Conservation Decisions Research Articles

Integrated machine learning and geospatial analysis enhanced gully erosion susceptibility modeling in the Erer watershed in Eastern Ethiopia

Land degradation from gully erosion poses a significant threat to the Erer watershed in Eastern Ethiopia, particularly due to agricultural activities and resource exploitation. Identifying erosion-prone areas and underlying factors using advanced machine learning algorithms (MLAs) and geospatial analysis is crucial for addressing this problem and prioritizing adaptive and mitigating strategies. However, previous studies have not leveraged machine learning (ML) and GIS-based approaches to generate susceptibility maps identifying these areas and conditioning factors, hindering sustainable watershed management solutions. This study aimed to predict gully erosion susceptibility (GES) and identify underlying areas and factors in the Erer watershed. Four ML models, namely, XGBoost, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), were integrated with geospatial analysis using 22 geoenvironmental predictors and 1,200 inventory points (70% used for training and 30% for testing). Model performance and robustness were validated through the area under the curve (AUC), accuracy, precision, sensitivity, specificity, kappa coefficient, F1 score, and logarithmic loss. The relative slope position is most influential, with 100% importance in SVM and RF and 95% importance in XGBoost, while annual rainfall (AR) dominated ANN (100% importance). Notably, XGBoost demonstrated robustness and superior prediction/mapping, achieving an AUC of 0.97, 91% accuracy, 92% precision, and 81% kappa while maintaining a low logloss (0.0394). However, SVM excelled in classifying gully resistant/susceptible areas (97% sensitivity, 98% specificity, and 91% F1 score). The ANN model predicted the most areas with very high gully susceptibility (13.74%), followed by the SVM (11.69%), XGBoost (10.65%), and RF (7.85%) models, while XGBoost identified the most areas with very low susceptibility (70.19%). The ensemble technique was employed to further enhance GES modeling, and it outperformed the individual models, achieving an AUC of 0.99, 93.5% accuracy, 92.5% precision, 97.5% sensitivity, 95.4% specificity, 85.8% kappa, and 94.9% F1 score. This technique also classified the GES of the watershed as 36.48% very low, 26.51% low, 16.24% moderate, 11.55% high, and 9.22% very high. Furthermore, district-level analyses revealed the most susceptible areas, including the Babile, Fedis, Harar, and Meyumuluke districts, with high GES areas of 32.4%, 21.3%, 14.3%, and 13.6%, respectively. This study offers robust and flexible ML models with comprehensive validation metrics to enhance GES modeling and identify gully prone areas and factors, thereby supporting decision-making for sustainable watershed conservation and land degradation prevention.

Land Cover Mapping in a Mangrove Ecosystem Using Hybrid Selective Kernel-Based Convolutional Neural Networks and Multi-Temporal Sentinel-2 Imagery

Mangrove ecosystems provide numerous ecological services and serve as vital habitats for a wide range of flora and fauna. Thus, accurate mapping and monitoring of relevant land covers in mangrove ecosystems are crucial for effective conservation and management efforts. In this study, we proposed a novel approach for mangrove ecosystem mapping using a Hybrid Selective Kernel-based Convolutional Neural Network (HSK-CNN) framework and multi-temporal Sentinel-2 imagery. A time series of the Normalized Difference Vegetation Index (NDVI) products derived from Sentinel-2 imagery was produced to capture the temporal behavior of land cover types in the dynamic ecosystem of the study area. The proposed algorithm integrated Selective Kernel-based feature extraction techniques to facilitate the effective learning and classification of multiple land cover types within the dynamic mangrove ecosystems. The model demonstrated a high Overall Accuracy (OA) of 94% in classifying eight land cover classes, including mangrove, tidal zone, water, mudflat, urban, and vegetation. The HSK-CNN demonstrated superior performance compared to other algorithms, including random forest (OA = 85%), XGBoost (OA = 87%), Three-Dimensional (3D)-DenseNet (OA = 90%), Two-Dimensional (2D)-CNN (OA = 91%), Multi-Layer Perceptron (MLP)-Mixer (OA = 92%), and Swin Transformer (OA = 93%). Additionally, it was observed that the structure of the network, such as the types of convolutional layers and patch sizes, affected the classification accuracy using the proposed model and, thus, the optimum scenarios and values of these parameters should be determined to obtain the highest possible classification accuracy. Overall, it was observed that the produced map could offer valuable insights into the distribution of different land cover types in the mangrove ecosystem, facilitating informed decision-making for conservation and sustainable management efforts.

Vicariant speciation resulting from biogeographic barriers in the Australian tropics: The case of the red-cheeked dunnart (Sminthopsis virginiae).

Global biodiversity loss continues unabated, and in Australia, the rate of recent mammal extinctions is among the worst in the world. Meanwhile, the diversity among and within many endemic mammal species remains undescribed. This information is crucial to delineate species boundaries and thus inform decision-making for conservation. Sminthopsis virginiae (the red-cheeked dunnart) is a small, dasyurid marsupial found in four disjunct populations around the northern coast of Australia and New Guinea. There are three currently recognized subspecies, each occupying a distinct geographic location. Sminthopsis v. virginiae occurs in Queensland, S. v. rufigenis is distributed across New Guinea and the Aru Islands, and S. v. nitela has populations in the Top End of the Northern Territory and the Kimberley region of Western Australia. Previous molecular work has suggested the current subspecies definitions are not aligned with DNA sequence data, though the sampling was limited. We undertook a comprehensive genetic and morphological review of S. virginiae to clarify relationships within the species. This included mitochondrial (CR, 12S, and cytb) and nuclear (omega-globin, IRBP, and bfib7) loci, and morphometric analysis of skulls and whole wet-preserved specimens held in museums. Maximum Likelihood and Bayesian phylogenetic analyses resolved samples into two distinct clades, demarcated by the Gulf of Carpentaria in Australia's north. Sminthopsis. v. nitela was consistently separated from S. v. virginiae and S. v. rufigenis, based on the overall body and skull size and craniodental features, while S. v. virginiae and S. v. rufigenis were more difficult to distinguish from each other. Thus, we redescribed S. virginiae, recognizing two species, S. nitela (raised from subspecies) and S. virginiae (now comprising the subspecies S. v. virginiae and S. v. rufigenis). This study highlights the importance of recognizing cryptic mammal fauna to help address the gap in our knowledge about diagnosing diversity during a time of conservation crisis.

Taking the Pulse of Resilience in Conserving Seagrass Meadows.

Foundational habitats such as seagrasses and coral reefs are at severe risk globally from climate warming. Infectious disease associated with warming events is both a cause of decline and an indicator of stress in both habitats. Since new approaches are needed to detect refugia and design climate-smart networks of marine protected areas, we test the hypothesis that the health of eelgrass (Zostera marina) in temperate ecosystems can serve as a proxy indicative of higher resilience and help pinpoint refugia. Eelgrass meadows worldwide are at risk from environmental stressors, including climate warming and disease. Disease outbreaks of Labyrinthula zosterae are associated with recent, widespread declines in eelgrass meadows throughout the San Juan Islands, Washington, USA. Machine language learning, drone surveys, and molecular diagnostics reveal climate impacts on seagrass wasting disease prevalence (proportion of infected individuals) and severity (proportion of infected leaf area) from San Diego, California, to Alaska. Given that warmer temperatures favor many pathogens such as L. zosterae, we hypothesize that absent or low disease severity in meadows could indicate eelgrass resilience to climate and pathogenic stressors. Regional surveys showed the San Juan Islands as a hotspot for both high disease prevalence and severity, and surveys throughout the Northeast Pacific indicated higher prevalence and severity in intertidal, rather than subtidal, meadows. Further, among sites with eelgrass declines, losses were more pronounced at sites with shallower eelgrass meadows. We suggest that deeper meadows with the lowest disease severity will be refuges from future warming and pathogenic stressors in the Northeast Pacific. Disease monitoring may be a useful conservation approach for marine foundation species, as low or absent disease severity can pinpoint resilient refugia that should be prioritized for future conservation efforts. Even in declining or at-risk habitats, disease surveys can help identify meadows that may contain especially resilient individuals for future restoration efforts. Our approach of using disease as a pulse point for eelgrass resilience to multiple stressors could be applied to other habitats such as coral reefs to inform conservation and management decisions.

Reproductive Capabilities of Female Nilgai (Boselaphus tragocamelus) in Southern Texas.

Free-ranging nilgai antelope (Boselaphus tragocamelus) are an understudied species, both on their native ranges of India, Pakistan, and Nepal and on their introduced ranges in southern Texas. Basic data related to population sizes, survival, reproduction, and recruitment are needed throughout their range to inform management and conservation decisions. We collected nilgai fetuses from 3 ranches in southern Texas, including East Foundation's El Sauz and Santa Rosa ranches, and the Norias Division of the King Ranch® from 2018-2021. We calculated the percentage of individuals that were pregnant in each of the sample years and overall. We determined monthly average, maximum, and minimum fetus length. Of 488 nilgai cows, we found 386 to be pregnant (79%) and 214 to be pregnant with twins (56%). We found nilgai cows as young as 1-year old to have fetuses and therefore to have reached sexual maturity. Sex ratios of fetuses during any sampling year did not differ. We found ample evidence supporting our hypothesis that nilgai are fecund on their introduced range of southern Texas. To prevent nilgai overpopulation and associated problems, harvest management strategies should be implemented, specifically on nilgai cows.

Conserving Evolutionary Potential: Combining Landscape Genomics with Established Methods to Inform Plant Conservation.

Biodiversity conservation requires conserving evolutionary potential-the capacity for wild populations to adapt. Understanding genetic diversity and evolutionary dynamics is critical for informing conservation decisions that enhance adaptability and persistence under environmental change. We review how emerging landscape genomic methods provide plant conservation programs with insights into evolutionary dynamics, including local adaptation and its environmental drivers. Landscape genomic approaches that explore relationships between genomic variation and environments complement rather than replace established population genomic and common garden approaches for assessing adaptive phenotypic variation, population structure, gene flow, and demography. Collectively, these approaches inform conservation actions, including genetic rescue, maladaptation prediction, and assisted gene flow. The greatest on-the-ground impacts from such studies will be realized when conservation practitioners are actively engaged in research and monitoring. Understanding the evolutionary dynamics shaping the genetic diversity of wild plant populations will inform plant conservation decisions that enhance the adaptability and persistence of species in an uncertain future.

IOT Based Air Conditioner Monitoring System

In the context of rising urbanization, the surge in air conditioner usage has become a significant contributor to energy consumption. To address this, our research introduces an innovative Air Conditioner Monitoring System (ACMS) designed to optimize energy usage, enhance system efficiency, and ensure optimal indoor conditions. The proposed system employs cutting-edge sensors to monitor crucial parameters, including current, voltage, power consumption, and room temperature in real-time. By integrating these metrics into a unified framework, the ACMS facilitates proactive management and enables users to make informed decisions for energy conservation. The collected data is processed through advanced algorithms to predict potential faults or inefficiencies, allowing for preemptive maintenance and reducing downtime. Furthermore, the system supports remote monitoring and control, providing users with the flexibility to manage air conditioning settings from any location. Practical implementation and performance evaluations demonstrate the system's effectiveness in improving energy efficiency, reducing operational costs, and enhancing overall sustainability. This research contributes to the development of intelligent solutions for air conditioning systems, addressing the growing need for energy conservation in the face of expanding urbanization. The modularity of the proposed system allows for seamless customization and integration of additional features, paving the way for future advancements in intelligent climate control systems.

WILDPOP: AN INTERACTIVE TOOL FOR ESTIMATING OCCUPANCY AND ABUNDANCE OF WILDLIFE POPULATIONS

Species abundance or population size is an ecological parameter of critical importance for wildlife management and conservation decisions. Widely used data collection methods, such as sign surveys and remote cameras, often count non-identifiable individuals or individuals prone to misidentification. In ecological modeling, these individuals are considered unmarked, and a state-of-the-art modeling approach for such data is occupancy-type modeling for unmarked individuals, which explicitly incorporates imperfect detection. Hierarchical modeling of this kind requires advanced statistical analyses, typically conducted using the R software platform and the “unmarked” package. However, these models can be explored only by researchers with programming skills and a thorough understanding of hierarchical analysis of wildlife population data. To help researchers and practitioners implement these models, we have developed a Shiny web-based interactive tool for wildlife population assessment, which works with data collected by scientists and wildlife managers. This app facilitates the use of occupancy-type modeling for unmarked individuals (single-season single species occupancy and N-mixture models) for non-coder users. The app performs simulations for single-season single species occupancy and N-mixture models with or without covariates, and estimates occupancy and abundance employing users provided data. The results are displayed as text, tables, and graphs, helping users understand hierarchical modeling and answering real-life wildlife management questions.

Comparative phylogeography, a tool to increase assessment efficiency of polar assemblage resilience and vulnerability

The Southern Ocean benthos is remarkably rich and diverse, and managed under a complexity of treaties and conventions, further complicated by geopolitical boundaries. Traditionally, conservation management is largely informed by species lists augmented, when data are available, by known vulnerability of the taxa. Species presence absence database resources are valuable tools with proven and positive management outcomes, however, in a vast, difficult to access and thus understudied region such as the Southern Ocean, there are large gaps in knowledge regarding the ecology, ecophysiology, life history and even species identity. Conservation biogeography identifies regions of conservation concern, rather than a species-by-species approach, but also relies on the availability of high-quality presence data from species lists and thus both approaches are undermined when species lists are inaccurate or species in general are poorly described. In addition, the data provide a snapshot of the current species diversity and have inadequate power to identify the processes underlying the patterns uncovered. Identifying historical processes common to shaping diversity (species or genetic) can be generalized across assemblages and regions, providing a more robust basis for conservation policy and decisions. In this study, largely based on consideration of Southern Ocean ophiuroids, we discuss the challenges inherent in using species lists, the power and limitations of genetic analyses, and revisit previous suggestions of building a spatial model of diversity that includes underlying evolutionary relationships transcending the simple species diversity approach, and that is applicable to assemblages, rather than just to individual taxa.

Assessment of Community Participation in Forest Conservation in Ondo State, Nigeria

This study examines community participation in forest conservation in Ondo State, Nigeria, exploring the socio-economic dynamics and the effectiveness of decentralized forest management. Amidst escalating environmental degradation and forest depletion, this research highlights the critical role local communities’ play in the stewardship of forest reserves. Data were collected through surveys, interviews, and field observations involving 200 households across multiple communities adjacent to forest reserves. The findings reveal a strong community willingness to engage in forest conservation, with a substantial proportion of respondents prepared to accept financial incentives to aid in conservation efforts. The study also uncovers a significant correlation between socioeconomic factors—such as income levels, educational attainment, and proximity to forests—and the willingness to participate in conservation initiatives. Despite the readiness to support forest preservation, the research identifies a glaring lack of awareness and involvement in existing forest conservation programs. It is recommended that enhanced community education on the benefits of forest conservation, the introduction of more robust economic incentives, increased community involvement in conservation decision-making, and the strengthening of legal frameworks to protect forest resources. These steps are essential for sustainable forest management and biodiversity conservation in the area, which could serve as a model for similar regions globally.

The Utility of the Koala Scat: A Scoping Review.

The use of samples or scats to provide important ecological, genetic, disease and physiology details on free-range populations is gaining popularity as an alternative non-invasive methodology. Koala populations in SE Queensland and NSW have recently been listed as endangered and continue to face anthropomorphic and stochastic environmental impacts that could potentially lead to their extinction. This scoping review examines the current and potential utility of the koala scat to contribute data relevant to the assessment of koala conservation status and decision making. Although we demonstrate that there is great potential for this methodology in providing details for both individual wild animal and population biology (distribution, abundance, sex ratio, immigration/emigration, genetic diversity, evolutionary significant unit, disease epidemiology, nutrition, reproductive status and stress physiology), the calibre of this information is likely to be a function of the quality of the scat that is sampled.

Seasonal movement behaviour of large male elephant in Hwange National Park, Zimbabwe

AbstractA great challenge for African conservationists is human–wildlife conflict outside the boundaries of protected areas (PAs). This is particularly true when considering large mobile species, such as elephants, that often disperse from PAs into community land. In Zimbabwe's Hwange National Park, studies on elephant dispersal have focused on breeding herds. Here, we analyse the movements of five older, large males fitted with GPS collars. We found some dispersal outside Hwange, but large males were largely restricted to the Park, especially during the late dry season when water was scarce. Our findings may be useful to regional conservation decision makers.

Seasonal monitoring of biochemical variables in natural rangelands using Sentinel-1 and Sentinel-2 data

ABSTRACT Rangelands are natural ecosystems that serve as essential sources of forage for domesticated livestock and wildlife. Therefore, accurately mapping nutrient levels in rangelands is crucial for sustainable development and effective management of grazing animals. Remote sensing tools offer a reliable means to explore nutrient concentrations across large spatial areas. This study aimed to estimate and map seasonal foliar concentrations of nitrogen (N), phosphorus, and neutral detergent fibre (NDF) in mesic tropical rangelands of Limpopo using Sentinel-1, Sentinel-2, and the integration of S1 and S2 data. Fieldwork was conducted to collect samples for seasonal foliar nutrients (N, P, and NDF) during early-summer (November-January 2020), winter (July-August 2021), and late-summer (February-March 2022). Various conventional and red-edge-based vegetation indices were computed. The results demonstrate that integration data from S1 and S2 can effectively estimate and predict foliar concentrations of N, P, and NDF in mesic rangelands throughout the seasons, achieving R2 values of 0.76, 0.78, and 0.71, with corresponding RMSE values of 0.13, 0.04, and 2.52. Notably, red-edge variables emerged as the most significant parameters for predicting seasonal N, P, and NDF concentrations. Additionally, factors such as season and slope significantly influenced the distribution and occurrence of these foliage nutrients, with higher foliage production observed during late-summer and on steeper slopes. The study concludes that the integration of S1 and S2 data can effectively monitor the seasonal dynamics of biochemical parameters. This finding holds significant implications for policymakers and rangeland users, offering a comprehensive understanding of the intricate variations within rangeland ecosystems. Further research could expand on these findings by applying the knowledge to various datasets, exploring different rangelands, and examining additional ecological factors such as slope altitude to detect foliar fibre biochemicals. Finally, the applications of this research extend beyond individual properties, providing practical tools for sustainable rangeland management and informed decision-making in resource utilization and conservation.

Influence of Executive Compensation on Firm Risk-Taking Behavior in Qatar

Purpose: The aim of the study was to assess the influence of executive compensation on firm risk-taking behavior in Qatar. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study indicated that the structure and magnitude of executive compensation can significantly impact the risk profile of a firm. When executives are rewarded with substantial stock options and performance-based incentives, they are often motivated to pursue riskier strategies that have the potential for higher returns, aligning their interests with those of shareholders. However, this can also lead to excessive risk-taking, which may jeopardize the firm's stability if not managed properly. Conversely, fixed salaries and lower levels of variable compensation tend to promote more conservative decision-making, reducing the propensity for risk. The balance and design of executive compensation packages are thus crucial in ensuring that executives take calculated risks that contribute to sustainable firm growth rather than engaging in hazardous financial behaviors that could lead to adverse outcomes. Implications to Theory, Practice and Policy: Agency theory, tournament theory and stewardship theory may be used to anchor future studies on assessing the influence of executive compensation on firm risk-taking behavior in Qatar. Implement compensation structures that strategically align executive incentives with organizational risk strategies. Collaborate with regulatory bodies to enhance governance frameworks that govern executive compensation.

Generalized nonlinearity in animal ecology: Research, review, and recommendations.

Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

Facilitating giant panda crossings of national highway in Wolong area of Giant Panda National Park amid human activities.

As human activities continue to expand, wildlife persistence faces escalating threats from roads. In Wolong area of Giant Panda National Park, the local giant pandas (Ailuropoda melanoleuca) are divided into two population groups along the National Highway G350 (NHG). Therefore, selecting suitable areas to help those giant pandas communicate across the NHG is necessary. In this research, we evaluated the presence of human activities and simulated their absence to analyze how they affect the giant panda's habitat in Wolong. Subsequently, based on the kernel density estimation (KDE) for giant pandas and the main human distribution locations, we selected suitable areas for the population link between the two road sections on the NHG. We simulated the absence of human activities on the two road sections to compare changes in the habitat suitability index (HSI) and connectivity value (CV) relative to their presence. We aimed to carefully select the area for future giant panda corridor plans and simulate whether eliminating human activities will significantly improve the HSI and CV of the area. Our results show that: (1) Human activities presence has led to subtle changes in the landscape pattern of suitable habitats and a decrease in Wolong by 78.76 km2 compared to their absence. (2) Human activities presence significantly reduced HSI and CV in the 1000 m buffer along the NHG compared to their absence. (3) The HSI and CV of the 1000 m buffer in the simulated absence of human activities for the two road sections were significantly higher than their presence. This research identified the optimal road section for crossing the NHG to link giant panda population groups and habitats in Wolong. These insights are significant for formulating conservation decisions and corridor plans and for promoting wildlife conservation in reserves amid high levels of human activity.

HBIM for Conservation of Built Heritage

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation.

Assessing marine ecosystem health using multi-omic analysis of blue mussel liquid biopsies: A case study within a national marine park

Marine ecosystems are under escalating threats from myriad environmental stressors, necessitating a deeper understanding of their impact on biodiversity and the health of sentinel organisms. In this study, we carried out a spatiotemporal multi-omic analysis of liquid biopsies collected from mussels (Mytilus spp.) in marine ecosystems of a national park. We delved into the epigenomic, transcriptomic, glycomic, proteomic, and microbiomic profiles to unravel the intricate interplay between ecosystem biodiversity and mussels' biological response to their environments. Our analysis revealed temporal fluctuations in the alpha diversity of the circulating microbiome associated with human activities. Analysis of the hemolymphatic circulating cell-free DNA (ccfDNA) provided information on the biodiversity and the presence of potential pathogens. Epigenomic analysis revealed widespread hypomethylation sites within the mitochondrial (mtDNA). Comparative transcriptomic and glycomic analyses highlighted differences in metabolic pathways and genes associated with immune and wound healing functions. This study demonstrates the potential of multi-omic analysis of liquid biopsy in sentinel to provide a holistic view of human activities' environmental impacts on marine coastal ecosystems. Overall, this approach has the potential to enhance the effectiveness and efficiency of various conservation efforts, leading to more informed decision-making and better outcomes for biodiversity and ecosystem conservation.

Spatial and temporal trends of Tamanend’s bottlenose dolphin (Tursiops erebennus) strandings in South Carolina, USA, 2006–2020

Marine mammal strandings data contribute towards overall assessments of cetacean populations, including seasonal, annual and life‐history trends which result from both natural and anthropogenic causes. We conducted spatial and temporal analyses of Tamanend’s bottlenose dolphin (Tursiops erebennus) strandings in the waters of South Carolina, USA, over a 15‐year period from 2006 to 2020, with the following objectives: (1) to determine spatial and temporal trends; (2) to analyse seasonal reproductive trends; (3) to determine life‐history parameters, such as sex ratio and age class; (4) to determine the extent to which human interaction contributed to strandings; and (5) to compare stranding patterns with historical data from 1992–2005. A total of 837 strandings occurred over the study period, with a mean of 55.8 strandings per annum. The season with most strandings was Spring (April–June), while March and April had the highest number of strandings. A relatively equal number of male and females dolphins stranded, and mortality was highest in neonates, first‐year calves and adults. Neonatal strandings comprised 22.1% of all strandings and were predominant in May. Ninety‐five human‐interaction (HI) cases were observed, representing 22.9% of strandings where HI or non‐HI could be determined. Confirmed crab‐pot buoy‐line entanglements were the predominant HI category (n = 31). Density maps and hot spot analysis showed most strandings, including live and neonatal strandings and HI cases, occurred in defined areas of Charleston and Beaufort Counties. While many trends were similar to historical data, some new trends emerged, particularly an increase of strandings in March and April. Neonatal strandings decreased in November and the historical spike during this month has essentially disappeared. The results of this analysis serve as a tool to predict stranding rates and inform conservation and management decisions to better protect bottlenose dolphins.

Amazon forest biogeography predicts resilience and vulnerability to drought.

Amazonia contains the most extensive tropical forests on Earth, but Amazon carbon sinks of atmospheric CO2 are declining, as deforestation and climate-change-associated droughts1-4 threaten to push these forests past a tipping point towards collapse5-8. Forests exhibit complex drought responses, indicating both resilience (photosynthetic greening) and vulnerability (browning and tree mortality), that are difficult to explain by climate variation alone9-17. Here we combine remotely sensed photosynthetic indices with ground-measured tree demography to identify mechanisms underlying drought resilience/vulnerability in different intact forest ecotopes18,19 (defined by water-table depth, soil fertility and texture, and vegetation characteristics). In higher-fertility southern Amazonia, drought response was structured by water-table depth, with resilient greening in shallow-water-table forests (where greater water availability heightened response to excess sunlight), contrasting with vulnerability (browning and excess tree mortality) over deeper water tables. Notably, the resilience of shallow-water-table forest weakened as drought lengthened. By contrast, lower-fertility northern Amazonia, with slower-growing but hardier trees (or, alternatively, tall forests, with deep-rooted water access), supported more-drought-resilient forests independent of water-table depth. This functional biogeography of drought response provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia's most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience.