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1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive. 4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions., Journal of Ecology, 111 (6), ISSN:0022-0477

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.

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., Frontiers in Ecology and Evolution, 9, ISSN:2296-701X

QUESTIONS: We examined the spatial patterns of dominant canopy species in a tropical forest to investigate: (a) what is the niche occupancy of canopy species with respect to topographic gradients; (b) what are the dominant ecological processes that explain their distribution; (c) what are the interactions among the most prevalent canopy species; and (d) what are the interactions between canopy species adults and juveniles trees? LOCATION: Rabi permanent CTFS‐ForestGEO plot, Gabon. METHODS: We selected the four most abundant canopy species and one timber species. We used Berman's test to determine the effect of three topographic variables on the distribution of each species and univariate analysis to model the spatial pattern of each species using either an inhomogeneous Poisson process or an inhomogeneous Cox process. We also used a bivariate form of the pair correlation function (PCF) to determine the spatial interaction between species and the correlation among conspecific adult and juvenile trees. RESULTS: Four of the five species had aggregated spatial patterns while Lophira alata showed spatial randomness. Most of the variance in the local tree density was explained by within‐population dispersal processes rather than environmental factors. Bivariate PCF tests showed significant segregation between species associations. Two species exhibited aggregation at small distances between young and adult trees, while others showed either complete spatial randomness at small inter‐tree distances or segregation at large distances. CONCLUSIONS: This study showed that the spatial pattern in the majority of canopy species was aggregation. Seed dispersal limitation mainly explained the observed aggregation pattern. Habitat filtering, as evidenced by the influence of topographic variables on niche occupancy, marginally, yet significantly, explained this pattern. The different spatial patterns of the principal species permit their coexistence. Spatial segregation among adult and juvenile trees reveals a strong pattern of either species‐specific seed predation or pathogens.

ForestGEO is a network of scientists and long-term forest dynamics plots (FDPs) spanning the Earth's major forest types. ForestGEO's mission is to advance understanding of the diversity and dynamics of forests and to strengthen global capacity for forest science research. ForestGEO is unique among forest plot networks in its large-scale plot dimensions, censusing of all stems ≥1 cm in diameter, inclusion of tropical, temperate and boreal forests, and investigation of additional biotic (e.g., arthropods) and abiotic (e.g., soils) drivers, which together provide a holistic view of forest functioning. The 71 FDPs in 27 countries include approximately 7.33 million living trees and about 12,000 species, representing 20% of the world's known tree diversity. With >1300 published papers, ForestGEO researchers have made significant contributions in two fundamental areas: species coexistence and diversity, and ecosystem functioning. Specifically, defining the major biotic and abiotic controls on the distribution and coexistence of species and functional types and on variation in species' demography has led to improved understanding of how the multiple dimensions of forest diversity are structured across space and time and how this diversity relates to the processes controlling the role of forests in the Earth system. Nevertheless, knowledge gaps remain that impede our ability to predict how forest diversity and function will respond to climate change and other stressors. Meeting these global research challenges requires major advances in standardizing taxonomy of tropical species, resolving the main drivers of forest dynamics, and integrating plot-based ground and remote sensing observations to scale up estimates of forest diversity and function, coupled with improved predictive models. However, they cannot be met without greater financial commitment to sustain the long-term research of ForestGEO and other forest plot networks, greatly expanded scientific capacity across the world's forested nations, and increased collaboration and integration among research networks and disciplines addressing forest science.

