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Forest structure is key to understanding the resilience of tropical forests (their ability to recover from disturbance) and predicting how these ecosystems will respond to future environmental and climatic fluctuations. Current resilience studies in the Amazon rely on passive and active remote sensing forest cover metrics that offer limited insight into nuanced forest canopy structural changes associated with degradation. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar provides detailed information on canopy structure, a key factor in forest recovery, resilience, and ability to provide ecosystem services. We evaluate GEDI spaceborne lidar’s capability to characterise forest structure along a gradient of degradation (e.g. primary unburned (PU), secondary recovering, fire frequency), and investigate the potential of quantifying forest structure to advance understanding of forest responses to disturbance across the Amazon. We assess the correspondence of GEDI structural metrics such as relative height (RH) and canopy cover to airborne lidar metrics across the Brazilian Amazon using Lin’s concordance correlation coefficient (CCC). We evaluate GEDI forest structure variation along a gradient of forest degradation. We explore the potential of principal component (PC) analysis applied to GEDI data to derive a continuous descriptor of forest structural state that characterises the forest degradation continuum, and use a multinomial logistic regression model (MNLR) to further evaluate this descriptor. The strongest positive correspondence for all sampled GEDI and airborne lidar footprints occurs at RH96 (CCC; 0.57) of the canopy height profile, with strongest agreement in primary forest burned at least three times (CCC; 0.91). Whilst canopy cover showed significant recovery 15–25 years after disturbance, forest canopy height and aboveground biomass density had not fully recovered to pre-disturbance levels within 38 years. The PCA identified the importance of RH75, RH96, foliage height diversity index and canopy directional gap probability, with PC1 and PC2 explaining 80% and 15% of variance, respectively. The MNLR suggests that the ratio of these PCs effectively characterises the forest degradation gradient. We demonstrate the ability of GEDI 2A/B structural metrics to differentiate forest structure along a gradient of PU to secondary severely degraded forest in the Amazon rainforest, despite some overlap in structural characterisations between similar degradation classes. Our new method for deriving a forest structural state metric supports future research on forest monitoring, conservation, and the study of Amazon-wide ecosystem resilience.

Eurasian beaver (Castor fiber) were nearly hunted to extinction but have recovered to occupy much of their former range. Beaver were extirpated from Great Britain c. 400 years ago but have recently been reintroduced. The River Otter catchment, Devon was the site of the first licensed wild release of beavers in England. With further releases being considered, there is a need to better understand population dynamics of this native, keystone species to inform conservation and management. Field signs were surveyed from 2015 to 2021. A semi‐automated territory detection method was adopted to estimate territory counts. A spatially explicit model was developed to estimate the ecological territory capacity of the catchment. Future territory expansion was modelled using logistic growth curves; initial growth rate was estimated from observed territory counts and the estimated territory capacity range was used to define the limiting value of the growth curve. Beaver territory removal was simulated, across a range of management intensities and start times, to determine potential impacts of translocation or lethal control upon population dynamics. Territory numbers increased from four to 18, inclusive of four additionally released individuals, during study period. In the absence of population management, the territory capacity of the catchment was estimated to range between 120 and 183; this may be reached between 2028 and 2057. Simulated territory removal, where territories were removed at a fixed rate from the sum of the estimated total population and the population increase for that year, demonstrated large uncertainties in predicted population responses. Simulations with territory removals >3/year all predicted potential population collapse. This finding emphasizes the need for caution when considering population management strategies; translocation of animals out of the catchment or culling should be considered only when populations are established and all alternatives have been considered. These results provide critical information for the expected rate and magnitude of beaver population change in the River Otter catchment. The methods provide a reproducible and generalizable approach for understanding beaver population change, which can inform policy on the reintroduction of beavers and the potential timing and intensity of future beaver population management. Beaver field signs were surveyed annually in the River Otter Catchment, England to map the distribution of beaver activity between 2015 and 2021. These data were used to estimate the territory expansion rate over the period which was then used to inform predictive models for future population change. A range of territory removal simulations were tested to predict the impact of potential future management strategies showing significant uncertainty in projected responses to population control.

Accurate assessment and mapping of biomass in tropical forests is essential for understanding the contributions of forests to the global carbon budget and informing environmental policies. Canopy height models, an important predictor for estimating above-ground biomass, can be enhanced using fine-resolution remote sensing including airborne Light Detection and Ranging (LiDAR). However, these methods are expensive to deploy, and airborne LiDAR lacks continuous global coverage. To address this, various studies have incorporated freely available Synthetic Aperture Radar (SAR) and optical data to extrapolate these fine-grained observations. This study aims to compare the performance of three machine learning algorithms (Multiple Linear Regression (MLR), Random Forest (RF), and Convolutional Neural Networks (CNN)) when using PlanetScope and Sentinel-2 imagery to improve the accuracy of height predictions. We suggest using RF due to its ease in model building, computational time, and efficient feature selection for predicting canopy height. The S2 data at 10 m spatial resolution combined with RF were most appropriate, yielding an R2 of 0.68, RMSE of 3.52 m, and MAE of 2.63 m. While a combined dataset of PlanetScope and Sentinel-2 had a slightly higher R2 of 0.69, PlanetScope data are not always freely available in certain geographical regions and in non-commercial applications, making S2 a more readily available and consistent data source. Overall, this study reveals that the inclusion of PlanetScope and the application of more complex CNN models is not necessary for canopy height predictions in tropical forests, as open-access optical data combined with simpler tree-based models, such as RF, demonstrate comparable performance.

