Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models

Coastal wetlands provide a range of ecosystem services, yet they are currently under threat from global change impacts. Thus, their monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small-scale studies using drones or national-/regional-/global-scale studies using satellite-derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board unoccupied aerial vehicles (UAVs) to calculate the plant fractional cover (PFC) in Sentinel-2 MultiSpectral Instrument (MSI) sensor grids. A random forest (RF) algorithm was trained and tested with vegetation indices (VIs) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a digital elevation model (DEM). After comparing the models, results show that using DEM with VIs can increase the prediction accuracy of PFC up to 2 times (R2 58%-70%). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes

Increasing dominance of large lianas in Amazonian forests

Ecological orthodoxy suggests that old-growth forests should be close to dynamic equilibrium, but this view has been challenged by recent findings that neotropical forests are accumulating carbon and biomass, possibly in response to the increasing atmospheric concentrations of carbon dioxide. However, it is unclear whether the recent increase in tree biomass has been accompanied by a shift in community composition. Such changes could reduce or enhance the carbon storage potential of old-growth forests in the long term. Here we show that non-fragmented Amazon forests are experiencing a concerted increase in the density, basal area and mean size of woody climbing plants (lianas). Over the last two decades of the twentieth century the dominance of large lianas relative to trees has increased by 1.7–4.6% a year. Lianas enhance tree mortality and suppress tree growth, so their rapid increase implies that the tropical terrestrial carbon sink may shut down sooner than current models suggest. Predictions of future tropical carbon fluxes will need to account for the changing composition and dynamics of supposedly undisturbed forests

Synergistic use of Sentinel-2 and UAV-derived data for plant fractional cover distribution mapping of coastal meadows with digital elevation models

Coastal wetlands provide a range of ecosystem services, yet they are currently under threat from global change impacts. Thus, their monitoring and assessment is vital for evaluating their status, extent and distribution. Remote sensing provides an excellent tool for evaluating coastal ecosystems, whether with small-scale studies using drones or national-/regional-/global-scale studies using satellite-derived data. This study used a fine-scale plant community classification of coastal meadows in Estonia derived from a multispectral camera on board unoccupied aerial vehicles (UAVs) to calculate the plant fractional cover (PFC) in Sentinel-2 MultiSpectral Instrument (MSI) sensor grids. A random forest (RF) algorithm was trained and tested with vegetation indices (VIs) calculated from the spectral bands extracted from the MSI sensor to predict the PFC. Additional RF models were trained and tested after adding a digital elevation model (DEM). After comparing the models, results show that using DEM with VIs can increase the prediction accuracy of PFC up to 2 times (R2 58 %–70 %). This suggests the use of ancillary data such as DEM to improve the prediction of empirical machine learning models, providing an appropriate approach to upscale local studies to wider areas for management and conservation purposes.</p

Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle

The ratio of leaf‐internal (ci) to ambient (ca) partial pressure of CO2, defined here as χ, is an index of adjustments in both leaf stomatal conductance and photosynthetic rate to environmental conditions. Measurements and proxies of this ratio can be used to constrain vegetation models uncertainties for predicting terrestrial carbon uptake and water use. We test a theory based on the least‐cost optimality hypothesis for modelling historical changes in χ over the 1951‐2014 period, across different tree species and environmental conditions, as reconstructed from stable carbon isotopic measurements across a global network of 103 absolutely‐dated tree‐ring chronologies. The theory predicts optimal χ as a function of air temperature, vapour pressure deficit, ca and atmospheric pressure. The theoretical model predicts 39% of the variance in χ values across sites and years, but underestimates the inter‐site variability in the reconstructed χ trends, resulting in only 8% of the variance in χ trends across years explained by the model. Overall, our results support theoretical predictions that variations in χ are tightly regulated by the four environmental drivers. They also suggest that explicitly accounting for the effects of plant‐available soil water and other site‐specific characteristics might improve the predictions

Physical activity as a preventive measure against overweight, obesity, infections, allergies and cardiovascular disease risk factors in adolescents: AFINOS Study protocol

<p>Abstract</p> <p>Background</p> <p>Prior studies addressing the impacts of regular physical activity or sedentary habits on the immune system have been conducted in adults and laboratory settings. Thus, it is practically unknown how a healthy active lifestyle could affect low-grade inflammation processes, infections or allergies in young persons. The AFINOS Study was designed to determine the relationship between the regular physical activity levels of adolescents and overweight, infection, and allergies along with the presence of metabolic and immunological biomarkers of a deteriorated health status. A further objective of the AFINOS Study is to assess the health status and lifestyle habits of an adolescent population in an effort to identify any protective factors that could be used as preventive measures, since many chronic diseases and their associated co-morbidities often persist from adolescence into adulthood.</p> <p>Methods/Design</p> <p>This study was conducted as three separate sub-studies in three different populations as follows: (a) Study 1 was performed on a population sample of adolescents; (b) Study 2 on the adolescents' parents; and (c) Study 3 on a subset of the adolescents from Study 1. Study 1 assessed health and lifestyle indicators through a questionnaire administered to a representative sample of adolescents from the Madrid Region (n = 2400) aged 13 to 16 years. In Study 2, the parents of the teenagers participating in Study 1 were required to fill out a questionnaire. Finally in Study 3, body composition, physical activity, health-related physical fitness, and blood measurements were determined in a subset (n = 200) of the individuals included in Study 1.</p> <p>Discussion</p> <p>This paper describes the rationale, design, and methodologies used in the AFINOS Study. This multidisciplinary, multicenter study seeks to evaluate several aspects of existing relationships between routine physical activity/sedentary behaviour and several health status markers, specifically those related to the immune system. The results of this cross-sectional study will serve for comparisons with the available data obtained in laboratory settings and in adults. In addition, knowledge regarding the health status and lifestyle habits of Spanish adolescents and their parents will be useful for designing preventive measures.</p

Pleistocene Climate, Phylogeny, and Climate Envelope Models: An Integrative Approach to Better Understand Species' Response to Climate Change

Mean annual temperature reported by the Intergovernmental Panel on Climate Change increases at least 1.1°C to 6.4°C over the next 90 years. In context, a change in climate of 6°C is approximately the difference between the mean annual temperature of the Last Glacial Maximum (LGM) and our current warm interglacial. Species have been responding to changing climate throughout Earth's history and their previous biological responses can inform our expectations for future climate change. Here we synthesize geological evidence in the form of stable oxygen isotopes, general circulation paleoclimate models, species' evolutionary relatedness, and species' geographic distributions. We use the stable oxygen isotope record to develop a series of temporally high-resolution paleoclimate reconstructions spanning the Middle Pleistocene to Recent, which we use to map ancestral climatic envelope reconstructions for North American rattlesnakes. A simple linear interpolation between current climate and a general circulation paleoclimate model of the LGM using stable oxygen isotope ratios provides good estimates of paleoclimate at other time periods. We use geologically informed rates of change derived from these reconstructions to predict magnitudes and rates of change in species' suitable habitat over the next century. Our approach to modeling the past suitable habitat of species is general and can be adopted by others. We use multiple lines of evidence of past climate (isotopes and climate models), phylogenetic topology (to correct the models for long-term changes in the suitable habitat of a species), and the fossil record, however sparse, to cross check the models. Our models indicate the annual rate of displacement in a clade of rattlesnakes over the next century will be 2 to 3 orders of magnitude greater (430-2,420 m/yr) than it has been on average for the past 320 ky (2.3 m/yr)

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Multi-messenger Observations of a Binary Neutron Star Merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ∼ 1.7 {{s}} with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of {40}-8+8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 {M}ȯ . An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ∼ 40 {{Mpc}}) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ∼ 9 and ∼ 16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta.</p