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Estimating relative abundance is critical for informing conservation and management efforts and for making inferences about the effects of environmental change on populations. Freshwater fisheries span large geographic regions, occupy diverse habitats and consist of varying species assemblages. Monitoring schemes used to sample these diverse populations often result in populations being sampled at different times and under different environmental conditions. Varying sampling conditions can bias estimates of abundance when compared across time, location and species, and properly accounting for these biases is critical for making inferences. We develop a joint species distribution model (JSDM) that accounts for varying sampling conditions due to the environment and time of sampling when estimating relative abundance. The novelty of our JSDM is that we explicitly model sampling effort as the product of known quantities based on time and gear type and an unknown functional relationship to capture seasonal variation in species life history. We use the model to study relative abundance of six freshwater fish species across the state of Minnesota, USA. Our model enables estimates of relative abundance to be compared both within and across species and lakes, and captures the inconsistent sampling present in the data. We discuss how gear type, water temperature and day of the year impact catchability for each species at the lake level and throughout a year. We compare our estimates of relative abundance to those obtained from a model that assumes constant catchability to highlight important differences within and across lakes and species. Synthesis and applications: Our method illustrates that assumptions relating indices of abundance to observed catch data can greatly impact model inferences derived from JSDMs. Specifically, not accounting for varying sampling conditions can bias inference of relative abundance, restricting our ability to detect responses to management interventions and environmental change. While our focus is on freshwater fisheries, this model architecture can be adopted to other systems where catchability may vary as a function of space, time and species.

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends. [ABSTRACT FROM AUTHOR]

This study aims to provide a comprehensive analysis of the impacts of high-intensity coal mining on vegetation in Liangbei Town, a typical deep coal mining area in central of China. Using Landsat remote sensing data from 2000 to 2023, processed by the Google Earth Engine (GEE) platform, the study calculates the Normalized Difference Vegetation Index (NDVI). Temporal and spatial distribution patterns of vegetation were assessed using LandTrendr algorithm, Sen's slope estimation, the Mann–Kendall test, the coefficient of variation, and the Hurst index. Vegetation growth dynamics were further analyzed through transfer matrix and intensity analysis frameworks. Driving factors influencing vegetation trends were evaluated using local climate data and surface deformation variables from SAR imagery. Temporal Dimension: From 2000 to 2023, the annual NDVI in Liangbei Township showed an upward trend with a growth rate of 0.0894 (10a)−1, peaking at 0.51 in 2020. Spatial Dimension: The NDVI distribution in Liangbei Township displayed a pattern of being lower in the center and higher around the edges, with values concentrated between 0.4 and 0.51, covering 50.34% of the total area. Trend of Change: Between 2000 and 2023, 83.28% of the area in Liangbei Township experienced significant improvement in the NDVI, with vegetation growth trends shifting primarily from slight to significant improvement, encompassing a total area of 10.98 km². This shift exhibited a marked tendency. Driving Factors: Deep mining in Liangbei Township is concentrated in the eastern part, with SAR imagery indicating a maximum surface subsidence of 0.26 m. As surface subsidence increases, the NDVI significantly decreases. The findings suggest that in the future, 91.13% of the vegetation in Liangbei Township will display an antipersistent change trend. The study offers critical insights into the interaction between mining activities and vegetation cover can serve as a reference for environmental evolution and management in similar mining areas. [ABSTRACT FROM AUTHOR]

Because coral reef ecosystems face threats from human activities and climate change, coral reef conservation programmes are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. However, current labour‐intensive methods result in a backlog of unsorted images, highlighting the need for automated classification. Few studies have simultaneously utilized accurate labels along with updated algorithms and datasets. This study aimed to create a dataset representing common coral reef conditions and associated stressors in the Indo‐Pacific. Concurrently, it assessed existing classification algorithms and proposed a new multilabel method for automatically detecting coral reef conditions and extracting ecological information. A dataset containing over 20,000 high‐resolution coral images of different health conditions and stressors was constructed based on the field survey. Seven representative deep learning architectures were tested on this dataset, and their performance was quantitatively evaluated using the F1 metric and the match ratio. Based on this evaluation, a new method utilizing the ensemble learning approach was proposed. The proposed method accurately classified coral reef conditions as healthy, compromised, dead and rubble; it also identified corresponding stressors, including competition, disease, predation and physical issues. This method can help develop the coral image archive, guide conservation activities and provide references for decision‐making for reef managers and conservationists. The proposed ensemble learning approach outperforms others on the dataset, showing state‐of‐the‐art (SOTA) performance. Future research should improve its generalizability and accuracy to support global coral reef conservation efforts. [ABSTRACT FROM AUTHOR]

This study assesses the hydrochemical dynamics of surface waters in Ukraine's border regions with Poland and Slovakia over a 15-year period. Key water quality parameters, including sulfates (SO42-), biochemical oxygen demand (BOD5), dissolved oxygen (DO), and total suspended solids (TSS), were analysed to determine the ecological state of these transboundary water bodies. The results indicate that sulfate concentrations remain below the maximum permissible concentrations (MPC) for both household and fishery water use. BOD5 and DO levels generally comply with environmental standards, though localized areas show signs of organic pollution. TSS concentrations remain within acceptable limits, likely influenced by natural erosion and occasional anthropogenic activities. Pearson correlation analysis revealed significant relationships between nitrogen and nitrates (r = 0.814), underscoring the role of agricultural runoff in nutrient dynamics. Negative correlations between DO and several pollutants suggest that organic and chemical contamination affects oxygen availability. These findings emphasize the need for continued monitoring and transboundary collaboration to safeguard water quality in the region. [ABSTRACT FROM AUTHOR]

For a long time, ecological monitoring across Australia has utilised a wide variety of different methodologies resulting in data that is difficult to analyse across place or time. In response to these limitations, a new systematic approach to ecological monitoring has been developed in collaboration between the Terrestrial Ecosystem Research Network and the Australian Department of Climate Change, Energy, the Environment and Water - the Ecological Monitoring System Australia (EMSA). A qualitative approach involving focus groups and semi-structured interviews was undertaken to review perceptions of the introduction of the EMSA protocols amongst Natural Resource Management practitioners and other key stakeholders. We found that environmental management stakeholders recognise there will be many advantages from the standardisation of ecological monitoring. However, key concerns emerged regarding the capacity needed to implement the standard protocols, the utility of the resultant data for regional projects, and the scope for adaptive co-management under the EMSA. Stakeholders emphasised the need for autonomy and flexibility, so their participation in protocol development can facilitate regional adoption of the standards. Respondents' concerns about a perceived lack of genuine consultation and acknowledgement of feedback revealed the importance of clear communication at all stages of an environmental management project aiming to standardise practices. Our findings indicate that reflexivity will be vital to address the complexity involved in standardisation of ecological monitoring. Formal processes of social learning will need to be integrated into environmental management approaches to account for the increasing complexity of socio-ecological systems as they are challenged by global change. [ABSTRACT FROM AUTHOR]

[Introduction] In response to the inability of existing communication conditions to meet the intelligent O&M and ecological monitoring needs of offshore wind farm, this article explores and proposes a 5G customized network scheme to solve the problems of poor signal accessibility, incomplete network coverage, and low smoothness in offshore wind farms. [Methods] In this paper, a comprehensive O&M and monitoring scheme was proposed by using 5G customized network technology, which was as follows: through the deployment of 5G macro base stations outdoors, 5G indoor distribution in towers, and underwater laying of optical networks, the 3D coverage of wind farm communication networks was realized; Based on 5G slicing technology, one network could be used for multiple purposes to meet the needs of offshore wind farms for network differentiation; computing nodes were deployed in the centralized control center computer room, and private network data was forwarded through the edge UPF (user plane function) to achieve computing-network integration. [Result] The intelligent management and ecological environment monitoring scheme for offshore wind farms based on 5G technology proposed in this article has been piloted and tested based on the project. The test results show that the maximum effective coverage radius of 5G base stations reaches 11.3 km, and the stable transmission uplink rate reaches 5 Mbps, meeting the needs of observation data return and unmanned ship video return in the sea area. By deploying two 2.1G 8TR enhanced base stations on the booster station and wind turbine to enhance sea area coverage, the pull-net test around the wind farm verified that the 5G private network can effectively cover wind farms, with a coverage rate of 98.4%, which can basically meet the coverage needs of the entire wind farm. [Conclusion] This scheme utilizes a 5G private network to cover the sea area of the wind farm and achieves underwater communication through STN (Smart Transport Network) and underwater optical networks. Consequently, it innovatively constructs a 3D ocean monitoring and communication network, laying the communication foundation for the intelligent management and ecological environment monitoring of offshore wind farms.

The article presents the results of applying an improved method for automatic segmentation of RGB imagery obtained using consumer-grade UAVs, based on the Mask R-CNN neural network architecture. Blocks for the preparation and post-processing of raster and vector files have been developed for working with geospatial data. The model was trained on 7000 crowns identified in pine forest of automorphic habitats in the mixed coniferous-broadleaf forest subzone. Training was carried out using cross-validation. Additional data of 1337 crowns were used for verification. During the sequential filtering by area, confidence level, and duplicate segments, the quality of the final segmentation results improved for all age groups of pine forests. The final average precision is 0.87, recall – 0.81, F1-score – 0.83. The results demonstrate the high efficiency of the filtering algorithm in reducing segment redundancy and increasing data reliability. The Mask R-CNN automatic segmentation method is an effective tool for analyzing the characteristics of pine canopies using RGB imagery from UAV surveys. It is capable of replicating the results of visual interpretation with high accuracy. This method particularly advantageous for scaling studies to large areas where manual delineation becomes labor-intensive.

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds (Zostera marina L.) which thrived in the bay's shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds' shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales. [ABSTRACT FROM AUTHOR]

Ecological monitoring is a vital tool to help us assess habitat condition and understand the mechanism(s) for habitat change. Yet many countries struggle to meet their monitoring requirements in part due to the high assessment workload. Rapid ecological assessment methods may have an important role to play in this regard. Following their success within several European habitats (e.g., semi-natural grasslands), they are now being developed for additional habitats such as heathlands, peatlands, and other agri-associated areas. Whilst some rapid assessments using ecological scorecards have been shown to be accurate compared to traditional ecological monitoring, less is known about the functionality of these scorecards in heterogenous landscapes. In this study, we selected four existing scorecards to test alongside a prototype. We assessed how these different scorecards measured habitat condition on the same heathland sites. We found that the choice of metrics, their score weighting, and the thresholds used for categorical scores cause scorecards to assess the same site with substantial variation (37%). Vegetation metrics were the primary cause of score variation, with vegetation structure and positive indicator species being the leading causes. Our study indicates that whilst current scorecards may be representative of project-specific goals, they may not be suitable for wider monitoring uses in their current form. Ecological scorecards have great potential to drastically increase the extent of monitoring, but caution is needed before adapting existing scorecards beyond the purposes from which they were designed. [ABSTRACT FROM AUTHOR]

Questions: Plant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non‐equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners. Location: The Netherlands, Western Europe. Methods: We used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero‐augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four‐year period. Thereafter we assessed trends in cover and in occurrence across all periods. Results: We found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence. Conclusions: Our model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets. [ABSTRACT FROM AUTHOR]

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Researchers often use surrogate species for assessing biodiversity—organisms that can indicate the presence of additional taxa or provide insight into environmental processes. As indicators of anthropogenic impact, "cross‐taxon" surrogates are theorized to be effective shortcuts for determining how communities respond to environmental change. However, few studies have implemented cross‐taxon surrogates given the challenge of validating the strength of the relationship between surrogates and their target taxa. Trophically transmitted parasites require multiple invertebrate and vertebrate taxa to complete their life cycles (e.g., annelids, mollusks, fishes, shorebirds), making them ideal cross‐taxon surrogates of community diversity. By sampling for these parasite surrogates, it is theoretically possible to infer the composition of the overall host community based on the species of parasites present. In our study, we tested the use of parasites as cross‐taxon surrogates of biodiversity by sampling for digenetic trematodes in the mudsnail Ilyanassa obsoleta, collected from coastal shoreline environments with or without artificial structures (e.g., bulkheads, seawalls). We found that trematode richness, evenness, and diversity were all greater in snails sampled from natural shorelines versus those with artificial structures. While parasite communities clustered by shoreline type (with vs. without artificial structures), we found no differences in multivariate dispersion, or beta‐diversity, between groups in our system. At the species level, trematodes requiring polychaetes and estuarine fishes as downstream hosts dominated parasite communities at shorelines with artificial structures. These taxa are ubiquitous but more abundant in degraded environments. In contrast, trematode species requiring mollusks and shorebirds as hosts was only documented from shorelines without artificial structures (i.e., natural), which may indicate that these areas are less degraded and have greater overall host diversity. High parasite diversity in easily collected hosts (e.g., mudsnails) provides evidence that the secondary (polychaetes, crustaceans) and tertiary (fishes, shorebirds) hosts required by the parasites are present in the system. Our study also helps validate the concept of surrogate species by demonstrating how a single species can represent broader taxonomic groups. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is an essential indicator for assessing vegetation growth and understanding the dynamics of forest ecosystems and is defined as the ratio of the total leaf surface area in the plant canopy to the corresponding surface area below it. LAI has applications for obtaining information on plant health, carbon cycling, and forest ecosystems. Due to their price and portability, mobile devices are becoming an alternative to measuring LAI. In this research, a new method for estimating LAI using a smart device with a fisheye lens (SFL) is proposed. The traditional Otsu method was enhanced to improve the accuracy and efficiency of foreground segmentation. The experimental samples were located in Gansu Ziwuling National Forest Park in Qingyang. In the accuracy parameter improvement experiment, the variance of the average LAI value obtained by using both zenith angle segmentation and azimuth angle segmentation methods was reduced by 50%. The results show that the segmentation of the front and back scenes of the new Otsu method is more accurate, and the obtained LAI values are more reliable. In the efficiency parameter improvement experiment, the time spent is reduced by 17.85% when the enhanced Otsu method is used to ensure that the data anomaly rate does not exceed 10%, which improves the integration of the algorithm into mobile devices and the efficiency of obtaining LAI. This study provides a fast and effective method for the near-ground measurement of forest vegetation productivity and provides help for the calculation of forest carbon sequestration efficiency, oxygen release rate, and forest water and soil conservation ability. [ABSTRACT FROM AUTHOR]

Common object detection and image segmentation methods are unable to accurately estimate the shape of the occluded fruit. Monitoring the growth status of shaded crops in a specific environment is challenging, and certain studies related to crop harvesting and pest detection are constrained by the natural shadow conditions. Amodal segmentation can focus on the occluded part of the fruit and complete the overall shape of the fruit. We proposed a Transformerbased amodal segmentation algorithm to infer the amodal shape of occluded tomatoes. Considering the high cost of amodal annotation, we only needed modal dataset to train the model. The dataset was taken from two greenhouses on the farm and contains rich occlusion information. We introduced boundary estimation in the hourglass structured network to provide a priori information about the completion of the amodal shapes, and reconstructed the occluded objects using a GAN network (with discriminator) and GAN loss. The model in this study showed accuracy, with average pairwise accuracy of 96.07%, mean intersection-over-union (mIoU) of 94.13% and invisible mIoU of 57.79%. We also examined the quality of pseudo-amodal annotations generated by our proposed model using Mask R-CNN. Its average precision (AP) and average precision with intersection over union (IoU) 0.5 (AP50) reached 63.91%,86.91% respectively. This method accurately and rationally achieves the shape of occluded tomatoes, saving the cost of manual annotation, and is able to deal with the boundary information of occlusion while decoupling the relationship of occluded objects from each other. Future work considers how to complete the amodal segmentation task without overly relying on the occlusion order and the quality of the modal mask, thus promising applications to provide technical support for the advancement of ecological monitoring techniques and ecological cultivation. [ABSTRACT FROM AUTHOR]

The Gore Glen pond is located in the forested floodplain of the Gore River, in the Mid-Lothian region near Edinburgh, Scotland. This work has considered in detail the hydrology, hydrochemistry, and biodiversity of the pond together with all their interactions and in addition interactions with the adjacent Gore River. It is important as it is one of few studies considering all these issues for a pond located in a riverine floodplain. This work shows the pond is connected to the Gore River for discharge events in the river larger than a 1 in 5-year return period. For these events, the pond acts as an online storage feature and will attenuate the flow, thus contributing towards the mitigation of downstream flooding. These large events are also a potential source of the large amount of sediments, as well as adsorbed chemicals, accumulated in the pond. The open water surface of the pond is covered by duckweed, Lemna minor, for most of the year, and that has profound implications for the hydrobiological community and biogeochemical cycling. The system is characterised by nutrient release from sediments due to the extensive hypolimnion anoxia in the summer/autumn period caused by the decomposition of organic matter. Algae are mostly represented by epiphytic diatoms and the cyanobacterium Microcystis, but their development appears to be constrained by light penetration. The eutrophic conditions are also conducive for the population of Difflugia (Protozoa, Rhizopoda). This work improves our understanding of the ecological relations of testate amoebae—an important group which has recently been used as biological indicators for the analysis of paleoecological relationships and paleogeographical reconstructions. [ABSTRACT FROM AUTHOR]

The well-known phenomenon of attracting untypical animals to disturbed territories has been poorly investigated in the polar mountains. We studied arthropod diversity in self-healing industrial clear-cuts and burn areas in the Khibiny Mountains, Kola Polar region. Fieldworks were conducted at four sites, including a control mountain taiga forest and its three transformed variants: burnt forest, uncleared clear-cut, and twice-disturbed burnt clear-cut. Arthropods were collected using formalin traps 2–3, 5–6, and 8–9 years after industrial deforestation in 2012 and an extensive grass-roots fire in 2013. Out of 124 identified species (spiders—61; ground beetles—41; and rove beetles—22), 79 (or 64%) were collected in disturbed, primarily burned areas and were absent in control forest. We note ten species of rove beetles, nine species of ground beetles, and eight species of spiders for the first time in the well-studied arthropod fauna of the Khibiny Mts. We found that grass-root fires transform the soil vegetation covers in the polar mountain forests more powerfully in comparison with extensive deforestation and attract a greater diversity of arthropods with different preferences, enriching the fauna of the polar mountains and the Subarctic region as a whole. The attraction effect persists for, at least, a decade after the violations. [ABSTRACT FROM AUTHOR]

Monitoring and evaluation of soil ecological environments are very important to ensure saline–alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent need for high-performance prediction of soil properties using multi-dimensional data in the WebGIS system. To this end, this study aims to develop a saline–alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction, and data management. The system visually displays the statistical results of air quality, vegetation index, and soil properties in the study area. It provides users with ecological risk assessment functions to analyze heavy metal pollution in the soil. Specially, the system introduces a tree-structured Parzan estimator (TPE)-optimized machine learning model to achieve accurate prediction of soil salinity. The TPE–RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE–GBDT model, which exhibited a strong nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline–alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline–alkali soil ecological protection. [ABSTRACT FROM AUTHOR]

The scarcity of long-term observational data has limited the use of statistical or machine-learning techniques for predicting intraannual ecological variation. However, time-stamped citizen-science observation records, supported by media data such as photographs, are increasingly available. In the present article, we present a novel framework based on the concept of relative phenological niche, using machine-learning algorithms to model observation records as a temporal sample of environmental conditions in which the represented ecological phenomenon occurs. Our approach accurately predicts the temporal dynamics of ecological events across large geographical scales and is robust to temporal bias in recording effort. These results highlight the vast potential of citizen-science observation data to predict ecological phenomena across space, including in near real time. The framework is also easily applicable for ecologists and practitioners already using machine-learning and statistics-based predictive approaches. [ABSTRACT FROM AUTHOR]

Passive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation. [ABSTRACT FROM AUTHOR]

Wet grasslands are crucial components of terrestrial ecosystems, known for their biodiversity and provision of ecosystem services such as flood attenuation and carbon sequestration. Given their ecological significance, monitoring biodiversity within these landscapes is of utmost importance for effective conservation and management strategies. This study, conducted in a wet grassland of Brandenburg, Germany, utilized unmanned aerial vehicles (UAVs) to facilitate the estimation of species richness by the integration of remotely sensed canopy features such as canopy height (CH), spectral data (Vegetation Indices, VI), and texture features (Gray-Level Co-occurrence Matrix, GLCM) using two machine learning methods (Partial Least Square regression (PLS) and Random Forest (RF)). Data was collected over two growing seasons under three different grass cutting regimes, employing multispectral sensors to capture detailed vegetation characteristics. The analysis revealed that the performance of the machine learning methods varied with the feature combinations. Models combining VI and GLCM features demonstrated the highest predictive accuracy, particularly in frequently cut grasslands, as indicated by higher R2 values (up to 0.52) and lower root mean square errors (rRMSE as low as 34.9 %). RF models generally outperformed PLS models across different feature sets, with the CH + VI + GLCM combination yielding the best results. These findings underscore the potential of spectral and textural data to effectively capture the ecological dynamics of wet grasslands, providing valuable insights into biodiversity patterns.

The soundscape encompasses all biological, geophysical, and anthropogenic sounds in an environment and serves as an important communication medium for biota. While macro-level observations have suggested a correlation between the acoustic and physical environments, detailed and synthesized empirical evidence establishing nuanced relationships remains limited. Establishing empirical relationships between the soundscape and the environment is essential to ensure monitoring programs are grounded in robust ecological principles. However, empirical evidence about the relationship between the soundscape, environmental attributes, and biotic communities is scarce and scattered. Here, we synthesize evidence about correlations between the soundscape and at least one environmental variable or indicator. We analyse where (location) and when (year) the relationships between the soundscape and the environment have been investigated, what study designs and methods have been used to quantify these relationships, and what relationships were found between the soundscape and environmental or biological attributes. Our key findings include (1) the relationship between the soundscape and the environment is underexplored in aquatic ecosystems – particularly freshwater; (2) riparian and underwater soundscapes are seldom monitored simultaneously; and (3) the relationships found are not tested across various climate regions, elevations, temporal and spatial scales, or study designs. Since most studies use acoustic indices to analyse their soundscape, future research needs to assess whether the relationships between the soundscape and the environment change under different audio processing methods. We also show there is potential to extend the likes of acoustic monitoring far beyond monitoring soniferous biota populations, to include monitoring ecological health, ecosystem processes, and non-soniferous biota. Finally, we present a list of key questions to increase our knowledge of the connection between the soundscape and the environment. These questions will promote a solid conceptual base for future acoustic monitoring programs that enhance the interpretation of acoustic data, develop new technologies, and consider a broader cross-system (land to water) context.

Forests around the world are under immense pressure from human land use and climate change. Old-growth and primary forests have been degraded in recent decades, yet are generally more resilient and resistant to climate change effects compared to human-modified forests. Nowhere is this more evident than in Russian Siberia, which contains almost one-fifth of the world’s forest area and has been subjected to a variety of land uses and disturbances since the mid-20th century. Although a number of related geospatial products exist, there are no large-scale maps of old-growth and primary forests across Siberia. However, remotely sensed metrics of forest stability have been shown to relate to old-growth and primary forests in tropical and boreal environments. Here we apply stability indices from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors across boreal Siberia from 2003 to 2020. Our results indicate that forests in the central and southern taiga contain most areas of high stability, but also distinct zones of disturbance and low stability. We identified three regions with particularly low forest stability: (i) the Zabaikal region in southern Siberia, (ii) a portion of the central taiga spanning the Republic of Sakha (Yakutia), and (iii) the West Siberian lowlands. This approach can be used to monitor Siberian boreal forest condition, and could be applied to other boreal forested regions.

This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands‐on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera‐captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture–recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time‐consuming compared with physical captures and tagging. [ABSTRACT FROM AUTHOR]

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities. [ABSTRACT FROM AUTHOR]

In the European Mediterranean Region, palm trees are a common element in cities and semi-urban landscapes and have become important habitat structures for local fauna. This study aimed to monitor the invertebrate and vertebrate fauna occurring on and associated with ornamental palms of the genus Phoenix, over the course of one year. Five study sites were used in southern Spain, with varying levels of management. Several complementary methods were applied monthly in order to assess the vertebrates and invertebrates associated with the full seasonal cycle of palms, including flowering and fruiting. The study resulted in the identification of 216 invertebrate families from seven different classes and 89 vertebrate species, consisting of 62 bird, 20 mammal (including bats), six reptile and one amphibian species associated with Phoenix palms. It thus highlights that Phoenix palms provide a habitat for many species and individuals over the course of one year. [ABSTRACT FROM AUTHOR]

The European Habitats Directive prescribes Member States to report on trends in abundance and distribution of protected species. One of these species is Graphoderus bilineatus (De Geer, 1774), a middle-sized predaceous aquatic beetle, listed in Annex II and IV of the Habitats Directive. In the Netherlands, a monitoring scheme for this species has been set up to assess the national trend as well as the trend in the national Natura2000 sites. In this scheme, a selection of 1 km2 squares is surveyed in cycles of six years using a standardized field method by professional fieldworkers. In each selected square, five locations were sampled per annum (September–October) by two different observers using hand netting. The data of the first two rounds (2011–2016 and 2017–2022) have been analysed using both an occupancy model and a Poisson GLMM. We found evidence of a declining trend in occupancy as well as in population size. The decreases were stronger outside than within Natura2000 sites and also stronger than those of three other beetle species that are often found together with G. bilineatus. In addition, considerable differences between observers were detected in the data, despite the application of a standardized field method. Implications for insect conservation: Graphoderus bilineatus is declining and the decline is stronger outside Natura2000 sites then within, most likely due to differences in the development of water quality, vegetation structure and water management. Further research is required to identify the exact causes of the decline; however water quality improvement seems essential to turn the tide. The study also shows the benefits of a monitoring scheme for trend assessments. The recent decline would probably not have been noticed based on distribution data alone. [ABSTRACT FROM AUTHOR]

Coral reefs are spatially variable ecosystems that form biogenic structures ranging in size from 10 to 1000s of meters. Their changes in response to anthropogenic stress are occurring across increasingly broad scales, yet our ability to detect, understand and respond to these changes at relevant scales is limited. Traditional in-water observation-based coral reef ecology and remote sensing-based methods both offer valuable insights into benthic change, but their relative scalability and use to-date must be understood to inform optimal future research approaches. We conducted a systematic literature review comparing the approaches used to quantify benthic habitat, through traditional in-water ecological studies and remote sensing studies, with respect to: (a) their geographic distribution, (b) reef zone selection, and c) their focal questions. Among the 199 studies reviewed, traditional ecological studies primarily concentrated on community composition (89%), using high-detail direct measurements, especially from the reef slope (80%). By contrast, remote sensing studies provided spatially explicit datasets at coarser spatial and thematic resolutions, with a predominant focus on benthic mapping (72%) across entire reef systems. Only 3% of studies integrated both approaches, combining comprehensive in-situ observations with broadscale remote sensing. As anthropogenic stressors continue to increase in scale, bridging these scientific disciplines offers a promising way to upscale observations to entire reef-scape scales. We identify steps to harness the strengths of both fields and integrate multiple tools at various levels of resolution and scale. Such bridging approaches offer a way forward in understanding and managing coral reef functioning in the Anthropocene. [ABSTRACT FROM AUTHOR]

Abstract Researchers often use surrogate species for assessing biodiversity—organisms that can indicate the presence of additional taxa or provide insight into environmental processes. As indicators of anthropogenic impact, “cross‐taxon” surrogates are theorized to be effective shortcuts for determining how communities respond to environmental change. However, few studies have implemented cross‐taxon surrogates given the challenge of validating the strength of the relationship between surrogates and their target taxa. Trophically transmitted parasites require multiple invertebrate and vertebrate taxa to complete their life cycles (e.g., annelids, mollusks, fishes, shorebirds), making them ideal cross‐taxon surrogates of community diversity. By sampling for these parasite surrogates, it is theoretically possible to infer the composition of the overall host community based on the species of parasites present. In our study, we tested the use of parasites as cross‐taxon surrogates of biodiversity by sampling for digenetic trematodes in the mudsnail Ilyanassa obsoleta, collected from coastal shoreline environments with or without artificial structures (e.g., bulkheads, seawalls). We found that trematode richness, evenness, and diversity were all greater in snails sampled from natural shorelines versus those with artificial structures. While parasite communities clustered by shoreline type (with vs. without artificial structures), we found no differences in multivariate dispersion, or beta‐diversity, between groups in our system. At the species level, trematodes requiring polychaetes and estuarine fishes as downstream hosts dominated parasite communities at shorelines with artificial structures. These taxa are ubiquitous but more abundant in degraded environments. In contrast, trematode species requiring mollusks and shorebirds as hosts was only documented from shorelines without artificial structures (i.e., natural), which may indicate that these areas are less degraded and have greater overall host diversity. High parasite diversity in easily collected hosts (e.g., mudsnails) provides evidence that the secondary (polychaetes, crustaceans) and tertiary (fishes, shorebirds) hosts required by the parasites are present in the system. Our study also helps validate the concept of surrogate species by demonstrating how a single species can represent broader taxonomic groups.

Abstract Passive acoustic recorders have emerged as powerful tools for ecological monitoring. However, effective monitoring is not simply an act of recording sounds. To have meaning for conservation and management, acoustic monitoring needs to be properly planned and analyzed to yield high quality information. Here, we provide a set of considerations for the design of an effective acoustic monitoring program. We argue that such a program, has the following attributes: (1) has established appropriate partnerships with landowners, Traditional Owners, researchers, or other relevant stakeholders, (2) is based on clear objectives and questions, (3) is explicit in its target sound signals, (4) has considered in‐field sensor placement for a range of factors, including experimental design, statistical power, background noise, and potential impacts on human privacy and animal disturbance, (5) has a justified recording schedule and periodicity, (6) has methods to process sound data in line with objectives, and (7) has protocols for permanent data storage and access. Acoustic monitoring is increasingly used in large‐scale programs and will be important in addressing global biodiversity targets and new biodiversity markets. It is critical that new monitoring programs are designed to effectively and efficiently capture data that address pertinent and emerging issues in conservation.

Riparian habitats are the most crucial yet the most fragile ecosystems, focal to safeguarding both the aquatic and terrestrial regions. Technology such as remote sensing, now powered by cloud-based server-side processing of high-resolution satellite imagery, and Big Data analytics, such as Google Earth Engine (GEE) combined with uncrewed aerial vehicles (UAVs), have accentuated ecological monitoring of natural habitats. This study leverages a nested approach to remote sensing, combining satellite data and UAV imagery to evaluate the present condition of riparian habitats along the Ganga River in the Upper Gangetic Plains. We used GEE to analyze Sentinel data and identify critical habitats, encompassing wetlands, grasslands, scrublands, plantations, river islands, and riparian forests in the study area. Strategic locations covering 291 km2 area were delineated, and over 1000 patches of 1 ha were isolated, with the largest patch of 23.99 km2 in Haiderpur. Furthermore, UAV-based data were collected for key identified regions. The status of a total of 284 field surveyed points was categorized as 29 intact grassland patches, 87 good habitat patches, 25 patches recently converted to agriculture, and 60 patches being converted to agriculture, remaining plantations, and waterbodies. UAV-based raster thematic maps of four key habitat regions generated using object-based image analysis classification found a promising approach for high-precision riparian habitat mapping, monitoring, and management, offering data quality, cost optimization, and time savings with an overall accuracy of 98% and kappa coefficient 0.97. UAV are, thus, effective tools for reach-level assessment of freshwater habitats, especially of smaller stream networks, retaining fine-scale riverscape information of the mosaic of land use and vegetation types.

Monitoring of global climate regulation ecosystem services is needed to inform national accounts, meet emission targets, and evaluate nature-based climate solutions. As carbon monitoring is context-dependent, the most useful methodological approach will depend on the spatial extent and resolution, temporal frequency, baseline, available data, funding, and dominant drivers of change, all of which will impact results and interpretation. Here, focusing on above and belowground carbon storage and sequestration, we review four groups of methods for estimating trends in carbon over time: (1) field-based measurements, (2) land cover maps with reference carbon values by land cover type, (3) statistical and machine learning models linking field measurements to remotely sensed data, and (4) mass balance models representing key carbon pools and flows between them. We discuss strengths, limitations, and best practices for each method to assist researchers in implementing an approach or critically evaluating whether an existing carbon dataset can be used for a different project. The best methods often account for spatial variability of carbon, ecosystem interconnections, and temporal stability of carbon stocks against future environmental changes. Effective carbon monitoring can help determine optimal conservation, restoration, and/or land management interventions with win-win outcomes for both conservation and nature-based climate solutions.

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends.

Classifying specimens is a critical component of ecological research, biodiversity monitoring and conservation. However, manual classification can be prohibitively time‐consuming and expensive, limiting how much data a project can afford to process.Computer vision, a form of machine learning, can help overcome these problems by rapidly, automatically and accurately classifying images of specimens. Given the diversity of animal species and contexts in which images are captured, there is no universal classifier for all species and use cases. As such, ecologists often need to train their own models. While numerous software programs exist to support this process, ecologists need a fundamental understanding of how computer vision works to select appropriate model workflows based on their specific use case, data types, computing resources and desired performance capabilities. Ecologists may also face characteristic quirks of ecological datasets, such as long‐tail distributions, 'unknown' species, similarity between species and polymorphism within species, which impact the efficacy of computer vision.Despite growing interest in computer vision for ecology, there are few resources available to help ecologists face the challenges they are likely to encounter. Here, we present a gentle introduction for species classification using computer vision. In this manuscript and associated GitHub repository, we demonstrate how to prepare training data, basic model training procedures, and methods for model evaluation and selection. Throughout, we explore specific considerations ecologists should make when training classification models, such as data domains, feature extractors and class imbalances.With these basics, ecologists can adjust their workflows to achieve research goals and/or account for uncertainty in downstream analysis. Our goal is to provide guidance for ecologists for getting started in or improving their use of machine learning for visual classification tasks. [ABSTRACT FROM AUTHOR]

The use of biological indicators in a bioassessment approach is important for inferences of anthropogenic stress in routine monitoring programs. One of the primary assumptions of bioassessment is that biological indicators observed at specific sampling sites will allow for inferences of local environmental quality; however, this assumption requires a reliable understanding of dispersal processes, which is particularly relevant in river systems due to their dendritic network. Inter-stream dispersal between different points of the river network could bias bioassessment, especially for highly mobile organisms like fish. Here, we examine standard biological metrics used in routine biomonitoring to determine how spatial variables, including dispersal, influence inferences between fish populations and environmental gradients. Using redundancy analysis (RDA) and variation partitioning, we tested the relative influence of both environmental and spatial variables on fish community structure and related community metrics. Fish were collected from 99 sampling sites distributed across 44 rivers and streams of the Great Morava River Basin, Serbia. Electroconductivity, the percentage of agricultural areas, dissolved oxygen, ammonia, and nitrate-nitrogen were found to be significant environmental variables, while ten spatial predictors from broad- to small-scales were found to influence fish community structure and community metrics. Our results suggest that contemporary dispersal among streams influences fish community composition, but that trait-based metrics are less sensitive than basic measures of diversity to spatial processes. Our results highlight the need for spatially independent sampling, as well as the need to consider dispersal-based processes in routine biomonitoring. [ABSTRACT FROM AUTHOR]

The outcome of the first tests of a measuring system designed for the monitoring of atmospheric aerosol and meteorological parameters is considered. Based on the measurement results from August 2022, the data of the system prototypes, a calibrated optical aerosol sensor, and the on-site meteorological station are compared. The revealed drawbacks of the system design are described. The plans to improve and extend the system capabilities are discussed. A blueprint of a future multipoint aerosol monitoring network in Yekaterinburg and its neighborhood is presented. [ABSTRACT FROM AUTHOR]

Abstract This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands‐on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera‐captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture–recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time‐consuming compared with physical captures and tagging.

Marine and estuarine habitat degradation threatens ecosystem function and delivery of ecosystem services. An increasing number of management interventions aiming to improve ecological condition within impacted marine and estuarine habitats are being implemented. Monitoring the ecological outcomes of management interventions to evaluate their effectiveness supports adaptive management. However, the lack of a standardised set of indicators has impeded reliable assessment and knowledge sharing. The objective of this research project is to develop a cross-ecosystem standardised indicator framework to assess changes in benthic habitat conditions. The rapid Marine and Estuarine Condition Assessment Tool (MarECAT) was developed for Queensland, Australia; however, it can be applied elsewhere. A literature review was undertaken to identify indicators and metrics for habitat condition assessment that were reviewed by subject matter experts through a series of Technical Group meetings. Three indicator groups were identified based on the presence or absence of structural macrobiota attributes (i.e., macroflora, macrofauna, and substrate-dominated). The panel of experts endorsed a list of 42 condition indicators with associated metrics representing all ecosystem components, enabling a comprehensive assessment of habitat condition for a rapid assessment tool. A level of confidence nominated by practitioners was allocated to each condition indicator metric to inform the interpretation of assessments. Another outcome of the expert workshops was the endorsement of 10 threat indicators representing key pressures in marine and estuarine habitats, along with a specific assessment scale. The scoring method developed for the MarECAT will facilitate reliable assessment of management intervention outcomes and implementation of adaptive management to improve project success.

This study introduces the AquaShan Index, an innovative approach to assessing river health, which integrates biological indicators with key environmental variables. This index represents an advancement over traditional methods, like the Shannon Diversity Index, by including both species diversity and environmental factors such as dissolved oxygen, pH, and water temperature. Utilizing data from the Stream/River Ecosystem Survey and Health Assessment in South Korea, covering 5,952 data over three years, the study combines species data of benthic macroinvertebrates with environmental parameters, applying Random Forest analysis to weight these variables. The AquaShan Index formula incorporates species’ pollution tolerance scores, offering a nuanced perspective on ecosystem resilience to environmental stressors. Our results show a wide range of river health conditions, with the AquaShan Index demonstrating greater sensitivity and specificity in identifying ecological impairments compared to traditional indices. The inclusion of pollution tolerance scores and environmental variables provides a more comprehensive assessment of river health, revealing a higher number of sites with ecological challenges than previously identified. The AquaShan Index's adaptability and comprehensive approach make it a valuable tool for informed decision-making in river conservation and management, offering an enhanced methodology for assessing and sustaining river ecosystem health.

At least 10 communities of macrozoobenthos are determined by modern statistical methods on soft sediments in the coastal areas of Peter the Great Bay with the depth of ≥ 5 m surveyed in 1992, 1993 and 1996. They were: I. Scoletoma spp. + Ophiura sarsii; II. Aphelochaeta pacifica (I–II — in the Strelok Bay and Rifovaya Inlet); III. O. sarsii + Amphiodia fissa; IV. Scoletoma spp. + Mactra chinensis; V. Scoletoma spp. + Maldane sarsi; VI. Acila insignis (III–VI — in the Posyet Bay and adjacent area); VII. Scoletoma spp. + Echinocardium cordatum; VIII. Dipolydora cardalia + M. sarsi + O. sarsii; IX. Spiophanes uschakovi + E. cordatum, and X. Ampelisca macrocephala (VII–X — in the area at the Tumannaya/Tumen River mouth). Their development was determined by such environmental imperatives as the bottom sediments properties (silting degree, mean grain size), depth, and level of pollution and eutrophication. Majority of these communities (I, III, IV, V, VII, VIII, IX, and X) were in good ecological condition. Their habitats were slightly disturbed (intact or almost intact) and rare or never polluted and eutrophicated. The community II occupied more disturbed habitats with moderate ecological status reflected in higher indices of pollution. This community was rather likely formed in the process of succession in conditions of decreasing pollution. The community VI had a lower ecological status; its differentiation was probably caused by specific oceanographic regime of the Posyet Bay and anthropogenic impacts.

At least 16 communities of macrozoobenthos are determined by modern statistical methods on soft sediments in the coastal areas with the depth of ≥ 5 m of Peter the Great Bay at Vladivostok surveyed in 2001–2019. They were: I. Scoletoma spp. + Sigambra bassi; II. Scoletoma spp. + Ophiura sarsii; III. Philine orientalis + Macoma sp.; IV. Aphelochaeta pacifica; V. A. pacifica + Capitella capitata; VI. O. sarsii + Macoma scarlatoi; VII. O. sarsii + Scoletoma spp. + Scoloplos armiger + Ennucula tenuis; VIII. Phoronopsis harmeri; IX. Maldane sarsi; X. A. pacifica + Dipolydora cardalia; XI. Scoletoma spp.; XII. Praxillella gracilis + Pelonaia corrugata, XIII O. sarsii + Acila insignis, XIV Scoletoma spp. + O. sarsii + M. sarsi; XV. Scoletoma spp. + M. scarlatoi, and XVI. A. pacifica. Their differentiation was determined by such environmental imperatives as the level of chemical pollution, eutrophication, and impact of terrigenous water discharge. By biotic indices (AMBI, M-AMBI, TPFbio), the most damaged and environmentally disturbed communities were IV, V, and XVI inhabited the Golden Horn Bay and Diomid Inlet. The communities of the East Bosphorus Strait (III, VI) have higher ecological status that increases even more in the Amur and Ussuri Bays (communities I, II, VII, XII, XIII, XV, XIV), except of the northern tips of both bays (IX, XI) and the eastern part of the Amur Bay (VIII, X). With such bioindicative approach, ecological status of the communities is described more concisely and visually than with indices of species richness, diversity, or W-statistics.

Ecological monitoring is an important part of environmental protection. To monitor the movement and abundance of animals in the airspace, an Aerial Ecological Monitoring System (AEMS) is developed by CMA Meteorological Observation Center for China's next-generation weather radar (CINRAD) network. Characteristics of weather radar clear air echo data and airborne biological scattering data are studied to identify biological echoes through fuzzy logic algorithm, and the system can monitor real-time ecological activities of insects such as biological density, migration path and space-time distribution.Weather Radar Airborne Ecological Monitoring System has been put into trial operation since May 2022. During the real-time monitoring period, it's found that the insect activity shows obvious spatial and temporal distribution characteristics. From August to September, pests are of large quantity and wide range, indicating urgent need of insect disaster prevention and control. In May, June, September, and October, insect activity gradually increases from 2000 BT every day, reaches its peak from 2200 BT to 2300 BT, gradually decreases thereafter, and disappears mostly by 0600 BT. In July and August, insect activity gradually increases from 2000 BT every day, with a peak from 2100 BT to 2200 BT. Insect activity begins during the daytime, increases at 0600 BT, becomes more frequent at 1300 BT, and gradually decreases thereafter. From May to July, there is a significant shift from south to north (i.e., northward migration process), and in late August, it quickly changes into a to southward migration. The southward migration process of insects is larger and more numerous than the northward migration process. It's verified that the system can effectively monitor real-time aerial ecology, providing technology and data support for precise pest control.However, characteristics of pests need further research and clear distribution of pests is an urgent need. Therefore, in-depth research will be carried out on aerial ecological classification technology, combined with other direct observation means such as real-time monitoring by drones to explore the relationship between radar detection and different pests, and improve the ability to identify different kinds of insects.

This study aims to provide a comprehensive analysis of the impacts of high-intensity coal mining on vegetation in Liangbei Town, a typical deep coal mining area in central of China. Using Landsat remote sensing data from 2000 to 2023, processed by the Google Earth Engine (GEE) platform, the study calculates the Normalized Difference Vegetation Index (NDVI). Temporal and spatial distribution patterns of vegetation were assessed using LandTrendr algorithm, Sen’s slope estimation, the Mann–Kendall test, the coefficient of variation, and the Hurst index. Vegetation growth dynamics were further analyzed through transfer matrix and intensity analysis frameworks. Driving factors influencing vegetation trends were evaluated using local climate data and surface deformation variables from SAR imagery. Temporal Dimension: From 2000 to 2023, the annual NDVI in Liangbei Township showed an upward trend with a growth rate of 0.0894 (10a)−1, peaking at 0.51 in 2020. Spatial Dimension: The NDVI distribution in Liangbei Township displayed a pattern of being lower in the center and higher around the edges, with values concentrated between 0.4 and 0.51, covering 50.34% of the total area. Trend of Change: Between 2000 and 2023, 83.28% of the area in Liangbei Township experienced significant improvement in the NDVI, with vegetation growth trends shifting primarily from slight to significant improvement, encompassing a total area of 10.98 km². This shift exhibited a marked tendency. Driving Factors: Deep mining in Liangbei Township is concentrated in the eastern part, with SAR imagery indicating a maximum surface subsidence of 0.26 m. As surface subsidence increases, the NDVI significantly decreases. The findings suggest that in the future, 91.13% of the vegetation in Liangbei Township will display an antipersistent change trend. The study offers critical insights into the interaction between mining activities and vegetation cover can serve as a reference for environmental evolution and management in similar mining areas.

The eastern coastline of James Bay is known to have been home to sizeable eelgrass beds (Zostera marina L.) which thrived in the bay’s shallow, subarctic waters. The region was subjected to substantial hydroelectric dams, large fires, and other human activities in the past half-century. To assess the impact of these factors on eelgrass beds, a historical reconstruction of eelgrass bed distribution was performed from images acquired by Landsat-5 Thematic Mapper (TM) in 1988, 1991, and 1996 and images of the Landsat-8 Operational Land Imager (OLI) in 2019. All the images were classified using the Random Forests classifier (RF) and assessed for accuracy each year on a bay-wide scale using an independent field validation dataset. The validation data were extracted from an eelgrass bed map established using aerial photos and field surveys in 1986, 1991, and 1995 and from a field survey in 2019. The overall validation accuracy of the classified images (between 72% and 85%) showed good agreement with the other datasets for most locations, providing reassurance about the reliability of the research. This makes it possible to use satellite imagery to detect past changes to eelgrass distribution within a bay. The classified images of 1988 and 1996 were also compared to aerial photos taken in years close to each other at ten sites to determine their ability to assess small eelgrass beds’ shape and presence. Such a comparison revealed that the classified images accurately portrayed eelgrass distribution even at finer scales.

The well-known phenomenon of attracting untypical animals to disturbed territories has been poorly investigated in the polar mountains. We studied arthropod diversity in self-healing industrial clear-cuts and burn areas in the Khibiny Mountains, Kola Polar region. Fieldworks were conducted at four sites, including a control mountain taiga forest and its three transformed variants: burnt forest, uncleared clear-cut, and twice-disturbed burnt clear-cut. Arthropods were collected using formalin traps 2–3, 5–6, and 8–9 years after industrial deforestation in 2012 and an extensive grass-roots fire in 2013. Out of 124 identified species (spiders—61; ground beetles—41; and rove beetles—22), 79 (or 64%) were collected in disturbed, primarily burned areas and were absent in control forest. We note ten species of rove beetles, nine species of ground beetles, and eight species of spiders for the first time in the well-studied arthropod fauna of the Khibiny Mts. We found that grass-root fires transform the soil vegetation covers in the polar mountain forests more powerfully in comparison with extensive deforestation and attract a greater diversity of arthropods with different preferences, enriching the fauna of the polar mountains and the Subarctic region as a whole. The attraction effect persists for, at least, a decade after the violations.