Search

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.

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.

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.

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.

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.

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.

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.

Artisanal gold mining in Amazon forests and rivers has been reported in all Amazonian countries. Amazon mining has a wide range of negative effects and severe environmental and social consequences. Given that the activity in the region is mostly illegal, there are few studies published in the scientific literature on recovery of areas degraded by gold mining. This study conducts an experimental reforestation project aimed to evaluate soil degradation and explore the seedling survivorship and early growth of 51 tropical tree species in gold mined areas at 5 study sites distributed across the Madre de Dios region, in the Peruvian Amazon. The study evaluates the effect of biochar amendments on the survivorship and growth of 51 tropical species. The study also analyzes the influence of species wood density on seedling performance one year after planting. In order to inform further restoration plantation strategies, species were chosen with the end goals of timber production, biodiversity enhancement, and soil restoration. Site degradation, soil properties and mercury levels were analyzed in degraded areas and paired reference forest patches. Soils after gold mining are found to be highly degraded, with soil C being nearly absent, cation content greatly decreased, and loss of fine sediment. Soil mercury levels were found below national and international environmental quality standards. A positive correlation and a statistically significant relationship were found between survivorship and wood density. This reveals that the higher the wood density of the species, the higher the survival percentage. Growth and overall performance of mid, and especially low wood density species were significantly increased by biochar additions, while no effect was recorded on high wood density species growth. The study provides guidance on the post-ASGM restoration potential for 51 common and useful tree species and gives practitioners recommendations for combinations of species and fertilization treatments to optimize restoration designs.

Liverworts and mosses are a major component of the epiphyte flora of tropical montane forest ecosystems. Canopy access was used to analyse the distribution and vertical stratification of bryophyte epiphytes within tree crowns at nine forest sites across a 3400 m elevational gradient in Peru, from the Amazonian basin to the high Andes. The stable isotope compositions of bryophyte organic material (13C/12C and18O/16O) are associated with surface water diffusive limitations and, along with C/N content, provide a generic index for the extent of cloud immersion. From lowland to cloud forest δ13C increased from −33‰ to −27‰, while δ18O increased from 16.3‰ to 18.0‰. Epiphytic bryophyte and associated canopy soil biomass in the cloud immersion zone was estimated at up to 45 t dry mass ha−1, and overall water holding capacity was equivalent to a 20 mm precipitation event. The study emphasizes the importance of diverse bryophyte communities in sequestering carbon in threatened habitats, with stable isotope analysis allowing future elevational shifts in the cloud base associated with changes in climate to be tracked.

Made available in DSpace on 2018-12-11T17:17:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-12-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Ministério da Ciência, Tecnologia e Inovação Ministério da Educação Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species' area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments. Coordenação de Botânica Museu Paraense Emílio Goeldi, Av. Magalhães Barata 376, C.P. 399 Programa de Pós-Graduação em Ciência Ambientais Universidade Federal Do Pará, Rua Augusto Corrêa 01 Biodiversity Dynamics Naturalis Biodiversity Center, PO Box 9517 Marine and Coastal Management, Deltares, Boussinesqweg 1 Coordenação de Biodiversidade Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 EMBRAPA Centro de Pesquisa Agroflorestal de Roraima, BR 174 km 8, Distrito Industrial Herbario Amazónico Colombiano Instituto SINCHI, Calle 20 No 5-44 Department of Integrative Biology University of California Universidad San Francisco de Quito Colegio de Ciencias Biológicas Diego de Robles y Vía Interoceánica Coordenação de Pesquisas em Ecologia Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 School of Geography University of Leeds, Woodhouse Lane Department of Wetland Ecology Institute of Geography and Geoecology Karlsruhe Institute of Technology - KIT, Josefstr 1 Biogeochemistry Max Planck Institute for Chemistry, Hahn-Meitner Weg 1 Departamento de Botânica Instituto de Pesquisas Científicas e Tecnológicas Do Amapá - IEPA Campus Do IEPA da Fazendinha, Rodovia JK Km 10 AMAP IRD Cirad CNRS INRA Université de Montpellier, TA A-51/PS2, Bd. de la Lironde Science and Education Field Museum, 1400 S. Lake Shore Drive Coordenação de Dinâmica Ambiental Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Jardín Botánico de Missouri Departamento de Ecologia Universidade Estadual Paulista - UNESP Instituto de Biociências - IB, Av. 24 A 1515 Centro de Biociências Departamento de Ecologia Universidade Federal Do Rio Grande Do Norte, Av. Senador Salgado Filho 3000 Divisao de Sensoriamento Remoto - DSR Instituto Nacional de Pesquisas Espaciais - INPE, Av. dos Astronautas 1758 Herbario Vargas Universidad Nacional de San Antonio Abad Del Cusco, Avenida de la Cultura, Nro 733 Departamento de Geografia Universidade Estadual Paulista - UNESP Instituto de Geociências e Ciências Extas - IGCe Departamento de Biologia Universidade Federal de Rondônia, Rodovia BR 364 s/n Km 9.5 Center for Tropical Conservation Duke University Nicholas School of the Environment Programa de Pós-Graduação em Biodiversidade e Biotecnologia - PPG-Bionorte Universidade Federal de Rondônia Campus Porto Velho, Km 9.5 Instituto Boliviano de Investigacion Forestal Santa Cruz, Av. 6 agosto 28 Km 14,Doble via La Guardia Casilla, 6204 Programa de Pós-Graduação em Ecologia e Conservação Universidade Do Estado de Mato Grosso Instituto de Investigaciones de la Amazonía Peruana - IIAP, Av. A. Quiñones km 2.5 Geography College of Life and Environmental Sciences University of Exeter Departamento de Ciencias Forestales Universidad Nacional de Colombia, Calle 64 x Cra 65 Agteca-Amazonica Facultad de Ciencias Agrícolas Universidad Autónoma Gabriel René Moreno Santa Cruz Prédio da Botânica e Ecologia Embrapa Recursos Genéticos Biotecnologia Parque Estação Biológica, Av. W5 Norte Projeto Dinâmica Biológica de Fragmentos Florestais Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Centre for Tropical Environmental and Sustainability Science College of Science and Engineering James Cook University Laboratório de Ecologia de Doenças Transmissíveis da Amazônia (EDTA) Instituto Leônidas e Maria Deane Fiocruz, Rua Terezina 476 Programa de Pós-graduação em Biodiversidade Saúde Instituto Oswaldo Cruz IOC/FIOCRUZ Pav. Arthur Neiva, Térreo Av. Brasil 4365 Centro de Investigaciones Ecológicas de Guayana Universidad Nacional Experimental de Guayana Calle Chile, urbaniz Chilemex Laboratorio de Ecología de Bosques Tropicales y Primatología Universidad de Los Andes, Carrera 1 # 18a- 10 Programa de Pós-Graduação em Biologia (Botânica) Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Institute of Biodiversity and Ecosystem Dynamics University of Amsterdam, Sciencepark 904 Programa de Ciencias Del Agro y El Mar Herbario Universitario (PORT) UNELLEZ-Guanare Endangered Species Coalition, 8530 Geren Rd MAUA Working Group Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Inventory and Monitoring Program National Park Service, 120 Chatham Lane Center for Conservation Education Sustainability Smithsonian Conservation Biology Institute, 1100 Jefferson Dr. SW, Suite 3123 Biologia Vegetal Universidade Estadual de Campinas, Caixa Postal 6109 Institute of Molecular Plant Sciences University of Edinburgh, Mayfield Rd Royal Botanic Garden Edinburgh, 20a Inverleith Row Programa de Pós-Graduação em Ecologia Instituto Nacional de Pesquisas da Amazônia - INPA, Av. André Araújo 2936 Forest Ecology and Forest Management Group Wageningen University Wageningen University and Research, Droevendaalsesteeg 3, P.O. Box 47 Natural Capital and Plant Health Royal Botanic Gardens Kew Ecosistemas Biodiversidad y Conservación de Especies Universidad Estatal Amazónica, Km. 2 1/2 vía a Tena (Paso Lateral) Museo de Historia Natural Noel Kempff Mercado Universidad Autónoma Gabriel Rene Moreno Santa Cruz, Avenida Irala 565 Casilla Postal 2489 Centro de Biociências Departamento de Botânica e Zoologia Universidade Federal Do Rio Grande Do Norte Campus Universitário Department of Biology University of Miami Coral Fairchild Tropical Botanic Garden Coral Instituto de Biociências Dept. Ecologia Universidade de Sao Paulo - USP, Rua do Matão, Trav. 14, no. 321 Diretoria de Pesquisas Científicas Instituto de Pesquisas Jardim Botânico Do Rio de Janeiro Escuela de Biología Herbario Alfredo Paredes Universidad Central, Ap. Postal 17.01.2177 International Center for Tropical Botany (ICTB) Department of Biological Sciences Florida International University, 11200 SW 8th Street, OE 243 Grupo de Ecología de Ecosistemas Terrestres Tropicales Universidad Nacional de Colombia Sede Amazonía Institute of Biological and Health Sciences Federal University of Alagoas, Av. Lourival Melo Mota, s/n Museu Universitário Centro de Ciências Biológicas e da Natureza Laboratório de Botânica e Ecologia Vegetal Universidade Federal Do Acre Universidad Regional Amazónica IKIAM, Km 7 via Muyuna Cirad UMR Ecofog AgrosParisTech CNRS INRA Univ Guyane Campus Agronomique Iwokrama International Programme for Rainforest Conservation Department of Biological Sciences Humboldt State University, 1 Harpst Street New York Botanical Garden, 2900 Southern Blvd Servicios de Biodiversidad EIRL, Jr. Independencia 405 Herbario Nacional de Bolivia Universitario UMSA, Casilla 10077 Correo Central Missouri Botanical Garden, P.O. Box 299 School of Environmental Sciences University of East Anglia Laboratoire Evolution et Diversité Biologique CNRS Université Paul Sabatier UMR 5174 EDB Department of Forestry Management Universidad Nacional Agraria la Molina, Avenido La Molina, Apdo. 456 School of Geosciences University of Edinburgh, 201 Crew Building, King's Buildings Biology Department Center for Energy Environment and Sustainability Wake Forest University, 1834 Wake Forest Rd Department of Anthropology University of Texas at Austin, SAC 5.150, 2201 Speedway Stop C3200 Andes to Amazon Biodiversity Program Madre de Dios Fundación Puerto Rastrojo, Cra 10 No. 24-76 Oficina 1201 Colegio de Ciencias Biológicas y Ambientales-COCIBA Galapagos Institute for the Arts and Sciences-GAIAS Universidad San Francisco de Quito-USFQ Department of Wildlife Ecology Conservation University of Florida, 110 Newins-Ziegler Hall Fundación Estación de Biología, Cra 10 No. 24-76 Oficina 1201 Embrapa Amazônia Oriental Trav. Dr. Enéas Pinheiro S/no Instituto de Ciencias Naturales Universidad Nacional de Colombia, Apartado, 7945 Amazon Conservation Team, Doekhieweg Oost #24 Facultad de Ciencias Forestales y Medio Ambiente Universidad Nacional de San Antonio Abad Del Cusco, Jirón San Martín 451 Laboratory of Human Ecology Instituto Venezolano de Investigaciones Científicas - IVIC, Ado 20632 Departement EV Muséum National d'Histoire Naturelle de Paris, 16 rue Buffon Instituto de Ciência Agrárias Universidade Federal Rural da Amazônia, Av. Presidente Tancredo Neves 2501 PROTERRA Instituto de Investigaciones de la Amazonía Peruana (IIAP), Av. A. Quiñones km 2 5 ACEER Foundation, Jirón Cusco No 370 Universidad Autónoma Del Beni José Ballivián Campus Universitario Final, Av. Ejercito Regional Norte Amazónico Centro de Investigación y Promoción Del Campesinado, C/Nicanor Gonzalo Salvatierra No 362 Environmental Change Institute Oxford University Centre for the Environment Dyson Perrins Building, South Parks Road School of Agriculture and Food Sciences ARC Centre of Excellence for Environmental Decisions CEED University of Queensland Instituto de Investigaciones para El Desarrollo Forestal (INDEFOR) Universidad de Los Andes, Conjunto Forestal, C.P. 5101 School of Environmental and Forest Sciences University of Washington University of Nottingham, University Park Geography and the Environment University of Texas at Austin, 305 E. 23rd Street, CLA building Laboratório de Ciências Ambientais Universidade Estadual Do Norte Fluminense, Av. Alberto Lamego 2000 Department of Biology University of Maryland GeoIS, El Día 369 y El Telégrafo, 3o Piso Environmental Science and Policy Northern Arizona University FOMABO Manejo Forestal en Las Tierras Tropicales de Bolivia Escuela de Ciencias Forestales (ESFOR) Universidad Mayor de San Simon (UMSS) Agricultural Services Ministry of Agro-Industry and Food Security Agricultural Services Ministry of Agro-Industry and Food Security Mauritius Herbarium Department of Bioscience Aarhus University, Building 1540 Ny Munkegade Ciencias Biológicas Universidad de Los Andes, Carrera 1 # 18a- 10 Medio Ambiente PLUSPRETOL Center for Conservation Education and Sustainability Smithsonian's National Zoo Conservation Biology Institute, National Zoological Park, 3001 Connecticut Ave Tropenbos International, Lawickse Allee 11 PO Box 232 Instituto de Biodiversidade e Floresta Universidade Federal Do Oeste Do Pará Campus Tapajós, Rua Vera Paz Department of Biology University of Missouri Facultad de Biologia Universidad Nacional de la Amazonia Peruana, Pevas 5ta cdra School of Anthropology and Conservation University of Kent, Marlowe Building Herbario Nacional Del Ecuador Universidad Técnica Del Norte Ecology and Biodiversity Group Utrecht University, Padualaan 8 Systems Ecology Free University, De Boelelaan 1087 Departamento de Ecologia Universidade Estadual Paulista - UNESP Instituto de Biociências - IB, Av. 24 A 1515 Departamento de Geografia Universidade Estadual Paulista - UNESP Instituto de Geociências e Ciências Extas - IGCe CAPES: 407232/2013-3-PVE CNPq: 407232/2013-3-PVE Ministério da Ciência, Tecnologia e Inovação: 407232/2013-3-PVE Ministério da Educação: 407232/2013-3-PVE

Aim Tropical elevation gradients are natural laboratories to assess how changing climate can influence tropical forests. However, there is a need for theory and integrated data collection to scale from traits to ecosystems. We assess predictions of a novel trait-based scaling theory, including whether observed shifts in forest traits across a broad tropical temperature gradient are consistent with local phenotypic optima and adaptive compensation for temperature. Location An elevation gradient spanning 3,300 m and consisting of thousands of tropical tree trait measures taken from 16 1-ha tropical forest plots in southern Peru, where gross and net primary productivity (GPP and NPP) were measured. Time period April to November 2013. Major taxa studied Plants; tropical trees. Methods We developed theory to scale from traits to communities and ecosystems and tested several predictions. We assessed the covariation between climate, traits, biomass and GPP and NPP. We measured multiple traits linked to variation in tree growth and assessed their frequency distributions within and across the elevation gradient. We paired these trait measures across individuals within 16 forests with simultaneous measures of ecosystem net and gross primary productivity. Results Consistent with theory, variation in forest NPP and GPP primarily scaled with forest biomass, but the secondary effect of temperature on productivity was much less than expected. This weak temperature dependence appears to reflect directional shifts in several mean community traits that underlie tree growth with decreases in site temperature. Main conclusions The observed shift in traits of trees that dominate in more cold environments is consistent with an ‘adaptive/acclimatory’ compensation for the kinetic effects of temperature on leaf photosynthesis and tree growth. Forest trait distributions across the gradient showed overly peaked and skewed distributions, consistent with the importance of local filtering of optimal growth traits and recent shifts in species composition and dominance attributable to warming from climate change. Trait-based scaling theory provides a basis to predict how shifts in climate have and will influence the trait composition and ecosystem functioning of tropical forests.

Solar irradiance and precipitation are the most likely drivers of the seasonal variation of net primary productivity (NPP) in tropical forests. Since their roles remain poorly understood, we use litter traps, dendrometer bands and census data collected from one hectare permanent plots to quantify the seasonality of above-groundNPPcomponents and weather parameters in 13 sites distributed along a 2800-m altitudinal gradient ranging from lowland Amazonia to the high Andes. We combine canopy leaf area index and litterfall data to describe the seasonality of canopy production. We hypothesize that solar irradiance is the primary driver of canopy phenology in wetter sites, whereas precipitation drives phenology in drier systems. The seasonal rhythm of canopyNPPcomponents is in synchrony with solar irradiance at all altitudes. Leaf litterfall peaks in the late dry season, both in lowland (averaging 0.54 ± 0.08 Mg C ha y−1, n = 5) and montane forests (averaging 0.29 ± 0.04 Mg C ha y−1, n = 8). Peaks in above-ground coarse woodyNPPappears to be triggered by the onset of rainfall in seasonal lowland rain forests (averaging 0.26 ± 0.04 Mg C ha y−1, n = 5, in November), but not in montane cloud forests.

Global climate models suggest enhanced warming of the tropical mid and upper troposphere, with larger temperature rise rates at higher elevations. Changes in fire activity are amongst the most significant ecological consequences of rising temperatures and changing hydrological properties in mountainous ecosystems, and there is a global evidence of increased fire activity with elevation. Whilst fire research has become popular in the tropical lowlands, much less is known of the tropical high Andean region (2000 masl, from Colombia to Bolivia). This study examines fire trends in the high Andes for three ecosystems, the Puna, the Paramo and the Yungas, for the period 1982-2006. We pose three questions: (i) is there an increased fire response with elevation? (ii) does the El Niño- Southern Oscillation control fire activity in this region? (iii) are the observed fire trends human driven (e.g., human practices and their effects on fuel build-up) or climate driven? We did not find evidence of increased fire activity with elevation but, instead, a quasicyclic and synchronous fire response in Ecuador, Peru and Bolivia, suggesting the influence of high-frequency climate forcing on fire responses on a subcontinental scale, in the high Andes. ENSO variability did not show a significant relation to fire activity for these three countries, partly because ENSO variability did not significantly relate to precipitation extremes, although it strongly did to temperature extremes. Whilst ENSO did not individually lead the observed regional fire trends, our results suggest a climate influence on fire activity, mainly through a sawtooth pattern of precipitation (increased rainfall before fire-peak seasons (t-1) followed by drought spells and unusual low temperatures (t0), which is particularly common where fire is carried by low fuel loads (e.g., grasslands and fine fuel). This climatic sawtooth appeared as the main driver of fire trends, above local human influences and fuel build-up cyclicity.

One of the major challenges in ecology is to understand how ecosystems respond to changes inenvironmental conditions, and how taxonomic and functional diversity mediate these changes. Inthis study, we use a trait-spectra and individual-based model, to analyse variation in forest primaryproductivity along a 3.3 km elevation gradient in the Amazon-Andes. The model accuratelypredicted the magnitude and trends in forest productivity with elevation, with solar radiation andplant functional traits (leaf dry mass per area, leaf nitrogen and phosphorus concentration, andwood density) collectively accounting for productivity variation. Remarkably, explicit representationof temperature variation with elevation was not required to achieve accurate predictions offorest productivity, as trait variation driven by species turnover appears to capture the effect oftemperature. Our semi-mechanistic model suggests that spatial variation in traits can potentiallybe used to estimate spatial variation in productivity at the landscape scale. Fil: Fyllas, Nikolaos M.. University of Oxford; Reino Unido Fil: Patrick Bentley, Lisa. University of Oxford; Reino Unido Fil: Shenkin, Alexander. University of Oxford; Reino Unido Fil: Asner, Gregory P.. Carnegie Institution for Science. Department of Global Ecology; Reino Unido Fil: Atkin, Owen K.. The Australian National University. ARC Centre of Excellence in Plant Energy Biology. Research School of Biology; Australia Fil: Díaz, Sandra Myrna. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina Fil: Enquist, Brian J.. Arizona State University; Estados Unidos Fil: Farfan Rios, William. University Wake Forest; Estados Unidos Fil: Gloor, Emanuel. University of Leeds; Reino Unido Fil: Guerrieri, Rossella. Universitat Autònoma de Barcelona; España. University of Edinburgh; Reino Unido Fil: Huaraca Huasco, Walter. Universidad Nacional de San Antonio Abad del Cusco; Perú Fil: Ishida, Yoko. James Cook University; Australia Fil: Martin, Roberta E.. Carnegie Institution for Science. Department of Global Ecology; Estados Unidos Fil: Meir, Patrick. University of Edinburgh; Reino Unido. The Australian National University. Research School of Biology. Division of Plant Sciences; Australia Fil: Phillips, Oliver. University of Leeds; Reino Unido Fil: Salinas, Norma. University of Oxford; Reino Unido. Pontificia Universidad Católica de Perú; Perú Fil: Silman, Miles. University Wake Forest; Estados Unidos Fil: Weerasinghe, Lasantha K.. The Australian National University. Research School of Biology. Division of Plant Sciences; Australia Fil: Zaragoza Castells, Joana. The Australian National University. Research School of Biology. Division of Plant Sciences; Australia. University of Exeter; Reino Unido Fil: Malhi, Yadvinder. University of Oxford; Reino Unido

Unmanned aerial vehicles (UAVs) offer new opportunities to monitor pollution and provide valuable information to support remediation. Their low-cost, ease of use, and rapid deployment capability make them ideal for environmental emergency response. Here we present a UAV-based study of the third largest coal ash spill in the United States. Coal ash from coal combustion is a toxic industrial waste material present worldwide. Typically stored in settling ponds in close proximity to waterways, coal ash poses significant risk to the environment and drinking water supplies from both chronic contamination of surface and ground water and catastrophic pond failure. We sought to provide an independent estimate of the volume of coal ash and contaminated water lost during the rupture of the primary coal ash pond at the Dan River Steam Station in Eden, NC, USA and to demonstrate the feasibility of using UAVs to rapidly respond to and measure the volume of spills from ponds or containers that are open to the air. Using structure-from-motion (SfM) imagery analysis techniques, we reconstructed the 3D structure of the pond bottom after the spill, used historical imagery to estimate the pre-spill waterline, and calculated the volume of material lost. We estimated a loss of 66,245 ± 5678 m3 of ash and contaminated water. The technique used here allows rapid response to environmental emergencies and quantification of their impacts at low cost, and these capabilities will make UAVs a central tool in environmental planning, monitoring, and disaster response.

Tropical forests are threatened by many human disturbances – two of the most important of which are deforestation and climate change. To mitigate the impacts of these disturbances, it is important to understand their potential effects on the distributions of species. In the tropics, such understanding has been hindered by poor knowledge of the current distributions and range limits of most species. Here, we use herbarium collection records to model the current and future distributions of ca. 3000 Amazonian plant species. We project these distributions into the future under a range of different scenarios related to the magnitude of climate change and extent of deforestation as well as the response of species to changes in temperature, precipitation, and atmospheric concentrations of CO2 . We find that the future of Amazonian diversity will be dependant primarily on the ability of species to tolerate or adapt to rising temperatures. If the thermal niches of tropical plant species are fixed and incapable of expanding under rapid warming, then the negative effects of climate change will overshadow the effects of deforestation, greatly reducing the area of suitable habitat available to most species and potentially leading to massive losses of biodiversity throughout the Amazon. If tropical species are generally capable of tolerating warmer temperatures, rates of habitat loss will be greatly reduced but many parts of Amazonia may still experience rapid losses of diversity, with the effects of enhanced seasonal water stress being similar in magnitude to the effects of deforestation.

Aim Species distribution models (SDMs) use the locations of collection records to map the distributions of species, making them a powerful tool in conservation biology, ecology and biogeography. However, the accuracy of range predictions may be reduced by temporally autocorrelated biases in the data. We assess the accuracy of SDMs in predicting the ranges of tropical plant species on the basis of different sample sizes while incorporating real-world collection patterns and biases. Location Tropical South American moist forests. Methods We use dated herbarium records to model the distributions of 65 Amazonian and Andean plant species. For each species, we use the first 25, 50, 100, 125 and 150 records collected and available for each species to analyse changes in spatial aggregation and climatic representativeness through time. We compare the accuracy of SDM range estimates produced using the time-ordered data subsets to the accuracy of range estimates generated using the same number of collections but randomly subsampled from all available records. Results We find that collections become increasingly aggregated through time but that additional collecting sites are added resulting in progressively better representations of the species’ full climatic niches. The range predictions produced using time-ordered data subsets are less accurate than predictions from random subsets of equal sample sizes. Range predictions produced using time-ordered data subsets consistently underestimate the extent of ranges while no such tendency exists for range predictions produced using random data subsets. Main conclusions These results suggest that larger sample sizes are required to accurately map species ranges. Additional attention should be given to increasing the number of records available per species through continued collecting, better distributed collecting, and/or increasing access to existing collections. The fact that SDMs generally under-predict the extent of species ranges means that extinction risks of species because of future habitat loss may be lower than previously estimated.

Fire in tropical montane cloud forests (TMCFs) is not as rare as once believed. Andean TMCFs sit immediately below highly flammable, high-altitude grasslands (Puna/Paramo) that suffer from recurrent anthropogenic fire. This treeline is a zone of climatic tension where substantial future warming is likely to force upward tree migrations, while increased fire presence and fire impacts are likely to force it downwards. TMCFs contain large carbon stocks in their peat soils and their loss through fire is a currently unaccounted for regional source of CO2. This study, conducted in the southern Peruvian Andes (>2800 m), documents differences in live tree biomass, fine root biomass, fallen and standing dead wood, and soil organic carbon in 4 paired-sample plots (burned versus control) following the severe ground fires that occurred during the 2005 Andean drought. Peat soils contributed the most to biomass burning emissions, with lower values corresponding to an 89% mean stock difference compared to the controls (mean ± SE) (54.1 ± 22.3 vs. 5.8 ± 5.3 MgC ha−1). Contrastingly, carbon stocks from live standing trees differed by a non-significant 37% lower value in the burned plots compared to the controls, largely compensated by vigorous resprouting (45.5 ± 17.4 vs. 69.2 ± 13.4 MgC ha−1). Both standing dead trees and fallen dead wood were significantly higher in the burned plots with a three-fold difference from the controls: dead Trees 45.2 ± 9.4 vs. 16.4 ± 4.4 MgC ha−1, and ca. a 2 fold difference for the fallen dead wood: 11.2 ± 5 vs. 6.7 ± 3.2 MgC ha−1 for the burned plots versus their controls. A preliminary estimate of the regional contribution of biomass burning emissions from Andean TMCFs for the period 2000–2008, resulted in mean carbon emission rates of 1.3 TgC yr−1 (max-min: 1.8–0.8 TgC yr−1). This value is in the same order of magnitude than South American annual fire emissions (300 TgC yr−1) suggesting the need for further research on Andean forest fires. On-going projects on the region are working on the promotion of landowner participation in TMCFs conservation through REDD+ mechanism. The heart of the proposed initiative is reforestation of degraded lands with green fire breaks enriched with economically valuable Andean plant species. The cultivation of these species may contribute to reduce deforestation pressure on the Amazonian cloud forest by providing an alternative income to local communities, at the same time that they prevent the spread of fire into Manu National Park and adjacent community-held forests, protecting forest and reducing CO2 emissions.

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca . ±5% and ca . ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

Improved management of carbon storage by terrestrial biomes has significant value for mitigating climate change. The carbon value of such management has the potential to provide additional income to rural communities and provide biodiversity and climate adaptation co-benefits. Here, we quantify the carbon stores in a 49,300-ha landscape centered on the cloud forest–grassland transition of the high Andes in Manu National Park, Peru. Aboveground carbon densities were measured across the landscape by field sampling of 70 sites above and below the treeline. The forest near the treeline contained 63.4 ± 5.2 Mg C ha−1 aboveground, with an additional 13.9 ± 2.8 Mg C ha−1 estimated to be stored in the coarse roots, using a root to shoot ratio of 0.26. Puna grasslands near the treeline were found to store 7.5 ± 0.7 Mg C ha−1 in aboveground biomass. Comparing our result to soil data gathered by Zimmermann and others (Ecosystems 13:62–74, 2010), we found the ratio of belowground:aboveground carbon decreased from 15.8 on the puna to 8.6 in the transition zone and 2.1 in the forest. No significant relationships were found between carbon densities and slope, altitude or fire disturbance history, though grazing (for puna) was found to reduce aboveground carbon densities significantly. We scaled our study sites to the study region with remote sensing observations from Landsat. The carbon sequestration potential of improved grazing management and assisted upslope treeline migration was also estimated. Afforestation of puna at the treeline could generate revenues of US $1,374 per ha over the project lifetime via commercialization of the carbon credits from gains in aboveground carbon stocks. Uncertainties in the fate of the large soil carbon stocks under an afforestation scenario exist.

The net primary productivity, carbon (C) stocks and turnover rates (i.e. C dynamics) of tropical forests are an important aspect of the global C cycle. These variables have been investigated in lowland tropical forests, but they have rarely been studied in tropical montane forests (TMFs). This study examines spatial patterns of above- and belowground C dynamics along a transect ranging from lowland Amazonia to the high Andes in SE Peru. Fine root biomass values increased from 1.50 Mg C ha−1 at 194 m to 4.95 ± 0.62 Mg C ha−1 at 3020 m, reaching a maximum of 6.83 ± 1.13 Mg C ha−1 at the 2020 m elevation site. Aboveground biomass values decreased from 123.50 Mg C ha−1 at 194 m to 47.03 Mg C ha−1 at 3020 m. Mean annual belowground productivity was highest in the most fertile lowland plots (7.40 ± 1.00 Mg C ha−1 yr−1) and ranged between 3.43 ± 0.73 and 1.48 ± 0.40 Mg C ha−1 yr−1 in the premontane and montane plots. Mean annual aboveground productivity was estimated to vary between 9.50 ± 1.08 Mg C ha−1 yr−1 (210 m) and 2.59 ± 0.40 Mg C ha−1 yr−1 (2020 m), with consistently lower values observed in the cloud immersion zone of the montane forest. Fine root C residence time increased from 0.31 years in lowland Amazonia to 3.78 ± 0.81 years at 3020 m and stem C residence time remained constant along the elevational transect, with a mean of 54 ± 4 years. The ratio of fine root biomass to stem biomass increased significantly with increasing elevation, whereas the allocation of net primary productivity above- and belowground remained approximately constant at all elevations. Although net primary productivity declined in the TMF, the partitioning of productivity between the ecosystem subcomponents remained the same in lowland, premontane and montane forests.

Aim Species distribution models are a potentially powerful tool for predicting the effects of global change on species distributions and the resulting extinction risks. Distribution models rely on relationships between species occurrences and climate and may thus be highly sensitive to georeferencing errors in collection records. Most errors will not be caught using standard data filters. Here we assess the impacts of georeferencing errors and the importance of improved data filtering for estimates of the elevational distributions, habitat areas and predicted relative extinction risks due to climate change of nearly 1000 Neotropical plant species. Location The Amazon basin and tropical Andes, South America. Methods We model the elevational distributions, or ‘envelopes’, of 932 Amazonian and Andean plant species from 35 families after performing standard datafiltering,andagainusingonlydatathathavepassedthroughanadditionallayer ofdatafiltering.Wetestforagreementintheelevationsrecordedwiththecollection and the elevation inferred from a digital elevation model (DEM) at the collection coordinates. From each dataset we estimate species range areas and extinction risks due to the changes in habitat area caused by a 4.5 � C increase in temperature. Results Amazonian and Andean plant species have a median elevational range of 717 m. Using only standard data filters inflates range limits by a median of 433 m (55%). This is equivalent to overestimating the temperature tolerances of species by over 3 � C ‐ only slightly less than the entire regional temperature change predicted over the next 50‐100 years. Georeferencing errors tend to cause overestimates in the amount of climatically suitable habitat available to species and underestimates in species extinction risks due to global warming. Georeferencing error artefacts are sometimes so great that accurately predicting whether species habitat areas will decrease or increase under global warming is impossible. The drawback of additional data filtering is large decreases in the number of species modelled, with Andean species being disproportionately eliminated. Main conclusions Even with rigorous data filters, distribution models will mischaracterize the climatic conditions under which species occur due to errors in the collection data. These errors affect predictions of the effects of climate change on species ranges and biodiversity, and are particularly problematic in mountainous areas. Additional data filtering reduces georeferencing errors but eliminates many species due to a lack of sufficient ‘clean’ data, thereby limiting our ability to predict the effects of climate change in many ecologically important and sensitive regions such as the Andes Biodiversity Hotspot.

Reliable soil organic carbon (SOC) stock measurements of all major ecosystems are essential for predicting the influence of global warming on global soil carbon pools, but hardly any detailed soil survey data are available for tropical montane cloud forests (TMCF) and adjacent high elevation grasslands above (puna). TMCF are among the most threatened of ecosystems under current predicted global warming scenarios. We conducted an intensive soil sampling campaign extending 40 km along the tree line in the Peruvian Andes between 2994 and 3860 m asl to quantify SOC stocks of TMCF, puna grassland, and shrubland sites in the transition zone between the two habitats. SOC stocks from the soil surface down to the bedrock averaged (±standard error SE) 11.8 (±1.5, N = 24) kg C/m2 in TMCF, 14.7 (±1.4, N = 9) kg C/m2 in the shrublands and 11.9 (±0.8, N = 35) kg C/m2 in the grasslands and were not significantly different (P > 0.05 for all comparisons). However, soil profile analysis revealed distinct differences, with TMCF profiles showing a uniform SOC distribution with depth, shrublands a linear decrease, and puna sites an exponential decrease in SOC densities with soil depth. Organic soil layer thickness reached a maximum (~70 cm) at the upper limit of the TMCF and declined with increasing altitude toward puna sites. Within TMCF, no significant increase in SOC stocks with increasing altitude was observed, probably because of the large variations among SOC stocks at different sites, which in turn were correlated with spatial variation in soil depth.

