TY - JOUR AU - Torresani, M. AU - Rocchini, D. AU - Ceola, G. AU - de, Vries J.P.R. AU - Feilhauer, H. AU - Moudrý, V. AU - Bartholomeus, H. AU - Perrone, M. AU - Anderle, M. AU - Gamper, H.A. AU - Chieffallo, L. AU - Guatelli, E. AU - Gatti, R.C. AU - Kleijn, D. TI - Grassland vertical height heterogeneity predicts flower and bee diversity: an UAV photogrammetric approach JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 14 PY - 2024 IS - 1 SN - 2045-2322 DO - 10.1038/s41598-023-50308-9 UR - https://m2.mtmt.hu/api/publication/34562370 ID - 34562370 N1 - Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano/Bozen, Piazza Universitá/Universitätsplatz 1, Bolzano/Bozen, 39100, Italy BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, via Irnerio 42, Bologna, 40126, Italy Department of Spatial Sciences, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcka 129, Praha - Suchdol, 16500, Czech Republic Plant Ecology and Nature Conservation Group, Wageningen University, Droevendaalsesteeg 3a, Wageningen, 6708PB, Netherlands Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, Germany German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany Department of Remote Sensing, Helmholtz-Centre for Environmental Research - UFZ, Permoserstr. 15, Leipzig, 04318, Germany Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, P.O. Box 47, Wageningen, 6700 AA, Netherlands Eurac Research, Inst. for Alpine Environment, Bolzano, Italy Department of Environmental Science and Policy, University of Milan, Milan, Italy Visual art, FEIMC, Bolzano, Italy Export Date: 6 February 2024 Correspondence Address: Rocchini, D.; Department of Spatial Sciences, Kamýcka 129, Czech Republic; email: duccio.rocchini@unibo.it LA - English DB - MTMT ER - TY - JOUR AU - Burrascano, Sabina AU - Chianucci, Francesco AU - Trentanovi, Giovanni AU - Kepfer-Rojas, Sebastian AU - Sitzia, Tommaso AU - Tinya, Flóra AU - Doerfler, Inken AU - Paillet, Yoan AU - Nagel, Thomas Andrew AU - Mitic, Bozena AU - Morillas, Lourdes AU - Munzi, Silvana AU - Van der Sluis, Theo AU - Alterio, Edoardo AU - Balducci, Lorenzo AU - de Andrade, Rafael Barreto AU - Bouget, Christophe AU - Giordani, Paolo AU - Lachat, Thibault AU - Matosevic, Dinka AU - Napoleone, Francesca AU - Nascimbene, Juri AU - Paniccia, Chiara AU - Roth, Nicolas AU - Aszalós, Réka AU - Brazaitis, Gediminas AU - Cutini, Andrea AU - D'Andrea, Ettore AU - De Smedt, Pallieter AU - Heilmann-Clausen, Jacob AU - Janssen, Philippe AU - Kozák, Daniel AU - Mårell, Anders AU - Mikoláš, Martin AU - Nordén, Björn AU - Matula, Radim AU - Schall, Peter AU - Svoboda, Miroslav AU - Ujhazyova, Mariana AU - Vandekerkhove, Kris AU - Wohlwend, Michael AU - Xystrakis, Fotios AU - Aleffi, Michele AU - Ammer, Christian AU - Archaux, Frederic AU - Asbeck, Thomas AU - Avtzis, Dimitrios AU - Ayasse, Manfred AU - Bagella, Simonetta AU - Balestrieri, Rosario AU - Barbati, Anna AU - Basile, Marco AU - Bergamini, Ariel AU - Bertini, Giada AU - Biscaccianti, Alessandro Bruno AU - Boch, Steffen AU - Bölöni, János AU - Bombi, Pierluigi AU - Boscardin, Yves AU - Brunialti, Giorgio AU - Bruun, Hans Henrik AU - Buscot, François AU - Byriel, David Bille AU - Campagnaro, Thomas AU - Campanaro, Alessandro AU - Chauvat, Matthieu AU - Ciach, Michał AU - Čiliak, Marek AU - Cistrone, Luca AU - Pereira, Joao Manuel Cordeiro AU - Daniel, Rolf AU - De Cinti, Bruno AU - De Filippo, Gabriele AU - Dekoninck, Wouter AU - Di Salvatore, Umberto AU - Dumas, Yann AU - Elek, Zoltán AU - Ferretti, Fabrizio AU - Fotakis, Dimitrios AU - Frank, Tamás AU - Frey, Julian AU - Giancola, Carmen AU - Gomoryová, Erika AU - Gosselin, Marion AU - Gosselin, Frederic AU - Gossner, Martin M. AU - Götmark, Frank AU - Haeler, Elena AU - Hansen, Aslak Kappel AU - Hertzog, Lionel AU - Hofmeister, Jeňýk AU - Hošek, Jan AU - Johannsen, Vivian Kvist AU - Justensen, Mathias Just AU - Korboulewsky, Nathalie AU - Kovács, Bence AU - Lakatos, Ferenc AU - Landivar, Carlos Miguel AU - Lens, Luc AU - Lingua, Emanuele AU - Lombardi, Fabio AU - Máliš, František AU - Marchino, Luca AU - Marozas, Vitas AU - Matteucci, Giorgio AU - Mattioli, Walter AU - Møller, Peter Friis AU - Müller, Jörg AU - Németh, Csaba AU - Ónodi, Gábor AU - Parisi, Francesco AU - Perot, Thomas AU - Perret, Sandrine AU - Persiani, Anna Maria AU - Portaccio, Alessia AU - Posillico, Mario AU - Preikša, Žydrūnas AU - Rahbek, Carsten AU - Rappa, Nolan James AU - Ravera, Sonia AU - Romano, Antonio AU - Samu, Ferenc AU - Scheidegger, Christoph AU - Schmidt, Inger Kappel AU - Schwegmann, Sebastian AU - Sicuriello, Flavia AU - Spinu, Andreea Petronela AU - Spyroglou, Gavriil AU - Stillhard, Jonas AU - Topalidou, Eleni AU - Tøttrup, Anders P. AU - Ujházy, Karol AU - Veres, Katalin AU - Verheyen, Kris AU - Weisser, Wolfgang W. AU - Zapponi, Livia AU - Ódor, Péter TI - Where are we now with European forest multi-taxon biodiversity and where can we head to? JF - BIOLOGICAL CONSERVATION J2 - BIOL CONSERV VL - 284 PY - 2023 PG - 13 SN - 0006-3207 DO - 10.1016/j.biocon.2023.110176 UR - https://m2.mtmt.hu/api/publication/34042004 ID - 34042004 N1 - Sapienza University of Rome, Rome, 00185, Italy National Biodiversity Future Center, Palermo, Italy Council for Agricultural Research and Economics (CREA), Research Centre for Forestry and Wood, V.le Santa Margherita 80, AR, Arezzo, I-52100, Italy Research Institute on Terrestrial Ecosystems, National Research Council, FI, Sesto Fiorentino, 50019, Italy University of Copenhagen, Department of Geosciences and Natural Resource Management, Frederiksberg, 1958, Denmark Department Land, Environment, Agriculture and Forestry, Università degli Studi di Padova, PD, Legnaro, 35020, Italy Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, 2163, Hungary University of Oldenburg, Oldenburg, 26129, Germany Technical University of Munich, Freising, Germany Univ. Grenoble Alpes, INRAE, Lessem, Saint Martin d'Heres, 38402, France University of Ljubljana, Department of Forestry and Renewable Forest Resources, Biotechnical Faculty, Ljubljana, 1000, Slovenia University of Zagreb, Faculty of Science, Department of Biology, Zagreb, 10000, Croatia University of Seville, Department of Vegetal biology and Ecology, Seville, 1749-016, Spain Universidade de Lisboa, Center for Ecology, Evolution and Environmental Changes & CHANGE - Global Change and Sustainability Institute, Lisbon, Portugal Centro Interuniversitário de História das Ciências e da Tecnologia, Lisbon, Portugal Wageningen Environmental Research, Wageningen, 6800 AA, Netherlands University of Maryland, College Park, 20742, United States INRAE, UR EFNO, Nogent-sur-Vernisson, 45290, France DIFAR, University of Genova, Genova, 16148, Italy Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Zollikofen, 3052, Switzerland Swiss Federal Research Institute WSL, Birmensdorf, Switzerland Croatian Forest Research Institute, Jastrebarsko, 10450, Croatia BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum, University of Bologna, Bologna, 40126, Italy Institute for Alpine Environment, Eurac Research, Bolzano, 39100, Italy Department of Forest Science, Vytautas Magnus University, Kaunas dist., 53361, Lithuania Research Institute on Terrestrial Ecosystems, National Research Council, Porano, 05010, Italy Ghent University, Melle-Gontrode, 9090, Belgium Center for Macroecology, Evolution and Climate, Globe Institute, University of Copenhagen, Copenhagen, DK-2100, Denmark Czech University of life Sciences in Prague, Prague, 16500, Czech Republic INRAE, UR EFNO, Nogent-sur-Vernisson, 45290, France Norwegian Institute for Nature Research, Oslo, 0855, Norway Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, 37077, Germany Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, Zvolen, 96001, Slovakia INBO-Research Institute for Nature and Forests, Geraardsbergen, 9500, Belgium Albert-Ludwigs-University Freiburg, Freiburg, 79085, Germany Forest Research Institute, Hellenic Agricultural Organization Demeter, Vassilika, 5700, Greece School of Biosciences, University of Camerino, MC, Camerino, 62032, Italy Unique landuse GmbH, Freiburg, 79098, Germany Forest Research Institute, Hellenic Agricultural Organization Demeter, Vassilika, 57006, Greece Ulm University, Institute of Evolutionary Ecology and Conservation Genomics, Ulm, Germany University of Sassari, Department of Chemical, Physical, Mathematical and Natural Sciences, Sassari, Italy Department of Marine Animal Conservation and Public Engagement, Anton Dohrn Zoological Station, Napoli, 00015, Italy University of Tuscia, Department for Innovation in Biological, Agro-food and Forest systems (DIBAF), Viterbo, 01100, Italy Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, 8903, Switzerland Dept. PAU, Mediterranea University of Reggio Calabria, Reggio Calabria, 89124, Italy WSL Swiss Federal Research Institute, Birmensdorf, 8903, Switzerland Institute of Research on Terrestrial Ecosystems, National Research Council, Montelibretti, Italy TerraData Environmetrics, Spin-Off Company of the University of Siena, Monterotondo Marittimo, 58025, Italy University of Copenhagen, Dept Biology, Copenhagen, 2100, Denmark Helmholtz Centre for Environmental Research (UFZ), Halle (Saale), 06120, Germany German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany University of Copenhagen, Department of Geosciences and Natural Resource Management, Rolighedsvej 23, Frederiksberg C, 1958, Denmark Council for Agricultural Research and Economics (CREA), Research Centre for Plant Protection and Certification, Via Lanciola 12A, FI, Impruneta, I-50125, Italy Normandie UNIV, UNIROUEN, INRAE, ECODIV, Mont Saint Aignan, 76821, France University of Agriculture in Krakow, Department of Forest Biodiversity, Krakow, Poland Forestry and Conservation, Cassino, 03043, Italy Albert-Ludwigs-University of Freiburg, Chair of Nature Conservation and Landscape Ecology, Freiburg, 79106, Germany Genomic and Applied Microbiology & Göttingen Genomics Laboratory, Institute of Microbiology and Genetics, Georg-August University of Göttingen, Grisebachstr. 8, Göttingen, 37077, Germany Istitute of Wildlife Management, Napoli, 80126, Italy Royal Belgian Institute for Natural Sciences, Brussels, Belgium Council for Agricultural Research and Economics (CREA), Research Centre for Agricultural Policies and Bioeconomy, Via Lombardia, c.da Bucceri, PE, Cepagatti, I-65012, Italy Department of Biostatistics, University of Veterinary Medicine, István utca 2, Budapest, 1078, Hungary Albert-Ludwigs-University of Freiburg, Chair of Forest Growth and Dendroecology, Freiburg, 79106, Germany Università degli Studi del Molise, Department of Biosciences and Territory, Campobasso, Italy Technical University in Zvolen, Zvolen, 96001, Slovakia University of Gothenburg, Göteborg, 40530, Sweden Federal Research and Training Centre for Forests, Natural Hazards and Landscape (BFW), Vienna, 1131, Austria Natural History Museum of Denmark, Univeristy of Copenhagen, Copenhagen, 2100, Denmark Thünen Institute of Biodiversity, Braunschweig, 38116, Germany Czech University of Life Sciences Prague, Faculty of Forestry and Wood Sciences, Department of Forest Ecology, Praha, 16500, Czech Republic Ecological Services, Hořovice, 26801, Czech Republic University of Sopron, Sopron, H-9400, Hungary Albert-Ludwigs-University of Freiburg, Dept of Biometry and Environmental System Analysis, Freiburg, 79106, Germany Ghent University, Ghent, Belgium Agraria Department, Mediterranean University of Reggio Calabria, Reggio Calabria, 89122, Italy Agriculture Academy, Vytautas Magnus University, Kaunas distr., 53361, Lithuania National Research Council of Italy, Institute of BioEconomy (CNR-IBE), FI, Sesto Fiorentino, 50019, Italy Council for Agricultural Research and Economics (CREA), Research Centre for Forestry and Wood, Via Valle della Quistione 27, Rome, I-00166, Italy Geological Survey of Denmark and Greenland, Copenhagen K, 1350, Denmark University Würzburg, Rauhenebrach, 96181, Germany Nationalpark Bavarian Forest, Grafenau, Germany National Laboratory for Water Science and Water Security, Balaton Limnological Research Institute, Tihany, H-8237, Hungary Reparto Carabinieri Biodiversità Castel di Sangro, Castel di Sangro, 67031, Italy Department of Biological, Chemical and Pharmaceutical Sciences and Technologies (STEBICEF), University of Palermo, Palermo, 90123, Italy Institute for Bioeconomy, National Research Council of Italy, Roma, 00100, Italy Plant Protection Institute, Centre for Agricultural Research, ELKH, Budapest, H-1029, Hungary Albert-Ludwigs-University of Freiburg, Chair of Silviculture, Freiburg, 79106, Germany Natural History Museum of Denmark, Univeristy of Copenhagen, Copenhagen, DK-1350, Denmark Faculty of Forestry, Technical University in Zvolen, Zvolen, 96001, Slovakia Technical University of Munich, Terrestrial Ecology Research Group, Department of Life Science Systems, School of Life Sciences, Freising, 85354, Germany Institute of BioEconomy, National Research Council, TN, San Michele All'adige, 38098, Italy University of Sopron, Institute of Environmental Protection and Nature Conservation, Sopron, Hungary Piazza Marina 61, Palermo, 90133, Italy ELKH-DE Anthropocene Ecology Research Group, University of Debrecen, Egyetem tér 1, Debrecen, 4032, Hungary Export Date: 19 July 2023 CODEN: BICOB Correspondence Address: Burrascano, S.; Sapienza University of RomeItaly; email: sabina.burrascano@uniroma1.it LA - English DB - MTMT ER - TY - JOUR AU - Davison, Charles W. AU - Assmann, Jakob J. AU - Normand, Signe AU - Rahbek, Carsten AU - Morueta-Holme, Naia TI - Vegetation structure from LiDAR explains the local richness of birds across Denmark JF - JOURNAL OF ANIMAL ECOLOGY J2 - J ANIM ECOL VL - 92 PY - 2023 IS - 7 SP - 1332 EP - 1344 PG - 13 SN - 0021-8790 DO - 10.1111/1365-2656.13945 UR - https://m2.mtmt.hu/api/publication/34330027 ID - 34330027 AB - Classic ecological research into the determinants of biodiversity patterns emphasised the important role of three-dimensional (3D) vegetation heterogeneity. Yet, measuring vegetation structure across large areas has historically been difficult. A growing focus on large-scale research questions has caused local vegetation heterogeneity to be overlooked compared with more readily accessible habitat metrics from, for example, land cover maps.Using newly available 3D vegetation data, we investigated the relative importance of habitat and vegetation heterogeneity for explaining patterns of bird species richness and composition across Denmark (42,394 km(2)).We used standardised, repeated point counts of birds conducted by volunteers across Denmark alongside metrics of habitat availability from land-cover maps and vegetation structure from rasterised LiDAR data (10 m resolution). We used random forest models to relate species richness to environmental features and considered trait-specific responses by grouping species by nesting behaviour, habitat preference and primary lifestyle. Finally, we evaluated the role of habitat and vegetation heterogeneity metrics in explaining local bird assemblage composition.Overall, vegetation structure was equally as important as habitat availability for explaining bird richness patterns. However, we did not find a consistent positive relationship between species richness and habitat or vegetation heterogeneity; instead, functional groups displayed individual responses to habitat features. Meanwhile, habitat availability had the strongest correlation with the patterns of bird assemblage composition.Our results show how LiDAR and land cover data complement one another to provide insights into different facets of biodiversity patterns and demonstrate the potential of combining remote sensing and structured citizen science programmes for biodiversity research. With the growing coverage of LiDAR surveys, we are witnessing a revolution of highly detailed 3D data that will allow us to integrate vegetation heterogeneity into studies at large spatial extents and advance our understanding of species' physical niches. LA - English DB - MTMT ER - TY - JOUR AU - Kuusinen, Nea AU - Hovi, Aarne AU - Rautiainen, Miina TI - Estimation of boreal forest floor lichen cover using hyperspectral airborne and field data JF - SILVA FENNICA J2 - SILVA FENN VL - 57 PY - 2023 IS - 1 PG - 19 SN - 0037-5330 DO - 10.14214/sf.22014 UR - https://m2.mtmt.hu/api/publication/34330028 ID - 34330028 AB - Lichens are sensitive to competition from vascular plants, intensive silviculture, pollution and reindeer and caribou grazing, and can therefore serve as indicators of environmental changes. Hyperspectral remote sensing data has been proved promising for estimation of plant diversity, but its potential for forest floor lichen cover estimation has not yet been studied. In this study, we investigated the use of hyperspectral data in estimating ground lichen cover in boreal forest stands in Finland. We acquired airborne and in situ hyperspectral data of lichen-covered forest plots, and applied multiple endmember spectral mixture analysis to estimate the fractional cover of ground lichens in these plots. Estimation of lichen cover based on in situ spectral data was very accurate (coefficient of determination (r2) 0.95, root mean square error (RMSE) 6.2). Estimation of lichen cover based on airborne data, on the other hand, was fairly good (r2 0.77, RMSE 11.7), but depended on the choice of spectral bands. When the hyperspectral data were resampled to the spectral resolution of Sentinel-2, slightly weaker results were obtained. Tree canopy cover near the flight plots was weakly related to the difference between estimated and measured lichen cover. The results also implied that the presence of dwarf shrubs could influence the lichen cover estimates. LA - English DB - MTMT ER - TY - JOUR AU - Mandl, Lisa AU - Stritih, Ana AU - Seidl, Rupert AU - Ginzler, Christian AU - Senf, Cornelius TI - Spaceborne LiDAR for characterizing forest structure across scales in the European Alps JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2023 PG - 16 SN - 2056-3485 DO - 10.1002/rse2.330 UR - https://m2.mtmt.hu/api/publication/33940536 ID - 33940536 AB - The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height-related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability-particularly in topographically complex terrain-remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape-scale, we evaluated the ability of GEDIs sample-based approach to characterize complex mountain landscapes by comparing it to wall-to-wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision, carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R-2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape-level, however, the agreement between GEDI and ALS was generally high, with R-2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape-scale analyses in the context of ecosystem dynamics and management. LA - English DB - MTMT ER - TY - JOUR AU - Tanács, Eszter AU - Vári, Ágnes AU - Bede-Fazekas, Ákos AU - Báldi, András AU - Csákvári, Edina AU - Endrédi, Anett AU - Fabók, Veronika AU - Kisné Fodor, Lívia AU - Kiss, Márton AU - Koncz, Péter AU - Kovács-Hostyánszki, Anikó AU - Mészáros, János AU - Pásztor, László AU - Rezneki, Rita AU - Standovár, Tibor AU - Zsembery, Zita AU - Török, Katalin TI - Finding the Green Grass in the Haystack? Integrated National Assessment of Ecosystem Services and Condition in Hungary, in Support of Conservation and Planning JF - SUSTAINABILITY J2 - SUSTAINABILITY-BASEL VL - 15 PY - 2023 IS - 11 PG - 28 SN - 2071-1050 DO - 10.3390/su15118489 UR - https://m2.mtmt.hu/api/publication/33883696 ID - 33883696 N1 - Centre for Ecological Research Institute of Ecology and Botany, Alkotmány u. 2–4, VácrátótH-2163, Hungary Department of Plant Systematics, Ecology and Theoretical Biology, ELTE Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, H-1117, Hungary Department of Natural Resource Sciences, Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada Department of Environmental and Landscape Geography, Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, H-1117, Hungary Centre for Ecological Research Institute of Aquatic Ecology, Centre for Ecological Research, Budapest, H-1113, Hungary Ministry of Agriculture, Nature Conservation Department, Budapest, H-1055, Hungary Department of Climatology and Landscape Ecology, University of Szeged, Szeged, H-6725, Hungary Institute for Soil Sciences Centre for Agricultural Research, Budapest, H-1022, Hungary Export Date: 26 June 2023 Correspondence Address: Tanács, E.; Centre for Ecological Research Institute of Ecology and Botany, Alkotmány u. 2–4, Vácrátót, Hungary; email: tanacs.eszter@ecolres.hu AB - Human well-being needs healthy ecosystems, providing multiple ecosystem services. Therefore, the assessment of ecosystems on large scales is a priority action. In Hungary, this work (MAES-HU) took place between 2016 and 2022. Twelve ecosystem services (ES) were mapped and assessed along with several ecosystem condition (EC) indicators. Their integrated spatial analysis aimed to identify patterns of ES multifunctionality, reveal relationships between EC and ES and delineate ES bundles. The results show outstanding multifunctionality of natural ecosystem types compared with the more artificial types, emphasizing the importance of natural areas in order to fulfil human needs. Native forests provide the most varied range of services, which underlines the importance of forest management to consider multiple services. There is a positive correlation between condition and multifunctionality in forests; areas in better condition (in terms of species composition and structure) provide more services at an outstanding level. ES bundles mainly reflect the major ecosystem types, topography and forest condition. Our analysis represents an example of synthesizing national MAES results with a combination of methods. Finding ES hotspots on a national scale and connecting them with an assessment of EC may help in finding optimal strategies to balance conservation targets and competing land uses. LA - English DB - MTMT ER - TY - JOUR AU - Toivonen, Janne AU - Kangas, Annika AU - Maltamo, Matti AU - Kukkonen, Mikko AU - Packalen, Petteri TI - Assessing biodiversity using forest structure indicators based on airborne laser scanning data JF - FOREST ECOLOGY AND MANAGEMENT J2 - FOREST ECOL MANAG VL - 546 PY - 2023 PG - 22 SN - 0378-1127 DO - 10.1016/j.foreco.2023.121376 UR - https://m2.mtmt.hu/api/publication/34273429 ID - 34273429 AB - The role of forests in biodiversity assessment and planning is substantial as these ecosystems support approxi-mately 80% of the world's terrestrial biodiversity. Forests provide food, shelter, and nesting environments for numerous species, and deliver multiple ecosystem services. It has been widely recognised that forest vegetation structure and its complexity influence local variations in biodiversity. As forests are facing threats globally caused by human activities, there is a need to map the biodiversity of these ecosystems. The main objective of this review was to summarise the use of airborne laser scanning (ALS) data in biodiversity-related assessment of forests. We draw attention to topics related to animal ecology, structural diversity, dead wood, fragmentation and forest habitat classification. After conducting a thorough literature search, we categorised scientific articles based on their topics, which served as the basis for the section division in this paper. The majority of the research was found to be conducted in Europe and North America, only a small fraction of the study areas was located elsewhere. Topics that have received the most attention were related to animal ecology (namely richness and diversity of forest fauna), assessment of dead trees and tree species diversity measures. Not all studies used ALS data only, as it were often fused with other remote sensing data - especially with aerial or satellite images. The fusion of spectral information from optical images and the structural information provided by ALS was highly advantageous in studies where tree species were considered. Relevant ALS variables were found to be case -specific, so variables varied widely between forest biodiversity studies. We found that there was a lack of research in geographical areas and forest types other than temperate and boreal forests. Also, topics that considered functional diversity, community composition and the effect of spatial resolution at which ALS data and field information are linked, were covered to much lesser extent. LA - English DB - MTMT ER - TY - JOUR AU - Assmann, Jakob J. AU - Moeslund, Jesper E. AU - Treier, Urs A. AU - Normand, Signe TI - EcoDes-DK15: high-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set JF - EARTH SYSTEM SCIENCE DATA J2 - EARTH SYST SCI DATA VL - 14 PY - 2022 IS - 2 SP - 823 EP - 844 PG - 22 SN - 1866-3508 DO - 10.5194/essd-14-823-2022 UR - https://m2.mtmt.hu/api/publication/33431405 ID - 33431405 AB - Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as light detection and ranging (lidar). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote sensing knowledge. We processed Denmark's publicly available national airborne laser scanning (ALS) data set acquired in 2014/15, together with the accompanying elevation model, to compute 70 rasterised descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than 40 000 km(2) covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (similar to 94 GB, compressed 16.8 GB), and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark's national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterised data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond. The full data set is available on Zenodo: https://doi.org/10.5281/zenodo.4756556 (Assmann et al., 2021); a 5MB teaser subset is also available: https://doi.org/10.5281/zenodo.6035188 (Assmann et al., 2022a). LA - English DB - MTMT ER - TY - JOUR AU - Bourgouin, Maurane AU - Valeria, Osvaldo AU - Fenton, Nicole J. TI - Predictive mapping of bryophyte diversity associated with mature forests using LiDAR-derived indices in a strongly managed landscape JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 136 PY - 2022 PG - 10 SN - 1470-160X DO - 10.1016/j.ecolind.2022.108585 UR - https://m2.mtmt.hu/api/publication/33431404 ID - 33431404 AB - Recovery of bryophyte diversity following silvicultural treatments depends upon the reestablishment of favorable microhabitats and microclimatic conditions. Without sources of propagules (reproductive structures) within the managed landscape, however, even optimal habitat conditions would not be sufficient to ensure bryophyte diversity. To identify sources of propagules and ensure their protection, we used indices that were derived from a Digital Elevation Model (DEMs) and an airborne point cloud (LiDAR; Light Detection and Ranging) as explanatory variables to predict bryophyte biodiversity. Bryophytes were collected in the intensively managed Black Brook District of New Brunswick, Canada, in eight mature managed and unmanaged forest types (n = 38). Our results show a strong bryophyte community gradient between wetter stands (Cedar, riparian zone and SpruceFir) and drier stands (Tolerant Harwood and Plantation) forming two distinctive groups. Indices explaining bryophyte composition and richness were related to moisture (closest distance to a stream), canopy (canopy relief ratio, canopy closure and density) and microtopography (Topographic Position Index). Models obtained from these indices explained 75% of bryophyte composition and predicted composition with a certainty of 71% The predominance of the closest distance to a stream in our model reinforces the great importance of buffer along the hydrological network. Buffers represent a substantial propagule source for the landscape and notably increase its ecological connectivity. Although wetter sites had greater richness, the completely different composition find at drier sites suggest that biodiversity management efforts to maintain bryophytes should not be restricted to wetter stands. Our model demonstrates the potential of airborne LiDAR-derived indices as surrogates for field data in estimating and mapping bryophyte compositions to understand the variation in diversity across the managed landscape. This model can be used as a dynamic tool to target areas that represent the overall bryophyte diversity of the managed landscape to ensure protection of propagule sources and favors reestablishment. LA - English DB - MTMT ER - TY - JOUR AU - Brunbjerg, Ane Kirstine AU - Fløjgaard, Camilla AU - Frøslev, Tobias Guldberg AU - Andersen, Dagmar Kappel AU - Bruun, Hans Henrik AU - Dalby, Lars AU - Goldberg, Irina AU - Lehmann, Louise Juhl AU - Moeslund, Jesper Erenskjold AU - Ejrnæs, Rasmus TI - Scrub encroachment promotes biodiversity in temperate European wetlands under eutrophic conditions JF - ECOLOGY AND EVOLUTION J2 - ECOL EVOL VL - 12 PY - 2022 IS - 11 SN - 2045-7758 DO - 10.1002/ece3.9445 UR - https://m2.mtmt.hu/api/publication/33212288 ID - 33212288 N1 - Department of Ecoscience, Aarhus University, Aarhus, Denmark GLOBE Institute, University of Copenhagen, Copenhagen, Denmark Department of Biology, University of Copenhagen, Copenhagen, Denmark Department of Biology, Aarhus University, Aarhus, Denmark Export Date: 25 March 2023 Correspondence Address: Brunbjerg, A.K.; Section for Biodiversity and Conservation, Denmark; email: akb@ecos.au.dk LA - English DB - MTMT ER - TY - JOUR AU - Kissling, W. Daniel AU - Shi, Yifang AU - Koma, Zsofia AU - Meijer, Christiaan AU - Ku, Ou AU - Nattino, Francesco AU - Seijmonsbergen, Arie C. AU - Grootes, Meiert W. TI - Laserfarm - A high-throughput workflow for generating geospatial data products of ecosystem structure from airborne laser scanning point clouds JF - ECOLOGICAL INFORMATICS J2 - ECOL INFORM VL - 72 PY - 2022 PG - 17 SN - 1574-9541 DO - 10.1016/j.ecoinf.2022.101836 UR - https://m2.mtmt.hu/api/publication/33431402 ID - 33431402 AB - Quantifying ecosystem structure is of key importance for ecology, conservation, restoration, and biodiversity monitoring because the diversity, geographic distribution and abundance of animals, plants and other organisms is tightly linked to the physical structure of vegetation and associated microclimates. Light Detection And Ranging (LiDAR) - an active remote sensing technique - can provide detailed and high resolution information on ecosystem structure because the laser pulse emitted from the sensor and its subsequent return signal from the vegetation (leaves, branches, stems) delivers three-dimensional point clouds from which metrics of vegetation structure (e.g. ecosystem height, cover, and structural complexity) can be derived. However, processing 3D LiDAR point clouds into geospatial data products of ecosystem structure remains challenging across broad spatial extents due to the large volume of national or regional point cloud datasets (typically multiple terabytes con-sisting of hundreds of billions of points). Here, we present a high-throughput workflow called 'Laserfarm' enabling the efficient, scalable and distributed processing of multi-terabyte LiDAR point clouds from national and regional airborne laser scanning (ALS) surveys into geospatial data products of ecosystem structure. Laserfarm is a free and open-source, end-to-end workflow which contains modular pipelines for the re-tiling, normalization, feature extraction and rasterization of point cloud information from ALS and other LiDAR surveys. The workflow is designed with horizontal scalability and can be deployed with distributed computing on different in-frastructures, e.g. a cluster of virtual machines. We demonstrate the Laserfarm workflow by processing a country-wide multi-terabyte ALS dataset of the Netherlands (covering-34,000 km2 with-700 billion points and -16 TB uncompressed LiDAR point clouds) into 25 raster layers at 10 m resolution capturing ecosystem height, cover and structural complexity at a national extent. The Laserfarm workflow, implemented in Python and available as Jupyter Notebooks, is applicable to other LiDAR datasets and enables users to execute automated pipelines for generating consistent and reproducible geospatial data products of ecosystems structure from massive amounts of LiDAR point clouds on distributed computing infrastructures, including cloud computing environments. We provide information on workflow performance (including total CPU times, total wall-time estimates and average CPU times for single files and LiDAR metrics) and discuss how the Laserfarm workflow can be scaled to other LiDAR datasets and computing environments, including remote cloud infrastructures. The Laserfarm workflow allows a broad user community to process massive amounts of LiDAR point clouds for mapping vegetation structure, e.g. for applications in ecology, biodiversity monitoring and ecosystem restoration. LA - English DB - MTMT ER - TY - JOUR AU - Lenoir, Jonathan AU - Gril, Eva AU - Durrieu, Sylvie AU - Horen, Hélène AU - Laslier, Marianne AU - Lembrechts, Jonas AU - Zellweger, Florian AU - Alleaume, Samuel AU - Brasseur, Boris AU - Buridant, Jérôme AU - Dayal, Karun AU - De Frenne, Pieter AU - Gallet‐Moron, Emilie AU - Marrec, Ronan AU - Meeussen, Camille AU - Rocchini, Duccio AU - Van Meerbeek, Koenraad AU - Decocq, Guillaume TI - Unveil the unseen: Using LiDAR to capture time‐lag dynamics in the herbaceous layer of European temperate forests JF - JOURNAL OF ECOLOGY J2 - J ECOL VL - 110 PY - 2022 IS - 2 SP - 282 EP - 300 PG - 19 SN - 0022-0477 DO - 10.1111/1365-2745.13837 UR - https://m2.mtmt.hu/api/publication/32616460 ID - 32616460 N1 - Funding Agency and Grant Number: Agence Nationale de la RechercheFrench National Research Agency (ANR)European Commission [ANR-19-CE32-0005-01, ANR-20-EBI5-0004]; Centre National de la Recherche ScientifiqueCentre National de la Recherche Scientifique (CNRS) [Defi INFINITI 2018]; Centre National d'Etudes Spatiale [TOSCA-CNES 4500070632]; Direction Regionale des Affaires Culturelles Hauts-de-France [PCR 2018-2022]; European Cooperation in Science and TechnologyEuropean Cooperation in Science and Technology (COST) [CA17134]; H2020 Environment [862480]; Research Foundation FlandersFWO [12P1819N]; H2020 European Research Council [757833]; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen ForschungAustrian Science Fund (FWF) [193645] Funding text: Agence Nationale de la Recherche, Grant/Award Number: ANR-19-CE32-0005-01 (IMPRINT) and ANR-20-EBI5-0004 (ASICS); Centre National de la Recherche Scientifique, Grant/Award Number: Defi INFINITI 2018 (MORFO); Centre National d'Etudes Spatiale, Grant/Award Number: TOSCA-CNES 4500070632 (FRISBEE); Direction Regionale des Affaires Culturelles Hauts-de-France, Grant/Award Number: PCR 2018-2022 (ARPEGE); European Cooperation in Science and Technology, Grant/Award Number: CA17134; H2020 Environment, Grant/Award Number: SHOWCASE Grant 862480; Research Foundation Flanders, Grant/Award Number: 12P1819N; H2020 European Research Council, Grant/Award Number: ERC Starting Grant FORMICA 757833; Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen Forschung, Grant/Award Number: 193645 LA - English DB - MTMT ER - TY - JOUR AU - Moeslund, Jesper Erenskjold AU - Clausen, Kevin Kuhlmann AU - Dalby, Lars AU - Flojgaard, Camilla AU - Partel, Meelis AU - Pfeifer, Norbert AU - Hollaus, Markus AU - Brunbjerg, Ane Kirstine AU - Disney, Mat AU - Zhang, Jian TI - Using airborne lidar to characterize North European terrestrial high-dark-diversity habitats JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON PY - 2022 PG - 16 SN - 2056-3485 DO - 10.1002/rse2.314 UR - https://m2.mtmt.hu/api/publication/33230295 ID - 33230295 N1 - Section for Biodiversity, Department of Ecoscience, Aarhus University, Aarhus C, Denmark Macroecology Group, Department of Botany, University of Tartu, Tartu, Estonia Research Unit Photogrammetry, Department of Geodesy and Geoinformation, Technical University of Vienna, Vienna, Austria Export Date: 12 December 2022 Correspondence Address: Moeslund, J.E.; Section for Biodiversity, Denmark; email: jesper.moeslund@ecos.au.dk AB - A key aspect of nature conservation is knowledge of which aspects of nature to conserve or restore to favor the characteristic diversity of plants in a given area. Here, we used a large plant dataset with >40 000 plots combined with airborne laser scanning (lidar) data to reveal the local characteristics of habitats having a high plant dark diversity-that is, absence of suitable species-at national extent (>43 000 km(2)). Such habitats have potential for reaching high realized diversity levels and hence are important in a conservation context. We calculated 10 different lidar based metrics (both terrain and vegetation structure) and combined these with seven different field-based measures (soil chemistry and species indicators). We then used Integrated Nested Laplace Approximation for modelling plant dark diversity across 33 North European habitat types (open landscapes and forests) selected by the European communities to be important. In open habitat types high-dark-diversity habitats had relatively low pH, high nitrogen content, tall homogenous vegetation, and overall relatively homogenous terrains (high terrain