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Aim: The size structure of a forest canopy is an important descriptor of the forest environment that may yield information on forest biomass and ecology. However, its variability at regional scales is poorly described or understood because of the still prohibitive cost of very high-resolution imagery as well as the lack of an appropriate methodology. We here employ a novel approach to describe and map the canopy structure of tropical forests. Location Amazonia. Methods: We apply Fourier transform textural ordination (FOTO) techniques to subsamples of very high-resolution satellite imagery freely available through virtual globe software (e.g. Google Earth®) to determine two key structural variables: apparent mean crown size and heterogeneity in crown size. A similar approach is used with artificial forest canopy images generated by the light interaction model (discrete anisotropic radiative transfer, DART) using three-dimensional stand models. The effects of sun and viewing angles are explored on both model and real data. Results: It is shown that in the case of canopies dominated by a modal size class our approach can predict mean canopy size to an accuracy of 5%. In Amazonia, we could evidence a clear macrostructure, despite considerable local variability. Apparent crown size indeed consistently increases from about 14 m in wet north-west Amazonia to more than 17 m for areas of intermediate dry season length (1–3 months) in south and east Amazonia, before decreasing again towards the ecotone with the Cerrado savanna biome. This general trend reflects the known variation of other forest physiognomic properties (height) reported for South America and Africa. Some regions show significantly greater canopy heterogeneity, a feature that may be related to substratum, perturbation rate and/or forest turnover rate. Main conclusions: Our results demonstrate the feasibility and interest of large-scale assessment of rain forest canopy structure

Hazelnut (Corylus avellana) cultivation is increasing worldwide. A 3D model of its structure could improve the managerial techniques such as pruning. This study aims to analyse, over two successive years, hazelnut architectural development to implement a functional structural plant model. 104 one-year-old shoots of own-rooted hazelnut trees were selected and analyzed in winter 2020 and 2021. Exploratory analyses, generalized linear models, and multinomial regression models were used to describe the architectural processes. The existence of sylleptic shoots on hazelnut one-year-old shoots, characterized by the presence of the male inflorescence on apical position, was detected. Along proleptic shoots the branching pattern was described by (1) blind nodes located in the proximal part (2) sylleptic shoots and mixed buds in the median part (3) vegetative buds in the distal part. Apical bud died during the growing season, suggesting that Tonda di Giffoni has a sympodial branching. The models revealed dependencies among buds located at the same node, in the case of proleptic shoots. Especially, the probability of a bud to burst depended on both its type (i.e., mixed or vegetative) and the presence of other buds, either mixed or vegetative. Based on these local models and on a flow diagram, which defines the steps that lead to the construction of hazelnut tree architecture, a first functional-structural plant model of hazelnut tree architecture was built. Further experiments will be needed and should be repeated over following years to extend this study toward the juvenility phase and tree architecture over time.

A better knowledge of tree vegetative growth phenology and its relationship to environmental variables is crucial to understanding forest growth dynamics and how climate change may affect it. Less studied than reproductive structures, vegetative growth phenology focuses primarily on the analysis of growing shoots, from buds to leaf fall. In temperate regions, low winter temperatures impose a cessation of vegetative growth shoots and lead to a well-known annual growth cycle pattern for most species. The humid tropics, on the other hand, have less seasonality and contain many more tree species, leading to a diversity of patterns that is still poorly known and understood. The work in this study aims to advance knowledge in this area, focusing specifically on herbarium scans, as herbariums offer the promise of tracking phenology over long periods of time. However, such a study requires a large number of shoots to be able to draw statistically relevant conclusions. We propose to investigate the extent to which the use of deep learning can help detect and type-classify these relatively rare vegetative structures in herbarium collections. Our results demonstrate the relevance of using herbarium data in vegetative phenology research as well as the potential of deep learning approaches for growing shoot detection.

In Amazonia, a growing body of studies has shown that rainforests were affected by human occupation? in many areas during pre-Columbian times, inducing changes in their floristic compositions. The northern part of Amazonia, and in particular the Guiana Shield, is much less studied, although past human occupations have also been documented in this region. Therefore, the actual impact of pre-Columbian societies on Guianan forests is still poorly known. Here we explore 12 sites in the dense forest of Nouragues, central French Guiana, ranging from a priori non-anthropogenic to clearly anthropogenic, using an anthracological approach. Soil charcoals were radiocarbon dated to assess the chronology of the past human occupations, and identified to determine shifts in vegetation cover. Our results show that two main periods of occupation can be distinguished on several sites in the Nouragues area: a first one between ca. 1,300 and 1,000 cal BP and a second one between ca. 600 and 400 cal BP. Charcoal identification reveals the presence of a secondary vegetation during this most recent period of occupation, as shown by the presence of pioneer and heliophilic taxa, suggesting that human activities induced and favored this kind of vegetation. The presence of valuable wood and edible species in the anthracological record could reflect selective exploitation of the former around dwelling areas and a concentration of the latter within anthropogenic sites. As shown by our anthracological results, all the sites which contained charcoal were once under forest cover, including those that are now covered by pseudo-bamboos or by liana forests. We therefore suggest that the type of human activity (e.g. dwelling or food production) may have had different impacts on the structure and composition of 2 subsequent vegetation resulting either in anthropogenic forests or liana and pseudo-bamboo patches after land abandonment.

