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Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark–resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season.

Tree diversity and composition in Amazonia are known to be strongly determined by the water supplied by precipitation. Nevertheless, within the same climatic regime, water availability is modulated by local topography and soil characteristics (hereafter referred to as local hydrological conditions), varying from saturated and poorly drained to well-drained and potentially dry areas. While these conditions may be expected to influence species distribution, the impacts of local hydrological conditions on tree diversity and composition remain poorly understood at the whole Amazon basin scale. Using a dataset of 443 1-ha non-flooded forest plots distributed across the basin, we investigate how local hydrological conditions influence 1) tree alpha diversity, 2) the community-weighted wood density mean (CWM-wd) – a proxy for hydraulic resistance and 3) tree species composition. We find that the effect of local hydrological conditions on tree diversity depends on climate, being more evident in wetter forests, where diversity increases towards locations with well-drained soils. CWM-wd increased towards better drained soils in Southern and Western Amazonia. Tree species composition changed along local soil hydrological gradients in Central-Eastern, Western and Southern Amazonia, and those changes were correlated with changes in the mean wood density of plots. Our results suggest that local hydrological gradients filter species, influencing the diversity and composition of Amazonian forests. Overall, this study shows that the effect of local hydrological conditions is pervasive, extending over wide Amazonian regions, and reinforces the importance of accounting for local topography and hydrology to better understand the likely response and resilience of forests to increased frequency of extreme climate events and rising temperatures., This work was part of the MSc thesis of the first author developed at the Graduate Program in Ecology of the National Institute of Amazonian Research (INPA). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. Gabriel M. Moulatlet acknowledges the postdoctoral grant from the Project SEP-CONACYT CB-2017–2018 (grant no. A1-S34563)

Funding: Floristic identification in plots in the RAINFOR forest monitoring network have been supported by the Natural Environment Research Council (grants NE/B503384/1, NE/ D01025X/1, NE/I02982X/1, NE/F005806/1, NE/D005590/1 and NE/I028122/1) and the Gordon and Betty Moore Foundation. In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics. Publisher PDF

The number of reforestation projects worldwide is increasing. In many cases funding is obtained through the claimed carbon capture of the trees, presented as immediate and durable, whereas reforested plots need time and maintenance to realise their carbon capture potential. Further, claims usually overlook the environmental costs of natural or anthropogenic disturbances during the forest’s lifetime, and greenhouse gas (GHG) emissions associated with the reforestation are not allowed for. This study uses life cycle assessment to quantify the carbon footprint of setting up a reforestation plot in the Peruvian Amazon. In parallel, we combine a soil carbon model with an above- and below-ground plant carbon model to predict the increase in carbon stocks after planting. We compare our results with the carbon capture claims made by a reforestation platform. Our results show major errors in carbon accounting in reforestation projects if they (1) ignore the time needed for trees to reach their carbon capture potential; (2) ignore the GHG emissions involved in setting up a plot; (3) report the carbon capture potential per tree planted, thereby ignoring limitations at the forest ecosystem level; or (4) under-estimate tree losses due to inevitable human and climatic disturbances. Further, we show that applications of biochar during reforestation can partially compensate for project emissions.

Recent studies have demonstrated that ecological processes that shape community structure and dynamics change along environmental gradients. However, much less is known about how the emergence of the gradients themselves shape the evolution of species that underlie community assembly. In this study, we address how the creation of novel environments leads to community assembly via two nonmutually exclusive processes: immigration and ecological sorting of pre-adapted clades (ISPC), and recent adaptive diversification (RAD). We study these processes in the context of the elevational gradient created by the uplift of the Central Andes. We develop a novel approach and method based on the decomposition of species turnover into within- and among-clade components, where clades correspond to lineages that originated before mountain uplift. Effects of ISPC and RAD can be inferred from how components of turnover change with elevation. We test our approach using data from over 500 Andean forest plots. We found that species turnover between communities at different elevations is dominated by the replacement of clades that originated before the uplift of the Central Andes. Our results suggest that immigration and sorting of clades pre-adapted to montane habitats is the primary mechanism shaping tree communities across elevations.

The Matsigenka people living traditional lifestyles in remote areas of the Amazon rely on a fish-based diet that exposes them to methylmercury (MeHg) at levels that have been associated with decreased IQ scores. In this study, the association between Hg levels and working memory was explored using the framework of the Multicomponent Model. Working memory tasks were modified to fit the culture and language of the Matsigenka when needed and included measures for verbal storage (Word Span) visuospatial storage (Corsi Block Task) and a measure of executive functions, the Self-Ordered Pointing Task (SOPT). An innovation of the Trail Making Tests A & B (TMT A & B) was pilot tested as another potential measure of executive functions. The mean hair Hg levels of 30 participants, ages 12 to 55 years, from three different communities (Maizal, Cacaotal and Yomibato) was 7.0 ppm (sd = 2.40), well above the World Health Organization (WHO) limit for hair of 2.0 ppm and ranged from 1.8 to 14.2 ppm, with 98% of a broader sample of 152 individuals exceeding the WHO limit. Hair Hg levels showed significant associations with cognitive performance, but the degree varied in magnitude according to the type of task. Hg levels were negatively associated with executive functioning performance (SOPT errors), while Hg levels and years of education predicted visuospatial performance (Corsi Block accuracy). Education was the only predictor of Word Span accuracy. The results show that Hg exposure is negatively associated with working memory performance when there is an increased reliance on executive functioning. Based on our findings and the review of the experimental research, we suggest that the SOPT and the Corsi Block have the potential to be alternatives to general intelligence tests when studying remote groups with extensive cultural differences.

Societal Impact Statement The approach that we take to our science is as important as the questions that we address if we would like our research to inform management. Here, we discuss our experience of using networks of permanent forest inventory plots to support sustainable management and conservation of intact tropical forests. A key conclusion is that to maximize the use of data from such large international networks within policymaking, it is crucial that leadership is widely shared among participants. Such an approach helps to address ethical concerns surrounding international collaborations and also achieves greater policy impact. Summary Long‐term data from permanent forest inventory plots have much to offer the management and conservation of intact tropical forest landscapes. Knowledge of the growth and mortality rates of economically important species, forest carbon balance, and the impact of climate change on forest composition are all central to effective management. However, this information is rarely integrated within the policymaking process. The problem reflects broader issues in using evidence to influence environmental management, and in particular, the need to engage with potential users beyond the collection and publication of high‐quality data. To ensure permanent plot data are used, (a) key “policy windows”—opportunities to integrate data within policy making—need to be identified; (b) long‐term relationships need to be developed between scientists and policy makers and policymaking organizations; and (c) leadership of plot networks needs to be shared among all participants, and particularly between institutions in the global north and those in tropical countries. Addressing these issues will allow permanent plot networks to make tangible contributions to ensuring that intact tropical forest persists over coming decades.

Species interactions generating consistent landscape or seascape patterns are important to ecosystem health and function. One widely-documented pattern in shallow, tropical reefscapes is sand “halos” that separate coral patches from surrounding vegetation. Multiple hypotheses for this pattern have been proposed, but individual explanations are rarely integrated to reflect the complexity of systems where halos occur. To evaluate and synthesize multiple hypotheses of halo formation and maintenance into a single model, connect the halo pattern with similar terrestrial phenomena, and identify targets to guide future research and conservation. We reviewed halo accounts from over fifty years in the scientific literature and combined them with related research on marine nutrients, bioturbation, analogous terrestrial processes, and new field measurements to create a conceptual model of halo formation and maintenance. Though halos are widely attributed to herbivory, grazing alone cannot explain patterns at all locations. Nutrients have large effects on tropical reefscapes but remain understudied for halos. Coupling nutrients and grazing explains a variety of observed halo patterns. Fish and invertebrate bioturbators also appear important. We combine these factors to explain pattern variability and present a framework for more comprehensive halo research. Explanations for halos to date have been overly simplistic. Halos are common patterns with complex underlying causes, including foraging behavior and nutrient cycling. Not all halo-like patterns may be created or maintained by the same mechanism(s). Our integrated model provides a more complete understanding of factors contributing to these patterns, a framework for ecological interpretations, and a guide for conservation.

Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques’ use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras’ potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns.

International audience; Despite its importance for forest regeneration, food webs, and human economies, changes in tree fecundity with tree size and age remain largely unknown. The allometric increase with tree diameter assumed in ecological models would substantially overestimate seed contributions from large trees if fecundity eventually declines with size. Current estimates are dominated by overrepresentation of small trees in regression models. We combined global fecundity data, including a substantial representation of large trees. We compared size–fecundity relationships against traditional allometric scaling with diameter and two models based on crown architecture. All allometric models fail to describe the declining rate of increase in fecundity with diameter found for 80% of 597 species in our analysis. The strong evidence of declining fecundity, beyond what can be explained by crown architectural change, is consistent with physiological decline. A downward revision of projected fecundity of large trees can improve the next generation of forest dynamic models.

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old-growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi-deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n = 47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water-stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions. © 2021 The Authors. Global Change Biology published by John Wiley & Sons Ltd. This study is a product of the Global Ecosystem Monitoring network (GEM), Andes Biodiversity and Ecosystem Research Group (ABERG), Amazon Forest Inventory Network (RAINFOR), Stability of Altered Forest Ecosystem (SAFE), and Instituto de Investigaciones de la Amazonia Peruana (IIAP). WHH was funded by Peruvian FONDECYT/CONCYTEC (grant contract number 213-2015-FONDECYT). The GEM network was supported by a European Research Council Advanced Investigator Grant to YM (GEM-TRAITS: 321131) under the European Union's Seventh Framework Programme (FP7/2007-2013). The field data collection was funded NERC Grants NE/D014174/1 and NE/J022616/1 for in Peru, BALI (NE/K016369/1) for work in Malaysia, the Royal Society-Leverhulme Africa Capacity Building Programme for work in Ghana and Gabon and ESPA-ECOLIMITS (NE/1014705/1) in Ghana and Ethiopia. Plot inventories in South America were supported by funding from the US National Science Foundation Long-Term Research in Environmental Biology program (LTREB; DEB 1754647) and the Gordon and Betty Moore Foundation Andes-Amazon Program. GEM data in Gabon were collected under authorization to YM and supported by the Gabon National Parks Agency. Y.M. is supported by the Jackson Foundation. We would like to acknowledge the GEM team across the tropical regions and countries of Bolivia, Brazil, Ghana, Gabon, Ethiopia, Malaysia, and Peru.

Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment.Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques’ use by addressing fish movement and feeding behavior related to grazing halos, a well-documented benthic pattern in shallow tropical reefscapes.Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months, and collected time-lapse images during daylight hours for a total of over 100,000 images. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fish, and diver surveys revealed that the camera images accurately represented local fish communities.The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras’ potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns.

Understanding how evolutionary constraints shape the elevational distributions of tree lineages provides valuable insight into the future of tropical montane forests under global change. With narrow elevational ranges, high taxonomic turnover, frequent habitat specialization, and exceptional levels of endemism, tropical montane forests and trees are predicted to be highly sensitive to environmental change. Using plot census data from a gradient traversing > 3,000 m in elevation on the Amazonian flank of the Peruvian Andes, we employ phylogenetic approaches to assess the influence of evolutionary heritage on distribution trends of trees at the genus‐level. We find that closely related lineages tend to occur at similar mean elevations, with sister genera pairs occurring a mean 254 m in elevation closer to each other than the mean elevational difference between non‐sister genera pairs. We also demonstrate phylogenetic clustering both above and below 1,750 m a.s.l, corresponding roughly to the cloud‐base ecotone. Belying these general trends, some lineages occur across many different elevations. However, these highly plastic lineages are not phylogenetically clustered. Overall, our findings suggest that tropical montane forests are home to unique tree lineage diversity, constrained by their evolutionary heritage and vulnerable to substantial losses under environmental changes, such as rising temperatures or an upward shift of the cloud‐base.