openness) although with a rather high degree of local microtopographical variations. High-dark-diversity habitats in forests had relatively tall vegetation, few natural-forest indicators, low potential solar radiation input and a low cover of small woody plants. Our results highlight important vegetation, terrain- and soil-related factors that managers and policymakers should be aware of in conservation and restoration projects to ensure a natural plant diversity, for example low nutrient loads, natural microtopography and possibly also open forests with old-growth elements such as dead wood and rot attacks. LA - English DB - MTMT ER - TY - JOUR AU - Anderson, C. T. AU - Dietz, S. L. AU - Pokswinski, S. M. AU - Jenkins, A. M. AU - Kaeser, M. J. AU - Hiers, J. K. AU - Pelc, B. D. TI - Traditional field metrics and terrestrial LiDAR predict plant richness in southern pine forests JF - FOREST ECOLOGY AND MANAGEMENT J2 - FOREST ECOL MANAG VL - 491 PY - 2021 PG - 7 SN - 0378-1127 DO - 10.1016/j.foreco.2021.119118 UR - https://m2.mtmt.hu/api/publication/32329451 ID - 32329451 AB - Terrestrial LiDAR is a promising tool for providing accurate and consistent measurements of forest structure at fine scales and has the potential to address some of the drawbacks associated with traditional vegetation monitoring methods. To compare terrestrial LiDAR to traditional methods, we conducted vegetation surveys using common methods of estimating cover and structure, and scanned surveyed areas using a terrestrial LiDAR device, the Leica BLK360. We developed simple methods for using point cloud data to make approximations of complex forest structure metrics and compared the ability of both data collection types to predict species richness. Hybrid models accurately predicted total, herb, and shrub richness in southern pine forests using combinations of metrics collected from terrestrial LiDAR and traditional field-based sampling methodology. Our findings indicate terrestrial LiDAR data may be used to accurately predict species richness in community types where structure and richness are related. In addition, our results suggest terrestrial LiDAR technology has the potential to address the limitations of traditional methods used to quantify vegetation structure and improve our ability for studying forest structure-richness relationships. LA - English DB - MTMT ER - TY - JOUR AU - Bohlin, Inka AU - Maltamo, Matti AU - Hedenas, Henrik AU - Lamas, Tomas AU - Dahlgren, Jonas AU - Mehtatalo, Lauri TI - Predicting bilberry and cowberry yields using airborne laser scanning and other auxiliary data combined with National Forest Inventory field plot data JF - FOREST ECOLOGY AND MANAGEMENT J2 - FOREST ECOL MANAG VL - 502 PY - 2021 PG - 14 SN - 0378-1127 DO - 10.1016/j.foreco.2021.119737 UR - https://m2.mtmt.hu/api/publication/33431406 ID - 33431406 AB - The increasing availability of wall-to-wall remote sensing datasets in combination with accurate field data enables the mapping of different ecosystem services more accurately and over larger areas than before. The provision of wild berries is an essential ecosystem service, and berries are the most used non-wood forest products in Nordic countries. The aim of the study was to 1) develop general prediction models for bilberry and cowberry yield based on metrics derived from airborne laser scanning (ALS) data and other existing wall-to-wall data and 2) to identify laser-based structural features of forests that can be linked to locations of the highest berry yields. We used the indirect approach where the correlation between forest structure described by the ALS data and the berry yields are utilized. Berry data collected in the Swedish National Forest Inventory (NFI) 2007-2016 were used for training the models and ALS data from 2009 to 2014 from the national ALS campaign of Sweden. Berry yields were modelled using generalised linear mixed models (GLMMs), and forest structural differences were demonstrated in histograms of presence/absence data. The ALS-based canopy cover was an important variable both in bilberry and cowberry models. Other significant variables were ALS-based height variance, shrub cover, height above sea level, slope, soil wetness and terrain ruggedness, satellite-based species-specific volume and percentage, seasonality of temperature and precipitation and annual precipitation, inventory year, soil type and land use class. In addition, the time difference between the inventory day and the Julian day when berries were expected to be ripe showed a 1.5% decrease for bilberry and a 1.1% decrease for cowberry yield per day during the season. The highest bilberry yield was identified in forests with a canopy cover of 50% and the highest cowberry yield in forests with a canopy cover close to zero. The canopy height of 15 m reflected the highest bilberry yield, whereas a canopy height close to 0 m resulted in the highest cowberry yield. The shrub cover was close to zero both with highest bilberry and cowberry yields. This is the first study combining ALS metrics with other wall-to-wall variables and NFI field data to model bilberry and cowberry yields. Prediction models can be used to produce maps showing the most potential locations for berry picking. Further, the models may, in the future, be imported into forest planning systems to obtain stand-level prognoses of berry yield development under different forest management strategies. LA - English DB - MTMT ER - TY - JOUR AU - Koma, Zsófia AU - Zlinszky, András AU - Bekő, László AU - Burai, Péter AU - Seijmonsbergen, Arie C. AU - Kissling, W. Daniel TI - Quantifying 3D vegetation structure in wetlands using differently measured airborne laser scanning data JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 127 PY - 2021 SN - 1470-160X DO - 10.1016/j.ecolind.2021.107752 UR - https://m2.mtmt.hu/api/publication/32077627 ID - 32077627 AB - Mapping and quantifying 3D vegetation structure is essential for assessing and monitoring ecosystem structure and function within wetlands. Airborne Laser Scanning (ALS) is a promising data source for developing indicators of 3D vegetation structure, but derived metrics are often not compared with 3D structural field measurements and the acquisition of ALS data is rarely standardized across different remote sensing surveys. Here, we compare a set of Light Detection And Ranging (LiDAR) metrics derived from ALS datasets with varying characteristics to a standardized set of field measurements of vegetation height, biomass and Leaf Area Index (LAI) across three Hungarian lakes (Lake Balaton, Lake Fert.o and Lake Tisza). The ALS datasets differed in whether the recording type was full waveform (FWF) or discrete return, and in their point density (4 pt/m(2) and 21 pt/m(2)). A total of eight LiDAR metrics captured radiometric information as well as descriptors of vegetation cover, height and vertical variability. Multivariate regression models with field-based measurements of vegetation height, biomass or LAI as response variable and LiDAR metrics as predictors showed major differences between ALS recording types, and were affected by differences in spatial resolution, temporal offset and seasonality between field and ALS data acquisition. Vegetation height could be estimated with high to intermediate accuracy (FWF ALS data only: R-2 = 0.84; combination of ALS datasets: R-2 = 0.67), demonstrating its potential as a robust indicator of 3D vegetation structure across