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The world’s coral reefs are being degraded, and the need to reduce local pressures to offset the effects of increasing global pressures is now widely recognized. This study investigates the spatial and temporal dynamics of coral cover, identifies the main drivers of coral mortality, and quantifies the rates of potential recovery of the Great Barrier Reef. Based on the world’s most extensive time series data on reef condition (2,258 surveys of 214 reefs over 1985–2012), we show a major decline in coral cover from 28.0% to 13.8% (0.53% y ⁻¹), a loss of 50.7% of initial coral cover. Tropical cyclones, coral predation by crown-of-thorns starfish (COTS), and coral bleaching accounted for 48%, 42%, and 10% of the respective estimated losses, amounting to 3.38% y ⁻¹ mortality rate. Importantly, the relatively pristine northern region showed no overall decline. The estimated rate of increase in coral cover in the absence of cyclones, COTS, and bleaching was 2.85% y ⁻¹, demonstrating substantial capacity for recovery of reefs. In the absence of COTS, coral cover would increase at 0.89% y ⁻¹, despite ongoing losses due to cyclones and bleaching. Thus, reducing COTS populations, by improving water quality and developing alternative control measures, could prevent further coral decline and improve the outlook for the Great Barrier Reef. Such strategies can, however, only be successful if climatic conditions are stabilized, as losses due to bleaching and cyclones will otherwise increase.

Diagnosis and management of ocular tuberculosis (OTB) poses a significant challenge. Mixed ocular tissue involvement and lack of agreement on best practice diagnostic tests together with the global variations in therapeutic management contributed to the existing uncertainties regarding the outcome of the disease. The current review aims to update recent progress on OTB. In particular, the Collaborative Ocular Tuberculosis Study (COTS) group recently standardized a nomenclature system for defining clinical phenotypes, and also proposed consensus guidelines and an algorithmic approach for management of different clinical phenotypes of OTB. Recent developments in experimental research and innovations in molecular diagnostics and imaging technology have provided a new understanding in the pathogenesis and natural history of the disease.

This paper proposes a practical physical tampering detection mechanism using inexpensive commercial off-the-shelf (COTS) Wi-Fi endpoint devices with a deep neural network (DNN) on channel state information (CSI) in the Wi-Fi signals. Attributed to the DNN that identifies physical tampering events due to the multi-subcarrier characteristics in CSI, our methodology takes effect using only one COTS Wi-Fi endpoint with a single embedded antenna to detect changes in the relative orientation between the Wi-Fi infrastructure and the endpoint, in contrast to previous sophisticated, proprietary approaches. Preliminary results show that our detectors manage to achieve a 95.89% true positive rate (TPR) with no worse than a 4.12% false positive rate (FPR) in detecting physical tampering events.

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference source must be detected, classified, its purpose determined, and localized to eliminate it. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This article introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-processing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human-in-the-loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers being deployed, resulting in a significantly higher probability of intercept (POI). The architecture of the monitoring system is described in detail in this article, including an analysis of the energy consumption and optimization. Controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches. The ML results show that accurate and reliable detection and classification are possible with COTS hardware.

The metabolic costs of animal movement have been studied extensively under laboratory conditions, although frequently these are a poor approximation of the costs of operating in the natural, heterogeneous environment. Construction of “energy landscapes,” which relate animal locality to the cost of transport, can clarify whether, to what extent, and how movement properties are attributable to environmental heterogeneity. Although behavioral responses to aspects of the energy landscape are well documented in some fields (notably, the selection of tailwinds by aerial migrants) and scales (typically large), the principles of the energy landscape extend across habitat types and spatial scales. We provide a brief synthesis of the mechanisms by which environmentally driven changes in the cost of transport can modulate the behavioral ecology of animal movement in different media, develop example cost functions for movement in heterogeneous environments, present methods for visualizing these energy landscapes, and derive specific predictions of expected outcomes from individual- to population- and species-level processes. Animals modulate a suite of movement parameters (e.g., route, speed, timing of movement, and tortuosity) in relation to the energy landscape, with the nature of their response being related to the energy savings available. Overall, variation in movement costs influences the quality of habitat patches and causes nonrandom movement of individuals between them. This can provide spatial and/or temporal structure to a range of population- and species-level processes, ultimately including gene flow. Advances in animal-attached technology and geographic information systems are opening up new avenues for measuring and mapping energy landscapes that are likely to provide new insight into their influence in animal ecology.

The Muskat problem models the evolution of the interface between two different fluids in porous media. The Rayleigh-Taylor condition is natural to reach linear stability of the Muskat problem. We show that the Rayleigh-Taylor condition may hold initially but break down in finite time. As a consequence of the method used, we prove the existence of water waves turning.

Crown-of-thorns starfish (COTS) outbreaks are a globally significant driver of coral mortality in the Indo-Pacific and work synergistically with other disturbances. We argue that our improved understanding of COTS ecology and ability to monitor their populations, combined with new efficiencies in COTS control technologies, provides a sound basis for attempting to suppress COTS outbreaks on the GBR before they reach full outbreak status. This conclusion is based on research on the GBR which we have synthesised to develop a new, pragmatic approach to managing primary outbreaks. We also outline key actions that must be undertaken in order for suppression to be successful, providing clear direction for future management action. The next COTS outbreak on the GBR is predicted to begin between 2025 and 2027, consequently efforts to detect and suppress pre-initiation populations must start no later than the summer of 2022–2023.

We investigated how density and aggregation influence crown-of-thorns starfish Acanthaster planci reproductive success, using an empirically-tuned, individual-based simulation model that incorporates spatial and temporal biological stochasticity associated with spawning, and a kinetics model of fertilisation that explicitly incorporates the probability of polyspermy. Greater aggregation of individuals relieved Allee fertilisation dynamics, particularly at low densities, leading to higher rates of successful monospermic fertilisation and allowing populations to produce many more zygotes. This is likely more important to smaller, rather than larger, populations, due to limited and more variable reproductive success. In higher density populations a fertilisation optimum was observed at moderate levels of aggregation, above which monospermic fertilisation plateaued or even declined. This was likely due to 2 factors: the spatial dynamics of gamete plume dispersal and polyspermic fertilisation. Comparison of in situ natural spawning aggregation with model results indicates a cost-benefit equilibrium may exist between aggregation and reproductive success, and that relief from mechanisms limiting aggregation (for example, decreased relative predator abundance) may permit increased aggregation resulting in greater fertilisation and zygote production. We propose an Allee threshold of 3 starfish ha−1 (for starfish of a mean diameter 345 mm), below which reproductive capacity is greatly reduced regardless of aggregation level. These preliminary findings posit aggregation as a key factor in outbreak formation that may feasibly be incorporated into preventative management strategies to detect and define incipient outbreak conditions and to mitigate subsequent risk.

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R2 = 0.8, RMSE = 115 Mg ha−1, Bias = 2 Mg ha−1). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R2 = 0.8, RMSE = 152 Mg ha−1, Bias = 9 Mg ha−1), including higher AGB values (>400 Mg ha−1) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders. Social media abstract Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Most of the transcription factors (TFs) responsible for controlling seed development are not yet known. To identify TF genes expressed at specific stages of seed development, including those unique to seeds, we used Affymetrix GeneChips to profile Arabidopsis genes active in seeds from fertilization through maturation and at other times of the plant life cycle. Seed gene sets were compared with those expressed in prefertilization ovules, germinating seedlings, and leaves, roots, stems, and floral buds of the mature plant. Most genes active in seeds are shared by all stages of seed development, although significant quantitative changes in gene activity occur. Each stage of seed development has a small gene set that is either specific at the level of the GeneChip or up-regulated with respect to genes active at other stages, including those that encode TFs. We identified 289 seed-specific genes, including 48 that encode TFs. Seven of the seed-specific TF genes are known regulators of seed development and include the LEAFY COTYLEDON (LEC) genes LEC1, LEC1-LIKE, LEC2, and FUS3. The rest represent different classes of TFs with unknown roles in seed development. Promoter-β-glucuronidase (GUS) fusion experiments and seed mRNA localization GeneChip datasets showed that the seed-specific TF genes are active in different compartments and tissues of the seed at unique times of development. Collectively, these seed-specific TF genes should facilitate the identification of regulatory networks that are important for programming seed development.