Background: Dead biomass, including woody debris (WD), is an important component of the carbon cycle in tropical forests. Aims: This study analyses WD (>2 cm) and other above-ground fluxes in mature tropical forest plots along an elevational gradient (210–3025 m above sea level) in southern Peru. Methods: This work was based on inventories of fine and coarse WD (FWD and CWD, respectively), above-ground biomass, and field-based and experimental respiration measurements. Results: Total WD stocks ranged from 6.26 Mg C ha−1 at 3025 m to 11.48 Mg C ha−1 at 2720 m. WD respiration was significantly correlated with moisture content (P < 0.001; R 2 = 0.25), temperature (P < 0.001; R 2 = 0.12) and wood density (P < 0.001; R 2 = 0.16). Controlled experiments showed that both water content and temperature increased respiration rates of individual WD samples. The full breadth of the temperature sensitivity coefficient, or Q 10, estimates, ranging from 1.14–2.13, was low compared to other studies. In addition, temperat...

Background: Tropical montane cloud forests (TMCF) are unique ecosystems with high biodiversity and large carbon reservoirs. To date there have been limited descriptions of the carbon cycle of TMCF. Aims: We present results on the production, allocation and cycling of carbon for two mid-elevation (1500–1750 m) tropical montane cloud forest plots in San Pedro, Kosnipata Valley, Peru. Methods: We repeatedly recorded the components of net primary productivity (NPP) using biometric measurements, and autotrophic (R a) and heterotrophic (R h) respiration, using gas exchange measurements. From these we estimated gross primary productivity (GPP) and carbon use efficiency (CUE) at the plot level. Results: The plot at 1500 m was found very productive, with our results comparable with the most productive lowland Amazonian forests. The plot at 1750 m had significantly lower productivity, possibly because of greater cloud immersion. Both plots had similar patterns of NPP allocation, a substantial seasonality in NPP com...

Dispersal affects community dynamics and vegetation response to global change. Understanding these effects requires descriptions of dispersal at local and regional scales and statistical models that permit estimation. Classical models of dispersal describe local or long-distance dispersal, but not both. The lack of statistical methods means that models have rarely been fitted to seed dispersal in closed forests. We present a mixture model of dispersal that assumes a range of disperal patterns, both local and long distance. The bivariate Student’s t or “2Dt” follows from an assumption that the distance parameter in a Gaussian model varies randomly, thus having a density of its own. We use an inverse approach to “compete” our mixture model against classical alternatives, using seed rain databases from temperate broadleaf, temperate mixed-conifer, and tropical floodplain forests. For most species, the 2Dt model fits dispersal data better than do classical models. The superior fit results from the potential f...

Unmanned aerial vehicles (UAVs) can provide new ways to measure forests and supplement expensive or labor-intensive inventory methods. Forest carbon, a key uncertainty in the global carbon cycle and also important for carbon conservation programs, is typically monitored using manned aircraft or extensive forest plot networks to estimate aboveground carbon density (ACD). Manned aircraft are only cost-effective when applied to large areas (>100,000 ha), while plot networks are most effective for total C stock estimation across large areas, not for quantifying spatially-explicit variation. We sought to develop an effective method for frequent and accurate ACD estimation at intermediate scales (100–100,000 ha) that would be sensitive to small-scale disturbance. Using small UAVs, we collected imagery of 516 ha of lowland forest in the Peruvian Amazon. We then used a structure-from-motion (SFM) approach to create a 3D model of forest canopy. Comparing SFM- and airborne Light Detection and Ranging (LiDAR)-derived estimates of canopy height and ACD, we found that SFM estimates of top-of-canopy height (TCH) and ACD were highly correlated with previous LiDAR estimates (r = 0.86–0.93 and r = 0.73–0.94 for TCH and ACD, respectively, at 0.1–4 ha grain sizes), with r = 0.92 for ACD determination at the 1 ha scale, despite SFM and LiDAR measurements being separated by two years in a dynamic forest. SFM and LiDAR estimates of mean TCH and mean ACD were highly similar, differing by only 0.4% and 0.04%, respectively, within mature forest. The technique allows inexpensive, near-real-time monitoring of ACD for ecological studies, payment for ecosystem services (PES) ventures, such as reducing emissions from deforestation and forest degradation (REDD+), forestry enterprises, and governance.

A continuous 48,000-year-long paleoecological record from Neotropical lower montane forest reveals a consistent forest presence and an ice-age cooling of approximately 5 degrees to 9 degrees C. After 30,000 years of compositional stability, a steady turnover of species marks the 8000-year-long transition from ice-age to Holocene conditions. Although the changes were directional, the rates of community change were no different during this transitional period than in the preceding 30,000-year period of community stability. The warming rate of about 1 degrees C per millennium during the Pleistocene-Holocene transition was an order of magnitude less than the projected changes for the 21st century.