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Queen snapper (Etelis oculatus) is of interest from an ecological and management perspective as it is the second most landed finfish species (by total pounds) as determined by Puerto Rico commercial landings (2010–2019). As fishing activities progressively expand into deeper waters, it is critical to gather data on deep-sea fish populations to identify essential fish habitats (EFH). In the U.S. Caribbean, the critically data-deficient nature of this species has made this challenging. We investigated the use of ensemble species distribution modeling (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using occurrence data and terrain attributes derived from bathymetric datasets at different resolutions, we developed species distribution models unique to each sampling region (west, northeast, and southeast Puerto Rico) using seven different algorithms. Then, we developed ESDM models to analyze fish distribution using the highest-performing algorithms for each region. Model performance was evaluated for each ensemble model, with all depicting 'excellent' predictive capability (AUC > 0.8). Additionally, all ensemble models depicted 'substantial agreement' (Kappa > 0.7). We then used the models in combination with existing knowledge of the species' range to produce binary maps of potential queen snapper distributions. Variable importance differed across spatial resolutions of 30 m (west region) and 8 m (northeast and southeast region); however, bathymetry was consistently one of the best predictors of queen snapper suitable habitat. Positive detections showed strong regional patterns localized around large bathymetric features, such as seamounts and ridges. Despite the data-deficient condition of queen snapper population dynamics, these models will help facilitate the analysis of their spatial distribution and habitat preferences at different spatial scales. Our results therefore provide a first step in designing long-term monitoring programs targeting queen snapper, and determining EFH and the general distribution of this species in Puerto Rico. [ABSTRACT FROM AUTHOR]

This editorial published in the journal Frontiers in Marine Science discusses the field of marine geomorphometry, which involves the quantitative characterization of the seabed terrain. The editorial highlights advancements in seabed mapping technologies and the increasing availability of data, which provide opportunities for innovation in marine geomorphometry. The document introduces a research topic on Frontiers in Marine Geomorphometry, aiming to disseminate novel approaches and applications of quantitative analysis of seafloor mapping datasets. The editorial emphasizes the importance of understanding, monitoring, and managing the oceans, especially in the context of the United Nations' Decade of Ocean Science for Sustainable Development. The document concludes by discussing various applications of marine geomorphometry, including habitat mapping, ecological analysis, and geohazard assessment, and mentions state-of-the-art approaches such as deep learning for automating marine geomorphometric and morphological analyses. [Extracted from the article]

Physical structures generated from ecosystem engineers can have a cascade of impacts on the ecological community and the surrounding landscape. The Eastern oyster Crassostrea virginica can form extensive intertidal reefs, whose three‐dimensional structures provide ecosystem services like nursery and foraging habitat for fishes and invertebrates and shoreline stabilization. Measurements of the structural properties of these reefs provide opportunities to quantitatively assess associated services. There is a growing variety of tools available for measuring three‐dimensional (3D) properties of intertidal habitats, including two remote sensing methods that capture 3D structural metrics in a number of environments. We surveyed reefs using a terrestrial laser scanner (TLS, LiDAR) and imagery from unoccupied aircraft systems (UAS, or drones) processed through Structure from Motion photogrammetry. Comparisons of digital elevation models from repetitive flights over an oyster reef to checkpoints yielded mean horizontal and vertical root mean square errors (RMSE) of −0.54 ± 0.47 cm and 0.97 ± 1.0 cm (Mean ± SD), respectively, indicating high accuracy among UAS surveys. Compared to TLS products, point cloud densities from UAS‐derived products were more consistent across the reef elevation gradient and much denser overall except in the low reef zone, which was proximal to most of the TLS scan locations. Comparisons of structural metrics between UAS and TLS showed similarities in metrics like profile and planform curvatures, yet indicated UAS surveys produced higher values of surface complexity and slope. Results indicate that UAS photogrammetry can produce robust oyster reef structural metrics that can be highly useful in oyster conservation and restoration. [ABSTRACT FROM AUTHOR]

In this study, we combined a machine learning pipeline and human supervision to identify and label swallow and martin roost locations on data captured from 2000 to 2020 by 12 Weather Surveillance Radars in the Great Lakes region of the US. We employed radar theory to extract the number of birds in each roost detected by our technique. With these data, we set out to investigate whether roosts formed consistently in the same geographic area over two decades and whether consistency was also predictive of roost size. We used a clustering algorithm to group individual roost locations into 104 high‐density regions and extracted the number of years when each of these regions was used by birds to roost. In addition, we calculated the overall population size and analyzed the daily roost size distributions. Our results support the hypothesis that more persistent roosts are also gathering more birds, but we found that on average, most individuals congregate in roosts of smaller size. Given the concentrations and consistency of roosting of swallows and martins in specific areas throughout the Great Lakes, future changes in these patterns should be monitored because they may have important ecosystem and conservation implications. [ABSTRACT FROM AUTHOR]

In soft‐bottom marine ecosystems, bedform variation is induced by wind‐ and tidal‐driven hydrodynamics. The resulting megaripples, sand waves and sandbanks form a spatially and temporally heterogeneous seafloor landscape. The strong physical forces imposed by the migration of these bedforms are important determinants for the occurrence of different macrozoobenthic species. Quantifying the effect of these forces can help in differentiating natural‐ and anthropogenically induced physical stressors. However, large‐scale mapping of seabed morphology at high resolution using multibeam echosounder is challenging, costly and time‐consuming, especially in shallow seas, prohibiting wide swaths. Instead, their bathymetry is typically studied using single‐beam transects that are interpolated to bathymetric grids with a relatively coarse resolution (20 m). However, this leaves out information on smaller scale (<20 m) bedforms that can be ecologically relevant. In the Dutch Wadden sea, a shallow tidal system, we characterized bedform variation at high resolution using single‐beam data for the first time. We calculated a 2‐D Terrain Ruggedness Index (TRI) at sub‐meter resolution along the single‐beam transects and interpolated the results to a full 3‐D grid. We then validated the result by relating TRI to independently modeled hydrodynamic parameters and to the distribution of macrozoobenthic species. We found that TRI successfully integrates the variation of tidal‐driven bed shear stress and wave‐driven orbital velocity. In addition, we found TRI to be a good predictor of the occurrence of macrozoobenthic species. The inferred small‐scale bedforms provide valuable information for separating the relative importance of natural dynamics versus anthropogenic disturbances such as dredging and bottom trawling activities. We discuss that by repurposing already available single‐beam data in this way, bedforms can be characterized at high resolution without the need for additional equipment or mapping campaigns, yielding novel input to decision‐making on marine management and conservation. [ABSTRACT FROM AUTHOR]

Wind energy is a major and rapidly expanding renewable energy source. Horizontal‐axis wind turbines, the main tool in this industry, induce mortality in flying animals and consequently bring about conservation concerns and regulatory restrictions. We utilized a unique combination of RADAR, LIDAR and ultrasonic acoustic recorders to test the utility of a novel technology meant to prevent wind turbine‐related mortality in bats. Our drone‐mounted deterrent device produces a pulsating combination of strong auditory and visual signals while moving through the air. LIDAR was used to assess the device's impact below its flight altitude and RADAR to assess its influence above its flight altitude. Continuous acoustic recordings from ground level to ~400 m above‐ground‐level were used to monitor bat activity in the research site. We recorded the nightly altitudinal distributions of multiple bat species throughout the experiment. Analysis revealed a significant change in activity while the deterrent was flying compared to baseline conditions. We also recorded a significant ~40% decrease below and a significant ~50% increase above the deterrent's flight altitude during its operation compared to the post‐flight control. The tested technology is independent of wind farm activities and does not require modifying wind turbine form or operation procedures. The device differs from previously proposed solutions by being dynamic – moving in the airspace and emitting constantly changing signals – thus decreasing the probability of animal habituation. Our findings suggest that the deterrent could dramatically decrease wind turbine‐related mortality by deterring bats from approaching rotor‐swept airspace. Focused implementation in conditions where bat activity and energy production are in conflict may provide a practical, cost‐effective mortality mitigation solution compared to current alternatives. Thus, our results should be considered by the wind‐turbine industry and environmental monitoring and animal conservation organizations, as well as by regulatory agencies, when pursuing alleviation of wind turbine‐related mortality. [ABSTRACT FROM AUTHOR]

Long‐term vegetation plots represent one of the largest types of research investments in ecology, but efforts to interrelate data on plants with that on animals are constrained because of the disturbance produced by human observers. Recent advances in the automated identification of animal sounds on large datasets of autonomously collected audio recordings hold the potential to describe plant–animal interactions, such as between frugivorous birds and fruiting trees, without such disturbance. We deployed an array of nine autonomous recording units (ARUs) on the 400 × 500 m Bubeng Forest Dynamics Plot, in Xishuangbanna, southwest China, and collected a total of 1965 h of recordings across two seasons. Animal Sound Identifier (ASI) software was used to detect the vocalizations of five frugivorous bird species, and the probability of detection was related to the number of mature fruiting trees within a 50 m radius of the ARUs. There were more significant positive relationships than would be expected by chance in analyses that investigated bird/tree interactions across 3 months, both in the wet season and the dry season, as well as in short‐term analyses within the dry season months of October and November. The analysis identified 54 interactions between bird and tree species with significant positive relationships. Follow‐up observations of birds on the plot validated that such interactions were more likely to be observed than other interactions. We demonstrate that ARUs and automated voice identification can map the distribution and/or movement of vocal animals across large vegetation plots, allowing this data on animals to be inter‐related to that on plants. We suggest that ARUs be added to the standardized protocols of the plot network, leveraging their vast amount of information about vegetation to describe plant–animal interactions currently, and monitor changes in the future. [ABSTRACT FROM AUTHOR]

Each year, billions of birds migrate across the globe, and interpretation of weather radar signals is increasingly being used to document the spatial and temporal migration patterns in Europe and America. Such approaches are yet to be applied in the East Asian‐Australasian Flyway (EAAF), one of the most species‐rich and threatened flyways in the world. Logistical challenges limit direct on‐ground monitoring of migratory birds in many parts of the EAAF, resulting in knowledge gaps on population status and site use that limit evidence‐based conservation planning. Weather radar data have great potential for achieving comprehensive migratory bird monitoring along the EAAF. In this study, we discuss the feasibility and challenges of using weather radar to complement on‐ground bird migration surveys in the flyway. We summarize the location, capacity and data availability of weather radars across EAAF countries, as well as the spatial coverage of the radars with respect to migrants' geographic distribution and migration hotspots along the flyway, with an exemplar analysis of biological movement patterns extracted from Chinese weather radars. There are more than 430 weather radars in EAAF countries, covering on average half of bird species' passage and non‐breeding distributions, as well as 70% of internationally important sites for migratory shorebirds. We conclude that the weather radar network could be a powerful resource for monitoring bird movements over the full annual cycle throughout much of the EAAF, providing estimates of migration traffic rates, site use, and long‐term population trends, especially in remote and less‐surveyed regions. Analyses of weather radar data would complement existing ornithological surveys and help understand the past and present status of the avian community in a highly threatened flyway. [ABSTRACT FROM AUTHOR]

An automated algorithm for passive acoustic detection of blue whale D‐calls was developed based on established deep learning methods for image recognition via the DenseNet architecture. The detector was trained on annotated acoustic recordings from the Antarctic, and performance of the detector was assessed by calculating precision and recall using a separate independent dataset also from the Antarctic. Detections from both the human analyst and automated detector were then inspected by an independent judge to identify any calls missed by either approach and to adjudicate whether the apparent false‐positive detections from the automated approach were actually true positives. A final performance assessment was conducted using double‐observer methods (via a closed‐population Huggins mark–recapture model) to assess the probability of detection of calls by both the human analyst and automated detector, based on the assumption of false‐positive‐free adjudicated detections. According to our double‐observer analysis, the automated detector showed superior performance with higher recall and fewer false positives than the original human analyst, and with performance similar to existing top automated detectors. To understand the performance of both detectors we inspected the time‐series and signal‐to‐noise ratio (SNR) of detections for the test dataset, and found that most of the advantages from the automated detector occurred at low and medium SNR. [ABSTRACT FROM AUTHOR]

