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Recent advances in remote sensing such as airborne laser scanning have revolutionised our ability to accurately map forest canopy gaps, with huge implications for tracking forest dynamics at scale. However, few studies have explored how canopy gaps vary among forests at different successional stages following disturbances, such as those caused by logging. Moreover, most studies have focused exclusively on the size distribution of gaps, ignoring other key features such as their spatial distribution and shape. Here we test a series of hypotheses about how the number, size, spatial configuration and geometry of gaps vary across a logging disturbance gradient in Malaysian Borneo. As predicted, we found that recently logged forests had much higher gap fraction compared to old-growth forests, a result of having both a greater total number of gaps and a higher proportion of large gaps. Regrowing forests, on the other hand, fell at the opposite end of the spectrum, being characterised by both fewer and smaller gaps compared to nearby old-growth forests. Across all successional stage gaps were found to be spatially clustered. However, logging significantly diluted the degree of spatial aggregation and led to the formation of gaps with much more complex geometries. Our results showcase how logging and subsequent regrowth substantially alter not just the number and size of gaps in a forest, but also their spatial arrangement and shape. Linking these emergent patterns to their underlying processes is key to better understanding the impacts of human disturbance on the structure and function of tropical forests., The acquisition of the ALS data was funded through a grant awarded to the BALI consortium through NERC's Human Modified Tropical Forests Programme (grant code: NE/K016377/1). TJ was supported by a NERC Independent Research Fellowship (grant code: NE/S01537X/1) and through a Research Project Grant from the Leverhulme Trust which also funded FJF (grant code: RPG-2020-341). BZ was funded by a Scholarship from the China Scholarship Council (grant code: 202008320276).

How ecologists think about above-ground forest structure and dynamics is fundamentally shaped by the data we can collect. This has historically been limited to what is possible with simple equipment such as a tape measure, which has often led to the three-dimensional complexity of the form of trees being reduced to the diameter of a trunk, and perhaps the height of the tree. While simple and pragmatic, this trunk-centric framework has some obvious limitations, as many of the major processes that influence how trees grow and interact with one another happen up in the canopy. For instance, the structural complexity of individual trees' leaf and branch arrangements and how trees fill canopy space are direct drivers of individual tree and whole-forest productivity and dynamics, but remain poorly studied because they have traditionally been challenging to measure.However, recent advances in remote sensing and data processing are revolutionising our ability to accurately measure tree and forest structure from leaves to landscapes. Not only do we have access to more accurate data on structure, but we also have data spanning a much broader range of spatial, temporal and ecological scales, and new avenues of research that challenge how we understand forests are emerging.Synthesis. Here, we review the new opportunities these technologies bring us to measure the physical structure of trees and highlight the technological developments needed to maximise their value to the field of forest ecology. Today, perhaps for the first time, how ecologists choose to study above-ground forest structure and dynamics is limited more by creativity than by what we can measure.

Funder: Agua Salud Project, Funder: U.S. Department of Energy; Id: http://dx.doi.org/10.13039/100000015, Funder: CAPES; Id: http://dx.doi.org/10.13039/501100002322, Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest monitoring programmes. However, these data can be surprisingly hard to come by, particularly for certain regions of the world and for specific taxonomic groups, posing a real barrier to progress in these fields. To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4.0 licence and can be access from: https://doi.org/10.5281/zenodo.6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height. In doing so, we provide a key resource for field ecologists, remote sensing researchers and the modelling community working together to better understand the role that trees play in regulating the terrestrial carbon cycle.

Spatially heterogeneous plant mortality rates are an important predictor of the distribution of vegetation carbon in Amazonia. Reproducing the spatial gradients of vegetation carbon in Amazonia and the observed decline in the intact Amazonian carbon sink since 1990 is a challenge faced by dynamic global vegetation models (DGVMs). In this paper, we implement spatially variable mortality rates in TRIFFID, the DGVM currently coupled to the Joint UK Land Environment Simulator (JULES), and compare with the standard model which assumes a homogeneous mortality rate. Spatially variable gridded fields of Amazonian tree mortality are created using a well-known relationship between mortality and wood density, and three independent wood density maps. The diversified mortality scheme substantially improves the representation of vegetation carbon in TRIFFID when compared to observations, with a 90 % reduction in model bias and an increase in the Pearson correlation coefficient with observed biomass. JULES now captures the observed variability of both mortality and vegetation carbon to a greater extent, demonstrating the potential of using easily-measured traits, like wood density, to add spatial and functional diversity into DGVMs. Despite this, the spatial variation of vegetation carbon simulated with the new mortality fields (with standard deviation 15 MgCha-1) is still less than half of the variation in the observed data (standard deviation 35 MgCha-1). Future work should consider the effects of additional processes, like fire, drought and the phosphorus cycle, on the simulated distribution of vegetation carbon in the Amazon.