different ALS datasets. In contrast, the estimation of biomass and LAI in these wetlands was sensitive to variation in ALS characteristics and to the discrepancies between field and ALS data in terms of spatial resolution, temporal offset and seasonality (biomass: R-2 = 0.20-0.22; LAI: R-2 = 0.08-0.30). We recommend the use of FWF ALS data within wetlands because it captures more vegetation structural details in dense reed and marshland vegetation. We further suggest that ecologists and remote sensing scientist should better coordinate the simultaneous and standardized acquisition of field and ALS data for testing the robustness of quantitative descriptors of vegetation cover, height and vertical variability within wetlands. This is important for establishing operational and spatially contiguous ALS-based indicators of 3D ecosystem structure across wetlands. LA - English DB - MTMT ER - TY - JOUR AU - Kopecky, Martin AU - Macek, Martin AU - Wild, Jan TI - Topographic Wetness Index calculation guidelines based on measured soil moisture and plant species composition JF - SCIENCE OF THE TOTAL ENVIRONMENT J2 - SCI TOTAL ENVIRON VL - 757 PY - 2021 PG - 10 SN - 0048-9697 DO - 10.1016/j.scitotenv.2020.143785 UR - https://m2.mtmt.hu/api/publication/32329453 ID - 32329453 AB - Soil moisture controls environmental processes and spedes distributions, but it is difficult to measure and interpolate across space. Topographic Wetness Index (TWI) derived from digital elevation model is therefore often used as a proxy for soil moisture. However, different algorithms can be used to calculate TWI and this potentially affects TWI relationship with soil moisture and species assemblages.To disentangle insufficiently-known effects of different algorithms on TWI relation with soil moisture and plant species composition, we measured the root-zone soil moisture throughout a growing season and recorded vascular plants and bryophytes in 45 temperate forest plots. For each plot, we calculated 26 TWI variants from a LiDAR-based digital terrain model and related these TWI variants to the measured soil moisture and moisture-controlled species assemblages of vascular plants and bryophytes.A flow accumulation algorithm determined the ability of the TWI to predict soil moisture, while the flow width and slope algorithms had only a small effects. The TWI calculated with the most often used single-flow D8 algorithm explained less than half of the variation in soil moisture and species composition explained by the TWI calculated with the multiple-flow FD8 algorithm. Flow dispersion used in the 1D8 algorithm strongly affected the TWI performance, and a flow dispersion dose to 1.0 resulted in the TWI best related to the soil moisture and spedes assemblages. Using downslope gradient instead of the local slope gradient can strongly decrease TWI performance.Our results clearly showed that the method used to calculate TWI affects study conclusion. However, TMI calculation is often not specified and thus impossible to reproduce and compare among studies. We therefore provide guidelines for TWI calculation and recommend the FD8 flow algorithm with a flow dispersion close to 1.0, flow width equal to the raster cell size and local slope gradient for TWI calculation. (C) 2020 Elsevier B.V. All rights reserved. LA - English DB - MTMT ER - TY - JOUR AU - Lai, G.-Y. AU - Liu, H.-C. AU - Chung, C.-H. AU - Wang, C.-K. AU - Huang, C.-Y. TI - Lidar-derived environmental drivers of epiphytic bryophyte biomass in tropical montane cloud forests JF - REMOTE SENSING OF ENVIRONMENT J2 - REMOTE SENS ENVIRON VL - 253 PY - 2021 SN - 0034-4257 DO - 10.1016/j.rse.2020.112166 UR - https://m2.mtmt.hu/api/publication/31824274 ID - 31824274 N1 - Department of Geography, National Taiwan University, Taipei, 10617, Taiwan Department of Forestry and Natural Resources, National Ilan University, Ilan, 26047, Taiwan Department of Geomatics, National Cheng Kung University, Tainan, 70101, Taiwan Research Center for Future Earth, National Taiwan University, Taipei, 10617, Taiwan Export Date: 22 January 2021 CODEN: RSEEA Correspondence Address: Huang, C.-Y.; Department of Geography, Taiwan; email: choying@ntu.edu.tw LA - English DB - MTMT ER - TY - JOUR AU - Valdez, J.W. AU - Brunbjerg, A.K. AU - Fløjgaard, C. AU - Dalby, L. AU - Clausen, K.K. AU - Pärtel, M. AU - Pfeifer, N. AU - Hollaus, M. AU - Wimmer, M.H. AU - Ejrnæs, R. AU - Moeslund, J.E. TI - Relationships between macro-fungal dark diversity and habitat parameters using LiDAR JF - FUNGAL ECOLOGY J2 - FUNGAL ECOL VL - 51 PY - 2021 SN - 1754-5048 DO - 10.1016/j.funeco.2021.101054 UR - https://m2.mtmt.hu/api/publication/31999550 ID - 31999550 N1 - Department of Bioscience, Aarhus University, Grenåvej 14, Rønde, 8410, Denmark Department of Botany, Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, Tartu, 51005, Estonia Department of Geodesy and Geoinformation, Technische Universität Wien, Wiedner Hauptstraße 8/E120Vienna 1040, Austria German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstrasse 4, Leipzig, 04103, Germany Export Date: 5 May 2021 Correspondence Address: Moeslund, J.E.; Department of Bioscience, Grenåvej 14, Denmark; email: jesper.moeslund@bios.au.dk LA - English DB - MTMT ER - TY - JOUR AU - Vries, Jan Peter Reinier AU - Koma, Zsófia AU - WallisDeVries, Michiel F. AU - Kissling, W. Daniel AU - Tingley, Reid TI - Identifying fine‐scale habitat preferences of threatened butterflies using airborne laser scanning JF - DIVERSITY AND DISTRIBUTIONS J2 - DIVERS DISTRIB VL - 27 PY - 2021 SN - 1366-9516 DO - 10.1111/ddi.13272 UR - https://m2.mtmt.hu/api/publication/31956636 ID - 31956636 N1 - Institute of Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, Netherlands De Vlinderstichting/Dutch Butterfly Conservation, Wageningen, Netherlands Plant Ecology and Nature Conservation Group, Wageningen University, Wageningen, Netherlands Cited By :11 Export Date: 12 December 2022 CODEN: DIDIF Correspondence Address: Kissling, W.D.; Institute of Biodiversity and Ecosystem Dynamics (IBED), Netherlands; email: wdkissling@gmail.com LA - English DB - MTMT ER - TY - JOUR AU - Brunbjerg, Ane Kirstine AU - Bruun, Hans Henrik AU - Dalby, Lars AU - Classen, Aimee T. AU - Flojgaard, Camilla AU - Froslev, Tobias G. AU - Hansen, Oskar Liset Pryds AU - Hoye, Toke Thomas AU - Moeslund, Jesper Erenskjold AU - Svenning, Jens-Christian AU - Ejrnaes, Rasmus TI - Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation JF - OIKOS J2 - OIKOS VL - 129 PY - 2020 IS - 9 SP - 1381 EP - 1392 PG - 12 SN - 0030-1299 DO - 10.1111/oik.07145 UR - https://m2.mtmt.hu/api/publication/31523270 ID - 31523270 AB - Amidst the global biodiversity crisis, identifying general principles for variation of biodiversity remains a key challenge. Scientific consensus is limited to a few macroecological rules, such as species richness increasing with area, which provide limited guidance for conservation. In fact, few agreed ecological principles apply at the scale of sites or reserve management, partly because most community-level studies are restricted to single habitat types and species groups. We used the recently proposed ecospace framework and a comprehensive data set for aggregating environmental variation to predict multi-taxon diversity. We studied richness of plants, fungi and arthropods in 130 sites representing the major terrestrial habitat types in Denmark. We found the abiotic environment (ecospace position) to be pivotal for the richness of primary producers (vascular plants, mosses and lichens) and, more surprisingly, little support for ecospace continuity as a driver. A peak in richness at intermediate productivity adds new empirical evidence to a long-standing debate over biodiversity responses to productivity. Finally, we discovered a dominant and positive response of fungi and insect richness to organic matter accumulation and diversification (ecospace expansion). Two simple models of producer and consumer richness accounted for 77% of the variation in multi-taxon species richness suggesting a significant potential for generalization beyond individual species responses. Our study widens the traditional conservation focus on vegetation and vertebrate populations unravelling the importance of diversification of carbon resources for diverse heterotrophs, such as fungi and insects. LA - English DB - MTMT ER - TY - JOUR AU - Cerrejon, Carlos AU - Valeria, Osvaldo AU - Mansuy, Nicolas AU - Barbe, Marion AU - Fenton, Nicole J. TI - Predictive mapping of bryophyte richness patterns in boreal forests using species distribution models and remote sensing data JF - ECOLOGICAL INDICATORS J2 - ECOL INDIC VL - 119 PY - 2020 PG - 13 SN - 1470-160X DO - 10.1016/j.ecolind.2020.106826 UR - https://m2.mtmt.hu/api/publication/31773035 ID - 31773035 AB - Bryophytes represent an essential component of global biodiversity and play a significant role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial exploitation of natural resources threatens bryophyte species and the ecological processes and services they support. However, the consideration of bryophytes in conservation issues is limited by current knowledge gaps on their distribution and diversity patterns. This is mainly due to the ineffectiveness of traditional field surveys to acquire information over large areas. Using remote sensing data in combination with species distribution models (SDMs), we aim to predict and map diversity patterns (in terms of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) in 28,436 km(2) of boreal forests of Quebec (Canada). A bryophyte presence/absence database was used to develop four response variables: total bryophyte richness, moss richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental predictors including climate, topography, soil moisture and drainage as well as vegetation. Then a final set of predictors was selected individually for each response variable through a two-step selection procedure. The Random Forest (RF) algorithm was used to develop spatially explicit regression models and to generate predictive cartography at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics were provided. Our models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze-thaw events) consistently appeared among the most important predictors for all bryophyte groups modeled. However, guild-level models identified differences in important factors determining the richness of each of the guilds and, therefore, in their predicted richness patterns. For example, the predictor number of days > 30 degrees C was especially relevant for liverworts, while drainage class, topographic position index and PALSAR HH-polarized L-band were identified among the most important predictors for sphagna. These differences have important implications for management and conservation strategies for bryophytes. This study provides evidence of the potential of remote sensing for assessing and making predictions on bryophyte diversity across the landscape. LA - English DB - MTMT ER - TY - JOUR AU - Koma, Zsófia AU - Seijmonsbergen, Arie C. AU - Kissling, W. Daniel ED - Pettorelli, Nathalie ED - Disney, Mat TI - Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning JF - REMOTE SENSING IN ECOLOGY AND CONSERVATION J2 - REMOTE SENS ECOL CON VL - 6 PY - 2020 SN - 2056-3485 DO - 10.1002/rse2.170 UR - https://m2.mtmt.hu/api/publication/31349502 ID - 31349502 LA - English DB - MTMT ER - TY - JOUR AU - Miguel Olano, Jose AU - Martinez-Rodrigo, Raquel AU - Miguel Altelarrea, Jose AU - Agreda, Teresa AU - Fernandez-Toiran, Marina AU - Garcia-Cervigon, Ana I AU - Rodriguez-Puerta, Francisco AU - Agueda, Beatriz TI - Primary productivity and climate control mushroom yields in Mediterranean pine forests JF - AGRICULTURAL AND FOREST METEOROLOGY J2 - AGR FOREST METEOROL VL - 288 PY - 2020 PG - 8 SN - 0168-1923 DO - 10.1016/j.agrformet.2020.108015 UR - https://m2.mtmt.hu/api/publication/31523269 ID - 31523269 AB - Mushrooms play a provisioning ecosystem service as wild food. The abundance of this resource shows high annual and interannual variability, particularly in Mediterranean ecosystems. Climate conditions have been considered the main factor promoting mushroom production variability, but several evidences suggest that forest composition, age and growth play also a role.Long-term mushroom production datasets are critical to understand the factors behind mushroom productivity. We used 22 and 24 year-long time series of mushroom production in Pinus pinaster and Pinus sylvestris forests in Central Spain to evaluate the effect of climate and forest productivity on mushroom yield. We combined climatic data (precipitation and temperature) and remote sensing data (soil moisture and the Normalized Difference Vegetation Index, NDVI, a surrogate of primary productivity) to model mushroom yields for each forest and for the main edible species of economic interest ( Boletus edulis and Lactarius deliciosus).We hypothesized that mushroom yield would be related to (i) forest primary productivity inferred from NDVI affects mushroom yields, that (ii) soil moisture inferred from remote sensors will equal the predictive power precipitation data, and that (iii) combining climatic and remote sensing will improve mushroom yield models.We found that (i) previous year NDVI correlated (r = 0.41-0.6) with mushroom yields; (ii) soil moisture from remote sensors rivaled the predictive power of precipitation (r = 0.63-0.72); and (iii) primary production and climate variances were independent, thus the combination of climatic and remote sensing data improved models with mean R-adj(2) as high as 0.629.On the light of these results, we propose as a working hypothesis that mushroom production might be modelled as a two step process. Previous year primary productivity would favour resource accumulation at tree level, potentially increasing resources for mycelia growth, climatic conditions during the fruiting season control the ability of mycelia to transform available resources into fruiting bodies. LA - English DB - MTMT ER - TY - JOUR AU - Valbuena, R. AU - O'Connor, B. AU - Zellweger, F. AU - Simonson, W. AU - Vihervaara, P. AU - Maltamo, M. AU - Silva, C. A. AU - Almeida, D. R. A. AU - Danks, F. AU - Morsdorf, F. AU - Chirici, G. AU - Lucas, R. AU - Coomes, D. A. AU - Coops, N. C. TI - Standardizing Ecosystem Morphological Traits from 3D Information Sources JF - TRENDS IN ECOLOGY & EVOLUTION J2 - TRENDS ECOL EVOL VL - 35 PY - 2020 IS - 8 SP - 656 EP - 667 PG - 12 SN - 0169-5347 DO - 10.1016/j.tree.2020.03.006 UR - https://m2.mtmt.hu/api/publication/31519782 ID - 31519782 AB - 3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits - height, cover, and structural complexity - that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources - satellites, aircrafts, drones, or ground data - allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported. LA - English DB - MTMT ER -