A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km2) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km$^2$ (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin

The global abundance of tree palms

Aim: Palms are an iconic, diverse and often abundant component of tropical ecosys-tems that provide many ecosystem services. Being monocots, tree palms are evo-lutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon se-questration and storage) and in terms of responses to climate change. We quanti-fied global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change.Location: Tropical and subtropical moist forests.Time period: Current.Major taxa studied: Palms (Arecaceae).Methods: We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co-occurring non-palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure.Results: On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly asso-ciated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long-term climate stability. Life-form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non-tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above-ground biomass, but the mag-nitude and direction of the effect require additional work.Conclusions: Tree palms are not only quintessentially tropical, but they are also over-whelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests

Local hydrological conditions influence tree diversity and composition across the Amazon basin

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

The global abundance of tree palms

Aim Palms are an iconic, diverse and often abundant component of tropical ecosystems that provide many ecosystem services. Being monocots, tree palms are evolutionarily, morphologically and physiologically distinct from other trees, and these differences have important consequences for ecosystem services (e.g., carbon sequestration and storage) and in terms of responses to climate change. We quantified global patterns of tree palm relative abundance to help improve understanding of tropical forests and reduce uncertainty about these ecosystems under climate change. Location Tropical and subtropical moist forests. Time period Current. Major taxa studied Palms (Arecaceae). Methods We assembled a pantropical dataset of 2,548 forest plots (covering 1,191 ha) and quantified tree palm (i.e., ≥10 cm diameter at breast height) abundance relative to co‐occurring non‐palm trees. We compared the relative abundance of tree palms across biogeographical realms and tested for associations with palaeoclimate stability, current climate, edaphic conditions and metrics of forest structure. Results On average, the relative abundance of tree palms was more than five times larger between Neotropical locations and other biogeographical realms. Tree palms were absent in most locations outside the Neotropics but present in >80% of Neotropical locations. The relative abundance of tree palms was more strongly associated with local conditions (e.g., higher mean annual precipitation, lower soil fertility, shallower water table and lower plot mean wood density) than metrics of long‐term climate stability. Life‐form diversity also influenced the patterns; palm assemblages outside the Neotropics comprise many non‐tree (e.g., climbing) palms. Finally, we show that tree palms can influence estimates of above‐ground biomass, but the magnitude and direction of the effect require additional work. Conclusions Tree palms are not only quintessentially tropical, but they are also overwhelmingly Neotropical. Future work to understand the contributions of tree palms to biomass estimates and carbon cycling will be particularly crucial in Neotropical forests

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Abstract Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km2) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin.</jats:p