Eurasian beaver (Castor fiber) populations are expanding across Europe. Depending on location, beaver dams bring multiple benefits and/or require management. Using nationally available data, we developed: a Beaver Forage Index (BFI), identifying beaver foraging habitat, and a Beaver Dam Capacity (BDC) model, classifying suitability of river reaches for dam construction, to estimate location and number of dams at catchment scales. Models were executed across three catchments, in Great Britain (GB), containing beaver. An area of 6747 km2 was analysed for BFI and 16,739 km of stream for BDC. Field surveys identified 258 km of channel containing beaver activity and 89 dams, providing data to test predictions. Models were evaluated using a categorical binomial Bayesian framework to calculate probability of foraging and dam construction. BFI and BDC models successfully categorised the use of reaches for foraging and damming, with higher scoring reaches being preferred. Highest scoring categories were ca. 31 and 79 times more likely to be used than the lowest for foraging and damming respectively. Zero-inflated negative binomial regression showed that modelled dam capacity was significantly related (p = 0.01) to observed damming and was used to predict numbers of dams that may occur. Estimated densities of dams, averaged across each catchment, ranged from 0.4 to 1.6 dams/km, though local densities may be up to 30 dams/km. These models provide fundamental information describing the distribution of beaver foraging habitat, where dams may be constructed and how many may occur. This supports the development of policy and management concerning the reintroduction and recolonisation of beaver.

Non‐forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non‐forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low‐stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave‐one‐out cross‐validation of 3.9%. Biomass per‐unit‐of‐height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much‐needed information required to calibrate and validate the vegetation models and satellite‐derived biomass products that are essential to understand vulnerable and understudied non‐forested ecosystems around the globe. Working at sites across the globe, we used a standardized protocol to collect and analyse drone data in order to measure the size of many different plants in non‐forest ecosystems. These measurements of canopy height allowed the prediction of aboveground biomass and carbon storage of different plants accurately across the landscape. This new approach to measuring plants enables detailed monitoring of vegetation dynamics and responses to differences in climate or disturbance that can help us understand the changes happening in important and vulnerable non‐forest ecosystems around the world.

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, yet are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely-sensed biomass products and are undersampled by in-situ monitoring. Current global change threats emphasise the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we assess whether canopy height inferred from drone photogrammetry allows the estimation of aboveground biomass (AGB) across low-stature plant species sampled through a global site network. We found mean canopy height is strongly predictive of AGB across species, demonstrating standardised photogrammetric approaches are generalisable across growth forms and environmental settings. Biomass per-unit-of-height was similar within, but different among, plant functional types. We find drone-based photogrammetry allows for monitoring of AGB across large spatial extents and can advance understanding of understudied and vulnerable non-forested ecosystems across the globe. Competing Interest Statement The authors have declared no competing interest.

Montgomery County lobbyist Blair Lee complained last month [Close to Home, Sept. 13] that the \"College Park campus is under siege by some ambitious Baltimoreans who think the university belongs in Baltimore, not Prince George's County.\"

Metastasis—the disseminated growth of tumours in distant organs—underlies cancer mortality. Breast-to-brain metastasis (B2BM) is a common and disruptive form of cancer and is prevalent in the aggressive basal-like subtype, but is also found at varying frequencies in all cancer subtypes. Previous studies revealed parameters of breast cancer metastasis to the brain, but its preference for this site remains an enigma. Here we show that B2BM cells co-opt a neuronal signalling pathway that was recently implicated in invasive tumour growth, involving activation by glutamate ligands of N -methyl- d -aspartate receptors (NMDARs), which is key in model systems for metastatic colonization of the brain and is associated with poor prognosis. Whereas NMDAR activation is autocrine in some primary tumour types, human and mouse B2BM cells express receptors but secrete insufficient glutamate to induce signalling, which is instead achieved by the formation of pseudo-tripartite synapses between cancer cells and glutamatergic neurons, presenting a rationale for brain metastasis. Breast-to-brain metastasis is enabled by activation of an N -methyl- d -aspartate receptor, which is achieved via the formation of pseudo-tripartite synapses between cancer cells and glutamatergic neurons.

Dietary salt plays a major role in the regulation of blood pressure, and the mineralocorticoid hormone aldosterone controls salt homeostasis and extracellular volume. Recent observations suggest that a small increase in plasma sodium concentration may contribute to the pressor response of dietary salt. Because endothelial cells are (i) sensitive to aldosterone, (ii) in physical contact with plasma sodium, and (iii) crucial regulators of vascular tone, we tested whether acute changes in plasma sodium concentration, within the physiological range, can alter the physical properties of endothelial cells. The tip of an atomic force microscope was used as a nanosensor to measure stiffness of living endothelial cells incubated for 3 days in a culture medium containing aldosterone at a physiological concentration (0.45 nM). Endothelial cell stiffness was unaffected by acute changes in sodium concentration <135 mM but rose steeply between 135 and 145 mM. The increase in stiffness occurred within minutes. Lack of aldosterone in the culture medium or treatment with the epithelial sodium channel inhibitor amiloride prevented this response. Nitric oxide formation was found down-regulated in cells cultured in aldosterone-containing high sodium medium. The results suggest that changes in plasma sodium concentration per se may affect endothelial function and thus control vascular tone.