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2. Understanding climate change from a global analysis of city analogues.

Author

Jean-Francois Bastin, Emily Clark, Thomas Elliott, Simon Hart, Johan van den Hoogen, Iris Hordijk, Haozhi Ma, Sabiha Majumder, Gabriele Manoli, Julia Maschler, Lidong Mo, Devin Routh, Kailiang Yu, Constantin M Zohner, and Thomas W Crowther

Subjects
Medicine, Science
Abstract

Combating climate change requires unified action across all sectors of society. However, this collective action is precluded by the 'consensus gap' between scientific knowledge and public opinion. Here, we test the extent to which the iconic cities around the world are likely to shift in response to climate change. By analyzing city pairs for 520 major cities of the world, we test if their climate in 2050 will resemble more closely to their own current climate conditions or to the current conditions of other cities in different bioclimatic regions. Even under an optimistic climate scenario (RCP 4.5), we found that 77% of future cities are very likely to experience a climate that is closer to that of another existing city than to its own current climate. In addition, 22% of cities will experience climate conditions that are not currently experienced by any existing major cities. As a general trend, we found that all the cities tend to shift towards the sub-tropics, with cities from the Northern hemisphere shifting to warmer conditions, on average ~1000 km south (velocity ~20 km.year-1), and cities from the tropics shifting to drier conditions. We notably predict that Madrid's climate in 2050 will resemble Marrakech's climate today, Stockholm will resemble Budapest, London to Barcelona, Moscow to Sofia, Seattle to San Francisco, Tokyo to Changsha. Our approach illustrates how complex climate data can be packaged to provide tangible information. The global assessment of city analogues can facilitate the understanding of climate change at a global level but also help land managers and city planners to visualize the climate futures of their respective cities, which can facilitate effective decision-making in response to on-going climate change.

Published
2019
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3. Recent deforestation drove the spike in Amazonian fires

Author

Adrián Cardil, Sergio de-Miguel, Carlos A Silva, Peter B Reich, David Calkin, Pedro H S Brancalion, Alexander C Vibrans, Javier G P Gamarra, M Zhou, Bryan C Pijanowski, Cang Hui, Thomas W Crowther, Bruno Hérault, Daniel Piotto, Christian Salas-Eljatib, Eben North Broadbent, Angelica M Almeyda Zambrano, Nicolas Picard, Luiz E O C Aragão, Jean-Francois Bastin, Devin Routh, Johan van den Hoogen, Pablo L Peri, and Jingjing Liang

Subjects
deforestation, fire, Amazon, forest policy, land-use change, tropical moist forest, Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999
Published
2020
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4. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Author

Laura Duncanson, James R. Kellner, John Armston, Ralph Dubayah, David M. Minor, Steven Hancock, Scott B Luthcke, Sean P. Healey, Paul L. Patterson, Svetlana Saarela, Suzanne Marselis, Carlos E. Silva, Jamis Bruening, Scott J. Goetz, Hao Tang, Michelle Hofton, Bryan Blair, Scott Luthcke, Lola Fatoyinbo, Katharine Abernethy, Alfonso Alonso, Hans-Erik Andersen, Paul Aplin, Timothy R. Baker, Nicolas Barbier, Jean Francois Bastin, Peter Biber, Pascal Boeckx, Jan Bogaert, Luigi Boschetti, Peter Brehm Boucher, Doreen S. Boyd, David F.R.P. Burslem, Sofia Calvo-Rodriguez, and Jermone Chave

Subjects
Earth Resources And Remote Sensing
Abstract

NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

Published
2022

5. Author response for 'Evenness mediates the global relationship between forest productivity and richness'

Author

null Iris Hordijk, null Daniel S. Maynard, null Simon P. Hart, null Mo Lidong, null Hans ter Steege, null Jingjing Liang, null Sergio de‐Miguel, null Gert‐Jan Nabuurs, null Peter B. Reich, null Meinrad Abegg, null C. Yves Adou Yao, null Giorgio Alberti, null Angelica M. Almeyda Zambrano, null Braulio V. Alvarado, null Alvarez‐Davila Esteban, null Patricia Alvarez‐Loayza, null Luciana F. Alves, null Christian Ammer, null Clara Antón‐Fernández, null Alejandro Araujo‐Murakami, null Luzmila Arroyo, null Valerio Avitabile, null Gerardo A. Aymard C, null Timothy Baker, null Radomir Bałazy, null Olaf Banki, null Jorcely Barroso, null Meredith L. Bastian, null Jean‐Francois Bastin, null Luca Birigazzi, null Philippe Birnbaum, null Robert Bitariho, null Pascal Boeckx, null Frans Bongers, null Olivier Bouriaud, null Pedro H. S. Brancalion, null Susanne Brandl, null Roel Brienen, null Eben N. Broadbent, null Helge Bruelheide, null Filippo Bussotti, null Roberto Cazzolla Gatti, null Ricardo G. César, null Goran Cesljar, null Robin Chazdon, null Han Y. H. Chen, null Chelsea Chisholm, null Emil Cienciala, null Connie J. Clark, null David B. Clark, null Gabriel Colletta, null David Coomes, null Fernando Cornejo Valverde, null Jose J. Corral‐Rivas, null Philip Crim, null Jonathan Cumming, null Selvadurai Dayanandan, null André L. de Gasper, null Mathieu Decuyper, null Géraldine Derroire, null Ben DeVries, null Ilija Djordjevic, null Amaral Iêda, null Aurélie Dourdain, null Engone Obiang Nestor Laurier, null Brian Enquist, null Teresa Eyre, null Adandé Belarmain Fandohan, null Tom M. Fayle, null Leandro V. Ferreira, null Ted R. Feldpausch, null Leena Finér, null Markus Fischer, null Christine Fletcher, null Lorenzo Frizzera, null Javier G. P. Gamarra, null Damiano Gianelle, null Henry B. Glick, null David Harris, null Andrew Hector, null Andreas Hemp, null Geerten Hengeveld, null Bruno Hérault, null John Herbohn, null Annika Hillers, null Eurídice N. Honorio Coronado, null Cang Hui, null Hyunkook Cho, null Thomas Ibanez, null Il Bin Jung, null Nobuo Imai, null Andrzej M. Jagodzinski, null Bogdan Jaroszewicz, null Vivian Johanssen, null Carlos A. Joly, null Tommaso Jucker, null Viktor Karminov, null Kuswata Kartawinata, null Elizabeth Kearsley, null David Kenfack, null Deborah Kennard, null Sebastian Kepfer‐Rojas, null Gunnar Keppel, null Mohammed Latif Khan, null Timothy Killeen, null Kim Hyun Seok, null Kanehiro Kitayama, null Michael Köhl, null Henn Korjus, null Florian Kraxner, null Diana Laarmann, null Mait Lang, null Simon Lewis, null Huicui Lu, null Natalia Lukina, null Brian Maitner, null Yadvinder Malhi, null Eric Marcon, null Beatriz Schwantes Marimon, null Ben Hur Marimon‐Junior, null Andrew Robert Marshall, null Emanuel Martin, null Olga Martynenko, null Jorge A. Meave, null Omar Melo‐Cruz, null Casimiro Mendoza, null Cory Merow, null Miscicki Stanislaw, null Abel Monteagudo Mendoza, null Vanessa Moreno, null Sharif A. Mukul, null Philip Mundhenk, null Maria G. Nava‐Miranda, null David Neill, null Victor Neldner, null Radovan Nevenic, null Michael Ngugi, null Pascal A. Niklaus, null Jacek Oleksyn, null Petr Ontikov, null Edgar Ortiz‐Malavasi, null Yude Pan, null Alain Paquette, null Alexander Parada‐Gutierrez, null Elena Parfenova, null Minjee Park, null Marc Parren, null Narayanaswamy Parthasarathy, null Pablo L. Peri, null Sebastian Pfautsch, null Oliver L. Phillips, null Nicolas Picard, null Maria Teresa Piedade, null Daniel Piotto, null Nigel C. A. Pitman, null Irina Polo, null Lourens Poorter, null Axel Dalberg Poulsen, null John R. Poulsen, null Hans Pretzsch, null Freddy Ramirez Arevalo, null Zorayda Restrepo‐Correa, null Mirco Rodeghiero, null Samir Rolim, null Anand Roopsind, null Francesco Rovero, null Ervan Rutishauser, null Purabi Saikia, null Christian Salas‐Eljatib, null Peter Schall, null Dmitry Schepaschenko, null Michael Scherer‐Lorenzen, null Bernhard Schmid, null Jochen Schöngart, null Eric B. Searle, null Vladimír Šebeň, null Josep M. Serra‐Diaz, null Douglas Sheil, null Anatoly Shvidenko, null Javier Silva‐Espejo, null Marcos Silveira, null James Singh, null Plinio Sist, null Ferry Slik, null Bonaventure Sonké, null Alexandre F. Souza, null Krzysztof Stereńczak, null Jens‐Christian Svenning, null Miroslav Svoboda, null Ben Swanepoel, null Natalia Targhetta, null Nadja Tchebakova, null Raquel Thomas, null Elena Tikhonova, null Peter Umunay, null Vladimir Usoltsev, null Renato Valencia, null Fernando Valladares, null Fons van der Plas, null Do Van Tran, null Michael E. Van Nuland, null Rodolfo Vasquez Martinez, null Hans Verbeeck, null Helder Viana, null Alexander C. Vibrans, null Simone Vieira, null Klaus von Gadow, null Hua‐Feng Wang, null James Watson, null Gijsbert D. A. Werner, null Susan K. Wiser, null Florian Wittmann, null Verginia Wortel, null Roderick Zagt, null Tomasz Zawila‐Niedzwiecki, null Chunyu Zhang, null Xiuhai Zhao, null Mo Zhou, null Zhi‐Xin Zhu, null Irie Casimir Zo‐Bi, and null Thomas W. Crowther

Published
2022

6. The number of tree species on Earth

Author

Roberto Cazzolla Gatti, Peter B. Reich, Javier G. P. Gamarra, Tom Crowther, Cang Hui, Albert Morera, Jean-Francois Bastin, Sergio de-Miguel, Gert-Jan Nabuurs, Jens-Christian Svenning, Josep M. Serra-Diaz, Cory Merow, Brian Enquist, Maria Kamenetsky, Junho Lee, Jun Zhu, Jinyun Fang, Douglass F. Jacobs, Bryan Pijanowski, Arindam Banerjee, Robert A. Giaquinto, Giorgio Alberti, Angelica Maria Almeyda Zambrano, Esteban Alvarez-Davila, Alejandro Araujo-Murakami, Valerio Avitabile, Gerardo A. Aymard, Radomir Balazy, Chris Baraloto, Jorcely G. Barroso, Meredith L. Bastian, Philippe Birnbaum, Robert Bitariho, Jan Bogaert, Frans Bongers, Olivier Bouriaud, Pedro H. S. Brancalion, Francis Q. Brearley, Eben North Broadbent, Filippo Bussotti, Wendeson Castro da Silva, Ricardo Gomes César, Goran Češljar, Víctor Chama Moscoso, Han Y. H. Chen, Emil Cienciala, Connie J. Clark, David A. Coomes, Selvadurai Dayanandan, Mathieu Decuyper, Laura E. Dee, Jhon Del Aguila Pasquel, Géraldine Derroire, Marie Noel Kamdem Djuikouo, Tran Van Do, Jiri Dolezal, Ilija Đ. Đorđević, Julien Engel, Tom M. Fayle, Ted R. Feldpausch, Jonas K. Fridman, David J. Harris, Andreas Hemp, Geerten Hengeveld, Bruno Herault, Martin Herold, Thomas Ibanez, Andrzej M. Jagodzinski, Bogdan Jaroszewicz, Kathryn J. Jeffery, Vivian Kvist Johannsen, Tommaso Jucker, Ahto Kangur, Victor N. Karminov, Kuswata Kartawinata, Deborah K. Kennard, Sebastian Kepfer-Rojas, Gunnar Keppel, Mohammed Latif Khan, Pramod Kumar Khare, Timothy J. Kileen, Hyun Seok Kim, Henn Korjus, Amit Kumar, Ashwani Kumar, Diana Laarmann, Nicolas Labrière, Mait Lang, Simon L. Lewis, Natalia Lukina, Brian S. Maitner, Yadvinder Malhi, Andrew R. Marshall, Olga V. Martynenko, Abel L. Monteagudo Mendoza, Petr V. Ontikov, Edgar Ortiz-Malavasi, Nadir C. Pallqui Camacho, Alain Paquette, Minjee Park, Narayanaswamy Parthasarathy, Pablo Luis Peri, Pascal Petronelli, Sebastian Pfautsch, Oliver L. Phillips, Nicolas Picard, Daniel Piotto, Lourens Poorter, John R. Poulsen, Hans Pretzsch, Hirma Ramírez-Angulo, Zorayda Restrepo Correa, Mirco Rodeghiero, Rocío Del Pilar Rojas Gonzáles, Samir G. Rolim, Francesco Rovero, Ervan Rutishauser, Purabi Saikia, Christian Salas-Eljatib, Dmitry Schepaschenko, Michael Scherer-Lorenzen, Vladimír Šebeň, Marcos Silveira, Ferry Slik, Bonaventure Sonké, Alexandre F. Souza, Krzysztof Jan Stereńczak, Miroslav Svoboda, Hermann Taedoumg, Nadja Tchebakova, John Terborgh, Elena Tikhonova, Armando Torres-Lezama, Fons van der Plas, Rodolfo Vásquez, Helder Viana, Alexander C. Vibrans, Emilio Vilanova, Vincent A. Vos, Hua-Feng Wang, Bertil Westerlund, Lee J. T. White, Susan K. Wiser, Tomasz Zawiła-Niedźwiecki, Lise Zemagho, Zhi-Xin Zhu, Irié C. Zo-Bi, Jingjing Liang, Cazzolla Gatti, Roberto, Reich, Peter B, Gamarra, Javier GP, Crowther, Tom, Keppel, Gunnar, Liang, Jingjing, Cazzolla Gatti R., Reich P.B., Gamarra J.G.P., Crowther T., Hui C., Morera A., Bastin J.-F., de-Miguel S., Nabuurs G.-J., Svenning J.-C., Serra-Diaz J.M., Merow C., Enquist B., Kamenetsky M., Lee J., Zhu J., Fang J., Jacobs D.F., Pijanowski B., Banerjee A., Giaquinto R.A., Alberti G., Almeyda Zambrano A.M., Alvarez-Davila E., Araujo-Murakami A., Avitabile V., Aymard G.A., Balazy R., Baraloto C., Barroso J.G., Bastian M.L., Birnbaum P., Bitariho R., Bogaert J., Bongers F., Bouriaud O., Brancalion P.H.S., Brearley F.Q., Broadbent E.N., Bussotti F., Castro da Silva W., Cesar R.G., Cesljar G., Chama Moscoso V., Chen H.Y.H., Cienciala E., Clark C.J., Coomes D.A., Dayanandan S., Decuyper M., Dee L.E., Del Aguila Pasquel J., Derroire G., Djuikouo M.N.K., Van Do T., Dolezal J., Dordevic I.D., Engel J., Fayle T.M., Feldpausch T.R., Fridman J.K., Harris D.J., Hemp A., Hengeveld G., Herault B., Herold M., Ibanez T., Jagodzinski A.M., Jaroszewicz B., Jeffery K.J., Johannsen V.K., Jucker T., Kangur A., Karminov V.N., Kartawinata K., Kennard D.K., Kepfer-Rojas S., Keppel G., Khan M.L., Khare P.K., Kileen T.J., Kim H.S., Korjus H., Kumar A., Laarmann D., Labriere N., Lang M., Lewis S.L., Lukina N., Maitner B.S., Malhi Y., Marshall A.R., Martynenko O.V., Monteagudo Mendoza A.L., Ontikov P.V., Ortiz-Malavasi E., Pallqui Camacho N.C., Paquette A., Park M., Parthasarathy N., Peri P.L., Petronelli P., Pfautsch S., Phillips O.L., Picard N., Piotto D., Poorter L., Poulsen J.R., Pretzsch H., Ramirez-Angulo H., Restrepo Correa Z., Rodeghiero M., Rojas Gonzales R.D.P., Rolim S.G., Rovero F., Rutishauser E., Saikia P., Salas-Eljatib C., Schepaschenko D., Scherer-Lorenzen M., Seben V., Silveira M., Slik F., Sonke B., Souza A.F., Sterenczak K.J., Svoboda M., Taedoumg H., Tchebakova N., Terborgh J., Tikhonova E., Torres-Lezama A., van der Plas F., Vasquez R., Viana H., Vibrans A.C., Vilanova E., Vos V.A., Wang H.-F., Westerlund B., White L.J.T., Wiser S.K., Zawila-Niedzwiecki T., Zemagho L., Zhu Z.-X., Zo-Bi I.C., Liang J., Purdue University [West Lafayette], University of Wisconsin-Madison, FAO Forestry, Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), Département Systèmes Biologiques (Cirad-BIOS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie]), Cazzolla Gatti, Roberto [0000-0001-5130-8492], Reich, Peter B [0000-0003-4424-662X], Hui, Cang [0000-0002-3660-8160], Morera, Albert [0000-0002-6777-169X], de-Miguel, Sergio [0000-0002-9738-0657], Svenning, Jens-Christian [0000-0002-3415-0862], Serra-Diaz, Josep M [0000-0003-1988-1154], Alberti, Giorgio [0000-0003-2422-3009], Bongers, Frans [0000-0002-8431-6189], Bouriaud, Olivier [0000-0002-8046-466X], Brancalion, Pedro HS [0000-0001-8245-4062], César, Ricardo Gomes [0000-0002-3392-8089], Chen, Han YH [0000-0001-9477-5541], Cienciala, Emil [0000-0002-1254-4254], Coomes, David [0000-0002-8261-2582], Djuikouo, Marie Noel Kamdem [0000-0003-0064-5151], Van Do, Tran [0000-0001-9059-5842], Feldpausch, Ted R [0000-0002-6631-7962], Jaroszewicz, Bogdan [0000-0002-2042-8245], Jeffery, Kathryn J [0000-0002-2632-0008], Kennard, Deborah K [0000-0003-4842-8260], Kim, Hyun Seok [0000-0002-3440-6071], Labrière, Nicolas [0000-0002-8037-2001], Maitner, Brian S [0000-0002-2118-9880], Malhi, Yadvinder [0000-0002-3503-4783], Peri, Pablo Luis [0000-0002-5398-4408], Phillips, Oliver L [0000-0002-8993-6168], Poorter, Lourens [0000-0003-1391-4875], Poulsen, John R [0000-0002-1532-9808], Salas-Eljatib, Christian [0000-0002-8468-0829], Schepaschenko, Dmitry [0000-0002-7814-4990], Silveira, Marcos [0000-0003-0485-7872], Slik, Ferry [0000-0003-3988-7019], Sonké, Bonaventure [0000-0002-4310-3603], Terborgh, John [0000-0003-1853-8311], Wiser, Susan K [0000-0002-8938-8181], Liang, Jingjing [0000-0001-9439-9320], Apollo - University of Cambridge Repository, and Coomes, David A [0000-0002-8261-2582]

Subjects
Cambios Antropogénicos, Richness, SAMPLE, Earth, Planet, Rarity, Bos- en Landschapsecologie, DIVERSITY, Forests, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Trees, forest, Bioma, Laboratory of Geo-information Science and Remote Sensing, Biome, espèce (taxon), HETEROGENEITY, Forest and Landscape Ecology, Forest Biodiversity, hyperdominance, Riqueza de Especies, Ecosystem Services, biodiversity, forests, hyperdominance, rarity, richness, biodiversity, Multidisciplinary, Hyperdominance, Overall Scale, F70 - Taxonomie végétale et phytogéographie, Biodiversity, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, Écologie des populations, PE&RC, COVERAGE, Boscos i silvicultura, Biometris, Forest Ecosystems, ABUNDANCE, Anthropogenic Changes, Vegetatie, Bos- en Landschapsecologie, Biodiversité, леса, Conservation of Natural Resources, F40 - Écologie végétale, Servicios de los Ecosistemas, Vulnerability, ECOLOGIA DE POPULAÇÕES, Arbre, ECOLOGY, Biodiversidad, forests, rarity, richness, Ecosistemas Forestales, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, COMPLETENESS, Árboles, Settore BIO/07 - ECOLOGIA, Richness Species, Bosecologie en Bosbeheer, Laboratorium voor Geo-informatiekunde en Remote Sensing, K70 - Dégâts causés aux forêts et leur protection, Biodiversidad Forestal, Escala Global, Vegetatie, деревья, Vegetation, Forest Ecology and Forest Management, biodiversité forestière, биоразнообразие, PATTERNS, Vegetation, Forest and Landscape Ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Vulnerabilidad
Abstract

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

Published
2022

7. Co-limitation towards lower latitudes shapes global forest diversity gradients

Author

Jingjing Liang, Javier G. P. Gamarra, Nicolas Picard, Mo Zhou, Bryan Pijanowski, Douglass F. Jacobs, Peter B. Reich, Thomas W. Crowther, Gert-Jan Nabuurs, Sergio de-Miguel, Jingyun Fang, Christopher W. Woodall, Jens-Christian Svenning, Tommaso Jucker, Jean-Francois Bastin, Susan K. Wiser, Ferry Slik, Bruno Hérault, Giorgio Alberti, Gunnar Keppel, Geerten M. Hengeveld, Pierre L. Ibisch, Carlos A. Silva, Hans ter Steege, Pablo L. Peri, David A. Coomes, Eric B. Searle, Klaus von Gadow, Bogdan Jaroszewicz, Akane O. Abbasi, Meinrad Abegg, Yves C. Adou Yao, Jesús Aguirre-Gutiérrez, Angelica M. Almeyda Zambrano, Jan Altman, Esteban Alvarez-Dávila, Juan Gabriel Álvarez-González, Luciana F. Alves, Bienvenu H. K. Amani, Christian A. Amani, Christian Ammer, Bhely Angoboy Ilondea, Clara Antón-Fernández, Valerio Avitabile, Gerardo A. Aymard, Akomian F. Azihou, Johan A. Baard, Timothy R. Baker, Radomir Balazy, Meredith L. Bastian, Rodrigue Batumike, Marijn Bauters, Hans Beeckman, Nithanel Mikael Hendrik Benu, Robert Bitariho, Pascal Boeckx, Jan Bogaert, Frans Bongers, Olivier Bouriaud, Pedro H. S. Brancalion, Susanne Brandl, Francis Q. Brearley, Jaime Briseno-Reyes, Eben N. Broadbent, Helge Bruelheide, Erwin Bulte, Ann Christine Catlin, Roberto Cazzolla Gatti, Ricardo G. César, Han Y. H. Chen, Chelsea Chisholm, Emil Cienciala, Gabriel D. Colletta, José Javier Corral-Rivas, Anibal Cuchietti, Aida Cuni-Sanchez, Javid A. Dar, Selvadurai Dayanandan, Thales de Haulleville, Mathieu Decuyper, Sylvain Delabye, Géraldine Derroire, Ben DeVries, John Diisi, Tran Van Do, Jiri Dolezal, Aurélie Dourdain, Graham P. Durrheim, Nestor Laurier Engone Obiang, Corneille E. N. Ewango, Teresa J. Eyre, Tom M. Fayle, Lethicia Flavine N. Feunang, Leena Finér, Markus Fischer, Jonas Fridman, Lorenzo Frizzera, André L. de Gasper, Damiano Gianelle, Henry B. Glick, Maria Socorro Gonzalez-Elizondo, Lev Gorenstein, Richard Habonayo, Olivier J. Hardy, David J. Harris, Andrew Hector, Andreas Hemp, Martin Herold, Annika Hillers, Wannes Hubau, Thomas Ibanez, Nobuo Imai, Gerard Imani, Andrzej M. Jagodzinski, Stepan Janecek, Vivian Kvist Johannsen, Carlos A. Joly, Blaise Jumbam, Banoho L. P. R. Kabelong, Goytom Abraha Kahsay, Viktor Karminov, Kuswata Kartawinata, Justin N. Kassi, Elizabeth Kearsley, Deborah K. Kennard, Sebastian Kepfer-Rojas, Mohammed Latif Khan, John N. Kigomo, Hyun Seok Kim, Carine Klauberg, Yannick Klomberg, Henn Korjus, Subashree Kothandaraman, Florian Kraxner, Amit Kumar, Relawan Kuswandi, Mait Lang, Michael J. Lawes, Rodrigo V. Leite, Geoffrey Lentner, Simon L. Lewis, Moses B. Libalah, Janvier Lisingo, Pablito Marcelo López-Serrano, Huicui Lu, Natalia V. Lukina, Anne Mette Lykke, Vincent Maicher, Brian S. Maitner, Eric Marcon, Andrew R. Marshall, Emanuel H. Martin, Olga Martynenko, Faustin M. Mbayu, Musingo T. E. Mbuvi, Jorge A. Meave, Cory Merow, Stanislaw Miscicki, Vanessa S. Moreno, Albert Morera, Sharif A. Mukul, Jörg C. Müller, Agustinus Murdjoko, Maria Guadalupe Nava-Miranda, Litonga Elias Ndive, Victor J. Neldner, Radovan V. Nevenic, Louis N. Nforbelie, Michael L. Ngoh, Anny E. N’Guessan, Michael R. Ngugi, Alain S. K. Ngute, Emile Narcisse N. Njila, Melanie C. Nyako, Thomas O. Ochuodho, Jacek Oleksyn, Alain Paquette, Elena I. Parfenova, Minjee Park, Marc Parren, Narayanaswamy Parthasarathy, Sebastian Pfautsch, Oliver L. Phillips, Maria T. F. Piedade, Daniel Piotto, Martina Pollastrini, Lourens Poorter, John R. Poulsen, Axel Dalberg Poulsen, Hans Pretzsch, Mirco Rodeghiero, Samir G. Rolim, Francesco Rovero, Ervan Rutishauser, Khosro Sagheb-Talebi, Purabi Saikia, Moses Nsanyi Sainge, Christian Salas-Eljatib, Antonello Salis, Peter Schall, Dmitry Schepaschenko, Michael Scherer-Lorenzen, Bernhard Schmid, Jochen Schöngart, Vladimír Šebeň, Giacomo Sellan, Federico Selvi, Josep M. Serra-Diaz, Douglas Sheil, Anatoly Z. Shvidenko, Plinio Sist, Alexandre F. Souza, Krzysztof J. Stereńczak, Martin J. P. Sullivan, Somaiah Sundarapandian, Miroslav Svoboda, Mike D. Swaine, Natalia Targhetta, Nadja Tchebakova, Liam A. Trethowan, Robert Tropek, John Tshibamba Mukendi, Peter Mbanda Umunay, Vladimir A. Usoltsev, Gaia Vaglio Laurin, Riccardo Valentini, Fernando Valladares, Fons van der Plas, Daniel José Vega-Nieva, Hans Verbeeck, Helder Viana, Alexander C. Vibrans, Simone A. Vieira, Jason Vleminckx, Catherine E. Waite, Hua-Feng Wang, Eric Katembo Wasingya, Chemuku Wekesa, Bertil Westerlund, Florian Wittmann, Verginia Wortel, Tomasz Zawiła-Niedźwiecki, Chunyu Zhang, Xiuhai Zhao, Jun Zhu, Xiao Zhu, Zhi-Xin Zhu, Irie C. Zo-Bi, Cang Hui, Liang, Jingjing, Gamarra, Javier GP, Picard, Nicolas, Zhou, Mo, Keppel, Gunnar, Hui, Cang, Liang J., Gamarra J.G.P., Picard N., Zhou M., Pijanowski B., Jacobs D.F., Reich P.B., Crowther T.W., Nabuurs G.-J., de-Miguel S., Fang J., Woodall C.W., Svenning J.-C., Jucker T., Bastin J.-F., Wiser S.K., Slik F., Herault B., Alberti G., Keppel G., Hengeveld G.M., Ibisch P.L., Silva C.A., ter Steege H., Peri P.L., Coomes D.A., Searle E.B., von Gadow K., Jaroszewicz B., Abbasi A.O., Abegg M., Yao Y.C.A., Aguirre-Gutierrez J., Zambrano A.M.A., Altman J., Alvarez-Davila E., Alvarez-Gonzalez J.G., Alves L.F., Amani B.H.K., Amani C.A., Ammer C., Ilondea B.A., Anton-Fernandez C., Avitabile V., Aymard G.A., Azihou A.F., Baard J.A., Baker T.R., Balazy R., Bastian M.L., Batumike R., Bauters M., Beeckman H., Benu N.M.H., Bitariho R., Boeckx P., Bogaert J., Bongers F., Bouriaud O., Brancalion P.H.S., Brandl S., Brearley F.Q., Briseno-Reyes J., Broadbent E.N., Bruelheide H., Bulte E., Catlin A.C., Cazzolla Gatti R., Cesar R.G., Chen H.Y.H., Chisholm C., Cienciala E., Colletta G.D., Corral-Rivas J.J., Cuchietti A., Cuni-Sanchez A., Dar J.A., Dayanandan S., de Haulleville T., Decuyper M., Delabye S., Derroire G., DeVries B., Diisi J., Do T.V., Dolezal J., Dourdain A., Durrheim G.P., Obiang N.L.E., Ewango C.E.N., Eyre T.J., Fayle T.M., Feunang L.F.N., Finer L., Fischer M., Fridman J., Frizzera L., de Gasper A.L., Gianelle D., Glick H.B., Gonzalez-Elizondo M.S., Gorenstein L., Habonayo R., Hardy O.J., Harris D.J., Hector A., Hemp A., Herold M., Hillers A., Hubau W., Ibanez T., Imai N., Imani G., Jagodzinski A.M., Janecek S., Johannsen V.K., Joly C.A., Jumbam B., Kabelong B.L.P.R., Kahsay G.A., Karminov V., Kartawinata K., Kassi J.N., Kearsley E., Kennard D.K., Kepfer-Rojas S., Khan M.L., Kigomo J.N., Kim H.S., Klauberg C., Klomberg Y., Korjus H., Kothandaraman S., Kraxner F., Kumar A., Kuswandi R., Lang M., Lawes M.J., Leite R.V., Lentner G., Lewis S.L., Libalah M.B., Lisingo J., Lopez-Serrano P.M., Lu H., Lukina N.V., Lykke A.M., Maicher V., Maitner B.S., Marcon E., Marshall A.R., Martin E.H., Martynenko O., Mbayu F.M., Mbuvi M.T.E., Meave J.A., Merow C., Miscicki S., Moreno V.S., Morera A., Mukul S.A., Muller J.C., Murdjoko A., Nava-Miranda M.G., Ndive L.E., Neldner V.J., Nevenic R.V., Nforbelie L.N., Ngoh M.L., N'Guessan A.E., Ngugi M.R., Ngute A.S.K., Njila E.N.N., Nyako M.C., Ochuodho T.O., Oleksyn J., Paquette A., Parfenova E.I., Park M., Parren M., Parthasarathy N., Pfautsch S., Phillips O.L., Piedade M.T.F., Piotto D., Pollastrini M., Poorter L., Poulsen J.R., Poulsen A.D., Pretzsch H., Rodeghiero M., Rolim S.G., Rovero F., Rutishauser E., Sagheb-Talebi K., Saikia P., Sainge M.N., Salas-Eljatib C., Salis A., Schall P., Schepaschenko D., Scherer-Lorenzen M., Schmid B., Schongart J., Seben V., Sellan G., Selvi F., Serra-Diaz J.M., Sheil D., Shvidenko A.Z., Sist P., Souza A.F., Sterenczak K.J., Sullivan M.J.P., Sundarapandian S., Svoboda M., Swaine M.D., Targhetta N., Tchebakova N., Trethowan L.A., Tropek R., Mukendi J.T., Umunay P.M., Usoltsev V.A., Vaglio Laurin G., Valentini R., Valladares F., van der Plas F., Vega-Nieva D.J., Verbeeck H., Viana H., Vibrans A.C., Vieira S.A., Vleminckx J., Waite C.E., Wang H.-F., Wasingya E.K., Wekesa C., Westerlund B., Wittmann F., Wortel V., Zawila-Niedzwiecki T., Zhang C., Zhao X., Zhu J., Zhu X., Zhu Z.-X., Zo-Bi I.C., Hui C., Purdue University [West Lafayette], Food and Agriculture Organization of the United Nations [Rome, Italie] (FAO), Groupement d'Interêt Public Ecosystèmes Forestiers GIP ECOFOR (GIP ECOFOR ), Forêts et Sociétés (UPR Forêts et Sociétés), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Département Environnements et Sociétés (Cirad-ES), Ecologie des forêts de Guyane (UMR ECOFOG), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Université de Guyane (UG)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université de Montpellier (UM), SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National Polytechnique Félix Houphouët-Boigny, and Stellenbosch University

Subjects
Bos- en Landschapsecologie, WASS, Plant Ecology and Nature Conservation, Forests, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, Co-limitation, Ontwikkelingseconomie, Forest and Nature Conservation Policy, Trees, Soil, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Development Economics, Laboratory of Geo-information Science and Remote Sensing, Settore BIO/07 - ECOLOGIA, Life Science, Laboratorium voor Moleculaire Biologie, Bos- en Natuurbeleid, Forest and Landscape Ecology, Bosecologie en Bosbeheer, Laboratorium voor Geo-informatiekunde en Remote Sensing, BIOS Plant Development Systems, Vegetatie, Ecology, Evolution, Behavior and Systematics, biogeography, biodiversity, Vegetation, Ecology, Biodiversity, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, Latitudinal gradients, PE&RC, Forest Ecology and Forest Management, Bioclimatic dominance, Biogeography, LATITUDE, Plantenecologie en Natuurbeheer, Vegetatie, Bos- en Landschapsecologie, Vegetation, Forest and Landscape Ecology, Laboratory of Molecular Biology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Corporate Governance & Legal Services, Tree, Global LDG
Abstract

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.

Published
2022

8. Protected area creation and its limited effect on deforestation: Insights from the Kiziba-Baluba hunting domain (DR Congo)

Author

Héritier Khoji Muteya, Médard Mpanda Mukenza, Ildephonse Kipili Mwenya, François Malaisse, Dieu-donné N'tambwe Nghonda, Nathan Kasanda Mukendi, Jean-François Bastin, Jan Bogaert, and Yannick Useni Sikuzani

Subjects
Anthropogenic pressure, Miombo woodland, Protected areas, Environmental legislation, Resource conservation, Ecosystem services, Forestry, SD1-669.5, Plant ecology, QK900-989
Abstract

The study examines the spatiotemporal dynamics of landscape anthropization in the Kiziba-Baluba Hunting Domain (KBHD), near Lubumbashi in southeastern Democratic Republic of Congo, facing increasing human threats. It assesses these dynamics from 1989 to 2023 using remote sensing, Geographic Information Systems (GIS), and landscape ecology principles. The results reveal a significant decrease in forest cover, declining from 70.33 % in 1989 to 26.22 % in 2023, with an annual deforestation rate of -1.84 %. This deforestation has led to the expansion of savannas (63.93 %), agriculture (5.76 %), and built-up and bare soil (0.93 %) through patch creation and aggregation. The level of landscape disturbance has increased sixfold over 34 years, from 0.42 in 1989 to 2.81 in 2023. The reduction in the size of the largest forest patch and increased spatial isolation show rising fragmentation and dissection, often followed by the attrition of residual patches. These findings highlight the inefficiency of current conservation measures in KBHD, indicating a need for restructuring management, redefining protected area boundaries, developing a suitable management plan, implementing reforestation programs, strengthening enforcement of environmental laws, and actively involving local communities.

Published
2024
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9. Recent deforestation drove the spike in Amazonian fires

Author

Christian Salas-Eljatib, Devin Routh, Eben N. Broadbent, Mo Zhou, Jean-Francois Bastin, Thomas W. Crowther, Daniel Piotto, Javier G. P. Gamarra, Pedro H. S. Brancalion, Luiz E. O. C. Aragão, Alexander Christian Vibrans, Sergio de-Miguel, Jingjing Liang, David E. Calkin, Bryan C. Pijanowski, Angelica M. Almeyda Zambrano, Bruno Hérault, Johan van den Hoogen, Nicolas Picard, Peter B. Reich, Pablo Luis Peri, Adrián Cardil, Cang Hui, and Carlos A. Silva

Subjects
Agricultural Practices, Amazonian, changement dans l'usage des terrres, Uso de las Tierras Forestales, Land Use, Forest Land Use, Forêt tropicale humide, Deforestation, AMAZÔNIA, General Environmental Science, Agroforestry, Agricultura, Environmental Degradation, Forest policy, Cambios en el Uso de las Tierras, Fire, E11 - Économie et politique foncières, Geography, Políticas Forestales, Biodiversity Conservation, Spike (software development), Land-use change, Tropical Rain Forests, Catastrophe causée par l'homme, Tropical moist forest, Degradación de Ambientes, Carbono, K70 - Dégâts causés aux forêts et leur protection, Amazon, Desforestation, Forestry Policies, Retrait des terres, Bosques Tropicales Húmedos, Renewable Energy, Sustainability and the Environment, gestion des incendies de forêt, Public Health, Environmental and Occupational Health, Carbon, Déboisement, Incendios, Deforestación, Incendie de forêt, Fire Cause, Conservación de la Biodiversidad
Abstract

Environmental Research Letters, 15 (12), ISSN:1748-9326, ISSN:1748-9318

Published
2020

10. Effective ecological monitoring requires a multi-scaled approach

Author

Ben Sparrow, Will Edwards, Samantha Munroe, Glenda Wardle, Greg Guerin, Jean-Francois Bastin, Beryl Morris, Rebekah Christensen, Stuart Phinn, and Andrew Lowe

Subjects
bepress|Life Sciences, bepress|Life Sciences|Ecology and Evolutionary Biology|Other Ecology and Evolutionary Biology, bepress|Life Sciences|Ecology and Evolutionary Biology
Abstract

Environmental monitoring data is fundamental to our understanding of environmental change and is vital to evidence-based policy and management. However, different types of ecological monitoring, along with their different applications, are often poorly understood and contentious. Varying definitions and strict adherence to a specific monitoring type can inhibit effective ecological monitoring, leading to poor program development, implementation, and outcomes. In an effort to develop a more consistent and clear understanding of environmental monitoring programs we review previous monitoring classifications and support the widespread adoption of three succinct categories of monitoring, namely targeted, surveillance and landscape monitoring. Landscape monitoring is conducted over large areas, provides spatial data, and enables us to address questions related to where and when environmental change is occurring. Surveillance monitoring uses standardised field methods to inform on what is changing in our environments and the direction and magnitude of that change, whilst targeted monitoring is designed around testable hypotheses over defined areas and is the best approach for determining the cause of environmental change. This classification system is ideal because it can incorporate different interests and objectives, and as well as different spatial scales and temporal frequencies. It is both comprehensive and flexible, while also providing valuable structure and consistency across distinct ecological monitoring programs. To support our argument, we examined the ability of each monitoring type to inform on six key types of questions that are routinely posed to ecological monitoring programs, such as where and when change is occurring, what is the magnitude of that change, and how to manage that change. As we demonstrate, each type of ecological monitoring has its own strengths and weaknesses, which should be carefully considered relative to the desired results. Using this scheme, users can compare how well different types of monitoring can answer different ecological questions, allowing scientists and managers to design programs best suited to their needs. Finally and most importantly, we assert that for our most serious environmental challenges, it is essential that we include information at each of these monitoring scales to inform on all facets of environmental change. This will be best achieved through close collaboration between practitioners of each form of monitoring. With a renewed understanding of the importance of each monitoring type along with greater commitment to monitor cooperatively, we will be well placed to address some of our greatest environmental challenges.

Published
2019

11. Response to Comment on 'The global tree restoration potential'

Author

Jean-Francois Bastin, Yelena Finegold, Claude Garcia, Danilo Mollicone, Marcelo Rezende, Devin Routh, Constantin M. Zohner, and Thomas W. Crowther

Subjects
Multidisciplinary, Time Factors, Climate, Climate Change, Carbon, Trees
Abstract

Our study quantified the global tree restoration potential and its associated carbon storage potential under existing climate conditions. Skidmore et al . dispute our findings, using as reference a yearly estimation of carbon storage that could be reached by 2050. We provide a detailed answer highlighting misunderstandings in their interpretation, notably that we did not consider any time limit for the restoration process.

Published
2019

12. Land Cover Dynamics in the Northwestern Virunga Landscape: An Analysis of the Past Two Decades in a Dynamic Economic and Security Context

Author

Charles Mumbere Musavandalo, Kouagou Raoul Sambieni, Jean-Pierre Mate Mweru, Jean-François Bastin, Chantale Shalukoma Ndukura, Timothée Besisa Nguba, Julien Bwazani Balandi, and Jan Bogaert

Subjects
forest loss, protected area, North Kivu, land cover, army faction, Agriculture
Abstract

The Beni region in the eastern Democratic Republic of Congo is grappling with socioeconomic development and security challenges that have affected its natural ecosystems, especially those located in the northern Virunga National Park. This study aims to document the anthropization of the northwestern Virunga landscape from 1995 to 2021 in the context of insecurity. Using a cartographic approach and ecological-landscape-analysis tools, this study delves into the overall landscape changes through a comparative analysis of protected and unprotected areas. These investigations focus on landscape composition, transitions between land-cover classes, and the spatial transformation process. The northwestern Virunga landscape is undergoing significant land cover changes due to the influence of insecurity on socioeconomic activities, primarily agriculture. Agricultural land encompasses a larger area than other land-cover types. However, its expansion has decelerated since the 2000s. The loss of forested area is discontinuous. During relatively stable periods (1995–2005), forests exhibited a reduction of up to 2.90% in area, while in the period of the return of Iturian refugees to their province, followed by terrorist insecurity in Beni (2005–2021), the forested area increased by 2.07%. Savannah areas, which are mainly located in the graben rift valley and near Butembo, have been more heavily affected by human activity than forests. Ultimately, the apparent stability of the landscape can be attributed to its protected areas, especially Virunga National Park.

Published
2024
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13. Urban Sprawl and Changes in Landscape Patterns: The Case of Kisangani City and Its Periphery (DR Congo)

Author

Julien Bwazani Balandi, Jean Pierre Pitchou Meniko To Hulu, Kouagou Raoul Sambieni, Yannick Useni Sikuzani, Jean-François Bastin, Charles Mumbere Musavandalo, Timothée Besisa Nguba, Jacques Elangi Langi Molo, Tresor Mbavumoja Selemani, Jean-Pierre Mate Mweru, and Jan Bogaert

Subjects
urbanization, peri-urbanization indexes, decline in urban density, diffusion–coalescence, Kisangani, Agriculture
Abstract

The rapid population growth in sub-Saharan Africa requires regular monitoring of the spatial expansion of cities in order to facilitate efficient urban planning. In this study, we quantified the dynamics of urban and peri-urban areas in the city of Kisangani from 1987 to 2021, based on morphological criteria. Results demonstrate continuous urban and peri-urban growth, with respective average annual change rates of 8.2% and 7.6%. The urban core area expanded from 13.49 km2 to 100.49 km2, resulting from an alternating process of diffusion and coalescence. Peri-urbanization indexes developed to assess the trend of the decline in urban densities indicate a phase of urban densification over the period 1987–2010 succeeded by a decline in urban density over the period 2010–2021 that is characterized by a large expansion of the peri-urban area. However, despite this trend observed between 2010 and 2021, the decrease in urban density was not effective between 1987 and 2021 in Kisangani, as the fraction of peri-urban area observed in 1987 remains equivalent to that observed in 2021. This suggests a continuity of urban densification despite increasing peri-urbanization.

Published
2023
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14. Mapping and Quantification of Miombo Deforestation in the Lubumbashi Charcoal Production Basin (DR Congo): Spatial Extent and Changes between 1990 and 2022

Author

Héritier Khoji Muteya, Dieu-donné N’Tambwe Nghonda, Franco Mwamba Kalenda, Harold Strammer, François Munyemba Kankumbi, François Malaisse, Jean-François Bastin, Yannick Useni Sikuzani, and Jan Bogaert

Subjects
anthropogenic pressure, charcoal, miombo woodland, landscape ecology, GIS/remote sensing, Agriculture
Abstract

Population growth in the city of Lubumbashi in the southeastern Democratic Republic of the Congo (DR Congo) is leading to increased energy needs, endangering the balance of the miombo woodland in the rural area referred to as the Lubumbashi charcoal production basin (LCPB). In this study, we quantified the deforestation of the miombo woodland in the LCPB via remote sensing and landscape ecology analysis tools. Thus, the analysis of Landsat images from 1990, 1998, 2008, 2015 and 2022 was supported by the random forest classifier. The results showed that the LCPB lost more than half of its miombo woodland cover between 1990 (77.90%) and 2022 (39.92%) and was converted mainly to wooded savannah (21.68%), grassland (37.26%), agriculture (2.03%) and built-up and bare soil (0.19). Consecutively, grassland became the new dominant land cover in 2022 (40%). Therefore, the deforestation rate (−1.51%) is almost six-times higher than the national average (−0.26%). However, persistent miombo woodland is characterised by a reduction, over time, in its largest patch area and the complexity of its shape. Consequently, because of anthropogenic activities, the dynamics of the landscape pattern are mainly characterised by the attrition of the miombo woodland and the creation of wooded savannah, grassland, agriculture and built-up and bare soil. Thus, it is urgent to develop a forest management plan and find alternatives to energy sources and the sedentarisation of agriculture by supporting local producers to reverse these dynamics.

Published
2023
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15. Using Model Analysis to Unveil Hidden Patterns in Tropical Forest Structures

Author

Nicolas Picard, Frédéric Mortier, Pierre Ploton, Jingjing Liang, Géraldine Derroire, Jean-François Bastin, Narayanan Ayyappan, Fabrice Bénédet, Faustin Boyemba Bosela, Connie J. Clark, Thomas W. Crowther, Nestor Laurier Engone Obiang, Éric Forni, David Harris, Alfred Ngomanda, John R. Poulsen, Bonaventure Sonké, Pierre Couteron, and Sylvie Gourlet-Fleury

Subjects
forest structure, forest typology, null model, pattern and process, rain forest, correlation, Evolution, QH359-425, Ecology, QH540-549.5
Abstract

When ordinating plots of tropical rain forests using stand-level structural attributes such as biomass, basal area and the number of trees in different size classes, two patterns often emerge: a gradient from poorly to highly stocked plots and high positive correlations between biomass, basal area and the number of large trees. These patterns are inherited from the demographics (growth, mortality and recruitment) and size allometry of trees and tend to obscure other patterns, such as site differences among plots, that would be more informative for inferring ecological processes. Using data from 133 rain forest plots at nine sites for which site differences are known, we aimed to filter out these patterns in forest structural attributes to unveil a hidden pattern. Using a null model framework, we generated the anticipated pattern inherited from individual allometric patterns. We then evaluated deviations between the data (observations) and predictions of the null model. Ordination of the deviations revealed site differences that were not evident in the ordination of observations. These sites differences could be related to different histories of large-scale forest disturbance. By filtering out patterns inherited from individuals, our model analysis provides more information on ecological processes.

Published
2021
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16. Quantification and Simulation of Landscape Anthropization around the Mining Agglomerations of Southeastern Katanga (DR Congo) between 1979 and 2090

Author

Héritier Khoji Muteya, Dieu-Donné N’Tambwe Nghonda, François Malaisse, Salomon Waselin, Kouagou Raoul Sambiéni, Sylvestre Cabala Kaleba, François Munyemba Kankumbi, Jean-François Bastin, Jan Bogaert, and Yannick Useni Sikuzani

Subjects
Southeastern Katanga, mining agglomeration, landscape anthropization, land cover change, Agriculture
Abstract

In Southeastern Katanga, mining activities are (in)directly responsible for deforestation, ecosystem degradation and unplanned building densification. However, little is known about these dynamics at the local level. First, we quantify the landscape anthropization around four agglomerations of Southeastern Katanga (Lubumbashi, Likasi, Fungurume and Kolwezi) in order to assess the applicability of the Nature–Agriculture-Urbanization model based on the fact that natural landscapes are replaced by anthropogenic landscapes, first dominated by agricultural production, and then built-up areas. Secondly, we predict evolutionary trends of landscape anthropization by 2090 through the first-order Markov chain. Mapping coupled with landscape ecology analysis tools revealed that the natural cover that dominated the landscape in 1979 lost more than 60% of its area in 41 years (1979–2020) around these agglomerations in favor of agricultural and energy production, the new landscape matrix in 2020, but also built-up areas. These disturbances, amplified between 2010 and 2020, are more significant around Lubumbashi and Kolwezi agglomerations. Built-up areas which spread progressively will become the dominant process by 2060 in Lubumbashi and by 2075 in Kolwezi. Our results confirm the applicability of the Nature–Agriculture-Urbanization model to the tropical context and underline the urgency to put in place a territorial development plan and alternatives regarding the use of charcoal as a main energy source in order to decrease the pressure on natural ecosystems, particularly in peri-urban areas.

Published
2022
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17. Using fragmentation to assess degradation of forest edges in Democratic Republic of Congo

Author

Aurélie C. Shapiro, Naikoa Aguilar-Amuchastegui, Patrick Hostert, and Jean-François Bastin

Subjects
Forest degradation, REDD, Fragmentation, Biomass, Emissions, Conservation, Environmental sciences, GE1-350
Abstract

Abstract Background Recent studies have shown that fragmentation is an increasing threat to global forests, which has major impacts on biodiversity and the important ecosystem services provided by forested landscapes. Several tools have been developed to evaluate global patterns of fragmentation, which have potential applications for REDD+. We study how canopy height and above ground biomass (AGB) change across several categories of forest edges determined by fragmentation analysis. We use Democratic Republic of Congo (DRC) as an example. Results An analysis of variance of different edge widths and airborne estimated canopy height found that canopy heights were significantly different in forest edges at a distance of 100 m from the nonforest edge. Biomass was significantly different between fragmentation classes at an edge distance of 300 m. Core forest types were found to have significantly higher canopy height and greater AGB than forest edges and patches, where height and biomass decrease significantly as the level of fragmentation increases. A change analysis shows that deforestation and degradation are increasing over time and biomass loss associated with degradation account for at least one quarter of total loss. We estimate that about 80 % of primary forests are intact, which decreases 3.5 % over the 15 year study period, as primary forest is either deforested or transitioned to forest edge. While the carbon loss per hectare is lower than that of deforestation, degradation potentially affects up to three times more area than deforestation alone. Conclusions When defining forest degradation by decreased biomass without any loss in forest area, assessing transitions of core forest to edges over time can contribute an important element to REDD+MRV systems. The estimation of changes between different forest fragmentation types and their associated biomass loss can provide an estimate of degradation carbon emission factors. Forest degradation and emissions due to fragmentation are often underestimated and should comprise an essential component of MRV systems.

Published
2016
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18. Wood Specific Gravity Variations and Biomass of Central African Tree Species: The Simple Choice of the Outer Wood.

Author

Jean-François Bastin, Adeline Fayolle, Yegor Tarelkin, Jan Van den Bulcke, Thales de Haulleville, Frederic Mortier, Hans Beeckman, Joris Van Acker, Adeline Serckx, Jan Bogaert, and Charles De Cannière

Subjects
Medicine, Science
Abstract

Wood specific gravity is a key element in tropical forest ecology. It integrates many aspects of tree mechanical properties and functioning and is an important predictor of tree biomass. Wood specific gravity varies widely among and within species and also within individual trees. Notably, contrasted patterns of radial variation of wood specific gravity have been demonstrated and related to regeneration guilds (light demanding vs. shade-bearing). However, although being repeatedly invoked as a potential source of error when estimating the biomass of trees, both intraspecific and radial variations remain little studied. In this study we characterized detailed pith-to-bark wood specific gravity profiles among contrasted species prominently contributing to the biomass of the forest, i.e., the dominant species, and we quantified the consequences of such variations on the biomass.Radial profiles of wood density at 8% moisture content were compiled for 14 dominant species in the Democratic Republic of Congo, adapting a unique 3D X-ray scanning technique at very high spatial resolution on core samples. Mean wood density estimates were validated by water displacement measurements. Wood density profiles were converted to wood specific gravity and linear mixed models were used to decompose the radial variance. Potential errors in biomass estimation were assessed by comparing the biomass estimated from the wood specific gravity measured from pith-to-bark profiles, from global repositories, and from partial information (outer wood or inner wood).Wood specific gravity profiles from pith-to-bark presented positive, neutral and negative trends. Positive trends mainly characterized light-demanding species, increasing up to 1.8 g.cm-3 per meter for Piptadeniastrum africanum, and negative trends characterized shade-bearing species, decreasing up to 1 g.cm-3 per meter for Strombosia pustulata. The linear mixed model showed the greater part of wood specific gravity variance was explained by species only (45%) followed by a redundant part between species and regeneration guilds (36%). Despite substantial variation in wood specific gravity profiles among species and regeneration guilds, we found that values from the outer wood were strongly correlated to values from the whole profile, without any significant bias. In addition, we found that wood specific gravity from the DRYAD global repository may strongly differ depending on the species (up to 40% for Dialium pachyphyllum).Therefore, when estimating forest biomass in specific sites, we recommend the systematic collection of outer wood samples on dominant species. This should prevent the main errors in biomass estimations resulting from wood specific gravity and allow for the collection of new information to explore the intraspecific variation of mechanical properties of trees.

Published
2015
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19. Nest grouping patterns of bonobos (Pan paniscus) in relation to fruit availability in a forest-savannah mosaic.

Author

Adeline Serckx, Marie-Claude Huynen, Jean-François Bastin, Alain Hambuckers, Roseline C Beudels-Jamar, Marie Vimond, Emilien Raynaud, and Hjalmar S Kühl

Subjects
Medicine, Science
Abstract

A topic of major interest in socio-ecology is the comparison of chimpanzees and bonobos' grouping patterns. Numerous studies have highlighted the impact of social and environmental factors on the different evolution in group cohesion seen in these sister species. We are still lacking, however, key information about bonobo social traits across their habitat range, in order to make accurate inter-species comparisons. In this study we investigated bonobo social cohesiveness at nesting sites depending on fruit availability in the forest-savannah mosaic of western Democratic Republic of Congo (DRC), a bonobo habitat which has received little attention from researchers and is characterized by high food resource variation within years. We collected data on two bonobo communities. Nest counts at nesting sites were used as a proxy for night grouping patterns and were analysed with regard to fruit availability. We also modelled bonobo population density at the site in order to investigate yearly variation. We found that one community density varied across the three years of surveys, suggesting that this bonobo community has significant variability in use of its home range. This finding highlights the importance of forest connectivity, a likely prerequisite for the ability of bonobos to adapt their ranging patterns to fruit availability changes. We found no influence of overall fruit availability on bonobo cohesiveness. Only fruit availability at the nesting sites showed a positive influence, indicating that bonobos favour food 'hot spots' as sleeping sites. Our findings have confirmed the results obtained from previous studies carried out in the dense tropical forests of DRC. Nevertheless, in order to clarify the impact of environmental variability on bonobo social cohesiveness, we will need to make direct observations of the apes in the forest-savannah mosaic as well as make comparisons across the entirety of the bonobos' range using systematic methodology.

Published
2014
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20. Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

Author

Adia Bey, Alfonso Sánchez-Paus Díaz, Danae Maniatis, Giulio Marchi, Danilo Mollicone, Stefano Ricci, Jean-François Bastin, Rebecca Moore, Sandro Federici, Marcelo Rezende, Chiara Patriarca, Ruth Turia, Gewa Gamoga, Hitofumi Abe, Elizabeth Kaidong, and Gino Miceli

Subjects
land monitoring, augmented visual interpretation, assessment, land use, land use change, very high resolution imagery, open source, Google Earth, Collect Earth, Science
Abstract

Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover.

Published
2016
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