Ecological Monitoring Research Articles

Effective ecological monitoring using passive acoustic sensors: Recommendations for conservation practitioners

AbstractPassive 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.

InsectSound1000 An insect sound dataset for deep learning based acoustic insect recognition

InsectSound1000 is a dataset comprising more than 169000 labelled sound samples of 12 insects. The insect sound level spans from very loud (Bombus terrestris) to inaudible to human ears (Aphidoletes aphidimyza). The samples were extracted from more than 1000 h of recordings made in an anechoic box with a four-channel low-noise measurement microphone array. Each sample is a four-channel wave-file of 2500 kHz length, at 16 kHz sample rate and 32 bit resolution. Acoustic insect recognition holds great potential to form the basis of a digital insect sensor. Such sensors are desperately needed to automate pest monitoring and ecological monitoring. With its significant size and high-quality recordings, InsectSound1000 can be used to train data-hungry deep learning models. Used to pretrain models, it can also be leveraged to enable the development of acoustic insect recognition systems on different hardware or for different insects. Further, the methodology employed to create the dataset is presented in detail to allow for the extension of the published dataset.

A review of the satellite remote sensing techniques for assessment of runoff and sediment in soil erosion

Abstract Soil erosion monitoring is essential for the ecological evaluation and dynamic monitoring of land resources via remote sensing technology. In this paper, we provide new insights into the existing problems and development directions of traditional models, which are supported by new technologies. An important role is played by remote sensing information acquisition technology in the qualitative and quantitative evaluation of soil erosion, and the data and technical support provided are systematically reviewed. We provide a detailed overview of the research progress associated with empirical statistical models and physically driven process models of soil erosion, and the limitations of their application are also summarized. The preliminary integration of remote sensing data sources with high spatial and temporal resolution and new technologies for soil erosion monitoring enables the high-precision quantitative estimation of sediment transport trajectories, the watershed river network density, and the terrain slope, enhancing the accuracy of erosion factor identification, such as spectral feature recognition from erosion information, gully erosion feature extraction, and vegetation coverage estimation. However, the current erosion models, driven by algorithms and models, are not comprehensive enough, particularly in terms of the spatial feature extraction of erosion information, and there are limitations in the applicability and accurate estimation of such models.

Research on Hyperspectral Surface Reflectance Dataset of Typical Ore Concentration Area in Hami Remote Sensing Test Field

Abstract. Surface reflectance data is the basic data source for the hyperspectral parametric remote sensing products and remote sensing quantitative application, which is widely used in various application fields such as natural resources and ecological environment monitoring. At present, multispectral data takes the leading role among the common land surface reflectance datasets and the reflectance data mainly involves the types of ground objects such as farmland, forest land, water body, soil, etc., while the datasets relatively less targets the types of rock and mineral surface objects, yet especially the reflectance datasets with the combination of time series and multi-scale satellite-earth are even more scarce. In order to better promote the application of hyperspectral surface reflectance and explore the advantages of joint application of satellite-earth multi-scale reflectance data, on the basis of field-measured rock and mineral target spectral, a comprehensive surface reflectance dataset was generated by using domestically produced hyperspectral satellite data as the data source in this study, mainly focusing on the typical ore concentration area in the Hami Remote Sensing test field in Xinjiang. The dataset includes multi-period hyperspectral satellite surface reflectance images, field measured rock and mineral spectral data, and multi-period sub-pixel spectral data collected based on ground spectral measured points, which can provide significant support for the research and development and accuracy verification as well as performance evaluation of algorithms such as surface reflectance inversion, mineral identification and ground object classification.

Ecological Monitoring of Pollution of Surface Waters of the Volga River Basin with Phenol and its Derivatives

The results of gas chromatographic control of the content of phenol and its derivatives in the surface waters of the Volga River basin are presented. The assessment of the water quality of the studied reservoir by categories of pollution at different times of the year was carried out. The results of environmental monitoring of the surface waters of the Volga River basin, taking into account the transformation of phenols into more toxic compounds, are discussed.

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.

Analysis of spatial and temporal changes in vegetation cover and its drivers in the Aksu River Basin, China

Exploring vegetation dynamics in arid areas and their responses to different natural and anthropogenic factors is critical for understanding ecosystems. Based on the monthly MOD13Q1 (250 m) remote sensing data from 2000 to 2019, this study analyzed spatio-temporal changes in vegetation cover in the Aksu River Basin and predicted future change trends using one-dimensional linear regression, the Mann–Kendall test, and the Hurst index. Quantitative assessment of the magnitude of anthropogenic and natural drivers was performed using the Geodetector model. Eleven natural and anthropogenic factors were quantified and analyzed within five time periods. The influence of the driving factors on the changes in the normalized difference vegetation index (NDVI) in each period was calculated and analyzed. Four main results were found. (1) The overall vegetation cover in the region significantly grew from 2000 to 2019. The vegetation cover changes were dominated by expected future improvements, with a Hurst index average of 0.45. (2) Land use type, soil moisture, surface temperature, and potential vapor dispersion were the main drivers of NDVI changes, with annual average q-values above 0.2. (3) The driving effect of two-factor interactions was significantly greater than that of single factors, especially land use type interacts with other factors to a greater extent on vegetation cover. (4) The magnitude of the interaction between soil moisture and potential vapor dispersion and the magnitude of the interaction between anthropogenic factors and other factors showed an obvious increasing trend. Current soil moisture and human activities had a positive influence on the growth of vegetation in the area. The findings of this study are important for ecological monitoring and security as well as land desertification control.

The First International eDNA Workshop in Hong Kong: A beginner's guide for the next‐generation eDNA researcher

AbstractThe field of environmental DNA (eDNA) has rapidly advanced in recent years, providing a non‐invasive and time‐saving method for assessing biodiversity. The First International Environmental DNA (eDNA) Workshop in Hong Kong was held from 16 to 27 October 2023 and provided early career professionals with hands‐on training and collaboration opportunities in eDNA research. With support from The Croucher Foundation Limited (Hong Kong), the workshop covered all stages of an eDNA‐based research project, including study design, field sampling, eDNA extraction, library preparation, bioinformatics, statistical data analysis, and ethics in scientific research. Participants gained insights into the principles and practical applications of eDNA technology, emphasizing the importance of careful experimental design and data interpretation. The workshop also highlighted the need for standardized protocols, comprehensive and local DNA reference databases, and careful selection of primer sets to overcome current issues and limitations. Workshop participants expressed enthusiasm for the potential of eDNA metabarcoding as a valuable tool for ecological monitoring, biodiversity assessment, and conservation decision‐making. The future of eDNA research looks promising overall, with continued advancements in technology, collaboration among researchers, and the integration of eDNA into large‐scale ecological monitoring. Future iterations of the Hong Kong International eDNA workshop will continue to provide hands‐on training and collaboration opportunities for early career professionals interested in eDNA research, focusing on addressing current limitations and challenges in the field.

Application and prospects of hyperspectral remote sensing in monitoring plant diversity in grassland

The biodiversity of grasslands is important for ecosystem function and health. The protection and mana-gement of grassland biodiversity requires the collection of the information on plant diversity. Hyperspectral remote sensing, with its unique advantages of extensive coverage and high spectral resolution, offers a new solution for long-term monitoring of plant diversity. We first reviewed the development history of hyperspectral remote sensing technology, emphasized its advantages in monitoring grassland plant diversity, and further analyzed its specific applications in this field. Finally, we discussed the challenges faced by hyperspectral remote sensing technology in its applications, such as the complexity of data processing, accuracy of algorithms, and integration with ground-based remote sensing data, and proposes prospects for future research directions. With the advancement of remote sensing technology and the integrated application of multi-source data, hyperspectral remote sensing would play an increasingly important role in grassland ecological monitoring and biodiversity conservation, which could provide scientific basis and technical support for global ecological protection and sustainable development.

Estimation of contemporary effective population size in plant populations: Limitations of genomic datasets.

Effective population size (N e) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate N e have been preferred over demographic methods because they rely on genetic data rather than time-consuming ecological monitoring. Methods based on linkage disequilibrium (LD), in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A software program based on the LD method, GONE, looks particularly promising to estimate contemporary and recent-historical N e (up to 200 generations in the past). Genomic datasets from non-model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNP genotyping is usually based on reduced-representation methods. In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating N e using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect N e estimation using the LD method, such as the occurrence of population structure. We show how accuracy and precision of N e estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data. We finally compare the N e estimates obtained with GONE for the last generations with the contemporary N e estimates obtained with the programs currentNe and NeEstimator.

Field collections and environmental DNA surveys reveal topographic complexity of coral reefs as a predictor of cryptobenthic biodiversity across small spatial scales

AbstractCoral reefs represent some of the most biodiverse ecosystems in the world but are currently undergoing large‐scale degradation due to anthropogenic stressors. Such degradation usually begins with coral bleaching, and if the stress condition is inflicted for too long may eventually result in loss of structural complexity (or “flattening”) of the reef, dramatically changing habitat availability for reef‐associated fauna. Despite having been linked to important ecosystem functions, cryptobenthic organisms are often overlooked in ecological monitoring programs, and their microhabitat dependencies are poorly understood. Here, we combined collection‐based biodiversity monitoring techniques with five different environmental DNA (eDNA) sampling techniques (Reef water, sediment, crevice water, slurp gun, and bulk sediment) to survey cryptobenthic fishes and annelids on a Maldivian fringing coral reef. Collectively, 176 fish and 140 annelid taxa were detected with eDNA across 14 surveyed sites, more than doubling the reported annelid taxa in the region with 88 new occurrences. Water filtered near the reef structure revealed the highest species richness out of the five eDNA sampling techniques tested. Furthermore, we found correlations between fish species richness and topographic complexity for both collection‐ and eDNA‐based techniques. This suggests that detection by eDNA may be linked to site‐specific predictors and reveal community differences across small spatial scales (tens of meters). We also report that reef flattening (going from structural complex to less complex sites) can cause a 50% reduction in fish diversity and that cryptobenthic fish species richness was highly associated with branching corals. In contrast, annelid communities showed no clear correlations with environmental predictors, but co‐amplification of non‐target, non‐annelid taxa may have distorted such correlations if present. This suggest that the predictive powers of eDNA for environmental gradients may be dependent on the targeted taxa.

GIS platform ecological water environment pollution automatic monitoring and analysis

This paper summarizes the application of GIS platform for automatic monitoring of ecological water environmental pollution. Firstly, the function of GIS platform and its combination with environmental monitoring are briefly introduced, and then the pollution status of ecological water environment is analyzed, including the source of pollution, classification and the impact on environment and human beings. This paper focuses on the application of GIS in data collection and integration, spatial distribution analysis of pollutants, simulation and prediction of pollution diffusion, design and implementation of monitoring system, etc. Through the introduction of GIS technology, the monitoring and management of ecological water environmental pollution can be realized more effectively, which provides a strong support for environmental protection.

PL-DINO: An Improved Transformer-Based Method for Plant Leaf Disease Detection

Agriculture is important for ecology. The early detection and treatment of agricultural crop diseases are meaningful and challenging tasks in agriculture. Currently, the identification of plant diseases relies on manual detection, which has the disadvantages of long operation time and low efficiency, ultimately impacting the crop yield and quality. To overcome these disadvantages, we propose a new object detection method named “Plant Leaf Detection transformer with Improved deNoising anchOr boxes (PL-DINO)”. This method incorporates a Convolutional Block Attention Module (CBAM) into the ResNet50 backbone network. With the assistance of the CBAM block, the representative features can be effectively extracted from leaf images. Next, an EQualization Loss (EQL) is employed to address the problem of class imbalance in the relevant datasets. The proposed PL-DINO is evaluated using the publicly available PlantDoc dataset. Experimental results demonstrate the superiority of PL-DINO over the related advanced approaches. Specifically, PL-DINO achieves a mean average precision of 70.3%, surpassing conventional object detection algorithms such as Faster R-CNN and YOLOv7 for leaf disease detection in natural environments. In brief, PL-DINO offers a practical technology for smart agriculture and ecological monitoring.

Spatiotemporal Pattern and Drivers of Ecological Quality in Inner Mongolia

With the escalating global climate change and frequent human activities, Inner Mongolia, as a crucial ecological barrier in the Beijing-Tianjin-Hebei region, Bohai Economic Rim, and even the whole country, confronts many ecosystem issues. To reveal the intricate interplay between natural processes, human activities, and policy interventions shaping the region’s ecological landscape, we provide a comprehensive analysis of ecological quality in Inner Mongolia from 2002 to 2021. Key findings include: (1) The research uncovers an overall improvement in ecological quality across Inner Mongolia, albeit with certain regions exhibiting a pattern of deterioration followed by improvement, particularly notable in the central part of the province. (2) Cropland, shrub, grassland, barren, wetland, and water are decreasing, while impervious, and forestland are increasing. (3) Precipitation, temperature, and elevation are significant drivers affecting the quality of the ecological environment. Interestingly, while human production and activities have altered ecological conditions, factors like GDP, population density, and night lighting index do not directly impact environmental quality significantly. This underscores the intricate interplay among various factors within the socioeconomic system, highlighting its complexity. (4) Based on these findings, our policy recommendations include strengthening ecological monitoring and management, promoting sustainable urban development, conducting public education and awareness campaigns, and fostering stakeholder collaboration for data sharing and collective understanding. Adopting sustainable management strategies is essential for promoting ecological resilience and mitigating environmental degradation in Inner Mongolia, not only benefiting the local ecosystem but also contributing to national and global environmental well-being.

Internet of Things assisted Unmanned Aerial Vehicle for Pest Detection with Optimized Deep Learning Model

IoT technologies & UAVs are frequently utilized in ecological monitoring areas. Unmanned Aerial Vehicles (UAVs) & IoT in farming technology can evaluate crop disease & pest incidence from the ground’s micro & macro aspects, correspondingly. UAVs could capture images of farms using a spectral camera system, and these images are been used to examine the presence of agricultural pests and diseases. In this research work, a novel IoT- assisted UAV- based pest detection with Arithmetic Crossover based Black Widow Optimization-Convolutional Neural Network (ACBWO-CNN) model is developed in the field of agriculture. Cloud computing mechanism is used for monitoring and discovering the pest during crop production by using UAVs. The need for this method is to provide data centers, so there is a necessary amount of memory storage in addition to the processing of several images. Initially, the collected input image by the UAV is assumed on handling the via-IoT-cloud server, from which the pest identification takes place. The pest detection unit will be designed with three major phases: (a) background &foreground Segmentation, (b) Feature Extraction & (c) Classification. In the foreground and background Segmentation phase, the K-means clustering will be utilized for segmenting the pest images. From the segmented images, it extracts the features including Local Binary Pattern (LBP) &improved Local Vector Pattern (LVP) features. With these features, the optimized CNN classifier in the classification phase will be trained for the identification of pests in crops. Since the final detection outcome is from the Convolutional Neural Network (CNN); its weights are fine-tuned through the ACBWO approach. Thus, the output from optimized CNN will portray the type of pest identified in the field. This method’s performance is compared to other existing methods concerning a few measures.

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial “technical tips”. Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956–0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA technologies showed a strong positive correlation (R2 = 0.58–0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

Exploring fish communities in the mud volcano polluted river using environmental DNA metabarcoding

In ecosystems subjected to environmental disturbances such as mud volcanoes, understanding the dynamics of fish communities is essential for ecological assessment and conservation. This study employs environmental DNA (eDNA) metabarcoding for the first time to explore fish communities within a challenging and unique environment—a mud volcano-polluted river. High-throughput sequencing was utilized to assess alpha and beta diversity, revealing distinct patterns in fish community composition in response to mud discharge activity. The results indicate that affected sites exhibit remarkably different communities during active and inactive mud discharge, with the impact extending downstream to the river mouth. Notably, the application of eDNA metabarcoding identified a vulnerable species, Rasbora lateristriata, emphasizing its utility in detecting high-risk species. Despite challenges in species identification and reference databases, the use of longer 16S rRNA fragments offered enhanced taxonomic resolution and interspecific sequence variation. This approach showcases the potential of eDNA metabarcoding in assessing fish diversity and community dynamics in polluted aquatic ecosystems, emphasizing its significance in biodiversity surveys and ecological monitoring. The study underscores the value of eDNA metabarcoding as an efficient and comprehensive tool for studying freshwater ecosystems and highlights the need for expanding reference databases to address genetic coverage gaps, particularly in less-studied environments.

Optical Design and Stray Light Analysis of Underwater Spectral Radiometer

Underwater spectral detection plays an important role in the study of the underwater environment, ecology, oceanography, and environmental monitoring. A kind of underwater spectral radiometer that can observe the distribution of underwater spectral radiation bidirectionally has been developed. The flat-field concave holographic grating is used as the only optical component, the optical design parameters are optimized, and the system is miniaturized. The mechanism of stray light generation in the spectrometer is studied, and a method of suppressing stray light is proposed and simulated. The level of stray light in the system is suppressed to the order of 10−4, the signal-to-noise ratio of the system is improved, and the ability to detect weak light is enhanced. The wavelength calibration experiment was completed, and the experimental results show that the wavelength resolution of the underwater spectral radiometer is better than 3 nm. Finally, the quantitative relationship between spectral irradiance and digital output is obtained through radiation in the calibration of the system.

Soil pollution of the city with machine-building production by heavy metals and arsenic and epidemiological risk to public health

Introduction. The paper presents the results of a study for the assessment of the epidemiological risk to public health and the level of soil contamination with heavy metals and arsenic in the city of Kovrov, a large industrial center of the Vladimir region.
 Materials and methods. The objects of the study are the morbidity in the population of the Kovrovsky district of the Vladimir region and the soil cover of the city of Kovrov, whose industrial enterprises specialize in the production of machine-building products. Soil samples were taken in the area of industrial enterprises, highways, and residential areas. The soil cover was examined by X-ray fluorescence method to determine the content of heavy metals and arsenic.
 Results. The conducted study on the assessment of probable risk allowed establishing in the Kovrov district, relative to background regional values, children to have a very high risk for 10 classes of diseases, adults have a very high risk for 6 classes of diseases. Contamination of the Carpet city soil with heavy metals and arsenic has been established. The priority pollutants of the Kovrov soil are zinc, lead and arsenic, which belong to the first hazard class. There was a significant variation in the content of heavy metals within both industrial and residential zones. Extremely dangerous chemical contamination of the soil with heavy metals has been detected. The assessment of the level of chemical soil pollution, as an indicator of adverse effects on public health, showed the residential part of the city, where most of the residents live, to be located in the zone of moderately dangerous or dangerous category of soil pollution.
 Limitations. The limitations of the study are related to one-time sampling and a small number of reference sites, which limits the possibilities of a broader interpretation of the data obtained.
 Conclusion. The unfavourable state of the soil cover in the residential area of Kovrov requires the manaagement and conducting constant ecological and hygienic monitoring of this territory. To reduce pollutants in particular, heavy metals, entering the soil of the city and, it is necessary to improve the system for cleaning gas-air emissions of machine-building enterprises of the city, as well as to ensure the effective arrangement of sanitary protection zones. To reduce the content of heavy metals in the soil, it is necessary to carry out measures to detoxify contaminated soils.

Leaf gene expression trajectories during the growing season are consistent between sites and years in American beech.

Transcriptomics provides a versatile tool for ecological monitoring. Here, through genome-guided profiling of transcripts mapping to 33 042 gene models, expression differences can be discerned among multi-year and seasonal leaf samples collected from American beech trees at two latitudinally separated sites. Despite a bottleneck due to post-Columbian deforestation, the single nucleotide polymorphism-based population genetic background analysis has yielded sufficient variation to account for differences between populations and among individuals. Our expression analyses during spring-summer and summer-autumn transitions for two consecutive years involved 4197 differentially expressed protein coding genes. Using Populus orthologues we reconstructed a protein-protein interactome representing leaf physiological states of trees during the seasonal transitions. Gene set enrichment analysis revealed gene ontology terms that highlight molecular functions and biological processes possibly influenced by abiotic forcings such as recovery from drought and response to excess precipitation. Further, based on 324 co-regulated transcripts, we focused on a subset of GO terms that could be putatively attributed to late spring phenological shifts. Our conservative results indicate that extended transcriptome-based monitoring of forests can capture diverse ranges of responses including air quality, chronic disease, as well as herbivore outbreaks that require activation and/or downregulation of genes collectively tuning reaction norms maintaining the survival of long living trees such as the American beech.