Climate change can affect the distribution and abundance of marine life, with consequences for goods and services provided to people. Because different models can lead to divergent conclusions about marine futures, we present an integrated global ocean assessment of climate change impacts using an ensemble of multiple climate and ecosystem models. It reveals that global marine animal biomass will decline under all emission scenarios, driven by increasing temperature and decreasing primary production. Notably, climate change impacts are amplified at higher food web levels compared with phytoplankton. Our ensemble projections provide the most comprehensive outlook on potential climate-driven ecological changes in the global ocean to date and can inform adaptive management and conservation of marine resources under climate change.While the physical dimensions of climate change are now routinely assessed through multimodel intercomparisons, projected impacts on the global ocean ecosystem generally rely on individual models with a specific set of assumptions. To address these single-model limitations, we present standardized ensemble projections from six global marine ecosystem models forced with two Earth system models and four emission scenarios with and without fishing. We derive average biomass trends and associated uncertainties across the marine food web. Without fishing, mean global animal biomass decreased by 5% (±4% SD) under low emissions and 17% (±11% SD) under high emissions by 2100, with an average 5% decline for every 1 °C of warming. Projected biomass declines were primarily driven by increasing temperature and decreasing primary production, and were more pronounced at higher trophic levels, a process known as trophic amplification. Fishing did not substantially alter the effects of climate change. Considerable regional variation featured strong biomass increases at high latitudes and decreases at middle to low latitudes, with good model agreement on the direction of change but variable magnitude. Uncertainties due to variations in marine ecosystem and Earth system models were similar. Ensemble projections performed well compared with empirical data, emphasizing the benefits of multimodel inference to project future outcomes. Our results indicate that global ocean animal biomass consistently declines with climate change, and that these impacts are amplified at higher trophic levels. Next steps for model development include dynamic scenarios of fishing, cumulative human impacts, and the effects of management measures on future ocean biomass trends.

The critical temperature beyond which photosynthetic machinery in tropical trees begins to fail averages approximately 46.7 °C (Tcrit)1. However, it remains unclear whether leaf temperatures experienced by tropical vegetation approach this threshold or soon will under climate change. Here we found that pantropical canopy temperatures independently triangulated from individual leaf thermocouples, pyrgeometers and remote sensing (ECOSTRESS) have midday peak temperatures of approximately 34 °C during dry periods, with a long high-temperature tail that can exceed 40 °C. Leaf thermocouple data from multiple sites across the tropics suggest that even within pixels of moderate temperatures, upper canopy leaves exceed Tcrit 0.01% of the time. Furthermore, upper canopy leaf warming experiments (+2, 3 and 4 °C in Brazil, Puerto Rico and Australia, respectively) increased leaf temperatures non-linearly, with peak leaf temperatures exceeding Tcrit 1.3% of the time (11% for more than 43.5 °C, and 0.3% for more than 49.9 °C). Using an empirical model incorporating these dynamics (validated with warming experiment data), we found that tropical forests can withstand up to a 3.9 ± 0.5 °C increase in air temperatures before a potential tipping point in metabolic function, but remaining uncertainty in the plasticity and range of Tcrit in tropical trees and the effect of leaf death on tree death could drastically change this prediction. The 4.0 °C estimate is within the ‘worst-case scenario’ (representative concentration pathway (RCP) 8.5) of climate change predictions2 for tropical forests and therefore it is still within our power to decide (for example, by not taking the RCP 6.0 or 8.5 route) the fate of these critical realms of carbon, water and biodiversity3,4.

Evidence exists that tree mortality is accelerating in some regions of the tropics1,2, with profound consequences for the future of the tropical carbon sink and the global anthropogenic carbon budget left to limit peak global warming below 2 °C. However, the mechanisms that may be driving such mortality changes and whether particular species are especially vulnerable remain unclear3–8. Here we analyse a 49-year record of tree dynamics from 24 old-growth forest plots encompassing a broad climatic gradient across the Australian moist tropics and find that annual tree mortality risk has, on average, doubled across all plots and species over the last 35 years, indicating a potential halving in life expectancy and carbon residence time. Associated losses in biomass were not offset by gains from growth and recruitment. Plots in less moist local climates presented higher average mortality risk, but local mean climate did not predict the pace of temporal increase in mortality risk. Species varied in the trajectories of their mortality risk, with the highest average risk found nearer to the upper end of the atmospheric vapour pressure deficit niches of species. A long-term increase in vapour pressure deficit was evident across the region, suggesting that thresholds involving atmospheric water stress, driven by global warming, may be a primary cause of increasing tree mortality in moist tropical forests.

Tropical forest degradation from selective logging, fire and edge effects is a major driver of carbon and biodiversity loss 1 – 3 , with annual rates comparable to those of deforestation 4 . However, its actual extent and long-term impacts remain uncertain at global tropical scale 5 . Here we quantify the magnitude and persistence of multiple types of degradation on forest structure by combining satellite remote sensing data on pantropical moist forest cover changes 4 with estimates of canopy height and biomass from spaceborne 6 light detection and ranging (LiDAR). We estimate that forest height decreases owing to selective logging and fire by 15% and 50%, respectively, with low rates of recovery even after 20 years. Agriculture and road expansion trigger a 20% to 30% reduction in canopy height and biomass at the forest edge, with persistent effects being measurable up to 1.5 km inside the forest. Edge effects encroach on 18% (approximately 206 Mha) of the remaining tropical moist forests, an area more than 200% larger than previously estimated 7 . Finally, degraded forests with more than 50% canopy loss are significantly more vulnerable to subsequent deforestation. Collectively, our findings call for greater efforts to prevent degradation and protect already degraded forests to meet the conservation pledges made at recent United Nations Climate Change and Biodiversity conferences. A global survey on the magnitude and persistence of moist forest cover change and canopy height following degradation using satellite remote sensing data finds that the effects are substantial and persist for decades.

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.

Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene.Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record.Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2).Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year.Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates.