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Phosphorus (P) is recognized as an important driver of terrestrial primary productivity across biomes. Several recent developments in process-based vegetation models aim at the concomitant representation of the carbon (C), nitrogen (N) and P cycles in terrestrial ecosystems, building upon the ecological stoichiometry and the processes that govern nutrient availability in soils. Thus, understanding the spatial distribution of P forms in soil is fundamental to initialize and/or evaluate process-based models that include the biogeochemical cycle of P. One of the major constraints for the large-scale application of these models is the lack of data related to the spatial patterns of the various forms of P present in soils, given the sparse nature of in situ observations. We applied a model selection approach based on random forest regressions models trained and tested for the prediction of different P forms (total, available, organic, and inorganic P) - obtained by the Hedley sequential extraction method. As input for the models, reference soil group and textural properties, geolocation, N and C contents, terrain elevation and slope, soil pH and mean annual precipitation and temperature from 108 sites of the RAINFOR network were used. The selected models were then applied to predict the target P forms using several spatially explicit datasets containing contiguous estimated values across the area of interest. Here, we present a set of maps depicting the distribution of total, available, organic, and inorganic P forms in the topsoil profile (0-30 cm) of the pan-Amazon region in the spatial resolution of 0.5 x 0.5 degrees. The random forest regression models presented a good level of mean accuracy for the total, available, organic, and inorganic P forms (77.37 %, 76,86 %, 75.14 %, and 68.23 % respectively). Our results confirm that the mapped area have generally very low total P concentration status with a clear gradient of soil development and nutrient content. Total N was the most important variable for the prediction of all target P forms and the analysis of partial dependence indicates several features that are also related with soil concentration of all target P forms. We observed that gaps in the data used to train and test the random forest models, especially in the most elevated areas, constitute a problem to the methods applied here. However, most of the area could be mapped with a good level of accuracy. Also, the biases of gridded data used for model prediction are introduced in the P maps. Nonetheless, the final map of total P resembles the expected geographical patterns. Our maps may be useful for the parametrization and evaluation of process-based terrestrial ecosystem models as well as other types of models. Also, they can promote the testing of new hypothesis about the gradient and status of P availability and soil-vegetation feedbacks in the pan-Amazon region. [ABSTRACT FROM AUTHOR]

Summary: Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund‐Potsdam‐Jena General Ecosystem Simulator, LPJ‐GUESS) to explore the main drivers of community assembly along an elevational gradient.In the model used here (LPJ‐GUESS‐NTD, where NTD stands for nutrient‐trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake.The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient‐use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient.Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions. [ABSTRACT FROM AUTHOR]

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio‐temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches – species distribution models, individual‐based forest models, and dynamic global vegetation models – as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change. [ABSTRACT FROM AUTHOR]

Global, fast and accessible monitoring of biodiversity is one of the main pillars of the efforts undertaken in order to revert it loss. The Group on Earth Observations Biodiversity Observation Network (GEO-BON) provided an expert-based definition of the biological properties that should be monitored, the Essential Biodiversity Variables (EBVs). Initiatives to provide indicators for EBVs rely on global, freely available remote sensing (RS) products in combination with empirical models and field data, and are invaluable for decision making. In this study, we provide alternatives for the expansion and improvement of the EBV indicators, by suggesting current and future data from the European Space Agencýs COPERNICUS and explore the potential of RS-integrated Dynamic Global Vegetation Models (DGVMs) for the estimation of EBVs. Our review found that mainly due to the inclusion of the Sentinel constellation, Copernicus products have similar or superior potential for EBV indicator estimation in relation to their NASA counterparts. DGVMs simulate the ecosystem level EBVs (ecosystem function and structure), and when integrated with remote sensing data have great potential to not only offer improved estimation of current states but to provide projection of ecosystem impacts. We suggest that focus on producing EBV relevant outputs should be a priority within the research community, to support biodiversity preservation efforts. [ABSTRACT FROM AUTHOR]

Deforestation in the tropics is not only responsible for direct carbon emissions but also extends the forest edge wherein trees suffer increased mortality. Here we combine high-resolution (30 m) satellite maps of forest cover with estimates of the edge effect and show that 19% of the remaining area of tropical forests lies within 100 m of a forest edge. The tropics house around 50 million forest fragments and the length of the world's tropical forest edges sums to nearly 50 million km. Edge effects in tropical forests have caused an additional 10.3 Gt (2.1-14.4 Gt) of carbon emissions, which translates into 0.34 Gt per year and represents 31% of the currently estimated annual carbon releases due to tropical deforestation. Fragmentation substantially augments carbon emissions from tropical forests and must be taken into account when analysing the role of vegetation in the global carbon cycle. [ABSTRACT FROM AUTHOR]