Search

Abstract Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems. EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher). Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research.

Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms. [ABSTRACT FROM AUTHOR]

Effective approaches are needed to conserve the planet's remaining wildlife and wilderness landscapes, especially concerning global biodiversity conservation targets. Here, we present a new software system called EarthRanger: an open‐source platform built to help monitor, research and manage ecosystems.EarthRanger consists of seven main components (Core Server, API, Storage, Gundi, Web App, Mobile App, Ecoscope) that provide functionality for data (i) aggregation & collection, (ii) storage & management, (iii) real‐time and post hoc analysis, (iv) visualisation and (v) dissemination. The mobile application provides field‐based data recording and visualisation tools. EarthRanger may be deployed for single project use or can aggregate across multiple geographies as a centralised hub. EarthRanger can be used to collect standardised tracking data (e.g. from wildlife collars, vehicles and ranger patrols) and configurable event information (e.g. a singular recording with associated user‐defined attribute information such as a wildlife sighting or encounter with a poacher).Since development began in 2015, the platform has (at the time of writing) been deployed at over 500 sites across 70 countries and with myriad configurations and objectives. EarthRanger has improved the ability to monitor data feeds and manage conservation‐related operations in real time. For instance, the deployment of EarthRanger by African Parks has led to the removal of over 50,000 snares, steady population growth of key species of concern and near cessation of poaching. In Liwonde's protected area, enhanced mitigation efforts supported by EarthRanger reduced the number of deaths from wildlife conflict by more than 91%. EarthRanger is also providing a platform to enhance standardisation, aggregation, transfer and long‐term storage of ecological information and promote collaboration between groups conducting protected area management and ecology and biodiversity research. [ABSTRACT FROM AUTHOR]

The Weddell seal (Leptonychotes weddellii) is a fast-ice obligate species that plays an important role as both predator and prey within the high-latitude, coastal Southern Ocean. Weddell seals are affected by pressures of marine resource extraction and variation in sea-ice extent and characteristics that are affected by climate. Thus, monitoring their population dynamics provides an indicator of the effectiveness of fisheries management, and impacts of changing climate in the high latitude Southern Ocean ecosystem. Satellite imagery is increasingly being used to monitor the populations of this species, but assessment techniques require a better understanding of the environmental factors influencing the likelihood that individuals will be on the sea-ice and therefore visible in a satellite image. Addressing that goal, we captured 5054 trail camera photos during spring 2017 in the 24-h light at Cape Royds, Antarctica, and then counted seals on the fast ice every 30 min over 59 days. Using a generalised additive model (63% deviance explained) we described the haulout behaviour of non-breeding Weddell seals according to time of day, date, air temperature, pressure, solar radiation, and wind speed. We found that the seals' haulout cycle is driven to a significant degree by weather variables, primarily temperature and wind speed. Quantifying these haulout patterns can be used to determine the time of day, and under what conditions, that most seals are hauled out. Integrating environmental parameters to correct time-of-day patterns would allow better cross-site abundance comparisons, leading to better Weddell seal population estimates for the Ross Sea region and the wider coastal Antarctica. [ABSTRACT FROM AUTHOR]

IntroductionAcoustic monitoring and soundscape analysis provide valuable data for the conservation and restoration of underwater habitats. However, before these methods can be widely implemented for management purposes, it is crucial to validate the ecological relevance of different sampling methodologies and quantify potential biases.MethodsWe investigated how the distance and orientation of an acoustic sensor relative to a target habitat influence the received soundscape. Using a spatial array of hydrophones, we recorded sound at different distances (1 m, 2 m, 5 m) and orientations (vertical vs. horizontal) from a shallow coral reef.ResultsHydrophones oriented horizontally toward the reef exhibited the expected decrease in sound levels with increasing distance. In contrast, hydrophones oriented vertically showed an inverse trend, with lower sound pressure levels at closer distances and higher levels further away.DiscussionThese findings indicate that sensor directivity significantly influences the received soundscape, introducing a potential methodological bias within and across acoustic datasets. To improve the accuracy and comparability of acoustic sampling in coastal habitats, sensor beam patterns should be carefully considered in experimental design.

Coral reefs are biodiverse marine ecosystems that are undergoing rapid changes, making monitoring vital as we seek to manage and mitigate stressors. Healthy reef soundscapes are rich with sounds, enabling passive acoustic recording and soundscape analyses to emerge as cost-effective, long-term methods for monitoring reef communities. Yet most biological reef sounds have not been identified or described, limiting the effectiveness of acoustic monitoring for diversity assessments. Machine learning offers a solution to scale such analyses but has yet to be successfully applied to characterize the diversity of reef fish sounds. Here we sought to characterize and categorize coral reef fish sounds using unsupervised machine learning methods. Pulsed fish and invertebrate sounds from 480 min of data sampled across 10 days over a 2-month period on a US Virgin Islands reef were manually identified and extracted, then grouped into acoustically similar clusters using unsupervised clustering based on acoustic features. The defining characteristics of these clusters were described and compared to determine the extent of acoustic diversity detected on these reefs. Approximately 55 distinct calls were identified, ranging in centroid frequency from 50 Hz to 1,300 Hz. Within this range, two main sub-bands containing multiple signal types were identified from 100 Hz to 400 Hz and 300 Hz-700 Hz, with a variety of signals outside these two main bands. These methods may be used to seek out acoustic diversity across additional marine habitats. The signals described here, though taken from a limited dataset, speak to the diversity of sounds produced on coral reefs and suggest that there might be more acoustic niche differentiation within soniferous fish communities than has been previously recognized. [ABSTRACT FROM AUTHOR]

Peatland degradation causes a number of environmental problems ranging from greenhouse gas (GHG) emissions to subsidence and ecosystem loss. Degraded peatlands, covering just 0.3 % of Earth’s land area (500,000 km2), disproportionately contribute 5 % of GHG emissions, exacerbating the climate crisis. Once degraded, restoring peatland ecosystem functions often presents a considerable challenge because degradation affects several interconnected components in peatlands: the peat body, hydrology, and vegetation. The planning and implementation of effective peatland restoration strategies requires the accurate mapping of degradation. This review examines how remote sensing has been used to examine the components of peatland degradation. A conceptual 3D space was created using the three components of peat, hydrology, and vegetation. Within this three-dimensional space, five groups of studies were identified, representing each one of the three dimensions individually, or addressing two or three of them simultaneously. Of 115 relevant articles, 54 % solely addressed vegetation degradation, 18 % evaluated two dimensions equally, while just 10 % of all studies examined all three dimensions. These results highlight a lack of remote sensing-based research that considers all aspects of peatland degradation and their interdependence simultaneously. By synthesizing our findings into three descriptive peatland degradation scenarios (a boreal landscape, a temperate large peatland complex and a temperate small raised bog), we present rapid and efficient methods and resources for a more holistic approach to degradation mapping and monitoring. Consequently, such comprehensive assessments integrating different historical and current remote sensing data should be carried out more frequently. They allow the visualization of ecological consequences of peatland degradation and promote peatland restoration in all parts of these tripartite ecosystems.

Background: In this study, citizen scientists gathered survey data by monitoring inland wetlands, recognized as carbon sinks, and verified the accuracy of the data for incorporation into ecosystem management policies. Results: In October 2022, citizen scientists conducted surveys on three taxonomical groups (plants, mammals, terrestrial insects) in three wetland protection areas. After capturing photographs with location information, these images were uploaded to a national ecological information bank (EcoBank) managed in Korea. The information collected by citizen scientists underwent cross-validation through two expert methods, involving ecology field experts. First, experts conducted a survey of invasive alien plants in the designated areas and compared their findings with those of citizen scientists. The choice of survey locations by citizen scientists was influenced by their proximity to their residences. Second, an expert scrutinized the accuracy of species names collected and uploaded to EcoBank by citizen scientists, presenting their findings. The classification accuracy for species names was 98.8% for vegetation (n = 83), 21.6% for terrestrial insects (n = 21), and 66.7% for mammals (n = 8). These results indicate that citizen scientists may lack detailed classification ability at the species level. Conclusions: Moving forward, it will be imperative to offer diverse forms of education to strengthen the capabilities of the citizen scientists, including sharing wetland survey results to enhance expertise in species identification, creating and distributing educational materials, and providing on-site education through professional surveyors.

Abstract Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems.

Abstract Aapa mires are waterlogged northern peatland ecosystems characterized by a patterned surface structure where water‐filled depressions (‘flarks’) alternate with drier hummock strings. As one of the EU Habitat Directive priority habitats, aapa mires are important for biodiversity and carbon cycling, harbouring several red‐listed species and supporting unique species communities. Due to their sensitivity to hydrological disturbances, reliable, up‐to‐date and systematic information on the hydrological condition and responses of mires is crucial and required for multiple purposes ranging from carbon exchange modelling to EU Habitats Directive reporting and conservation and ecosystem restoration planning. Here, we demonstrate the usability of Sentinel‐2 satellite data in a semi‐automatic cloud‐based approach to retrieve large‐scale information on aapa mire hydrological variability. Two satellite‐derived metrics, soil moisture index and the extent of water‐saturated surfaces based on pixel‐wise classification, are used to quantify monthly and interannual wetness variation between 2017 and 2020 across Natura 2000 aapa mires in Finland, including responses to the extreme drought of 2018. The results revealed high temporal variability in wetness, particularly in the southern parts of the aapa mire zone and generally in the late summer months interannually. Observations from the drought summer showed that one third of usually year‐round wet flark surfaces may be exposed to drying during climatic extremes. Responses varied between sites and regions, implicating the significance of environmental factors for drought resistance: some sites maintained high levels of moisture, whereas others lost wet surfaces completely. Our study provides the first comprehensive national‐level representation of seasonal and interannual wetness variability and drought‐sensitivity of pristine aapa mire sites. The approach and methods used here can be directly upscaled outside protected areas and to other EU countries. Thus, they provide a means for harmonized, systematic large‐scale monitoring of this priority habitat, as well as valuable information for other applications supporting peatland conservation and research.

Heavy metals, pervasive in the environment due to natural processes and human activities, pose substantial threats to ecosystems and human health. This study aims to delve into the sources, contamination pathways in natural waters, and subsequent bioaccumulation of heavy metals across various organisms. The overview encompasses an exploration of the environmental persistence, bioaccumulation dynamics, and ecotoxicological impacts of these metals. Methodologically, this research undertakes a comprehensive review synthesizing existing literature and studies on heavy metal contamination, bioaccumulation mechanisms, and ecotoxicity. Key findings highlight the protracted environmental persistence of heavy metals, perpetuating significant threats to ecological balance and human well-being. Notably, the transfer of these metals through food chains culminates in their bioaccumulation in diverse organisms, raising concerns about potential toxicity, including human exposure. The discussion underscores the imperative nature of assessing heavy metal pollution and its ramifications on ecosystems and human health. Emphasizing the essential role of bioindicators and biomarkers, this article elucidates their significance in evaluating heavy metal-induced environmental stressors and their impact on both biota and human populations. This comprehensive study contributes to a nuanced understanding of heavy metal dynamics, advocating for proactive measures in monitoring and mitigating their deleterious effects on ecosystems and human health. [ABSTRACT FROM AUTHOR]

Alpine ecosystems are particularly vulnerable to climate change. Monitoring the distribution of alpine vegetation is required to plan practical conservation activities. However, conventional field observations, airborne and satellite remote sensing are difficult in terms of coverage, cost and resolution in alpine areas. Ground‐based time‐lapse cameras have been used to observe the regions' snowmelt and vegetation phenology and offer significant advantages in terms of cost, resolution and frequency. However, they have not been used in research monitoring of vegetation distribution patterns. This study proposes a novel method for drawing georeferenced vegetation classification maps from ground‐based imagery of alpine regions. Our approach had two components: vegetation classification and georectification. The proposed vegetation classification method uses a pixel time series acquired from fall images, utilizing the fall leaf color patterns. We demonstrated that the performance of the vegetation classification could be improved using time‐lapse imagery and a Recurrent Neural Network. We also developed a novel method to accurately transform ground‐based images into georeferenced data. We propose the following approaches: (1) an automated procedure to acquire Ground Control Points and (2) a camera model that considers lens distortions for accurate georectification. We demonstrated that the proposed approach outperforms conventional methods, in addition to achieving sufficient accuracy to observe the vegetation distribution on a plant‐community scale. The evaluation revealed an F1 score and root‐mean‐square error of 0.937 and 3.4 m in the vegetation classification and georectification, respectively. Our results highlight the potential of inexpensive time‐lapse cameras to monitor the distribution of alpine vegetation. The proposed method can significantly contribute to the effective conservation planning of alpine ecosystems. [ABSTRACT FROM AUTHOR]

Aapa mires are waterlogged northern peatland ecosystems characterized by a patterned surface structure where water‐filled depressions ('flarks') alternate with drier hummock strings. As one of the EU Habitat Directive priority habitats, aapa mires are important for biodiversity and carbon cycling, harbouring several red‐listed species and supporting unique species communities. Due to their sensitivity to hydrological disturbances, reliable, up‐to‐date and systematic information on the hydrological condition and responses of mires is crucial and required for multiple purposes ranging from carbon exchange modelling to EU Habitats Directive reporting and conservation and ecosystem restoration planning. Here, we demonstrate the usability of Sentinel‐2 satellite data in a semi‐automatic cloud‐based approach to retrieve large‐scale information on aapa mire hydrological variability. Two satellite‐derived metrics, soil moisture index and the extent of water‐saturated surfaces based on pixel‐wise classification, are used to quantify monthly and interannual wetness variation between 2017 and 2020 across Natura 2000 aapa mires in Finland, including responses to the extreme drought of 2018. The results revealed high temporal variability in wetness, particularly in the southern parts of the aapa mire zone and generally in the late summer months interannually. Observations from the drought summer showed that one third of usually year‐round wet flark surfaces may be exposed to drying during climatic extremes. Responses varied between sites and regions, implicating the significance of environmental factors for drought resistance: some sites maintained high levels of moisture, whereas others lost wet surfaces completely. Our study provides the first comprehensive national‐level representation of seasonal and interannual wetness variability and drought‐sensitivity of pristine aapa mire sites. The approach and methods used here can be directly upscaled outside protected areas and to other EU countries. Thus, they provide a means for harmonized, systematic large‐scale monitoring of this priority habitat, as well as valuable information for other applications supporting peatland conservation and research. [ABSTRACT FROM AUTHOR]

Understanding and managing the response of marine ecosystems to human pressures including climate change requires reliable large-scale and multidecadal information on the state of key populations. These populations include the pelagic animals that support ecosystem services including carbon export and fisheries. The use of research vessels to collect information using scientific nets and acoustics is being replaced with technologies such as autonomous moorings, gliders, and meta-genetics. Paradoxically, these newer methods sample pelagic populations at ever-smaller spatial scales, and ecological change might go undetected in the time needed to build up large-scale, long time series. These global-scale issues are epitomised by Antarctic krill (Euphausia superba), which is concentrated in rapidly warming areas, exports substantial quantities of carbon and supports an expanding fishery, but opinion is divided on how resilient their stocks are to climatic change. Based on a workshop of 137 krill experts we identify the challenges of observing climate change impacts with shifting sampling methods and suggest three tractable solutions. These are to: improve overlap and calibration of new with traditional methods; improve communication to harmonise, link and scale up the capacity of new but localised sampling programs; and expand opportunities from other research platforms and data sources, including the fishing industry. Contrasting evidence for both change and stability in krill stocks illustrates how the risks of false negative and false positive diagnoses of change are related to the temporal and spatial scale of sampling. Given the uncertainty about how krill are responding to rapid warming we recommend a shift towards a fishery management approach that prioritises monitoring of stock status and can adapt to variability and change. [ABSTRACT FROM AUTHOR]

ABSTRACT Seasonal fluctuations profoundly affect marine microeukaryotic plankton composition and metabolism, but accurately tracking these changes has been a long-standing challenge. In this study, we present a year-long metatranscriptomic data set from the Southern Bight of the North Sea, shedding light on the seasonal dynamics in temperate plankton ecosystems. We observe distinct shifts in active plankton species and their metabolic processes in response to seasonal changes. We characterized the metabolic signatures of different seasonal phases in detail, thereby revealing the metabolic versatility of dinoflagellates, the heterotrophic dietary strategy of Phaeocystis during its late-stage blooms, and stark variations in summer and fall diatom abundance and metabolic activity across nearby sampling stations. Our data illuminate the varied contributions of microeukaryotic taxa to biomass production and nutrient cycling at different times of the year and allow delineation of their ecological niches.IMPORTANCEEcosystem composition and metabolic functions of temperate marine microeukaryote plankton are strongly influenced by seasonal dynamics. Although monitoring of species composition of microeukaryotes has expanded recently, few methods also contain seasonally resolved information on ecosystem functioning. We generated a year-long spatially resolved metatranscriptomic data set to assess seasonal dynamics of microeukaryote species and their associated metabolic functions in the Southern Bight of the North Sea. Our study underscores the potential of metatranscriptomics as a powerful tool for advancing our understanding of marine ecosystem functionality and resilience in response to environmental changes, emphasizing its potential in continuous marine ecosystem monitoring to enhance our ecological understanding of the ocean's eukaryotic microbiome.

Coral reefs are biodiverse marine ecosystems that are undergoing rapid changes, making monitoring vital as we seek to manage and mitigate stressors. Healthy reef soundscapes are rich with sounds, enabling passive acoustic recording and soundscape analyses to emerge as cost-effective, long-term methods for monitoring reef communities. Yet most biological reef sounds have not been identified or described, limiting the effectiveness of acoustic monitoring for diversity assessments. Machine learning offers a solution to scale such analyses but has yet to be successfully applied to characterize the diversity of reef fish sounds. Here we sought to characterize and categorize coral reef fish sounds using unsupervised machine learning methods. Pulsed fish and invertebrate sounds from 480 min of data sampled across 10 days over a 2-month period on a US Virgin Islands reef were manually identified and extracted, then grouped into acoustically similar clusters using unsupervised clustering based on acoustic features. The defining characteristics of these clusters were described and compared to determine the extent of acoustic diversity detected on these reefs. Approximately 55 distinct calls were identified, ranging in centroid frequency from 50 Hz to 1,300 Hz. Within this range, two main sub-bands containing multiple signal types were identified from 100 Hz to 400 Hz and 300 Hz–700 Hz, with a variety of signals outside these two main bands. These methods may be used to seek out acoustic diversity across additional marine habitats. The signals described here, though taken from a limited dataset, speak to the diversity of sounds produced on coral reefs and suggest that there might be more acoustic niche differentiation within soniferous fish communities than has been previously recognized.

Hybrid systems offer optimal solutions for unmanned aerial platforms, showcasing their technological development in parallel and series configurations and providing alternatives for future aircraft concepts. However, the limited energetic benefit of these configurations is primarily due to their weight, constituting one of the main constraints. Solar PV technology can provide an interesting enhancement to the autonomy of these systems. However, to create efficient propulsion architectures tailored for specific missions, a flexible framework is required. This work presents a methodology to assess hybrid solar-powered UAVs in distributed propulsion configurations through a two-level modeling scheme. The first stage consists of determining operational and design constraints through parametric models that estimate the baseline energetic requirements of flight. The second phase executes a nonlinear optimization algorithm tuned to find optimal propulsion configurations in terms of the degree of hybridization, number of propellers, different wing loadings, and the setup of electric distributed propulsion (eDP) considering fuel consumption as a key metric. The results of the study indicate that solar-hybrid configurations can theoretically achieve fuel savings of up to 80% compared to conventional configurations. This leads to a significant reduction in emissions during long-endurance flights where current battery technology is not yet capable of providing sustained flight.

Understanding and managing the response of marine ecosystems to human pressures including climate change requires reliable large-scale and multi-decadal information on the state of key populations. These populations include the pelagic animals that support ecosystem services including carbon export and fisheries. The use of research vessels to collect information using scientific nets and acoustics is being replaced with technologies such as autonomous moorings, gliders, and meta-genetics. Paradoxically, these newer methods sample pelagic populations at ever-smaller spatial scales, and ecological change might go undetected in the time needed to build up large-scale, long time series. These global-scale issues are epitomised by Antarctic krill (Euphausia superba), which is concentrated in rapidly warming areas, exports substantial quantities of carbon and supports an expanding fishery, but opinion is divided on how resilient their stocks are to climatic change. Based on a workshop of 137 krill experts we identify the challenges of observing climate change impacts with shifting sampling methods and suggest three tractable solutions. These are to: improve overlap and calibration of new with traditional methods; improve communication to harmonise, link and scale up the capacity of new but localised sampling programs; and expand opportunities from other research platforms and data sources, including the fishing industry. Contrasting evidence for both change and stability in krill stocks illustrates how the risks of false negative and false positive diagnoses of change are related to the temporal and spatial scale of sampling. Given the uncertainty about how krill are responding to rapid warming we recommend a shift towards a fishery management approach that prioritises monitoring of stock status and can adapt to variability and change.

In tropical countries, frequent anthropogenic disturbances are primary drivers of the reduction in community diversity and local extinction of many insect taxa, including Lepidoptera. We assessed the impact of anthropogenic disturbances on lepidopteran assemblages across five different land use types (Fragmented Forest, Crop Fields, Pasture Land, Rural Settlements and undisturbed Natural Forest) in the Choke Mountains, Ethiopia. Lepidoptera were sampled using 20 W UV LED lights in 19 sites for 12 consecutive months. A total of 4 559 specimens representing 14 families and 339 species were sampled. The highest diversity was obtained from the Natural Forest, followed by the Fragmented Forest, Rural Settlements, Pasture Land and Crop Fields. The monthly trends of the diversity estimates showed strong differences among the five land use types, with months when the highest Hill–Shannon and Hill– Simpson values were observed not in the Natural Forest, but in the Rural Settlements and Fragmented Forest. The highest dominance values were observed in the Crop Fields and Pasture Land, with dominant species percentages of about 10%. The multivariate results clearly highlight the separation of the Natural Forest sites from all other sites and, in general, great consistency within each land use. A high positive linear relationship between the number of vascular plants and sampled Lepidoptera species was observed. The results of this study will be useful for guiding conservation management priorities to prevent irreversible biodiversity loss and maintain ecosystem provisioning services that are essential for the sustainable development of rural communities. [ABSTRACT FROM AUTHOR]

IntroductionAn unprecedented amount of Earth Observations and in-situ data has become available in recent decades, opening up the possibility of developing scalable and practical solutions to assess and monitor ecosystems across the globe. Essential Biodiversity Variables are an example of the integration between Earth Observations and in-situ data for monitoring biodiversity and ecosystem integrity, with applicability to assess and monitor ecosystem structure, function, and composition. However, studies have yet to explore how such metrics can be organized in an effective workflow to create a composite Ecosystem Integrity Index and differentiate between local plots at the global scale.MethodsUsing available Essential Biodiversity Variables, we present and test a framework to assess and monitor forest ecosystem integrity at the global scale. We first defined the theoretical framework used to develop the workflow. We then measured ecosystem integrity across 333 forest plots of 5 km2. We classified the plots across the globe using two main categories of ecosystem integrity (Top and Down) defined using different Essential Biodiversity Variables.Results and discussion:We found that ecosystem integrity was significantly higher in forest plots located in more intact areas than in forest plots with higher disturbance. On average, intact forests had an Ecosystem Integrity Index score of 5.88 (CI: 5.53–6.23), whereas higher disturbance lowered the average to 4.97 (CI: 4.67–5.26). Knowing the state and changes in forest ecosystem integrity may help to deliver funding to priority areas that would benefit from mitigation strategies targeting climate change and biodiversity loss. This study may further provide decision- and policymakers with relevant information about the effectiveness of forest management and policies concerning forests. Our proposed method provides a flexible and scalable solution that facilitates the integration of essential biodiversity variables to monitor forest ecosystems.

Size spectra are used to assess the status and functioning of marine and freshwater ecosystems worldwide. Their use is underpinned by theory linking the dynamics of trophic interactions to a power-law decline of abundance with body size in ecological communities. Recent papers on empirical size spectrum estimation have argued for Maximum Likelihood Estimation of power-law probability distributions as a more accurate alternative to traditional linear regression approaches. One major limitation of currently used size spectrum estimators from Maximum Likelihood Estimation is that they cannot account for the use of multiple sampling protocols, nor the distortions caused by gear size selectivity, and therefore they become restricted to a relatively narrow taxonomic group and size range. Further progress in the field requires new methods that are flexible enough to combine multiple trophic groups and sampling gears into a single size spectrum estimate, while taking advantage of more accurate distributional approaches. The method we propose in this paper fills this gap by deriving the distribution of observed sizes explicitly from the underlying power-law spectrum and gear selectivity functions. It specifies likelihoods as a product of two components: (i) the probability of belonging to a given group and (ii) the probability distribution within the group. Using Bayesian estimation, we applied the method to surveys of phytoplankton, zooplankton, and fishes in lakes of Quetico Provincial Park, northwestern Ontario, using Van Dorn samplers, zooplankton nets, gillnets, and hydroacoustics. The results show that the spectra estimated from subsets of trophic groups or gears are weak predictors of more complete spectra, highlighting the importance of using more inclusive community data. The two-component partitioning of likelihoods also helped demonstrate the existence of between-group spectrum slopes that were overall steeper than within-group slopes, indicating that heterogeneity of trophic transfers across the size spectrum is an important factor structuring these ecosystems. [ABSTRACT FROM AUTHOR]

Due to the cold climate and altitude, to identify trends in the dynamic vegetation and the main factors that contribute to changes in vegetation cover in grassland areas is essential to understand climate change in mountainous regions. Landsat-8 OLI and Landsat-1 MSS images from 1973 to 2022 of Morraria do Urucum and Serra do Amolar were pre-processed in the GEE cloud platform and QGIS. The resampling method per pixel in the scale of values defined for vegetation Campos de Altitude was used to show changes in vegetation cover and its dynamics through the NDVI index. In both study areas, a continuous trend of significant reduction of vegetation in highland grasslands was observed over 50 years. The average decrease was 49% for Urucum (less 2,164 hectares) and 43% for Amolar (less 3,959 hectares). The use of GHG to obtain remote sensing data combined with temporal image analysis offers the potential to quickly perceive trends in large-and small-scale vegetation cover change

Canga ecosystems are iron-rich habitats and pose a challenge for conservation and environmental governance in Brazil. They support high levels of biodiversity and endemism and, at the same time, have suffered intense losses and degradation due to large-scale iron ore mining. The Peixe Bravo River Valley in the Brazilian savanna is one of the last natural canga areas that has yet to face the irreversible impacts of mining. However, there are vast gaps in data on the vegetation cover, location, spatial distribution, and area of occurrence of this ecosystem. Therefore, more information is needed on the appropriate scale, without which it is difficult to establish conservation planning and strategies to prevent, mitigate or compensate for impacts on canga ecosystems. In this study, we provide the first map of canga ecosystems in Brazil using the U-Net deep learning model and Sentinel-2 images. In addition, we estimate the degree of direct threat faced by ecosystems due to the spatial overlap of the mapped cangas and the location of mining concession areas for iron ore exploitation. The deep learning algorithm identified and segmented 762 canga patches (overall accuracy of 98.5%) in an area of 30,000 ha in the Peixe Bravo River Valley, demonstrating the high predictive power of the mapping approach. We conclude that the direct threat to canga ecosystems is high since 99.6% of the observed canga patches are included in mining concession areas. We also highlight that the knowledge acquired about the distribution of cangas through the application of an effective method of artificial intelligence and the use of open-source satellite images is especially important for supporting conservation strategies and environmental public policies. [ABSTRACT FROM AUTHOR]

Copyright of Terr@ Plural is the property of Terr@ Plural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Lepidoptera family Saturniidae is commonly utilized as a bioindicator for ecosystem monitoring. During six years of sampling, the study explored the dynamics of the Shannon-Wiener, Simpson, species richness, Pielou equity, dominance, and abundance ecological parameters under the effect of seasonality. The research took place in the Atlantic Forest remnants at Vale Natural Reserve in Linhares, Espirito Santo, Brazil. Blacklight traps were used to collect the samples. 1,445 specimens were collected and classified into four subfamilies, 30 genera, and 48 species. There is a large difference in richness and abundance due to temperature in different seasons. When the temperature drops, the abundance declines. When comparing years, equitability and dominance are not significant. During periods of more rainfall, there is a higher abundance of species (richness). Despite variations in abundance and diversity throughout the years, the Saturniidae are known for their low resistance and strong resilience.

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystemdynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series–based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems. [ABSTRACT FROM AUTHOR]

Ecosystem restoration and reforestation often operate at large scales, whereas monitoring practices are usually limited to spatially restricted field measurements that are (i) time- and labour-intensive, and (ii) unable to accurately quantify restoration success over hundreds to thousands of hectares. Recent advances in remote sensing technologies paired with deep learning algorithms provide an unprecedented opportunity for monitoring changes in vegetation cover at spatial and temporal scales. Such data can feed directly into adaptive management practices and provide insights into restoration and regeneration dynamics. Here, we demonstrate that convolutional neural network (CNN) segmentation algorithms can accurately classify the canopy cover of Portulacaria afra Jacq. in imagery acquired using different models of unoccupied aerial vehicles (UAVs) and under variable light intensities. Portulacaria afra is the target species for the restoration of Albany Subtropical Thicket vegetation, endemic to South Africa, where canopy cover is challenging to measure due to the dense, tangled structure of this vegetation. The automated classification strategy presented here is widely transferable to restoration monitoring as its application does not require any knowledge of the CNN model or specialist training, and can be applied to imagery generated by a range of UAV models. This will reduce the sampling effort required to track restoration trajectories in space and time, contributing to more effective management of restoration sites, and promoting collaboration between scientists, practitioners and landowners. [ABSTRACT FROM AUTHOR]

Context: Seagrasses are submerged marine plants that have been declining globally at increasing rates. Natural resource managers rely on monitoring programs to detect and understand changes in these ecosystems. Technological advancements are allowing for the development of patch-level seagrass maps, which can be used to explore seagrass meadow spatial patterns. Objectives: Our research questions involved comparing lacunarity, a measure of landscape configuration, for seagrass to assess cross-site differences in areal coverage and spatial patterns through time. We also discussed how lacunarity could help natural resource managers with monitoring program development and restoration decisions and evaluation. Methods: We assessed lacunarity of seagrass meadows for various box sizes (0.0001 ha to 400.4 ha) around Cat Island and Ship Island, Mississippi (USA). For Cat Island, we used seagrass data from 2011 to 2014. For Ship Island, we used seagrass data for seven dates between 1963 and 2014. Results: Cat Island, which had more continuous seagrass meadows, had lower lacunarity (i.e., denser coverage) compared to Ship Island, which had patchier seagrass beds. For Ship Island, we found a signal of disturbance and path toward recovery from Hurricane Camille in 1969. Finally, we highlighted how lacunarity curves could be used as one of multiple considerations for designing monitoring programs, which are commonly used for seagrass monitoring. Conclusions: Lacunarity can help quantify spatial pattern dynamics, but more importantly, it can assist with natural resource management by defining fragmentation and potential scales for monitoring. This approach could be applied to other environments, especially other coastal ecosystems. [ABSTRACT FROM AUTHOR]

The paper presents a diagnostic complex for plankton studies using the miniDHC (digital holographic camera). Its capabilities to study the rhythmic processes in plankton ecosystems were demonstrated using the natural testing in Lake Baikal in summer. The results of in situ measurements of plankton to detect the synchronization of collective biological rhythms with medium parameters are presented and interpreted. The most significant rhythms in terms of the correlation of their parameters with medium factors are identified. The study shows that the correlation with water temperature at the mooring site has the greatest significance and reliability. The results are verified with biodiversity data obtained by the traditional mesh method. The experience and results of the study can be used for the construction of a stationary station to monitor the ecological state of the water area through the digitalization of plankton behavior. [ABSTRACT FROM AUTHOR]

Ecosystem restoration and reforestation often operate at large scales, whereas monitoring practices are usually limited to spatially restricted field measurements that are (i) time- and labour-intensive, and (ii) unable to accurately quantify restoration success over hundreds to thousands of hectares. Recent advances in remote sensing technologies paired with deep learning algorithms provide an unprecedented opportunity for monitoring changes in vegetation cover at spatial and temporal scales. Such data can feed directly into adaptive management practices and provide insights into restoration and regeneration dynamics. Here, we demonstrate that convolutional neural network (CNN) segmentation algorithms can accurately classify the canopy cover of Portulacaria afra Jacq. in imagery acquired using different models of unoccupied aerial vehicles (UAVs) and under variable light intensities. Portulacaria afra is the target species for the restoration of Albany Subtropical Thicket vegetation, endemic to South Africa, where canopy cover is challenging to measure due to the dense, tangled structure of this vegetation. The automated classification strategy presented here is widely transferable to restoration monitoring as its application does not require any knowledge of the CNN model or specialist training, and can be applied to imagery generated by a range of UAV models. This will reduce the sampling effort required to track restoration trajectories in space and time, contributing to more effective management of restoration sites, and promoting collaboration between scientists, practitioners and landowners.

The use of Artificial substrates (AS) as sampling devices addresses challenges in macrofaunal quantitative sampling. While effectively capturing biodiversity patterns, the time-intensitive identification process at the species level remains a substantial challenge. The Taxonomic Sufficiency approach (TS), where only taxa above species level are identified, arises as a potential solution to be tested across different environmental monitoring scenarios. In this paper, we analyzed three AS macrobenthic datasets to evaluate the odds of TS in improving the cost-effective ratio in AS monitoring studies and establish the highest resolution level to detect assemblage changes under different environmental factors. Results indicated that the family level emerged as a pragmatic compromise, balancing precision and taxonomic effort. Cost/benefit analysis supported TS efficiency, maintaining correlation stability until the family level. Results also showed that reducing resolution to family does not entail a significant Loss of Information. This study contributes to the discourse on TS applicability, highlighting its practicality in monitoring scenarios, including spatial-temporal studies, and rapid biodiversity assessments. Additionally, it highlights the "second best approach" of family-level practicality depending on the specific monitoring scenario and recognizes the importance of the species-level "best approach" before applying TS in monitoring studies. • The family level provides an optimal cost-effective balance between identification accuracy and effort. • Cost/benefit analysis shows TS efficiency with consistent Spearman's correlation between species and family. • TS reduces identification time and costs while maintaining reliable ecological data. • Initial species-level data is essential to validate TS across different spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Standardized metrics that quantify a component of ecosystem functioning are essential for evaluating the current status of coastal marine habitats and for monitoring how ecologically important ecosystems are changing in response to global and local environmental change. Calcification accretion units (CAUs) are a standardized tool for quantifying net calcium carbonate accretion, early successional community structure, recruitment of algae and sessile invertebrates and other response metrics that can be determined from image analyses in coastal marine habitats.CAUs are comprised of paired‐settlement tiles that are separated by a spacer. This design mimics the presence of different representative habitats that are common in most marine systems such as exposed benthic surfaces, cryptic spaces inaccessible to grazers and shaded overhangings. The protected space between the tiles facilitates recruitment and inclusion of cryptic taxa in community assemblage estimates. After a period of deployment, CAUs are photographed for image analysis and then decalcified to quantify calcium carbonate accretion rates.The CAU methodology provides a cost‐effective, standardized protocol for evaluating structure and function in marine benthic habitats. We illustrate how CAU data can be used to compare accretion rates and the relative proportion of carbonate polymorphs in ecosystems across the globe.Here we provide a comprehensive standard operating procedure for building, deploying and processing CAUs, to ensure that a consistent protocol is used for accurate data collection and cross‐system comparative studies. [ABSTRACT FROM AUTHOR]

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil–Sen median method and Mann–Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year−1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends. [ABSTRACT FROM AUTHOR]

Pantropical degradation of coral reefs is prompting considerable investment in their active restoration. However, current measures of restoration success are based largely on coral cover, which does not fully reflect ecosystem function or reef health.Soundscapes are an important aspect of reef health; loud and diverse soundscapes guide the recruitment of reef organisms, but this process is compromised when degradation denudes soundscapes. As such, acoustic recovery is a functionally important component of ecosystem recovery.Here, we use acoustic recordings taken at one of the world's largest coral reef restoration projects to test whether successful restoration of benthic and fish communities is accompanied by a restored soundscape. We analyse recordings taken simultaneously on healthy, degraded (extensive historic blast fishing) and restored reefs (restoration carried out for 1–3 years on previously degraded reefs). We compare soundscapes using manual counts of biotic sounds (phonic richness), and two commonly used computational analyses (acoustic complexity index [ACI] and sound‐pressure level [SPL]).Healthy and restored reef soundscapes exhibited a similar diversity of biotic sounds (phonic richness), which was significantly higher than degraded reef soundscapes. This pattern was replicated in some automated analyses but not others; the ACI exhibited the same qualitative result as phonic richness in a low‐frequency, but not a high‐frequency bandwidth, and there was no significant difference between SPL values in either frequency bandwidth. Furthermore, the low‐frequency ACI and phonic richness scores were only weakly correlated despite showing a qualitatively equivalent overall result, suggesting that these metrics are likely to be driven by different aspects of the reef soundscape.Synthesis and applications. These data show that coral restoration can lead to soundscape recovery, demonstrating the return of an important ecosystem function. They also suggest that passive acoustic monitoring (PAM) might provide functionally important measures of ecosystem‐level recovery—but only some PAM metrics reflect ecological status, and those that did are likely to be driven by different communities of soniferous animals. Recording soundscapes represents a potentially valuable tool for evaluating restoration success across ecosystems, but caution must be exercised when choosing metrics and interpreting results. [ABSTRACT FROM AUTHOR]

Marine zooplankton are rapid-responders and useful indicators of environmental variability and climate change impacts on pelagic ecosystems on time scales ranging from seasons to years to decades. The systematic complexity and taxonomic diversity of the zooplankton assemblage has presented significant challenges for routine morphological (microscopic) identification of species in samples collected during ecosystem monitoring and fisheries management surveys. Metabarcoding using the mitochondrial Cytochrome Oxidase I (COI) gene region has shown promise for detecting and identifying species of some – but not all – taxonomic groups in samples of marine zooplankton. This study examined species diversity of zooplankton on the Northwest Atlantic Continental Shelf using 27 samples collected in 2002-2012 from the Gulf of Maine, Georges Bank, and Mid-Atlantic Bight during Ecosystem Monitoring (EcoMon) Surveys by the NOAA NMFS Northeast Fisheries Science Center. COI metabarcodes were identified using the MetaZooGene Barcode Atlas and Database (https://metazoogene.org/MZGdb) specific to the North Atlantic Ocean. A total of 181 species across 23 taxonomic groups were detected, including a number of sibling and cryptic species that were not discriminated by morphological taxonomic analysis of EcoMon samples. In all, 67 species of 15 taxonomic groups had ≥ 50 COI sequences; 23 species had >1,000 COI sequences. Comparative analysis of molecular and morphological data showed significant correlations between COI sequence numbers and microscopic counts for 5 of 6 taxonomic groups and for 5 of 7 species with >1,000 COI sequences for which both types of data were available. Multivariate statistical analysis showed clustering of samples within each region based on both COI sequence numbers and EcoMon counts, although differences among the three regions were not statistically significant. The results demonstrate the power and potential of COI metabarcoding for identification of species of metazoan zooplankton in the context of ecosystem monitoring.

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil–Sen median method and Mann–Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year−1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.