomass through grazing and cutting resulted in a rapid and a significant decrease of R s. Temperature-independent seasonal fluctuations of R s of an intensively managed pasture were closely related to changes in leaf area index (LAI). Across sites $$ R_{{{\text{s}}_{{{\text{10}}}} }} $$ increased with mean annual soil temperature (MAT), LAI and gross primary productivity (GPP), indicating that assimilate supply overrides potential acclimation to prevailing temperatures. Also annual R s was closely related to LAI and GPP. Because the latter two parameters were coupled to MAT, temperature was a suitable surrogate for deriving estimates of annual R s across the grasslands studied. These findings contribute to our understanding of regional patterns of soil C fluxes and highlight the importance of assimilate supply for soil CO2 emissions at various timescales.

Seven years of continuous eddy covariance measurements at an alpine meadow were used to investigate the impacts of climate drivers and ecosystem responses on the inter-annual variability (IAV) of the net ecosystem exchange (NEE). The annual cumulative value of NEE was positive (source) in 2003, 2005 and 2009 (50, 15 and 112 g m−2 respectively) and negative (sink) in 2004, 2006, 2007 and 2008 (29, 75, 110 and 28 g m−2 respectively). The IAV of carbon dioxide fluxes builds up in two phenological phases: the onset of the growing season (triggered by snow melting) and the canopy re-growth after mowing. Respiratory fluxes during the non-growing season were observed to increase IAV, while growing season uptake dampened it. A novel approach was applied to factor out the two main sources of IAV: climate drivers’ variability and changes in the ecosystem responses to climate. Annual values of carbon dioxide fluxes were calculated assuming (a) variable climate and variable ecosystem response among years, (b) variable climate and constant ecosystem response and (c) constant climate and variable ecosystem response. The analysis of flux variances calculated under these three assumptions indicates the occurrence of an important negative feedback between climate and ecosystem responses. Due to this feedback, the observed IAV of NEE is lower than one would expect for a given climate variability, because of the counteracting changes in ecosystem responses. This alpine meadow therefore demonstrates the ability to acclimatise and to limit the IAV of carbon fluxes induced by climate variability., JRC.H.7-Climate Risk Management

Estimates of leaf gas-exchange characteristics using standard clamp-on leaf chambers are prone to errors because of diffusion leaks. While some consideration has been given to CO(2) diffusion leaks, potential water vapour diffusion leaks through chamber gaskets have been neglected. We estimated diffusion leaks of two clamp-on Li-Cor LI-6400 (Li-Cor, Inc., Lincoln, NE, USA) leaf chambers with polymer foam gaskets and enclosing either 2 or 6 cm(2) leaf area, and conducted a sensitivity analysis of the diffusion leak effects on Farquhar et al. photosynthesis model parameters - the maximum carboxylase activity of ribulose 1 x 5-bisphosphate carboxylase/oxygenase (Rubisco) (V(cmax)), capacity for photosynthetic electron transport (J(max)) and non-photorespiratory respiration rate in light (R(d)). In addition, net assimilation rate (A(n)) versus intercellular CO(2) (C(i)) responses were measured in leaves of Mediterranean evergreen species Quercus ilex L. enclosing the whole leaf chamber in a polyvinyl fluoride bag flushed with the exhaust air of leaf chamber, thereby effectively reducing the CO(2) and water vapour gradients between ambient air and leaf chamber. For the empty chambers, average diffusion leak for CO(2), K(CO2), (molar flow rate corresponding to unit CO(2) mole fraction difference) was ca. 0.40 micromol s(-1). K(CO2) increased ca. 50% if a dead leaf was clamped between the leaf chamber. Average diffusion leak for H(2)O was ca. 5- to 10-fold larger than the diffusion leak for CO(2). Sensitivity analyses demonstrated that the consequence of a CO(2) diffusion leak was apparent enhancement of A(n) at high CO(2) mole fraction and reduction at lower CO(2) mole fraction, and overall compression of C(i) range. As the result of these modifications, Farquhar et al. model parameters were overestimated. The degree of overestimation increased in the order of V(cmax)J(max)R(d), and was larger for smaller chambers and for leaves with lower photosynthetic capacity, leading to overestimation of all three parameters by 70-290% for 2 cm(2), and by 10-60% for 6 cm(2) chamber. Significant diffusion corrections (5-36%) were even required for leaves with high photosynthetic capacity measured in largest chamber. Water vapour diffusion leaks further enhanced the overestimation of model parameters. For small chambers and low photosynthetic capacities, apparent C(i) was simulated to decrease with increasing A(n) because of simultaneous CO(2) and H(2)O diffusion leaks. Measurements in low photosynthetic capacity Quercus ilex leaves enclosed in 2 cm(2) leaf chamber exhibited negative apparent C(i) values at highest A(n). For the same leaves measured with the entire leaf chamber enclosed in the polyvinyl fluoride bag, C(i) and A(n) increased monotonically. While the measurements without the bag could be corrected for diffusion leaks, the required correction in A(n) and transpiration rates was 100-500%, and there was large uncertainty in Farquhar et al. model parameters derived from 'corrected'A(n)/C(i) response curves because of uncertainties in true diffusion leaks. These data demonstrate that both CO(2) and water vapour diffusion leaks need consideration in measurements with clamp-on leaf cuvettes. As plants in natural environments are often characterized by low photosynthetic capacities, cuvette designs need to be improved for reliable measurements in such species.

Mature non-senescent leaves of evergreen species become gradually shaded as new foliage develops and canopy expands, but the interactive effects of integrated light during leaf formation (Q(int)G), current light (Q(int)C) and leaf age on foliage photosynthetic competence are poorly understood. In Quercus ilex L., we measured the responses of leaf structural and physiological variables to Q(int)C and Q(int)G for four leaf age classes. Leaf aging resulted in increases in leaf dry mass per unit area (M(A)), and leaf dry to fresh mass ratio (D(F)) and decreases in N content per dry mass (N(M)). N content per area (N(A)) was independent of age, indicating that decreases in N(M) reflected dilution of leaf N because of accumulation of dry mass (NA = N(M) M(A)). M(A), D(F) and N(A) scaled positively with irradiance, whereas these age-specific correlations were stronger with leaf growth light than with current leaf light. Area-based maximum ribulose 1,5-bisphosphate carboxylase/oxygenase (Rubisco) carboxylase activity (V(cmax)A), capacity for photosynthetic electron transport (J(max)A) and the rate of non-photorespiratory respiration in light (R(d)A) were also positively associated with irradiance. Differently from leaf structural characteristics, for all data pooled, these relationships were stronger with current light with little differences among leaves of different age. Acclimation to current leaf light environment was achieved by light-dependent partitioning of N in rate-limiting proteins. Mass-based physiological activities decreased with increasing leaf age, reflecting dilution of leaf N and a larger fraction of non-photosynthetic N in older leaves. This resulted in age-dependent modification of leaf photosynthetic potentials versus N relationships. Internal diffusion conductance (g(m)) per unit area (g(m)A) increased curvilinearly with increasing irradiance for two youngest leaf age classes and was independent of light for older leaves. In contrast, g(m) per dry mass (g(m)M) was negatively associated with light in current-year leaves. Greater photosynthetic potentials and moderate changes in diffusion conductance resulted in greater internal diffusion limitations of photosynthesis in higher light. Both area- and mass-based g(m) decreased with increasing leaf age. The decrease in diffusion conductance was larger than changes in photosynthetic potentials, leading to larger CO2 drawdown from leaf internal air space to chloroplasts (delta(c)) in older leaves. The increases in diffusion limitations in older leaves and at higher light scaled with age- and light-dependent increases in MA and D(F). Overall, our study demonstrates a large potential of foliage photosynthetic acclimation to changes in leaf light environment, but also highlights enhanced structural diffusion limitations in older leaves that result from leaf structural acclimation to previous rather than to current light environment and accumulation of structural compounds with leaf age.

In their Report (“Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate,” 13 August, p. [834][1]), C. Beer et al. joined others ([ 1 ][2], [ 2 ][3]) in estimating global carbon fluxes and their relationship to climate. However, the global role of soil carbon

Mention d'édition : First Edition; International audience