International audience; Individual-based forest models (IBMs) are useful to investigate the effect of environment on forest structure and dynamics, but they are often restricted to site-specific applications. To build confidence for spatially distributed simulations, model transferability, i.e. the ability of the same model to provide reliable predictions at contrasting sites, has to be thoroughly tested. We tested the transferability of a spatially explicit forest IBM, TROLL, with a trait-based species parameterization and global gridded climate forcing, by applying it to two sites with sharply contrasting climate and floristic compositions across the tropics, one in South America and one in Southeast Asia. We identified which parameters are most influential for model calibration and assessed the model sensitivity to climatic conditions for a given calibration. TROLL produced realistic predictions of forest structure and dynamics at both sites and this necessitates the recalibration of only three parameters, namely photosynthesis efficiency, crown allometry and mortality rate. All three relate to key processes that constrain model transferability and warrant further model development and data acquisition, with mortality being a particular priority of improvement for the current generation of vegetation models. Varying the climatic conditions at both sites demonstrate similar, and expected, model responses: GPP increased with temperature and irradiance, while stem density and aboveground biomass declined as temperature increased. The climate dependence of productivity and biomass was mediated by plant respiration, carbon allocation and mortality, which has implications both on model development and on forecasting of future carbon dynamics. Our detailed examination of forest IBM transferability unveils key processes that need to improve in genericity before reliable large-scale implementations can be envisioned.

International audience; AbstractKey messageWe used lightweight terrestrial laser scanning (TLS) to detect over 3000 stems per hectare across a 12-ha permanent forest plot in French Guiana, 81% of them

International audience; Secondary forests are a prominent component of tropical landscapes, and they constitute a major atmospheric carbon sink. Rates of carbon accumulation are usually inferred from chronosequence studies, but direct estimates of carbon accumulation based on long-term monitoring of stands are rarely reported. Recent compilations on secondary forest carbon accumulation in the Neotropics are heavily biased geographically as they do not include estimates from the Guiana Shield. We analysed the temporal trajectory of aboveground carbon accumulation and floristic composition at one 25-ha secondary forest site in French Guiana. The site was clearcut in 1976, abandoned thereafter, and one large plot (6.25 ha) has been monitored continuously since. We used Bayesian modelling to assimilate inventory data and simulate the long-term carbon accumulation trajectory. Canopy change was monitored using two aerial lidar surveys conducted in 2009 and 2017. We compared the dynamics of this site with that of a surrounding old-growth forest. Finally, we compared our results with that from secondary forests in Costa Rica, which is one of the rare long-term monitoring programs reaching a duration comparable to our study. Twenty years after abandonment, aboveground carbon stock was 64.2 [95% credibility interval: 46.4;89.0] MgC/ha, and this stock increased to 101.3 [78.7;128.5] MgC/ha twenty years later. The time to accumulate half of the mean aboveground carbon stored in the nearby old-growth forest (185.6 [155.9;200.2] MgC/ha) was estimated at 35.0 [20.9;55.9] years. During the first 40 years, the contribution of the long-lived pioneer species Xylopia nitida, Goupia glabra and Laetia procera to the aboveground carbon stock increased continuously. Secondary forest mean-canopy height measured by lidar increased by 1.14 m in eight years, a canopy-height increase consistent with an aboveground carbon accumulation of 7.1 MgC/ha (or 0.89 MgC/ha/yr) during this period. Long-term AGC accumulation rate in Costa Rica was almost twice as fast as at our site in French Guiana. This may reflect higher fertility of Central American forest communities or a better adaptation of the forest tree community to intense and frequent disturbances. This finding may have important consequences for scaling-up carbon uptake estimates to continental scales.

International audience; Human activities modify the disturbance regimes of tropical forests. Since tropical forests host high biological diversity, understanding the role of biodiversity in ecosystem recovery pathways and the underlying ecological mechanisms is crucial to predict the fate of tropical ecosystems. Studies relying on regularly-censused forest plots rarely include disturbed forests, are not long enough to assess long-term forest dynamics and often lack repeatability. We used an individual-based model of tropical forest growth to assess the effect of species and functional diversity on long-term ecosystem recovery from disturbance. We manipulated the number of species and functional assemblages across a large number of simulations and simulated different levels of disturbance. To investigate the ecological mechanisms that underlie the effect of biodiversity on forest functioning along recovery pathways, we partitioned the net effect of biodiversity on ecosystem properties into complementarity and selection effects over time. 3. We found that functional diversity improved tropical forest resilience after a disturbance. The complementarity effect dominated soon after the disturbance but was progressively surpassed by a selection effect as more competitive species dominated the forest community. This pattern increased with the intensity of the disturbance. 4. Synthesis. We found that the mechanisms through which biodiversity influences forest functioning depend on the ecosystem state, shifting from a dominant complementarity effect in recently disturbed systems to a selection effect in systems disturbed a long time ago. Our results thus suggest that the time since the last disturbance is a key to understanding biodiversity-ecosystem functioning relationships in tropical forests and can help reconcile previous contrasting results obtained with snapshots of ecosystem state in empirical studies.

International audience; Allometry determines how tree shape and function scale with each other, related through size. Allometric relationships help scale processes from individual to global scale, and they constitute a core component of vegetation models. Allometric relationships have been expected to emerge from optimization theory, yet this does not suitably predict empirical data. Here we argue that the fusion of high-resolution data, such as those derived from airborne laser scanning, with individual-based forest modelling offers insight into how plant size contributes to large scale biogeochemical processes. We review the challenges in allometric scaling, how they can be tackled by advances in data-model fusion, and how individual-based models can serve as data integrators for dynamic global vegetation models.

Premise of the Study : Basic wood density is an important ecological trait for woody plants. It is used to characterize species performance and fitness in community ecology and to compute tree and forest biomass in carbon cycle studies. While wood density has been historically measured at 12% moisture, it is convenient for ecological purposes to convert this measure to basic wood density, i.e., the ratio of dry mass over green volume. Basic wood density can then be used to compute tree dry biomass from living tree volume. Methods: Here, we derive a new exact formula to compute the basic wood density Db from the density at moisture content w denoted Dw, the fiber saturation point S, and the volumetric shrinkage coefficient R. We estimated a new conversion factor using a global wood technology database where values to use this formula are available for 4022 trees collected in 64 countries (mostly tropical) and representing 872 species. Key Results: We show that previous conversion factors used to convert densities at 12% moisture into basic wood densities are inconsistent. Based on theory and data, we found that basic wood density could be inferred from the density at 12% moisture using the following formula: Db = 0.828D12. This value of 0.828 provides basic wood density estimates 4–5% smaller than values inferred from previous conversion factors. Conclusions: This new conversion factor should be used to derive basic wood densities in global wood density databases. Its use would prevent overestimating global forest carbon stocks and allow predicting better tree species community dynamics from wood density.

Premise of the studyBasic wood density is an important ecological trait for woody plants. It is used to characterize species performance and fitness in community ecology, and to compute tree and forest biomass in carbon cycle studies. While wood density has been historically measured at 12% moisture, it is convenient for ecological purposes to convert this measure to basic wood density, i.e. the ratio of dry mass over green volume. Basic wood density can then be used to compute tree dry biomass from living tree volume.MethodsHere, we derive a new, exact formula to compute the basic wood density Db from the density at moisture content w denoted Dw, the fibre saturation point S, and the volumetric shrinkage coefficient R. We estimated a new conversion factor using a global wood technology database where values to use this formula are available for 4022 trees collected in 64 countries (mostly tropical) and representing 872 species.Key resultsWe show that previous conversion factors used to convert densities at 12% moisture into basic wood densities are inconsistent. Based on theory and data, we found that basic wood density could be inferred from the density at 12% moisture using the following formula: Db = 0.828D122. This value of 0.828 provides basic wood density estimates 4-5% smaller than values inferred from previous conversion factors.ConclusionsThis new conversion factor should be used to derive basic wood densities in global wood density databases. This would prevent overestimating global forest carbon stocks and allow predicting better tree species community dynamics from wood density.