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Acknowledgements: This work was funded by NERC (Grant ref. NE/R000751/1) to I T L, A H, K H R, E T A M, C M A, and T R B; Charles University (PRIMUS/23/SCI/013) to AH; Charles University Research Centre program UNCE/24/SCI/006 to AH; Leverhulme Trust (grant ref. RPG-2018-306) to K H R, L E S C and C E W E N H C acknowledges support from her NERC Knowledge Exchange Fellowship (NE/V018760/1) and from Green Climate Fund and KOICA to PROFONANPE and IIAP to sample peatlands in the Datem del Marañón in Peru. A H and I T L acknowledge support from the Charles University and University of St Andrews Joint Seed Funding Programme. J E H, F W, and M T F P acknowledge support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq—process number 141727/2011-0 as well as Universal 14/2011) for Brazilian field sampling. J E H, J P J and M T acknowledge support from the Discovery Fund of Fort Worth, Texas, the Gordon and Betty Moore Foundation (Grant Nos. 484), the U.S. National Science Foundation (Grant No. 0717453), and the Programa de Ciencia y Tecnologia—FINCYT (co-financed by the Banco Internacional de Desarollo, BID) Grant Number PIBAP-2007-005. We thank SERNANP, SERFOR and GERFOR for providing research permits, and the different Indigenous and local communities, research stations and tourist companies for giving consent and allowing access to the forests, including the Santa Rosa de Lagarto community (Parinari, Loreto). We acknowledge the invaluable support of technicians J Irarica, J Sanchez, H Vásquez and R Flores, without whom much of the fieldwork would not have been possible. Finally, we would like to thank the reviewers for their insightful comments and suggestions which substantially improved the manuscript.Funder: Gordon and Betty Moore Foundation; doi: http://dx.doi.org/10.13039/100000936Funder: Banco InternacionalFunder: Programa de Ciencia y TecnologiaFunder: Charles University/ University of St Andrews Joint Seed FundingTropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km2) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Acknowledgements: This work was funded by NERC (Grant ref. NE/R000751/1) to I T L, A H, K H R, E T A M, C M A, and T R B; Charles University (PRIMUS/23/SCI/013) to AH; Charles University Research Centre program UNCE/24/SCI/006 to AH; Leverhulme Trust (grant ref. RPG-2018-306) to K H R, L E S C and C E W E N H C acknowledges support from her NERC Knowledge Exchange Fellowship (NE/V018760/1) and from Green Climate Fund and KOICA to PROFONANPE and IIAP to sample peatlands in the Datem del Marañón in Peru. A H and I T L acknowledge support from the Charles University and University of St Andrews Joint Seed Funding Programme. J E H, F W, and M T F P acknowledge support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq—process number 141727/2011-0 as well as Universal 14/2011) for Brazilian field sampling. J E H, J P J and M T acknowledge support from the Discovery Fund of Fort Worth, Texas, the Gordon and Betty Moore Foundation (Grant Nos. 484), the U.S. National Science Foundation (Grant No. 0717453), and the Programa de Ciencia y Tecnologia—FINCYT (co-financed by the Banco Internacional de Desarollo, BID) Grant Number PIBAP-2007-005. We thank SERNANP, SERFOR and GERFOR for providing research permits, and the different Indigenous and local communities, research stations and tourist companies for giving consent and allowing access to the forests, including the Santa Rosa de Lagarto community (Parinari, Loreto). We acknowledge the invaluable support of technicians J Irarica, J Sanchez, H Vásquez and R Flores, without whom much of the fieldwork would not have been possible. Finally, we would like to thank the reviewers for their insightful comments and suggestions which substantially improved the manuscript.Funder: Gordon and Betty Moore Foundation; doi: http://dx.doi.org/10.13039/100000936Funder: Banco InternacionalFunder: Programa de Ciencia y TecnologiaFunder: Charles University/ University of St Andrews Joint Seed FundingTropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km2) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin

Wind dispersed tree species have greater maximum height

Aim We test the hypothesis that wind dispersal is more common among emergent tree species given that being tall increases the likelihood of effective seed dispersal. Location Americas, Africa and the Asia-Pacific. Time period 1970–2020. Major taxa studied Gymnosperms and Angiosperms. Methods We used a dataset consisting of tree inventories from 2821 plots across three biogeographic regions (Americas, Africa and Asia-Pacific), including dry and wet forests, to determine the maximum height and dispersal strategy of 5314 tree species. A web search was used to determine whether species were wind-dispersed. We compared differences in tree species maximum height between biogeographic regions and examined the relationship between species maximum height and wind dispersal using logistic regression. We also tested whether emergent tree species, that is species with at least one individual taller than the 95% height percentile in one or more plots, were disproportionally wind dispersed in dry and wet forests within each biogeographic region. Results Our dataset provides maximum height values for 5314 tree species, of which more than half (2914) had no record of this trait in existing global databases. We found that, on average, tree species in the Americas have lower maximum heights compared to those in Africa and the Asia Pacific. The probability of wind dispersal increased significantly with tree species maximum height and was significantly higher among emergent than non-emergent tree species in both dry and wet forests in all three biogeographic regions. Main conclusion Wind dispersal is more prevalent in tall, emergent tree species than in non-emergent species and may thus be an important factor in the evolution of tree species maximum height. By providing the most comprehensive dataset so far of tree species maximum height and wind dispersal strategies, this study paves the way for advancing our understanding of the eco-evolutionary drivers of tree size

Dominance and rarity in tree communities across the globe: Patterns, predictors and threats

Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?. Location: Global. Time period: 1990–2017. Major taxa studied: Trees. Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation. Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices