The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo

Forest degradation through logging is pervasive throughout the world's tropical forests, leading to changes in the three-dimensional canopy structure that have profound consequences for wildlife, microclimate and ecosystem functioning. Quantifying these structural changes is fundamental to understanding the impact of degradation, but is challenging in dense, structurally complex forest canopies. We exploited discrete-return airborne LiDAR surveys across a gradient of logging intensity in Sabah, Malaysian Borneo, and assessed how selective logging had affected canopy structure (Plant Area Index, PAI, and its vertical distribution within the canopy). LiDAR products compared well to independent, analogue models of canopy structure produced from detailed ground-based inventories undertaken in forest plots, demonstrating the potential for airborne LiDAR to quantify the structural impacts of forest degradation at landscape scale, even in some of the world's tallest and most structurally complex tropical forests. Plant Area Index estimates across the plot network exhibited a strong linear relationship with stem basal area (R2 = 0.95). After at least 11–14 years of recovery, PAI was ~28% lower in moderately logged plots and ~52% lower in heavily logged plots than that in old-growth forest plots. These reductions in PAI were associated with near-complete lack of trees >30-m tall, which had not been fully compensated for by increasing plant area lower in the canopy. This structural change drives a marked reduction in the diversity of canopy environments, with the deep, dark understorey conditions characteristic of old-growth forests far less prevalent in logged sites. Full canopy recovery is likely to take decades. Synthesis and applications. Effective management and restoration of tropical forests requires detailed monitoring of the forest and its environment. We demonstrate that airborne LiDAR can effectively map the canopy architecture of the complex tropical forests of Borneo, capturing the three-dimensional impact of degradation on canopy structure at landscape scales, therefore facilitating efforts to restore and conserve these ecosystems

Classifying organisms and artefacts by their outline shapes

We often wish to classify objects by their shapes. Indeed, the study of shapes is an important part of many scientific fields, such as evolutionary biology, structural biology, image processing and archaeology. However, mathematical shape spaces are rather complicated and nonlinear. The most widely used methods of shape analysis, geometric morphometrics, treat the shapes as sets of points. Diffeomorphic methods consider the underlying curve rather than points, but have rarely been applied to real-world problems. Using a machine classifier, we tested the ability of several of these methods to describe and classify the shapes of a variety of organic and man-made objects. We find that one method, based on square-root velocity functions (SRVFs), outperforms all others, including a standard geometric morphometric method (eigenshapes), and that it is also superior to human experts using shape alone. When the SRVF approach is constrained to take account of homologous landmarks it can accurately classify objects of very different shapes. The SRVF method identifies a shortest path between shapes, and we show that this can be used to estimate the shapes of intermediate steps in evolutionary series. Diffeomorphic shape analysis methods, we conclude, now provide practical and effective solutions to many shape description and classification problems in the natural and human sciences.</p

Extreme and Highly Heterogeneous Microclimates in Selectively Logged Tropical Forests

Microclimate within forests influences ecosystem fluxes and demographic rates. Anthropogenic disturbances, such as selective logging can affect within-forest microclimate through effects on forest structure, leading to indirect effects on forests beyond the immediate impact of logging. However, the scope and predictability of these effects remains poorly understood. Here we use a microclimate thermal proxy (sensitive to radiative, convective, and conductive heat fluxes) measured at the forest floor in three 1-ha forest plots spanning a logging intensity gradient in Malaysian Borneo. We show (1) that thermal proxy ranges and spatiotemporal heterogeneity are doubled between old growth and heavily logged forests, with extremes often exceeding 45°C, (2) that nearby weather station air temperatures provide estimates of maximum thermal proxy values that are biased down by 5–10°C, and (3) that lower canopy density, higher canopy height, and higher biomass removal are associated with higher maximum temperatures. Thus, logged forests are less buffered from regional climate change than old growth forests, and experience much higher microclimate extremes and heterogeneity. Better predicting the linkages between regional climate and its effects on within-forest microclimate will be critical for understanding the wide range of conditions experienced within tropical forests

Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning

Borneo contains some of the world's most biodiverse and carbon-dense tropical forest, but this 750 000 km(2) island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remaining forests of Borneo hinge on recognizing the ecosystem services they provide, including their ability to store and sequester carbon. Airborne laser scanning (ALS) is a remote sensing technology that allows forest structural properties to be captured in great detail across vast geographic areas. In recent years ALS has been integrated into statewide assessments of forest carbon in Neotropical and African regions, but not yet in Asia. For this to happen new regional models need to be developed for estimating carbon stocks from ALS in tropical Asia, as the forests of this region are structurally and composition-ally distinct from those found elsewhere in the tropics. By combining ALS imagery with data from 173 permanent forest plots spanning the lowland rainforests of Sabah on the island of Borneo, we develop a simple yet general model for estimating forest carbon stocks using ALS-derived canopy height and canopy cover as input metrics. An advanced feature of this new model is the propagation of uncertainty in both ALS- and ground-based data, allowing uncertainty in hectare-scale estimates of carbon stocks to be quantified robustly. We show that the model effectively captures variation in aboveground carbon stocks across extreme disturbance gradients spanning tall dipterocarp forests and heavily logged regions and clearly outperforms existing ALS-based models calibrated for the tropics, as well as currently available satellite-derived products. Our model provides a simple, generalized and effective approach for mapping forest carbon stocks in Borneo and underpins ongoing efforts to safeguard and facilitate the restoration of its unique tropical forests.Peer reviewe

Fine root dynamics across pantropical rainforest ecosystems

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.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

Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

Funding Information: This work is a product of the Global Ecosystems Monitoring (GEM) network (gem.tropicalforests.ox.ac.uk). J.A.G. was funded by the Natural Environment Research Council (NERC; NE/T011084/1 and NE/S011811/1) and the Netherlands Organisation for Scientific Research (NWO) under the Rubicon programme with project number 019.162LW.010. The traits field campaign was funded by a grant to Y.M. from the European Research Council (Advanced Grant GEM-TRAIT: 321131) under the European Union‘s Seventh Framework Programme (FP7/2007-2013), with additional support from NERC Grant NE/D014174/1 and NE/J022616/1 for traits work in Peru, NERC Grant ECOFOR (NE/K016385/1) for traits work in Santarem, NERC Grant BALI (NE/K016369/1) for plot and traits work in Malaysia and ERC Advanced Grant T-FORCES (291585) to Phillips for traits work in Australia. Plot setup in Ghana and Gabon were funded by a NERC Grant NE/I014705/1 and by the Royal Society-Leverhulme Africa Capacity Building Programme. The Malaysia campaign was also funded by NERC GrantNE/K016253/1. Plot inventories in Peru 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. Plots inventories in Nova Xavantina (Brazil) were supported by the National Council for Scientific and Technological Development (CNPq), Long Term Ecological Research Program (PELD), Proc. 441244/2016-5, and the Foundation of Research Support of Mato Grosso (FAPEMAT), Project ReFlor, Proc. 589267/2016. During data collection, I.O. was supported by a Marie Curie Fellowship (FP7-PEOPLE-2012-IEF-327990). GEM trait data in Gabon was collected under authorisation to Y.M. and supported by the Gabon National Parks Agency. D.B. was funded by the Fondation Wiener-Anspach. W.D.K. acknowledges support from the Faculty Research Cluster ‘Global Ecology’ of the University of Amsterdam. M.S. was funded by a grant from the Ministry of Education, Youth and Sports of the Czech Republic (INTER-TRANSFER LTT19018). Y.M. is supported by the Jackson Foundation. We thank the two anonymous reviewers and Associate Editor G. Henebry for their insightful comments that helped improved this manuscript.Peer reviewedPostprin

Year in review in Intensive Care Medicine 2009: I. Pneumonia and infections, sepsis, outcome, acute renal failure and acid base, nutrition and glycaemic control

Journal ArticleReviewSCOPUS: re.jinfo:eu-repo/semantics/publishe

Fine root dynamics across pantropical rainforest ecosystems

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than above-ground 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, 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.Output Status: Forthcoming/Available Online Additional co-authors: Christopher E. Doughty, Imma Oliveras, Darcy F. Galiano Cabrera, Liliana Durand Baca, Filio Farfán Amézquita, Javier E. Silva Espejo, Antonio C.L. da Costa, Erick Oblitas Mendoza, Carlos Alberto Quesada, Fidele Evouna Ondo, Josué Edzang Ndong, Vianet Mihindou, Natacha N’ssi Bengone, Forzia Ibrahim, Shalom D. Addo-Danso, Akwasi Duah-Gyamfi, Gloria Djaney Djagbletey, Kennedy Owusu-Afriyie, Lucy Amissah, Armel T. Mbou, Toby R. Marthews, Daniel B. Metcalfe, Luiz E.O. Aragão, Ben H. Marimon-Junior, Beatriz S. Marimon, Noreen Majalap, Stephen Adu-Bredu, Miles Silman, Robert M. Ewers, Patrick Meir, Yadvinder Malh

Pantropical modelling of canopy functional traits using Sentinel-2 remote sensing data

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