The response of tropical rainforests to drought : lessons from recent research and future prospects

Key message: we review the recent findings on the influence of drought on tree mortality, growth or ecosystem functioning in tropical rainforests. Drought plays a major role in shaping tropical rainforests and the response mechanisms are highly diverse and complex. The numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical rainforests on the three continents. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance. - Context: tropical rainforest ecosystems are characterized by high annual rainfall. Nevertheless, rainfall regularly fluctuates during the year and seasonal soil droughts do occur. Over the past decades, a number of extreme droughts have hit tropical rainforests, not only in Amazonia but also in Asia and Africa. The influence of drought events on tree mortality and growth or on ecosystem functioning (carbon and water fluxes) in tropical rainforest ecosystems has been studied intensively, but the response mechanisms are complex.- Aims: herein, we review the recent findings related to the response of tropical forest ecosystems to seasonal and extreme droughts and the current knowledge about the future of these ecosystems. - Results: this review emphasizes the progress made over recent years and the importance of the studies conducted under extreme drought conditions or in through-fall exclusion experiments in understanding the response of these ecosystems. It also points to the great diversity and complexity of the response of tropical rainforest ecosystems to drought. - Conclusion: the numerous gaps identified here require the international scientific community to combine efforts in order to conduct comprehensive studies in tropical forest regions. These results are essential to simulate the future of these ecosystems under diverse climate scenarios and to predict the future of the global earth carbon balance

Climate impacts on tree growth in the Sierra Nevada

Rising temperatures and aridity may negatively impact tree growth, and therefore ecosystem services like carbon sequestration. In the Sierra Nevada in California, annual variation in precipitation is high, and forests have already been impacted by several recent severe droughts. In this study, we used growth census data from long-term plots in the Sierra Nevada to calibrate an annual climate-dependent growth model. Our results highlight a high diversity of responses to climate, although the effects of climate are small compared to those of tree size and competition. Some species grow less during dry years (Pinus contorta and Calocedrus decurrens) but, surprisingly, other species exhibit higher growth during dry years (Pinus monticola, Abies magnifica, Pinus jeffreyi, Quercus kelloggii). These results emphasize the need for growth models to take into account species variability, as well as spatial heterogeneity, when studying mixed conifer forests. So far, temperatures have increased in California, and tree growth of some species may drastically decrease in the Sierra Nevada if warming continues, leading to changes in forest structure and composition as well as potential changes in wood production and carbon sequestration

Modeling the forest dynamics of the Sierra Nevada under climate change using SORTIE-ND

Key message: Model simulation results suggest that forests in the Sierra Nevada mountains of California will tend to increase in density and basal area in the absence of fire over the next century, and that climate change will favor increases in drought-tolerant species. Context: Climate change is projected to intensify the natural summer drought period for Mediterranean-climate forests. Such changes may increase tree mortality, change species interactions and composition, and impact ecosystem services. Aims: To parameterize SORTIE-ND, an individual-based, spatially explicit forest model, for forests in the Sierra Nevada, and to model forest responses to climate change. Methods: We use 3 downscaled GCM projections (RCP 8.5) to project forest dynamics for 7 sites at different elevations. Results: Basal area and stem density tended to increase in the absence of fire. Climate change effects differed by species, with more drought-tolerant species such as Jeffrey pine (Pinus jeffreyi A.Murray bis) and black oak (Quercus kelloggii Newb.) exhibiting increases in basal area and/or density. Conclusion: Increasing forest density may favor carbon sequestration but could increase the risk of high-severity fires. Future analyses should include improved parameterization of reproduction and interactions of disturbance with climate effects

Identifying climatic drivers of tropical forest dynamics

In the context of climate change, identifying and then predicting the impacts of climatic drivers on tropical forest dynamics is becoming a matter of urgency. To look at these climate impacts, we used a coupled model of tropical tree growth and mortality, calibrated with forest dynamic data from the 20-year study site of Paracou, French Guiana, in order to introduce and test a set of climatic variables. Three major climatic drivers were identified through the variable selection procedure: drought, water saturation and temperature. Drought decreased annual growth and mortality rates, high precipitation increased mortality rates and high temperature decreased growth. Interactions between key functional traits, stature and climatic variables were investigated, showing best resistance to drought for trees with high wood density and for trees with small current diameters. Our results highlighted strong long-term impacts of climate variables on tropical forest dynamics, suggesting potential deep impacts of climate changes during the next century

Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions

Estimating tropical tree diversity indices from forestry surveys : a method to integrate taxonomic uncertainty

Analyses of tree diversity and community composition in tropical rain forests are usually based either on general herbarium data or on a restricted number of botanical plots. Despite their high taxonomic accuracy, both types of data are difficult to extrapolate to landscape scales. Meanwhile, forestry surveys provide quantitative occurrence data on large areas, and are thus increasingly used for landscape-scale analyses of tree diversity. However, the reliability of these approaches has been challenged because of the ambiguity of the common (vernacular) names used by foresters and the complexity of tree taxonomy in those hyper-diverse communities. We developed and tested a novel approach to evaluate taxonomic reliability of forestry surveys and to propagate the resulting uncertainty in the estimates of several diversity indicators (alpha and beta entropy, Fisher-alpha and Sørensen similarity). Our approach is based on Monte-Carlo processes that simulate communities by taking into account the expected accuracy and reliability of common names. We tested this method in French Guiana, on 9 one-hectare plots (4279 trees – DBH ⩾ 10 cm) for which both common names and standardized taxonomic determinations were available. We then applied our method of community simulation on large forestry inventories (560 ha) at the landscape scale and compared the diversity indices obtained for 10 sites with those computed from precise botanical determination situated at the same localities. We found that taxonomic reliability of forestry inventories varied from 22% (species level) to 83% (family level) in this Amazonian region. Indices computed directly with raw forestry data resulted in incorrect values, except for Gini–Simpson beta-diversity. On the contrary, our correction method provides more accurate diversity estimates, highly correlated with botanical measurements, for almost all diversity indices at both regional and local scales. We obtained a robust ranking of sites consistent with those shown by botanical inventories. These results show that (i) forestry inventories represent a significant part of taxonomic information, (ii) the relative diversity of regional sites can be successfully ranked using forestry inventory data using our method and (iii) forestry inventories can valuably contribute to the detection of large-scale diversity patterns when biases are well-controlled and corrected. The tools we developed as R-functions are available in supplementary material and can be adapted with local parameters to be used for forest management and conservation issues in other regional contexts

A generic weather-driven model to predict mosquito population dynamics applied to species of Anopheles, Culex and Aedes genera of southern France

An accurate understanding and prediction of mosquito population dynamics are needed to identify areas where there is a high risk of mosquito-borne disease spread and persistence. Simulation tools are relevant for supporting decision-makers in the surveillance of vector populations, as models of vector population dynamics provide predictions of the greatest risk periods for vector abundance, which can be particularly helpful in areas with a highly variable environment. We present a generic weather-driven model of mosquito population dynamics, which was applied to one species of each of the genera Anopheles, Culex, and Aedes, located in the same area and thus affected by similar weather conditions. The predicted population dynamics of Anopheles hyrcanus, Culex pipiens, and Aedes caspius were not similar. An. hyrcanus was abundant in late summer. Cx. pipiens was less abundant but throughout the summer. The abundance of both species showed a single large peak with few variations between years. The population dynamics of Ae. caspius showed large intra- and inter-annual variations due to pulsed egg hatching. Predictions of the model were compared to longitudinal data on host-seeking adult females. Data were previously obtained using CDC-light traps baited with carbon dioxide dry ice in 2005 at two sites (Marais du Viguerat and Tour Carbonniere) in a favourable temperate wetland of southern France (Camargue). The observed and predicted periods of maximal abundance for An. hyrcanus and Cx. pipiens tallied very well. Pearson's coefficients for these two species were over 75% for both species. The model also reproduced the major trends in the intra-annual fluctuations of Ae. caspius population dynamics, with peaks occurring in early summer and following the autumn rainfall events. Few individuals of this species were trapped so the comparison of predicted and observed dynamics was not relevant. A global sensitivity analysis of the species-specific models enabled us to identify the parameters most influencing the maximal abundance of mosquitoes. These key parameters were almost similar between species, but not with the same contributions. The emergence of adult mosquitoes was identified as a key process in the population dynamics of all of the three species considered here. Parameters associated with adult emergence therefore need to be precisely known to achieve accurate predictions. Our model is a flexible and efficient tool that predicts mosquito abundance based on local environmental factors. It is useful to and already used by a mosquito surveillance manager in France

