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Forest biodiversity is critical for many ecosystem functions and services. Yet, it remains uncertain how plant functional diversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We integrated remote sensing and terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple spatial scales for a heterogeneous forest ecosystem in Switzerland. We initialized forest structure and composition in the ecosystem demography model (ED2) through a combination of ground‐based surveys, airborne laser scanning and imaging spectroscopy for forest patches at 10 × 10‐m spatial grain. We derived morphological and physiological forest traits and productivity from model simulations at patch‐level to relate morphological and physiological aspects of functional diversity to the average productivity from 2006 to 2015 at 20 × 20 to 100 × 100‐m spatial extent. We did this for model simulations under observed and experimental conditions (mono‐soils, mono‐cultures and mono‐structures). Functional diversity increased productivity significantly (p< 0.001) across all simulations at 20 × 20 to 30 × 30 m scale, but at 100 × 100‐m scale positive relationships disappeared under homogeneous soil conditions potentially due to the low beta diversity of this forest and the saturation of functional richness represented in the model. Although local functional diversity was an important driver of productivity, environmental context underpinned the variation of productivity (and functional diversity) at larger spatial scales. In this study, we could show that the integration of remotely sensed information on forest composition and structure into terrestrial biosphere models is important to fill knowledge gaps about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas. We explored relationships between forest biodiversity and productivity at multiple spatial scales to better understand how plant traits and their diversity influence the functioning of forest ecosystems. To do this across large contiguous areas, we simulated a temperate mixed forest by combining process‐based terrestrial biosphere modeling with remotely sensed information about forest structure and composition. We found that higher functional diversity was associated with higher productivity at local spatial scale (tens of meters), while soil depth and texture were the main drivers of productivity at the hectare scale. At this larger scale, functional diversity tended to be saturated due to the low turnover of species and functional traits of the temperate mixed forest and the limited representation of physiological diversity in the model. We explored forest functional diversity—productivity relationships using a terrestrial biosphere model initialized with remote sensingHigher functional diversity at 20 × 20 to 30 × 30 m scale was associated with higher productivity (p< 0.001) across all simulationsSoil depth and texture were the main drivers of productivity at the hectare scale, while functional diversity saturated at coarser scales We explored forest functional diversity—productivity relationships using a terrestrial biosphere model initialized with remote sensing Higher functional diversity at 20 × 20 to 30 × 30 m scale was associated with higher productivity (p< 0.001) across all simulations Soil depth and texture were the main drivers of productivity at the hectare scale, while functional diversity saturated at coarser scales

Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high‐resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED‐2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED‐2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥66%) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems. In the Amazon, timber extraction and forest fires ignited by people are the chief causes of damages that we call forest degradation. Degradation is as widespread as deforestation and changes how forests behave. Degraded forests may pump less water to the atmosphere and absorb less carbon dioxide from the atmosphere. To understand the differences in behavior between degraded and intact forests, we used high‐resolution scanning laser data collected from aircraft flights over regions in the Amazon where we knew if and when forests were degraded. Then, we provided these data to a computer program that calculates the exchange of water and carbon between the forest and the atmosphere. We found that, during the dry season, degraded forests are 6.5°C warmer, pump one‐third less water (i.e., 400,000 L ha−1month−1), absorb one‐third less carbon (i.e., 1 tonC ha−1month−1), and show higher fire risk than intact forests. To our surprise, when the Amazon is hit by severe droughts, intact forests start to behave like degraded forests, because all forests run out of water and become hot. Our results are important because they show that forest degradation caused by people can have large impacts on dry‐season climate and favor more fire, especially during typical, nondrought years. Airborne lidar can be used to inform about degradation‐driven changes in structure to vegetation modelsForest degradation typically depletes evapotranspiration and productivity and increases flammabilityExtreme droughts reduce functional differences between degraded and intact tropical forests

There is large uncertainty whether Amazon forests will remain a carbon sink as atmospheric CO2increases. Hence, we simulated an old‐growth tropical forest using six versions of four terrestrial models differing in scale of vegetation structure and representation of biogeochemical (BGC) cycling, all driven with CO2forcing from the preindustrial period to 2100. The models were benchmarked against tree inventory and eddy covariance data from a Brazilian site for present‐day predictions. All models predicted positive vegetation growth that outpaced mortality, leading to continual increases in present‐day biomass accumulation. Notably, the two vegetation demographic models (VDMs) (ED2 and ELM‐FATES) always predicted positive stem diameter growth in all size classes. The field data, however, indicated that a quarter of canopy trees didn't grow over the 15‐year period, and while high interannual variation existed, biomass change was near neutral. With a doubling of CO2, three of the four models predicted an appreciable biomass sink (0.77 to 1.24 Mg ha−1year−1). ELMv1‐ECA, the only model used here that includes phosphorus constraints, predicted the lowest biomass sink relative to initial biomass stocks (+21%), lower than the other BGC model, CLM5 (+48%). Models projections differed primarily through variations in nutrient constraints, then carbon allocation, initial biomass, and density‐dependent mortality. The VDM's performance was similar or better than the BGC models run in carbon‐only mode, suggesting that nutrient competition in VDMs will improve predictions. We demonstrate that VDMs are comparable to nondemographic (i.e., “big‐leaf”) models but also include finer scale demography and competition that can be evaluated against field observations. We evaluate four terrestrial models, some in multiple versions, against a 15‐year dataset of forest dynamics in BrazilAnnual biomass increments from models were within field data uncertainty, but over 15 years large biomass accumulations emerged, inconsistent with field dataWith elevating CO2out to 2100, carbon‐only models predicted large sinks, demography reduced this sink, and the model with phosphorus constraints predicted a substantially lower sink

The coming of age of global positioning system telemetry, in conjunction with recent theoretical innovations for formulating quantitative descriptions of how different ecological forces and behavioral mechanisms shape patterns of animal space use, has led to renewed interest and insight into animal home-range patterns. This renaissance is likely to continue as a result of ongoing synergies between these empirical and theoretical advances. In this article I review key developments that have occurred over the past decade that are furthering our understanding of the ecology of animal home ranges. I then outline what I perceive as important future directions for furthering our ability to understand and predict mammalian home-range patterns. Interesting directions for future research include improved insights into the environmental and social context of animal movement decisions and resulting patterns of space use; quantifying the role of memory in animal movement decisions; and examining the relevance of these advances in our understanding of animal movement behavior and space use to questions concerning the demography and abundance of animal populations.