Height-diameter allometry of tropical forest trees

Tropical tree height-diameter (H:D) relationships may vary by forest type and region making large-scale estimates of above-ground biomass subject to bias if they ignore these differences in stem allometry. We have therefore developed a new global tropical forest database consisting of 39 955 concurrent H and D measurements encompassing 283 sites in 22 tropical countries. Utilising this database, our objectives were: 1. to determine if H:D relationships differ by geographic region and forest type (wet to dry forests, including zones of tension where forest and savanna overlap). 2. to ascertain if the H:D relationship is modulated by climate and/or forest structural characteristics (e.g. stand-level basal area, A). 3. to develop H:D allometric equations and evaluate biases to reduce error in future local-to-global estimates of tropical forest biomass. Annual precipitation coefficient of variation (PV), dry season length (SD), and mean annual air temperature (TA) emerged as key drivers of variation in H:D relationships at the pantropical and region scales. Vegetation structure also played a role with trees in forests of a high A being, on average, taller at any given D. After the effects of environment and forest structure are taken into account, two main regional groups can be identified. Forests in Asia, Africa and the Guyana Shield all have, on average, similar H:D relationships, but with trees in the forests of much of the Amazon Basin and tropical Australia typically being shorter at any given D than their counterparts elsewhere. The region-environment-structure model with the lowest Akaike\u27s information criterion and lowest deviation estimated stand-level H across all plots to within amedian −2.7 to 0.9% of the true value. Some of the plot-to-plot variability in H:D relationships not accounted for by this model could be attributed to variations in soil physical conditions. Other things being equal, trees tend to be more slender in the absence of soil physical constraints, especially at smaller D. Pantropical and continental-level models provided less robust estimates of H, especially when the roles of climate and stand structure in modulating H:D allometry were not simultaneously taken into account

Abiotic controls on macroscale variations of humid tropical forest height

Spatial variation of tropical forest tree height is a key indicator of ecological processes associated with forest growth and carbon dynamics. Here we examine the macroscale variations of tree height of humid tropical forests across three continents and quantify the climate and edaphic controls on these variations. Forest tree heights are systematically sampled across global humid tropical forests with more than 2.5 million measurements from Geoscience Laser Altimeter System (GLAS) satellite observations (2004–2008). We used top canopy height (TCH) of GLAS footprints to grid the statistical mean and variance and the 90 percentile height of samples at 0.5 degrees to capture the regional variability of average and large trees globally. We used the spatial regression method (spatial eigenvector mapping-SEVM) to evaluate the contributions of climate, soil and topography in explaining and predicting the regional variations of forest height. Statistical models suggest that climate, soil, topography, and spatial contextual information together can explain more than 60% of the observed forest height variation, while climate and soil jointly explain 30% of the height variations. Soil basics, including physical compositions such as clay and sand contents, chemical properties such as PH values and cation-exchange capacity, as well as biological variables such as the depth of organic matter, all present independent but statistically significant relationships to forest height across three continents. We found significant relations between the precipitation and tree height with shorter trees on the average in areas of higher annual water stress, and large trees occurring in areas with low stress and higher annual precipitation but with significant differences across the continents. Our results confirm other landscape and regional studies by showing that soil fertility, topography and climate may jointly control a significant variation of forest height and influencing patterns of aboveground biomass stocks and dynamics. Other factors such as biotic and disturbance regimes, not included in this study, may have less influence on regional variations but strongly mediate landscape and small-scale forest structure and dynamics.The research was funded by Gabon National Park (ANPN) under the contract of 011-ANPN/2012/SE-LJTW at UCLA. We thank IIASA, FAO, USGS, NASA, Worldclim science teams for making their data available. (011-ANPN/2012/SE-LJTW - Gabon National Park (ANPN) at UCLA

Leaf, tree and soil properties in a Eucalyptus saligna forest exhibiting canopy decline

The extent of eucalypt decline in moist coastal forests of south-eastern Australia is increasing with resultant losses in biodiversity and productivity. This survey aimed to identify factors associated with the decline of Eucalyptus saligna (Sydney Blue Gum) in Cumberland State Forest, a moist sclerophyll forest within urban Sydney. Eucalyptus saligna was the dominant overstorey species in six 20 m radius plots, which differed in floristic composition, structure and crown condition. One plot was colonised by bell miners (Manorina melanophrys). A range of leaf, tree and plot scale parameters were assessed including insect damage and free amino acid content, visual crown condition, floristics and soil chemistry. The plot permanently colonised by bell miners also had Eucalyptus saligna trees in the poorest condition. Both the weed Lantana camara and the soil pathogen Phytophthora cinnamomi were present in some of the plots but neither was strongly consistent with the severity of crown decline. There were, however, significant correlations among the foliar traits of insect damage, free amino acid content and relative chlorophyll content. Free amino acid content differed significantly between leaf age cohorts. Plots differed notably in topsoil organic matter and soil nitrogen, but the plot with the poorest visual crown condition score had intermediate mean values for both soil properties within the ranges presented by the six plots. Overall, crown condition score was weakly negatively correlated with topsoil organic carbon and total nitrogen content. The unhealthiest plot also had the highest density of shrubby understorey. Site factors that could influence both the quantity and quality of foliage (e.g. free amino acid content) in eucalypt crowns, and hence the population dynamics of herbivorous insects and bell miners, are discussed in relation to Eucalyptus saligna crown decline

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns

Soil Classification Resorting to Machine Learning Techniques

Soil classification is the act of resuming the most relevant information about a soil profile into a single class, from which we can infer a large amount of properties without extensive knowledge of the subject. These classes then make the communication of soils, and how they can best be used in areas such as agriculture and forestry, simpler and easier to understand. Unfortunately soil classification is expensive and requires that specialists perform varied experiments, to be able to precisely attribute a class to a soil profile. This master’s thesis focuses on machine learning algorithms for soil classification mainly based on its intrinsic attributes, in the Mexico region. The data set used contains 6 760 soil profiles, the 19 464 horizons that constitute them, as well as physical and chemical properties, such as pH or organic content, belonging to those horizons. Four data modelling methods were tested (i.e., standard depths, n first layers, thickness, and area weighted thickness), as well as different values for a k-Nearest Neighbours imputation. A comparison between state of the art machine learning algorithms was also made, namely Random Forests, Gradient Tree Boosting, Deep Neural Networks and Recurrent Neural Networks. All of our modelling methods provided very similar results, when properly parametrised, reaching Kappa values of 0.504 and an accuracy of 0.554, with the standard depths method providing the most consistent results. The k parameter for the imputation showed very little impact on the variation on the results. Gradient Tree Boosting was the algorithm with the best overall results, closely followed by the Random Forests model. The neuron based methods never achieved a Kappa score over 0.4, therefore providing substantially worse results.A classificação de solos é o ato de resumir a informação sobre um perfil do solo em uma única classe, da qual é possivel inferir várias propriedades, mesmo com a ausência de conhecimento sobre a área de estudo. Estas classes fazem a comunicação dos solos e de como estes podem ser usados, em áreas como a agricultura e silvicultura, mais simples de perceber. Infelizmente a classificação de solos é dispendiosa, demorada, e requer especialistas para realizar as experiências necessárias para classificar corretamente o solo em causa. A presente tese de mestrado focou-se na avaliação de algoritmos de aprendizagem automática para o problema de classificação de solos, baseada maioritariamente nos atributos intrínsecos destes, na região do México. Foi utilizada uma base de dados contendo 6 760 perfis de solos, os 19 464 horizontes que os constituem, e as propriedades químicas e físicas, como o pH e a percentagem de barro, pertencentes a esses horizontes. Quatro métodos de modelação de dados foram testados (standard depths, n first layers, thickness, e area weighted thickness), tal como diferentes valores para uma imputação baseada em k-Nearest Neighbours. Também foi realizada uma comparação entre algoritmos de aprendizagem automática, nomeadamente Random Forests, Gradient Tree Boosting, Deep Neural Networks e Recurrent Neural Networks. Todas as modelações de dados providenciaram resultados similares, quando propriamente parametrisados, atingindo valores de Kappa de 0.504 e accuracy de 0.554, sendo que o métdodo standard depths obteve uma performance mais consistente. O parâmetro k, referente ao método de imputação, revelou ter pouco impacto na variação dos resultados. O algoritmo Gradient Tree Boosting foi o que obteve melhores resultados, seguido de perto pelo modelo de Random Forests. Os métodos baseados em neurónios tiveram resultados substancialmente piores, nunca superando um valor de Kappa de 0.4

Soil biodiversity: functions, threats and tools for policy makers

Human societies rely on the vast diversity of benefits provided by nature, such as food, fibres, construction materials, clean water, clean air and climate regulation. All the elements required for these ecosystem services depend on soil, and soil biodiversity is the driving force behind their regulation. With 2010 being the international year of biodiversity and with the growing attention in Europe on the importance of soils to remain healthy and capable of supporting human activities sustainably, now is the perfect time to raise awareness on preserving soil biodiversity. The objective of this report is to review the state of knowledge of soil biodiversity, its functions, its contribution to ecosystem services and its relevance for the sustainability of human society. In line with the definition of biodiversity given in the 1992 Rio de Janeiro Convention, soil biodiversity can be defined as the variation in soil life, from genes to communities, and the variation in soil habitats, from micro-aggregates to entire landscapes. Bio Intelligence Service, IRD, and NIOO, Report for European Commission (DG Environment