Seagrass ecosystems are globally significant hot spots of blue carbon storage, coastal biodiversity and coastal protection, rendering them a so‐called natural climate solution. Their potential as a natural climate solution has been largely overlooked in national and international climate strategies and financing. This stems mainly from the lack of standardized, spatially explicit mapping and region‐specific carbon inventories. Here, we introduce a novel seagrass ecosystem accounting framework that harnesses machine learning, big satellite data analytics and open region‐specific reference data within the Google Earth Engine cloud computing platform. Leveraging a biennial percentile composite, assembled from 16 453 Sentinel‐2 surface reflectance image tiles at 10‐m spatial resolution, and 20 820 reference data points, we applied the cloud‐native framework to produce the first national inventories of seagrass extent and total seagrass carbon stocks in Kenya, Tanzania, Mozambique and Madagascar. We estimated 4316 km2 of regional seagrass extent (mean F1‐score of 59.3% and overall accuracy of 84.3%) up to 23 m of depth. Pairing country‐specific in situ carbon data and our spatially explicit seagrass extents, we calculated total regional seagrass blue carbon stocks between 11.2–40.2 million MgC, with the largest national carbon pool in Kenya (8–29.2 million MgC). We envisage that improvements in the remote sensing components of the framework guided by a necessary influx of region‐specific data on seagrass stocks and fluxes could reduce uncertainties in our current spatially explicit ecosystem extent and carbon accounts, enhancing the incorporation of seagrasses into Multilateral Environmental Agreements for future resilient ecosystems, societies and economies. [ABSTRACT FROM AUTHOR]

Longitudinal training dams (LTDs) are novel hydraulic engineering structures in the river Waal intended to facilitate intensive navigation and safe discharges in the main channel while providing sheltered habitats for aquatic biota in shore channels. Monitoring data collected using light detection and ranging, multibeam echosounder and aerial photography for the years during and after the construction of the LTDs were analysed in order to determine patterns of erosion and deposition, the retreat rate of steep eroding banks and shoreline length change through time. The LTD shore channels and two traditional groyne fields (references) were divided into nine mesohabitats based on physical attributes. Net erosion was estimated for eight out of the nine mesohabitats for the 2015–2020 period. Generally, there was a pattern of riverbed aggradation towards the LTDs and degradation or bank erosion towards the littoral zones of the LTD shore channels. This kind of continuous behaviour could be indicative of current or eminent channel and thus habitat stability. The bankline erosion in shore channels had mean retreat rates of 1.4–1.6 m/year. The shorelines were longer in sand‐dominated mesohabitats, which could be key for habitat heterogeneity. The LTD shore channels offered more complex relatively natural continuous littoral zones than the traditional groyne fields while maintaining the multifunctionality of the river. Thus, the development of sandy shorelines in the LTD shore channels should be encouraged through management in order to enhance biodiversity. Geomorphological monitoring of the shore channels should continue in the future in order to detect any long‐term changes in the sedimentary processes and ecological functions. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) involves recording the sounds of animals and environments for research and conservation. PAM is used in a range of contexts across terrestrial, marine and freshwater environments. However, financial constraints limit applications within aquatic environments; these costs include the high cost of submersible acoustic recorders. We quantify this financial constraint using a systematic literature review of all ecoacoustic studies published in 2020, demonstrating that commercially available autonomous underwater recording units are, on average, five times more expensive than their terrestrial equivalents. This pattern is more extreme at the low end of the price range; the cheapest available aquatic autonomous units are over 40 times more expensive than their terrestrial counterparts. Following this, we test a prototype low‐cost, low‐specification aquatic recorder called the 'HydroMoth': this device is a modified version of a widely used terrestrial recorder (AudioMoth), altered to include a waterproof case and customisable gain settings suitable for a range of aquatic applications. We test the performance of the HydroMoth in both aquaria and field conditions, recording artificial and natural sounds, and comparing outputs with identical recordings taken with commercially available hydrophones. Although the signal‐to‐noise ratio and the recording quality of HydroMoths are lower than commercially available hydrophones, the recordings with HydroMoths still allow for the identification of different fish and marine mammal species, as well as the calculation of ecoacoustic indices for ecosystem monitoring. Finally, we outline the potential applications of low‐cost, low‐specification underwater sound recorders for bioacoustic studies, discuss their likely limitations, and present important considerations of which users should be aware. Several performance limitations and a lack of professional technical support mean that low‐cost devices cannot meet the requirements of all PAM applications. Despite these limitations, however, HydroMoth facilitates underwater recording at a fraction of the price of existing hydrophones, creating exciting potential for diverse involvement in aquatic bioacoustics worldwide. [ABSTRACT FROM AUTHOR]

Remote Sensing in Ecology and Conservation, 1, 1 - 3. 31 Pittman, S.J. (2018) Seascape ecology. Active acoustic remote sensing technologies have historically been associated with military uses and the field of hydrography rather than with the remote sensing community of practice; this has slowed the integration of data processing and analysis methods that have proven effective in the study of terrestrial environments. Acoustic remote sensing can be passive (i.e., using hydrophones to capture sounds in the environment) or active (i.e., using sonars that produce directional sound and listen for returns); both have their place in support of marine ecology and conservation. Since its inception, the editorial board of I Remote Sensing in Ecology and Conservation i has welcomed contributions to coastal and marine ecology and conservation that rely on remote sensing (Pettorelli et al., 2015). [Extracted from the article]

We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and over 90% balanced accuracy in recognising running and rumbling behaviours from the seismic data. We release the dataset used in this study: SeisSavanna represents a unique collection of seismic signals with the associated wildlife species and behaviour. Our results suggest that seismic data offer substantial benefits for monitoring wildlife, and we propose to further develop our methods using dense arrays that could result in a seismic shift for wildlife monitoring. [ABSTRACT FROM AUTHOR]

Hydropower infrastructure represents a major driver of habitat loss and insular fragmentation worldwide, mostly across the tropics and sub‐tropics. Despite growing evidence of dam‐induced impacts on biodiversity, the effects of insular habitat fragmentation on species assemblages remain poorly understood, particularly for East Asian vertebrates. Here, we assess how insectivorous bats respond to forest fragmentation in Thousand Island Lake, a mega‐hydroelectric dam in East China. Bat assemblages were surveyed across 36 land‐bridge islands of different sizes and degrees of isolation, using AudioMoth recorders. Echolocation calls were classified into sonotypes, each corresponding to either single or multiple species, which were further classified according to their habitat affinities into forest or open‐space foragers. Based on 22 875 five‐min recordings from 108 detector‐nights, we recorded 15 bat sonotypes, eight of which we classified as forest sonotypes (2329 bat passes) and seven as open‐space sonotypes (52 277 bat passes). Overall, sonotype richness increased with island area, but only above a certain threshold (34 ha). Habitat affinity played an important role in ensemble‐level responses to fragmentation; forest sonotype richness increased with island area, whereas open‐space sonotype activity was higher in more isolated islands. Our results highlight the relevance of particularly large fragments (>1000 ha) to maintain area‐sensitive forest bat species. However, islands below 34 ha in size and those more isolated from the mainland are also of conservation value as they, correspondingly, harbour a variable (but substantial) number of species and concentrate higher activity of open‐space foragers. These findings further demonstrate that acoustic sampling methods, as the one presented here, are able to provide key information for evidence‐based policies aimed at halting the ongoing wave of dam‐induced biodiversity loss. [ABSTRACT FROM AUTHOR]

Offshore windfarms have recently emerged as a renewable energy solution. The effects of pile‐driving and long‐term impacts of operational noises on fish chorusing still, however, remain largely unknown. In this study, we investigated the variations of fish chorusing intensity and duration during the construction (2016) and operational phases (2017–2018) of a windfarm at the Eastern Taiwan Strait (ETS). At the ETS, two types of fish choruses (Types 1 and 2) were found to repeat over a diurnal pattern. In the 2 days after the pile driving, Type 1 chorusing showed lower intensity and longer duration, while Type 2 chorusing exhibited higher intensity and no changes in its duration. During the operational phases in 2017 and 2018, both choruses were longer in duration (2–3 h for Type 1; 0.5–1 h for Type 2). The intensity of Type 1 chorus increased by 5–10 dB, but no significant variation was recorded for Type 2. Our findings document, for the first time, different behavioral responses of two chorusing types exposed to pile‐driving and windfarm noise pressure. Fish choruses have been associated with several behavioral functions. Deviation from a regular pattern might affect fish reproductive success, cause migration, augmented predation, or physiological alterations. Because offshore windfarms installations are growing in magnitude and capacity worldwide, this study provides essential insights for policymakers and constitutes an important reference for assessing the effects of noise from pile driving and windfarms on fishes. [ABSTRACT FROM AUTHOR]