Species delimitation remains a crucial issue for widespread plants occurring across forest-savanna ecotone such as Lophira (Ochnaceae). Most taxonomists recognize two parapatric African tree species, widely distributed and morphologically similar but occurring in contrasted habitats: L. lanceolata in the Sudanian dry forests and savannahs and L. alata in the dense Guineo-Congolian forests. Both species co-occur along a ca. 3000 km long forest-savanna mosaic belt, constituting ideal models for investigating hybridization patterns and the impact of past glacial periods on the genetic structures in two types of ecosystems. We genotyped 10 nuclear microsatellites for 803 individuals sampled across the distribution range of Lophira. Both species exhibit similar levels of genetic diversity [He = 0.52 (L. alata); 0.44 (L. lanceolata)] and are well differentiated, consistent with taxonomic delimitation (FST = 0.36; RST = 0.49), refuting the hypothesis that they might constitute ecotypes rather than distinct species. Furthermore, L. alata displayed two deeply differentiated clusters (FST = 0.37; RST = 0.53) distributed in parapatry, one endemic to Western Gabon while another cluster extended over the remaining species range, suggests that L. alata is made of two cryptic species. We showed that rare hybrids occur in some contact zones between these three species, leaving a weak signal of introgression between L. lanceolata and the northern cluster of L. alata. At the intra-specific level, the latter species also show weak genetic structuring between Upper and Lower Guinea and the intensity did not differ strikingly between rainforest and savanna ecosystems. The discovery of a new species of Lophira with a narrow distribution in West Gabon where it is intensively exploited for its timber requires to evaluate its conservation status., SCOPUS: ar.j, info:eu-repo/semantics/published

Many tropical tree species have buttresses at the standard breast height (1.3 m above ground) of diameter measurement, with a presumable role in improving nutrient acquisition or tree anchorage in the ground (Newbery et al. 2009, Richter 1984). Measuring the diameter using standard dendrometrical tools such as callipers or graduated tapes, which require that the cross-section of the trunk has a convex shape, is then impossible (Nogueira et al. 2006). The recommended method in this case is to measure the diameter above the buttress (DAB), thus possibly leading to biased estimates of the basal area (West 2009), of tree above-ground biomass (Dean & Roxburgh 2006, Dean et al. 2003) and of tree growth (Metcalf et al. 2009). As an alternative, one can measure the basal area at breast height of buttressed trees, using a method that can deal with the irregular non-convex shape of the cross-section of the stem such as the Picus calliper, photogrammetry or 3D laser scanning (Badia et al. 2003, Dean 2003, Newbery et al. 2009).

In Gabon, two major species of Loranthaceae, Phragmanthera capitata (Sprengel) Balle and Globimetula braunii (Engl.) Danser to a lesser extent, parasitize Hevea brasiliensis (Mull.) Arg. The behavior of different clones of rubber trees facing the Loranthaceae parasitism has been studied in the large industrial plantation of Mitzic and in three neighboring smallholding plantations. The clones PR 261, RRIM 600 and PB 260, although never totally resistant, are far less susceptible to Loranthaceae than the clones AVROS 2037, PB 217, PB 235 and GT 1. The parasite development kinetics was more rapid on the most susceptible clones. Phellem thickness, cortical parenchyma thickness and tannin cell density were independent of susceptibility. The number of tree ramifications was significantly different between the clones. Interestingly, on the least susceptible trees, being also the most ramified, the parasite showed the slowest development.

The effect of Phragmanthera capitata (Sprengel) Balle, an epiphytic Loranthaceae, on latex production of three clones of rubber tree, Hevea brasiliensis (Mull. Arg.), was studied in the industrial plantation of Mitzic, in Gabon. The 22-year-old trees, presenting the largest trunk circumference, were the most parasitized and at the same time the best producers of latex. At first, this would suggest that mistletoe parasitism could have a favourable effect on latex yield. However, an accurate statistical analysis showed that, in fact, parasitism had no significant effect on latex yield. These data, discussed in relation to the biology of flowering parasitic plants, suggest that there is no urgency to eradicate Phragmanthera capitata for the moment.

Background A large proportion of the tropical rain forests of central Africa undergo periodic selective logging for timber harvesting. The REDD+ mechanism could promote less intensive logging if revenue from the additional carbon stored in the forest compensates financially for the reduced timber yield. Results Carbon stocks, and timber yields, and their associated values, were predicted at the scale of a forest concession in Gabon over a project scenario of 40 yr with reduced logging intensity. Considering that the timber contribution margin (i.e. the selling price of timber minus its production costs) varies between 10 and US$40 m −3, the minimum price of carbon that enables carbon revenues to compensate forgone timber benefits ranges between US$4.4 and US$25.9/tCO 2 depending on the management scenario implemented. Conclusions Where multiple suppliers of emission reductions compete in a REDD+ carbon market, tropical timber companies are likely to change their management practices only if very favourable conditions are met, namely if the timber contribution margin remains low enough and if alternative management practices and associated incentives are appropriately chosen.

International audience; Context Okoume (Aucoumea klaineana) is the most important timber species in Gabon, while being classified as vulnerable in the IUCN red list. Thousands of growth measurements for this species have been performed since the 1950s but, because of a lack of integrated analysis, did not bring a consistent view on its growth pattern.Aims This study aims at disentangling the effects of tree size and local competition on tree growth.Methods A growth model was fitted for okoume, using data from seven sites in Gabon and Congo. The growth model was designed to be useful for forest management, which means that it relied on variables that could be computed using forest inventory data.Results A lognormal growth model with a negative response to stand basal area was selected. A significant residual site effect on growth was found, with a slower growth in the sites near the border of the natural range of okoume than in the sites far from it.Conclusion Growth strongly responded to local stand attributes such as tree density and basal area. Growth decrease with stand age in monodominant okoume stands was correctly predicted, although tree age was not incorporated into the model.

When considering all trees irrespective of their species, natural tropical rain forests typically exhibit spatial patterns that range from random to regular. The regularity is often interpreted as a footprint of tree competition. Using 23 permanent sample plots totalling 61 ha in the rain forests of central Africa, we characterized their spatial patterns and modelled those that exhibited regularity by a Strauss point process. This Strauss process is obtained as a Markov point process whose interaction function is an exponential function of a competition index commonly used in forestry. The parameter of this Strauss process characterizes the strength of competition. The 23 plots in central Africa differed in tree density and basal area, and could be discriminated depending on the type of spatial patterns: plots having a large basal area with respect to their density had a non regular pattern, whereas those having a small basal area with respect to their density had a regular pattern. For those plots that exhibited regularity, average tree size could be used to predict the strength of competition. The parameter of the Strauss process was significantly related to the average size by a linear relationship, such that competition decreases as average tree size increases. This relationship extrapolated to a null value of the Strauss parameter when average tree size reaches 32 cm in diameter. This relationship between average tree size and spatial pattern is a testable feature for future studies on the relationship between competition and spatial pattern in natural forests.

The effect of Phragmanthera capitata (Sprengel) Balle, an epiphytic Loranthaceae, on latex production of three clones of rubber tree, Hevea brasiliensis (Müll. Arg.), was studied in the industrial plantation of Mitzic, in Gabon. The 22-year-old trees, presenting the largest trunk circumference, were the most parasitized and at the same time the best producers of latex. At first, this would suggest that mistletoe parasitism could have a favourable effect on latex yield. However, an accurate statistical analysis showed that, in fact, parasitism had no significant effect on latex yield. These data, discussed in relation to the biology of flowering parasitic plants, suggest that there is no urgency to eradicate Phragmanthera capitata for the moment.

L'azobé, Lophira alata, est un important bois d'oeuvre d'Afrique centrale classé vulnérable dans la liste rouge de l'UICN. Bien que de nombreuses mesures de croissance aient été faites pour cette espèce, aucun modèle de croissance ne semble exister actuellement. L'objectif de ce travail était de faire la part, dans la croissance de l'azobé, entre l'effet de la taille de l'arbre et celui de la compétition locale. Un modèle de croissance a été ajusté pour l'azobé en utilisant les données de quatre sites de mesures au Gabon. Ce modèle de croissance a été conçu pour être utile à l'aménagement forestier, c'est-à-dire qu'il ne repose que sur des variables qui peuvent être calculées à partir des données d'inventaire forestier. Un modèle log-normal avec une réponse négative à la densité du peuplement et à sa surface terrière a été sélectionné. La relation entre la croissance et la taille de l'arbre était unimodale avec un maximum à 60 cm de diamètre à hauteur de poitrine. Un effet résiduel significatif du statut social de l'arbre a été trouvé (avec une croissance plus lente pour les arbres dominés) tandis qu'aucun effet résiduel du site n'a été trouvé.