12 results on '"Hardy O.J."'
Search Results
2. Mitochondrial DNA hyperdiversity and its potential causes in the marine periwinkle Melarhaphe neritoides (Mollusca: Gastropoda)
- Author
-
Fourdrilis, S., Mardulyn, P., Hardy, O.J., Jordaens, K., de Frias Martins, A.M., and Backeljau, T.
- Abstract
We report the presence of mitochondrial DNA (mtDNA) hyperdiversity in the marine periwinkle Melarhaphe neritoides (Linnaeus, 1758), the first such case among marine gastropods. Our dataset consisted of concatenated 16S-COI-Cytb gene fragments. We used Bayesian analyses to investigate three putative causes underlying genetic variation, and estimated the mtDNA mutation rate, possible signatures of selection and the effective population size of the species in the Azores archipelago. The mtDNA hyperdiversity in M. neritoides is characterized by extremely high haplotype diversity (Hd = 0.999 ± 0.001), high nucleotide diversity (π = 0.013 ± 0.001), and neutral nucleotide diversity above the threshold of 5% (πsyn = 0.0677). Haplotype richness is very high even at spatial scales as small as 100m2. Yet, mtDNA hyperdiversity does not affect the ability of DNA barcoding to identify M. neritoides. The mtDNA hyperdiversity in M. neritoides is best explained by the remarkably high mutation rate at the COI locus (μ = 5.82 × 10−5 per site per year or μ = 1.99 × 10−4 mutations per nucleotide site per generation), whereas the effective population size of this planktonic-dispersing species is surprisingly small (Ne = 5, 256; CI = 1,312–3,7495) probably due to the putative influence of selection. Comparison with COI nucleotide diversity values in other organisms suggests that mtDNA hyperdiversity may be more frequently linked to high μ values and that mtDNA hyperdiversity may be more common across other phyla than currently appreciated.
- Published
- 2016
3. High selfing rate, limited pollen dispersal and inbreeding depression in the emblematic African rain forest tree Baillonella toxisperma – Management implications
- Author
-
Duminil, J., primary, Mendene Abessolo, D.T., additional, Ndiade Bourobou, D., additional, Doucet, J.-L., additional, Loo, J., additional, and Hardy, O.J., additional
- Published
- 2016
- Full Text
- View/download PDF
4. Genetic diversity of Niger onions (Allium cepaL.) assessed by simple sequence repeat markers (SSR)
- Author
-
Abdou, R., primary, Bakasso, Y., additional, Saadou, M., additional, Baudoin, J.P., additional, and Hardy, O.J., additional
- Published
- 2016
- Full Text
- View/download PDF
5. Nuclear microsatellites reveal contrasting pattern of genetic structure between western and southeastern European populations of the common ash (Fraxinus excelsior L.)
- Author
-
Heuertz M., Hausman J-F., Hardy O.J., Vendramin G.G., Frascaria-Lacoste N., and Vekeman X.
- Subjects
Bayesian methods ,postglacial recolonization ,population genetic structure ,Admixture ,microsatellites - Abstract
To determine extant patterns of population genetic structure in common ash and gain insight into postglacial recolonization processes, we applied multilocus-based Bayesian approaches to data from 36 European populations genotyped at five nuclear microsatellite loci. We identified two contrasting patterns in terms of population genetic structure: (1) a large area from the British Isles to Lithuania throughout central Europe constituted effectively a single deme, whereas (2) strong genetic differentiation occurred over short distances in Sweden and southeastern Europe. Concomitant geographical variation was observed in estimates of allelic richness and genetic diversity, which were lowest in populations from southeastern Europe, that is, in regions close to putative ice age refuges, but high in western and central Europe, that is, in more recently recolonized areas. We suggest that in southeastern Europe, restricted postglacial gene flow caused by a rapid expansion of refuge populations in a mountainous topography is responsible for the observed strong genetic structure. In contrast, admixture of previously differentiated gene pools and high gene flow at the onset of postglacial recolonization of western and central Europe would have homogenized the genetic structure and raised the levels of genetic diversity above values in the refuges.
- Published
- 2004
6. Chloroplast DNA variation and postglacial recolonization of common ash ( Fraxinus excelsior L.) in Europe
- Author
-
Heuertz M., Fineschi S., Anzidei M., Pastorelli R., Salvini D. Paule L. Frascaria-Lacoste N., Hardy O.J. Vekemans X., and Vendramin G.G
- Subjects
PCR-RFLP ,chloroplast DNA ,Fraxinus excelsior ,phylogeography ,chloroplast microsatellite - Abstract
We used chloroplast polymerase chain reaction-restriction-fragment length polymorphism (PCR-RFLP) and chloroplast microsatellites to assess the structure of genetic variation and postglacial history across the entire natural range of the common ash (Fraxinus excelsior L.), a broad-leaved wind-pollinated and wind-dispersed European forest tree. A low level of polymorphism was observed, with only 12 haplotypes at four polymorphic microsatellites in 201 populations, and two PCR-RFLP haplotypes in a subset of 62 populations. The clear geographical pattern displayed by the five most common haplotypes was in agreement with glacial refugia for ash being located in Iberia, Italy, the eastern Alps and the Balkan Peninsula, as had been suggested from fossil pollen data. A low chloroplast DNA mutation rate, a low effective population size in glacial refugia related to ash's life history traits, as well as features of postglacial expansion were put forward to explain the low level of polymorphism. Differentiation among populations was high (GST = 0.89), reflecting poor mixing among recolonizing lineages. Therefore, the responsible factor for the highly homogeneous genetic pattern previously identified at nuclear microsatellites throughout western and central Europe (Heuertz et al. 2004) must have been efficient postglacial pollen flow. Further comparison of variation patterns at both marker systems revealed that nuclear microsatellites identified complex differentiation patterns in south-eastern Europe which remained undetected with chloroplast microsatellites. The results suggest that data from different markers should be combined in order to capture the most important genetic patterns in a species.
- Published
- 2004
- Full Text
- View/download PDF
7. Contrasting patterns of gene flow between sister plant species in the understorey of African moist forests – The case of sympatric and parapatric Marantaceae species
- Author
-
Ley, A.C., primary and Hardy, O.J., additional
- Published
- 2014
- Full Text
- View/download PDF
8. Does water facilitate gene flow in spore-producing plants? Insights from the fine-scale genetic structure of the aquatic moss Rhynchostegium riparioides (Brachytheciaceae)
- Author
-
Hutsemékers, V., primary, Hardy, O.J., additional, and Vanderpoorten, A., additional
- Published
- 2013
- Full Text
- View/download PDF
9. Species delimitation in the Central African herbs Haumania (Marantaceae) using georeferenced nuclear and chloroplastic DNA sequences
- Author
-
Ley, A.C., primary and Hardy, O.J., additional
- Published
- 2010
- Full Text
- View/download PDF
10. New insights from fine-scale spatial genetic structure analyses in plant populations.
- Author
-
Vekemans, X. and Hardy, O.J.
- Subjects
- *
PLANT population genetics , *GENETIC markers , *AUTOCORRELATION (Statistics) , *PLANT populations , *PLANT breeding , *MOLECULAR ecology - Abstract
Many empirical studies have assessed fine-scale spatial genetic structure (SGS), i.e. the nonrandom spatial distribution of genotypes, within plant populations using genetic markers and spatial autocorrelation techniques. These studies mostly provided qualitative descriptions of SGS, rendering quantitative comparisons among studies difficult. The theory of isolation by distance can predict the pattern of SGS under limited gene dispersal, suggesting new approaches, based on the relationship between pairwise relatedness coefficients and the spatial distance between individuals, to quantify SGS and infer gene dispersal parameters. Here we review the theory underlying such methods and discuss issues about their application to plant populations, such as the choice of the relatedness statistics, the sampling scheme to adopt, the procedure to test SGS, and the interpretation of spatial autocorrelograms. We propose to quantify SGS by an ‘ Sp’ statistic primarily dependent upon the rate of decrease of pairwise kinship coefficients between individuals with the logarithm of the distance in two dimensions. Under certain conditions, this statistic estimates the reciprocal of the neighbourhood size. Reanalysing data from, mostly, published studies, the Sp statistic was assessed for 47 plant species. It was found to be significantly related to the mating system (higher in selfing species) and to the life form (higher in herbs than trees), as well as to the population density (higher under low density). We discuss the necessity for comparing SGS with direct estimates of gene dispersal distances, and show how the approach presented can be extended to assess (i) the level of biparental inbreeding, and (ii) the kurtosis of the gene dispersal distribution. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
- View/download PDF
11. Estimating seed vs. pollen dispersal from spatial genetic structure in the common ash.
- Author
-
Heuertz, M., Vekemans, X., Hausman, J.F., Paladas, M., and Hardy, O.J.
- Subjects
EUROPEAN ash ,POLLEN dispersal ,PLANT genetics - Abstract
Abstract Spatial genetic structure was analysed with five highly polymorphic microsatellite loci in a Romanian population of common ash (Fraxinus excelsior L.), a wind-pollinated and wind-dispersed tree species occurring in mixed deciduous forests over almost all of Europe. Contributions of seed and pollen dispersal to total gene flow were investigated by analysing the pattern of decrease in kinship coefficients among pairs of individuals with geographical distance and comparing it with simulation results. Plots of kinship against the logarithm of distance were decomposed into a slope and a shape component. Simulations showed that the slope is informative about the global level of gene flow, in agreement with theoretical expectations, whereas the shape component was correlated with the relative importance of seed vs. pollen dispersal. Hence, our results indicate that insights into the relative contributions of seed and pollen dispersal to overall gene flow can be gained from details of the pattern of spatial genetic structure at biparentally inherited loci. In common ash, the slope provided an estimate of total gene dispersal in terms of Wright's neighbourhood size of Nb = 519 individuals. No precise estimate of seed vs. pollen flow could be obtained from the shape because of the stochasticity inherent to the data, but the parameter combinations that best fitted the data indicated restricted seed flow, σ[sub s ] £ 14 m, and moderate pollen flow, 70 m £ σ[sub p ] £ 140 m. [ABSTRACT FROM AUTHOR]
- Published
- 2003
- Full Text
- View/download PDF
12. 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
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.