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R‐CNN

Tropical forests are a major component of the global carbon cycle and home to two-thirds of terrestrial species. Upper-canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2, which builds on the Mask R-CNN computer vision framework to recognize the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper-canopy trees. Detectree2 delineated 65 000 upper-canopy trees across 14 km2 of aerial images. The skill of the automatic method in delineating unseen test trees was good (F1 score = 0.64) and for the tallest category of trees was excellent (F1 score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate-size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open-source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration

Accurate delineation of individual tree crowns in tropical forests from aerial RGB imagery using Mask R-CNN

Tropical forests are a major component of the global carbon cycle and home to two-thirds of terrestrial species. Upper-canopy trees store the majority of forest carbon and can be vulnerable to drought events and storms. Monitoring their growth and mortality is essential to understanding forest resilience to climate change, but in the context of forest carbon storage, large trees are underrepresented in traditional field surveys, so estimates are poorly constrained. Aerial photographs provide spectral and textural information to discriminate between tree crowns in diverse, complex tropical canopies, potentially opening the door to landscape monitoring of large trees. Here we describe a new deep convolutional neural network method, Detectree2, which builds on the Mask R-CNN computer vision framework to recognise the irregular edges of individual tree crowns from airborne RGB imagery. We trained and evaluated this model with 3,797 manually delineated tree crowns at three sites in Malaysian Borneo and one site in French Guiana. As an example application, we combined the delineations with repeat lidar surveys (taken between 3 and 6 years apart) of the four sites to estimate the growth and mortality of upper-canopy trees. Detectree2 delineated 65,000 upper-canopy trees across 14 km² of aerial images. The skill of the automatic method in delineating unseen test trees was good (F₁ score = 0.64) and for the tallest category of trees was excellent (F₁ score = 0.74). As predicted from previous field studies, we found that growth rate declined with tree height and tall trees had higher mortality rates than intermediate-size trees. Our approach demonstrates that deep learning methods can automatically segment trees in widely accessible RGB imagery. This tool (provided as an open-source Python package) has many potential applications in forest ecology and conservation, from estimating carbon stocks to monitoring forest phenology and restoration

Linking Drone and Ground-Based Liana Measurements in a Congolese Forest

International audienceLianas are abundant and diverse in tropical forests and impact forest dynamics. They occupy part of the canopy, forming a layer of leaves overtopping tree crowns. Yet, their interaction with trees has been mainly studied from the ground. With the emergence of drone-based sensing, very high-resolution data may be obtained on liana distribution above canopies. Here, we assessed the relationship between common liana ground measurements and drone-determined liana leaf coverage over tree crowns, tested if this relationship is mediated by liana functional composition, and compared the signature of liana patches and tree crowns in our drone images. Using drone platforms, we acquired very high resolution RGB and multispectral images and LiDAR data over two 9-ha permanent plots located in northern Republic of Congo and delineated liana leaf coverage and individual tree crowns from these data. During a concomitant ground survey, we focused on 275 trees infested or not by lianas, for which we measured all lianas ≥ 1 cm in diameter climbing on them (n = 615) and estimated their crown occupancy index (COI). We additionally measured or recorded the wood density and climbing mechanisms of most liana taxa. Contrary to recent findings, we found significant relationships between most ground-derived metrics and the top-of-view liana leaf coverage over tree crowns. Tree crown infestation by lianas was primarily explained by the load of liana climbing on them, and negatively impacted by tree height. Liana leaf coverage over individual tree crowns was best predicted by liana basal area and negatively mediated by liana wood density, with a higher leaf area to diameter ratio for light-wooded lianas. COI scores were concordant with drone assessments, but two thirds differed from those obtained from drone measurements. Finally, liana patches had a higher light reflectance and variance of spectral responses than tree crowns in all studied spectra. However, the large overlap between them challenges the autodetection of liana patches in canopies. Overall, we illustrate that the joint use of ground and drone-based data deepen our understanding of liana-infestation pathways and of their functional and spectral diversity. We expect drone data to soon transform the field of liana ecology