Tropical forest ecosystems are undergoing rapid transformation as a result of changing environmental conditions and direct human impacts. However, we cannot adequately understand, monitor or simulate tropical ecosystem responses to environmental changes without capturing the high diversity of plant functional characteristics in the species-rich tropics. Failure to do so can oversimplify our understanding of ecosystems responses to environmental disturbances. Innovative methods and data products are needed to track changes in functional trait composition in tropical forest ecosystems through time and space. This study aimed to track key functional traits by coupling Sentinel-2 derived variables with a unique data set of precisely located in-situ measurements of canopy functional traits collected from 2434 individual trees across the tropics using a standardised methodology. The functional traits and vegetation censuses were collected from 47 field plots in the countries of Australia, Brazil, Peru, Gabon, Ghana, and Malaysia, which span the four tropical continents. The spatial positions of individual trees above 10 cm diameter at breast height (DBH) were mapped and their canopy size and shape recorded. Using geo-located tree canopy size and shape data, community-level trait values were estimated at the same spatial resolution as Sentinel-2 imagery (i.e. 10 m pixels). We then used the Geographic Random Forest (GRF) to model and predict functional traits across our plots. We demonstrate that key plant functional traits can be accurately predicted across the tropicsusing the high spatial and spectral resolution of Sentinel-2 imagery in conjunction with climatic and soil information. Image textural parameters were found to be key components of remote sensing information for predicting functional traits across tropical forests and woody savannas. Leaf thickness (R2 = 0.52) obtained the highest prediction accuracy among the morphological and structural traits and leaf carbon content (R2 = 0.70) and maximum rates of photosynthesis (R2 = 0.67) obtained the highest prediction accuracy for leaf chemistry and photosynthesis related traits, respectively. Overall, the highest prediction accuracy was obtained for leaf chemistry and photosynthetic traits in comparison to morphological and structural traits. Our approach offers new opportunities for mapping, monitoring and understanding biodiversity and ecosystem change in the most species-rich ecosystems on Earth.

Aims: Terrestrial ecosystems are changing in biodiversity, species composition and functional trait composition. To understand the underlying causes of these changes and predict the long-term resilience of the ecosystem to withstand future disturbances, we can evaluate changes in diversity and composition from fossil pollen records. Although diversity can be well estimated from pollen in temperate ecosystems, this is less clear for the hyperdiverse tropics. Moreover, it remains unknown whether functional composition of plant assemblages can be accurately predicted from pollen assemblage composition. Here, we evaluate how community-weighted mean (CWM) traits and diversity indices change along elevation.Location: Amazon–Andes elevation gradient in Peru.Methods: We used 82 modern pollen samples and 59 vegetation plots along the elevation gradient, and calculated CWM traits and diversity indices for each pollen sample and vegetation plot. We also quantified the degree to which taxa are over- or underrepresented by their pollen, by dividing the relative pollen abundance by the relative basal area abundance in the nearby vegetation survey plots (i.e. the R-rel values).Results: We found that CWM wood density increased, and CWM adult height and leaf area decreased with elevation. This change was well predicted by pollen assemblages, indicating that CWM trait–environment relationships based on pollen abundance data provide meaningful results. Diversity (richness, Shannon and Simpson) decreased with elevation for vegetation plots, but these trends could not be observed from pollen assemblages.Conclusions: Our results demonstrate that more research is needed to develop methods that lead to accurate diversity estimates from pollen data in these tropical ecosystems, but that CWM traits can be calculated from pollen data to assess spatial shifts in functional composition. This opens opportunities to calculate CWM traits from fossil pollen data sets in the tropics, with broad implications for improving our understanding and predictions of forest dynamics, functioning and resilience through time.

SummaryBiogeographic events occurring in the deep past can contribute to the structure of modern ecological communities. However, little is known about how the emergence of environmental gradients shape the evolution of species that underlie community assembly. In this study, we address how the creation of novel environments lead to community assembly via two non-mutually exclusive processes: 1) the immigration and ecological sorting of pre-adapted clades (ISPC), and 2) recent adaptive diversification (RAD). We study these processes in the context of the elevational gradient created by the uplift of the Central Andes.We develop a novel approach and method based on the decomposition of species turnover into within- and among-clade components, where clades correspond to lineages that originated before mountain uplift. Effects of ISPC and RAD can be inferred from how components of turnover change with elevation. We test our approach using data from over 500 Andean forest plots.We found that species turnover between communities at different elevations is dominated by the replacement of clades that originated before the uplift of the Central Andes.Our results suggest that immigration and sorting of clades pre-adapted to montane habitats is the primary mechanism shaping communities across elevations.

Forests are integral to global carbon cycling but are threatened by anthropogenic degradation and climate change. Assessing this global threat has been hindered by a lack of clear, flexible, and easy-to-use productivity models along with a lack of functional trait and productivity data for parameterizing and testing those models. Current productivity models are either extremely complex requiring up to hundreds of parameters, many sub-models, and considerable computational expense or rely on statistical trait-growth relationships that can be hard to extrapolate to new systems or climates. Here we provide a simple alternative: a remote sensing canopy functional model (RS-CFM) that uses remotely-sensed foliar traits and canopy structure data to efficiently map productivity at high-resolution and large spatial scales. We test this model by quantifying net primary productivity (NPP) at 0.01-ha resolution in 30,040 hectares of Peruvian tropical rainforest along a 3,322-m Amazon-to-Andes elevation gradient. Our model predicts local NPP and elevational shifts in NPP much more accurately and in greater detail than a prominent alternative method—NASA’s MODIS NPP product. Furthermore, we show how NPP estimates depend on light competition and identify the appropriate spatial resolution for remote productivity estimation. Our framework opens up possibilities to fully harness remote sensing data and reliably scale up from traits to map regional or global productivity in a more direct, efficient, and cost-effective manner.

The sizes and shapes of tree crowns are of fundamental importance in ecology, yet understanding the forces that determine them remains elusive. A cardinal question facing ecologists is the degree to which general and non-specific versus ecological and context-dependent processes are responsible for shaping tree crowns. Here, we test this question for the first time across diverse tropical ecosystems. Using trees from 20 plots varying in elevation, precipitation, and ecosystem type (savanna-forest transitions) across the paleo- and neo-tropics, we test the relationship between crown dimensions and tree size. By analyzing these scaling relationships across environmental gradients, biogeographic regions, and phylogenetic distance, we extend Metabolic Scaling Theory (MST) predictions to include how local selective pressures shape variation in crown dimensions. Across all sites, we find strong agreement between mean trends and MST predictions for the scaling of crown size and shape, but large variation around the mean. While MST explained approximately half of the observed variation in tree crown dimensions, we find that local, ecosystem, and phylogenetic predictors account for the half of the residual variation. Crown scaling does not change significantly across regions, but does change across ecosystem types, where savanna tree crowns grow more quickly with tree girth than forest tree crowns. Crowns of legumes were wider and more voluminous than those of other taxa. Thus, while MST can accurately describe the central tendency of tree crown size, local ecological conditions and evolutionary history appear to modify the scaling of crown shape. Importantly, our extension of MST incorporating these differences accounts for the mechanisms driving variation in the scaling of crown dimensions across the tropics. We present allometric equations for the prediction of crown dimensions across tropical ecosystems. These results are critical when scaling the function of individual trees to larger spatial scales or incorporating the size and shape of tree crowns in global biogeochemical models.

It is largely unknown how South America’s Andean forests affect the global carbon cycle, and thus regulate climate change. Here, we measure aboveground carbon dynamics over the past two decades in 119 monitoring plots spanning a range of >3000 m elevation across the subtropical and tropical Andes. Our results show that Andean forests act as strong sinks for aboveground carbon (0.67 ± 0.08 Mg C ha−1 y−1) and have a high potential to serve as future carbon refuges. Aboveground carbon dynamics of Andean forests are driven by abiotic and biotic factors, such as climate and size-dependent mortality of trees. The increasing aboveground carbon stocks offset the estimated C emissions due to deforestation between 2003 and 2014, resulting in a net total uptake of 0.027 Pg C y−1. Reducing deforestation will increase Andean aboveground carbon stocks, facilitate upward species migrations, and allow for recovery of biomass losses due to climate change., Here, the authors investigate the aboveground carbon sink efficiency of Andean forests. The study shows the high potential of these forests to serve as future carbon refuges, and urges to reduce deforestation and increase restoration.

Understanding how evolutionary constraints shape the elevational distributions of tree lineages provides valuable insight into the future of tropical montane forests under global change. With narrow elevational ranges, high taxonomic turnover, frequent habitat specialisation, and exceptional levels of endemism, tropical montane forests and trees are predicted to be highly sensitive to environmental change. Using plot census data from a gradient traversing >3000 m in elevation on the Amazonian flank of the Peruvian Andes, we employ phylogenetic approaches to assess the influence of evolutionary heritage on distribution trends of trees at the genus level. We find that closely related lineages tend to occur at similar mean elevations, with sister genera pairs occurring a mean 254 m in elevation closer to each other than the mean elevational difference for all genera pairs. We also demonstrate phylogenetic clustering both above and below 1750 m a.s.l, corresponding roughly to the cloud-base ecotone. Belying these general trends, some lineages occur across many different elevations. However, these highly plastic lineages are not phylogenetically clustered. Overall, our findings suggest that tropical montane forests are home to unique tree lineage diversity, constrained by their evolutionary heritage and vulnerable to substantial losses under environmental changes, such as rising temperatures or an upward shift of the cloud base.

Global warming is forcing many species to shift their distributions upward, causing consequent changes in the compositions of species that occur at specific locations. This prediction remains largely untested for tropical trees. Here we show, using a database of nearly 200 Andean forest plot inventories spread across more than 33.5° latitude (from 26.8° S to 7.1° N) and 3,000-m elevation (from 360 to 3,360 m above sea level), that tropical and subtropical tree communities are experiencing directional shifts in composition towards having greater relative abundances of species from lower, warmer elevations. Although this phenomenon of ‘thermophilization’ is widespread throughout the Andes, the rates of compositional change are not uniform across elevations. The observed heterogeneity in thermophilization rates is probably because of different warming rates and/or the presence of specialized tree communities at ecotones (that is, at the transitions between distinct habitats, such as at the timberline or at the base of the cloud forest). Understanding the factors that determine the directions and rates of compositional changes will enable us to better predict, and potentially mitigate, the effects of climate change on tropical forests. Fil: Fadrique, Belén. University of Miami; Estados Unidos Fil: Báez, Selene. Escuela Politécnica Nacional; Ecuador Fil: Duque, Álvaro. Universidad Nacional de Colombia; Colombia Fil: Malizia, Agustina. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina Fil: Blundo, Cecilia Mabel. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina Fil: Carilla, Julieta. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina Fil: Osinaga Acosta, Oriana. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina Fil: Malizia, Lucio Ricardo. Universidad Nacional de Jujuy. Facultad de Ciencias Agrarias; Argentina Fil: Silman, Miles. University Wake Forest; Estados Unidos Fil: Farfán Ríos, William. Universidad Nacional San Antonio Abad del Cusco; Perú. University Wake Forest; Estados Unidos Fil: Malhi, Yadvinder. University of Oxford; Reino Unido Fil: Young, Kenneth R.. University of Texas at Austin; Estados Unidos Fil: Cuesta C., Francisco. University of Amsterdam; Países Bajos Fil: Homeier, Jurgen. Universität Göttingen; Alemania Fil: Peralvo, Manuel. Consorcio para el Desarrollo Sostenible de la Ecorregión Andina; Ecuador Fil: Pinto, Esteban. Consorcio para el Desarrollo Sostenible de la Ecorregión Andina; Ecuador Fil: Jadan, Oswaldo. Universidad de Cuenca. Facultad de Ciencias Agropecuarias; Ecuador Fil: Aguirre, Nikolay. Universidad Nacional de Loja. Programa de Investigación Biodiversidad y Servicios Ecosistémicos; Ecuador Fil: Aguirre, Zhofre. Universidad Nacional de Loja. Programa de Investigación Biodiversidad y Servicios Ecosistémicos; Ecuador Fil: Feeley, Kenneth J.. University of Miami; Estados Unidos. Fairchild Tropical Botanic Garden; Estados Unidos

A paleoecological record from Lake Palotoa (1370 m elevation) in the Andean foothills of Peru spans the last 3800 years. Lake Palotoa lies near the modern cloud base in a location sensitive to changes in atmospheric moisture. In many areas, these forests have been destroyed, but Lake Palotoa shows no sign of human occupation today or in the past. The modern forest surrounding the lake is dominated by the Andean palm, Dictyocaryum lamarckianum, which is also the most abundant taxon in the fossil pollen record. Fossil pollen data show the vegetation assemblages have not experienced strong compositional changes in the late Holocene. Global-scale climatic events such as the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA) are identified within the record, though the vegetation responses are subtle. Hedyosmum and Sloanea pollen percentages increase near the onset of the MCA and may reflect decreased seasonality. The LIA coincides with increased Hedyosmum pollen percentages, and increases in Clethra and Begonia, two elements that tend to occupy forests now found at higher elevations. Our findings demonstrate the stability of montane forest systems to natural Holocene climate change.

Remote sensing of trait-based ecology can improve our understanding of the effect of trait diversity on ecosystem functioning., Spatially continuous data on functional diversity will improve our ability to predict global change impacts on ecosystem properties. We applied methods that combine imaging spectroscopy and foliar traits to estimate remotely sensed functional diversity in tropical forests across an Amazon-to-Andes elevation gradient (215 to 3537 m). We evaluated the scale dependency of community assembly processes and examined whether tropical forest productivity could be predicted by remotely sensed functional diversity. Functional richness of the community decreased with increasing elevation. Scale-dependent signals of trait convergence, consistent with environmental filtering, play an important role in explaining the range of trait variation within each site and along elevation. Single- and multitrait remotely sensed measures of functional diversity were important predictors of variation in rates of net and gross primary productivity. Our findings highlight the potential of remotely sensed functional diversity to inform trait-based ecology and trait diversity-ecosystem function linkages in hyperdiverse tropical forests.

Worldwide demand for gold has caused increased extractive activities in the Western Amazon, resulting in a wide-scale transformation of the Madre de Dios river basin (Peru) due to Artisanal and Small-scale Gold Mining (ASGM). These impactful activities profoundly affect the natural landscape and hydroscape as terra firme forest and floodplain habitats are deforested leaving a vast landscape of abandoned ponds that are subsequently colonized from surrounding water bodies. However, the metacommunity dynamics of these emergent networks of connected wetlands and their environmental drivers remain virtually unknown. Here, we present a one-year (May 2017–May 2018) study conducted to assess the composition, abundance and trophic structure of fish assemblages in three abandoned ASGM ponds and their interactions in relation to those of other freshwater groups (macroinvertebrates, phytoplankton and zooplankton). We aimed to determine the influence of environmental parameters on community composition, focused on flood pulse influence (FPI), seasonality, and to identify indicators for predicting aquatic communities’ assemblages. A remarkably abundance and diversity were found totaling 4601 sampled fish from 6 orders, 27 families, 68 genera and 103 species, as well as 87 macoinvertebrate, 71 phytoplankton and 44 zooplankton taxa. The two FPI ponds showed a more stable fish trophic structure throughout the year than the unique non-FPI, probably due to the floods provision of buffer against the lack of resources that occurs during the dry season. Conversely, fish of higher trophic levels were not detected during the dry and transition-to-wet seasons in the non-FPI pond surveyed. Community trajectory analysis showed greater stability of fish assemblages in the non-FPI pond compared to the FPI ponds, which experienced larger disturbance of environmental parameters and biotic inputs during flooding events. Detrended correspondence analysis showed floods as the key factor influencing aquatic communities and species accumulation. However, whereas floods had a large influence on fish and macroinvertebrates, seasonality was the main driver for plankton. FPI ponds showed six-fold higher fish and macroinvertebrate species richness than non-FPI ones, while plankton showed an opposite trend. FPI was the main factor determining the taxonomic composition of fish, followed by dissolved oxygen. Macroinvertebrates were also affected by FPI together with pH and the presence of grassed banks, while conductivity determined phytoplankton composition. Our study presents novel evidence on the highly heterogeneous communities of ASGM abandoned ponds, highlighting their important ecological value and potential role in providing of ecosystem services currently overlooked from conservation policies and management of the aquatic resources.

Gold mining is the largest source of mercury (Hg) pollution worldwide. The discharge of mercury in the environment bears direct human health risks and is likely to increase cascading effects throughout local food chains. In the Peruvian Amazon the mining process consists of slashing and burning trees, followed by extraction of gold-bearing sediment, amalgamation with Hg and gold recovery, leading each year to the degradation of 6,000-10,000 ha and the release of 180 metric tons of Hg per year to the enviroment. The purpose of this study was to determine soil Hg levels in soils of abandoned alluvial gold mine spoils and undisturbed forest in the Madre de Dios region, the epicenter of alluvial gold mining in Peru. We selected gold mine spoils of the two most important technologies locally applied for gold extraction, i.e., Minimally Mechanized Mining (MMM) and Highly Mechanized Mining (HMM), in the native communities of Laberinto and Kotzimba, respectively. We collected 127 and 35 soil samples (0-20cm depth) from potentially contaminated sites and undisturbed forest, respectively. Physicochemical analysis and determination of Hg levels were determined for all soil samples. None of the samples had Hg concentrations above Peruvian, Canadian and British Environmental Quality Standards for Agricultural Soil (6.6mg/kg). Hg levels in MMM and HMM were not significantly different between the two areas. The main variables explaining variation of soil Hg concentrations were the vegetation cover, soil organic matter, soil pH and clay particle content, which explained up to 80% of data set variation. Surprisingly, highest Hg concentrations were found in untouched old-growth forest bordering the mine spoils, but there was also a trend of increasing Hg concentrations with the regenerating vegetation. Our findings suggest that Hg concentrations in old mine spoils are low and shouldn’t stand in the way of efforts to restore soil conditions and develop sustainable land uses. However, it is urgent to end the use of Hg in mining operation to decrease human and environmental risks.

Amazonian forests support high biodiversity and provide valuable ecosystem services. Unfortunately, these forests are under extreme pressure from land use change and other anthropogenic disturbances. A recent study combined data from an Amazon-wide network of forest inventory plots with spatially explicit deforestation models to predict that by 2050, 36% or 57% of species will be ‘globally threatened’, as defined by IUCN Red List criteria, due to deforestation under Increased-Governance or Business-As-Usual scenarios, respectively. It was also predicted that the number of threatened species will drop by 29–44% if no deforestation occurs within protected areas. However, even the best-protected areas of the Amazon may still be susceptible to the effects of climate change and rising temperatures. To illustrate the potential dangers of climate change for Amazonian parks, we calculated the percentage of land area within all officially designated protected areas of tropical South America that will or will not have future temperature analogs under various scenarios of temperature change and park connectivity. We show that depending on the rate of warming and degree of connectivity, about 19–67% of protected areas will not have any temperature analogs in the near future (2050s). These results help to emphasize that protected areas are not immune to the effects of climate change and that large portions of Amazonian protected areas include ‘disappearing climates’. In the face of these disappearing climates, the biggest determinant of many species’ extinction risks may be their ability to migrate through non-protected habitats.