Journal of Animal Environment (E-ISSN 2717-1396, ISSN 2717-1388) is a Quaterly peer-reviewed academic journal focuses on research and studies related to the interactions between animals and their environments, including aspects of animal welfare, habitat, and ecological impact. Journal publishes articles in Farsi and English. Journal of Animal Environment articles have been referred, 2-4 reviewers per paper.
Predicting Wildlife Refugia in Hyperfragmented Tropical Forests Using Microclimate Mosaics and Fine-Scale Movement Patterns
The fragmentation of habitats in tropical forests alters microclimatic conditions and connectivity for movement, making it very challenging for wildlife. The key to successful conservation planning is thus in identifying climate-buffered refugia in hyperfragmented landscapes. This paper forecasts wildlife havens by applying coarse-scale animal movement patterns and high-resolution microclimate mosaics on a hyper-fragmented tropical forest. The data included in situ microclimate sensor data (temperature and relative humidity), remotely sensed canopy structural data, and GPS telemetry from several forest-related vertebrate species. Refugia detection was performed using a machine-learning framework that linked microclimate stability indices to step-selection and movement-resistance models. Areas with low thermal variance, maintained humidity, and seasonal, multi-species use were described as refugial patches. Findings show that refugia covered 12.4% of total forest cover, yet supported 46.7% of all locations of all animals recorded. The average daytime temperatures in refugia were 2.8 °C colder (p < 0.001) than those in non-refugial patches, and the persistence of relative humidity across seasons was 19.3% greater. The movement analyses showed that the likelihood of animals choosing microclimate-stable corridors rose 1.9-fold (0.64, SD = 0.11), although the travel distance in microclimate-stable corridors also increased. Those who inhabited refugia had 31% lower thermal exposure variance and a higher 24% site fidelity than those who used more open patches. These results indicate that heterogeneity in microclimates, rather than patch size, is the sole determinant of refugia formation in hyperfragmented tropical forests. Combining microclimate mosaics and animal movement behaviour provides a robust mechanism for delineating functionally significant refugia and ranking conservation measures to achieve climate resilience in swiftly shifting tropical landscapes.
Copyright (c) 2025 Journal of Animal Environment
Machine Learning Assessment of Climate-Driven Shifts in Wildlife Population Dynamics Across Fragmented Landscapes
The human factors that have a significant influence on wildlife population dynamics include climate change and habitat fragmentation, which impact the ways species are distributed, abundant, and resilient. The traditional ecological models do not usually exhibit good predictability, usually do not account for the nonlinear interactions of climate variables and landscape structure. In this paper, a machine learning-based model is used to evaluate climate-induced changes in wildlife populations in fragmented natural environments, determine the most important environmental and spatial predictors, and measure the predictive accuracy. Data on wildlife population, climate (change in temperature, variability of precipitation), and fragmentation (patch size, edge density, habitat isolation, and connectivity) were integrated and pre-processed. Models of ensemble machine learning, such as Random Forest, Gradient Boosting, and Neural Networks, were trained and cross-validated with cross-validation. The evaluation of model performance was done using MAE, RMSE, and R2, and these features of importance and partial dependence analysis established prominent drivers of climate and fragmentation. Higher vulnerability when compared to moderately fragmented (0.98 ± 0.14) and low-fragmentation areas (1.12 ± 0.08) was observed with populations in highly fragmented habitats having the lowest mean population index (0.74 ± 0.22). Gradient Boosting was the most predictive (RMSE=0.168, MAE=0.119, R 2=0.81). The importance of features showed that temperature anomaly (0.31) and patch size (0.27) are the most influential factors, and precipitation variability, habitat isolation, and edge density were also significant. Partial dependence plots revealed that there were nonlinear population changes in response to temperature changes, but these were sensitive to extreme weather conditions. Machine learning has the potential to learn complex interactions between climate and fragmentation, and is capable of identifying the major drivers of wildlife populations. The framework gives practical information to conservation planning to assist in restoring habitat connectivity and climate-adaptive management of fragmented landscapes.
Acoustic Soundscapes as Early Warning Indicators for Biodiversity Collapse in Restored Savanna Ecosystems
The rapid biodiversity loss in savanna habitats endangers ecosystem stability and long-term ecological restoration success, but detecting ecosystem collapse early is difficult because traditional biodiversity surveys are expensive and time-consuming. The research measures the biodiversity collapse of restored savanna as an indicator of acoustic soundscapes. Passive acoustic observations were performed at 18 restored and reference savanna sites over 24 months, revealing diel and seasonal variation in biotic sound. Quantitative measurements of acoustic indices, such as the Acoustic Complexity Index (ACI), Bioacoustic Index (BI), and Acoustic Entropy (H), were compared with vegetation structure, insect abundance, and vertebrate species richness data collected independently. Findings show that there was a consistent precedence of deterioration in soundscapes, with a resultant measurable reduction in biodiversity. The ecological degradation sites recorded a 2238% decrease in ACI and a 31% decrease in BI up to 69 months before the reporting of substantial losses in species richness. There were strong positive correlations between the acoustic indices and field-based biodiversity indices (e.g., ACI vs. species richness: r = 0.74, p < 0.001). Classification models trained with machine-learning methods on acoustic features accurately identified stable and collapsing sites with 87 % accuracy. Notably, the restored sites with a greater level of early acoustic diversity had a higher level of long-term resilience indicating that soundscapes are indicative of both the state of biodiversity and the functionality of an ecosystem. The paper has shown that acoustic soundscape analysis is a tool which can be scaled, non-invasive, and cost-effective in identifying the early warning of biodiversity breakage in restored savanna ecosystems. Acoustic monitoring can be effectively incorporated into the restoration assessment systems to help achieve better adaptive management and better conservation outcomes with the growing environmental stresses.
Integrating Predictive Modeling of Wildlife Population Dynamics with Ecosystem Service Conservation Strategies
The paper will examine how predictive modeling of wildlife population dynamics can be incorporated into ecosystem service conservation strategies to improve wildlife management and conservation across the board. Predictive modeling is an important mathematical and computational model for forecasting future population trends that are instrumental in the comprehension and management of the population of wildlife in changing environmental conditions. Wildlife populations have a profound effect on ecosystem services, which are the benefits that humans obtain because of ecosystems, but there is a gap in the current research on the possibility of using predictive modeling to inform conservation strategies that will lead to the preservation of ecosystem services. The paper explores the connection between the dynamics and the population of wildlife and ecosystem services and proposes a method where both areas are merged to provide more efficient conservation methods. The research involves the determination of a study area and species, wildlife population data collection, and the development of predictive models to determine the effects of the population dynamics on the ecological services. Findings reveal that there are strong correlations between wildlife population dynamics and ecosystem service delivery and that predictive models have the potential to be used in conservation planning. The discussion has brought out the implications of findings in the context of the existing research and the impact of those findings on the management strategies and suggestions for future research on the same. The research concludes that predictive modeling coupled with ecosystem service conservation is a promising way of solving conservation problems, and both the scientific research and conservation efforts should be advanced.
Behavioural Syndromes and Links to Extinction Risk and Population Viability Under Changing Land Use
The rapid change in land use driven by agricultural intensification, urbanization, and habitat fragmentation is an increasingly significant threat to biodiversity. Still, not all species respond to these environmental pressures in the same way. This paper analyses behavioural syndromes, which refer to stable associations between traits, including boldness, activity, and dispersal, and extinction risk and the viability of a population in changed landscapes. The behavioural and demographic data from 18 terrestrial vertebrate species were collected across 6 divergent land-use regimes, comprising 420 standardised behavioural trials and 35 long-term population datasets. Stochastic population viability models were fitted with behavioural traits to assess the survival, fecundity, dispersal, and extinction history of populations under progressive land-use scenarios. The finding indicates that high-risk behavioural syndrome populations had 0.42 units of survival per generation of adults and 1.6 disturbance encounters per time step of mortality events. Risk-averse syndromes, on the other hand, had reduced dispersal, leading to a 1.9-fold reduction in successful colonization events within fragmented habitat networks. Behaviour-informed models used behaviour information to predict extinction risk 21 log-units better than behaviour-neutral models. The influence of population growth on the viability results was reduced by 0.31 units in the high-land-use-intensity landscapes, with behavioural effects explaining 38 units of variance. These results show that behavioural syndromes are significant mediators of species' effects on land-use change, with individual behavioural consistency linked to population-level persistence. Assessment of conservation incorporating behavioural aspects will be a better predictor of extinction risk and of successful land-use management in the face of rapid environmental change.
Network-Optimized Design of Wildlife Corridors for Multi-Species Connectivity in Rapidly Urbanizing Regions
Habitat fragmentation is a significant cause brought about by urbanization, and it interferes with the connectivity of wildlife and poses a threat to biodiversity. The booming city growth and development of infrastructure causes the isolation of wildlife species and the inhibition of access to vital resources. This paper responds to the fact that there is a need to have proper wildlife corridors that can sustain the connectivity of various species within the urbanizing landscapes. This study utilizes the network optimization strategy, which combines cost surface analysis and genetic algorithms to develop wildlife corridors to fit the movement patterns of carnivores, herbivores, and small mammals. The area of study involves fragmental habitats, and the optimization process takes into consideration future urbanization projections in order to have a viable corridor in the long term. Resistance values, species specific habitat preferences, and dispersal distance of most landscape features were taken into consideration in the design of the corridor. The improved connectivity of 45 to 80% across species was achieved in the optimized corridors, and the most significant benefits were seen in large carnivores. The corridors were effective in terms of preventing fragmentation and genetic flow between geographically isolated populations. The urban infrastructure, including roads and buildings, was named as a significant obstacle, but the corridors easily avoided the obstacles and facilitated the species movement, making them less isolated. This paper suggests that network-based wildlife corridors can contribute significantly to the connectivity in urbanizing areas. The findings underscore the significance of design and urban development of corridors, especially regarding multi-species and multi-use. Fragmentation can be reduced by incorporating wildlife corridors in urban planning to promote conservation of biodiversity in the long run.
The Role of Social Learning in Buffering or Amplifying Climate Stress in Long-Distance Migratory Species
The current trend in climate change is the changing environmental conditions that migratory species face over long distances, resulting in increased physiological stress, diversion of migration routes, and decreased survival rates. Although the individual adaptive responses have been well researched, little is known about the process of social learning, including the transfer of information by groups during interaction, and how these moderates or exacerbates stress induced by climate change. The research question addressed in this study is whether social learning serves as a buffer, increasing resilience, or an amplifier, positively reinforcing maladaptive behavior in the context of climate stress. A comparative study of five migratory species, three avian and two marine, was used to identify a set of three GPS tracking data, climate exposure indices, and behavioral network metrics across 12 migration seasons (2012-2023). Stress responses were measured as deviations in migration timing and route flexibility, and the probability of survival and social learning was quantified using indices of group cohesion and leader-follower relationships. Findings show that moderate amounts of social learning effectively buffered climate stress, reducing route deviation errors by 27% and increasing migration success rates by 18% during high-temperature anomalies. Nevertheless, the intensification of stress effects by highly centralized social structures led to coordinated route failures and a 22% higher mortality rate during extreme climate events. Flexible species had better adaptive ability than species with strong information hierarchies. The results indicate that social learning has a dual impact on climate adaptation, both as a resilience factor and a risk factor. Considering social dynamics in conservation planning is thus essential for forecasting the outcomes of migration and for developing efficient, climate-adaptive management measures.
Leveraging Artificial Intelligence for Mapping Migratory Patterns and Enhancing Biodiversity Preservation
The study of migratory patterns and their importance in maintaining biodiversity is necessary because ecosystems are challenged by the growing issues of climate change and habitat devastation. Male migration studies in the traditional approach are always prone to struggle with scope, precision, and flexibility in the dynamic environmental conditions. The proposed research explores how artificial intelligence (AI) can help improve the study of migratory patterns and aid in the conservation of biodiversity in areas such as the African Savannah. Developed with species like wildebeests, zebras, and African elephants in mind, AI algorithms, mainly machine learning, neural networks, etc., are used to work with large amounts of data, such as GPS tracking, environmental variables, satellite images, etc. Such AI models can be used to forecast migration patterns, monitor elephant relocation, and determine the areas of importance that are at risk due to human actions and climate change. The findings indicate that AI can improve the precision of migration predictions and provide proactive conservation methods by mapping essential migration pathways. Moreover, AI solutions assist in determining the zones that are at risk because of habitat fragmentation and poaching, which is crucial in the conservation of African elephants and other migrating species. The paper will compare AI based methods with the traditional techniques of migration to show how AI provides more insights, improved conservation results, and sustainable management practices. The challenges facing it include the need to refine the model because its potential is limited by issues like data quality, model interpretation, and scalability. The paper presents the significance of the incorporation of AI into conservation management to facilitate more efficient and sustainable conservation of biodiversity amid the worldwide environmental transformations.
Multispecies Functional Connectivity Networks in the Context of Habitat Fragmentation and Warming Trends
Ecological connectivity is changing rapidly due to habitat fragmentation and climate warming, threatening the survival of species in heterogeneous landscapes. Though the functional connectivity of single species has been thoroughly studied, little is known about the interactions between fragmentation and warming in multispecies connectivity networks. This paper compares multispecies functional connectivity in fragmented, warming landscapes across 18 vertebrate taxa of interest as focal species. The distribution models of species were combined with the surfaces of resistance based on the land-cover fragmentation index and temperature anomalies 1985-2020. Graph-theoretic metrics and circuit-based modeling were used to quantify connectivity patterns for constructing multispecies functional connectivity networks. The findings show that connectivity is significantly degraded in both stressing conditions individually and in combination. Fragmentation of land, by itself, caused a 27.6% reduction in mean connectivity, whereas warming trends led to a 19.3% decrease in connectivity relative to the baseline. Connectivity loss was experienced to 38.9% when the interacting factors were fragmentation and warming, showing a cumulative effect. Modularity of the network increased from 0.41 to 0.63, indicating greater isolation between habitat clusters. Pathways that supported over 60% of the modeled species declined by 34%, and low-elevation and edge-dominated pathways experienced the greatest losses. Moreover, the probability of dispersal for species with a narrow thermal range decreased by 42% compared with generalist species, suggesting increased susceptibility to resistance under warming. These results indicate that multispecies functional connectivity networks are more vulnerable to the synergistic effects of habitat fragmentation and climate warming than to either pressure alone. The conservation policies, which place greater emphasis on climate-resilient corridors and ensure multi-species connectivity, can thus be vital in supporting the functionality of ecological networks in the face of current global change.
Drone-Based Monitoring of Biodiversity Responses to Targeted Forest Restoration in Tropical Habitat Mosaics
The research will determine the efficacy of drone-based surveillance in determining biodiversity responses to forest restoration in tropical habitat mosaics. The study contrasts the level of species richness, abundance, and vegetation health (NDVI) in restored and non-restored regions in two years. In the main, the findings include that: the species richness grew by 30% in Year 1, and 44% in Year 2, in the restored areas; but it rose by 5-7% in non-restored areas. The biodiversity index of Shannon Wiener of restored areas has increased 34%, from 2.3 to 3.1, but in non-restored regions, the improvement was only 12%. Drones were found to have a 95% species detection rate, and they significantly improve biodiversity observation in large and remote areas of restoration. The research also brought out the fact that the restored areas had significant vegetation health improvement in terms of their NDVI, which rose to 0.80 in Year 2 after restoration, as compared to the smaller rise in non restored areas (from 0.45 to 0.55). These results emphasize the usefulness of drones in scalable, cost effective biodiversity monitoring in conservation and restoration efforts. According to the research, drone technology will be able to take accurate and repeatable data to measure the success of the restoration and alter the adaptive management strategies. The next step in the field should be enhancing the accuracy of drone sensors, control of the distance of flight, and machine-learning species detection. Also, a combination of drone data, satellite imagery, and ground-based surveys can provide a more comprehensive view of biodiversity processes.
Urban Heat Islands as Evolutionary Laboratories for Rapid Thermal Adaptation in Synanthropic Birds
City heat islands (UHIs) are localized areas of high temperature that introduce special ecological stress to synanthropic birds that coexist with humans. These natural environments are adapting to rapidly changing thermal conditions, providing an appropriate background for observing evolutionary adaptation. This research examines, in the long term, the phenotypic and behavioral adaptations of bird populations living in urban areas to heat stress, as well as the main physiological characteristics, including basal metabolic rate, evaporative cooling efficiency, and thermal tolerance limits. Twelve species of the synanthropic species were observed in five metropolitan regions, with temperature loggers, observations of behaviour, and repeated tests of heat tolerance. Mixed-effects models and survival analyses showed significant interspecific differences in adaptation rates. The birds in the central urban areas had more evaporative cooling ability (mean change = 18.4% p < 0.01) and less activity in the extreme heat durations, as compared to the peri-urban ones. Moreover, genetic sampling reported early indicators of selection in thermoregulation-related loci in the rapidly adapted species. These findings indicate that UHIs act as evolutionary laboratories, which encourage rapid thermal adjustment through phenotypic plasticity and embryonic genetic modification. The paper emphasizes the significance of urban microclimate heterogeneity in determining adaptive patterns and lays emphasis on how synanthropic species may be used as prototype systems to evaluate evolutionary dynamics to climate change. Further study involving longitudinal genetic tracking and experimental manipulation of thermal conditions is advised to identify a means of separating a plastic and heritable response. The conservation and urban planning strategies must take into account the importance of the thermal variability of microhabitat in promoting the resilience of avians in progressively urbanized environments.
Modeling Behavioral Adaptations of Species to Climate Change in the Context of Ecological Restoration Frameworks
As climate change continues to accelerate, the survival of global biodiversity increasingly depends on species' capacity to adapt behaviorally to rapidly shifting environmental stressors. This research study examines how modern behavioral modeling can be integrated into ecological restoration models to enhance species resilience. Past restoration activities have tended to focus on the physical restoration of the habitat, which, despite ignoring dynamic behavioural responses such as changes in migration, foraging behaviour, and social interactions, ultimately defines the long-term viability of the population in the new ecosystems. Using contemporary computational tools, namely Artificial Intelligence (AI) and predictive analytics, this study assesses how species navigate a modified landscape and how evolutionary traps develop. The article provides an analysis of the available significant research perspectives to suggest a multidisciplinary framework of research in which behavioral flexibility becomes a key measure of restoration success. It examines how classical conservation approaches can be replaced by AI-based approaches, evaluating risk factors, including the contributions of invasive species and environmental pollution to aquatic and terrestrial life, and shows that incorporating behavioral data into models of the restoration process can significantly enhance the accuracy of ecosystem recovery predictions and support the development of proactive measures for climate adoption decisions. Moreover, the research shows the economic merits of employing AI monitors to provide sustainability of ecosystem services. The study represents a critical roadmap to scientists and policymakers: it allows linking theoretical modeling and field implementation to make sure that restoration activities will be scientifically sound and behaviorally informed in a warming world.
Small Habitat Patches and Conservation Value in Fragmented Protected Areas
Habitat fragmentation is a critical issue for biodiversity, especially in conservation areas, where conservation initiatives are usually directed at large, continuous habitats. Nonetheless, small patches of habitat can be seen to be very important in terms of sustaining species diversity, ecological connectivity, and general conservation value in fragmented landscapes. This paper compares the conservation value of small habitat patches, using three metrics, which are species richness, abundance, and habitat quality, in three fragmented protected areas. The field surveys were also performed on 45 patches, and the presence of plant and animal species, habitat structure and level of anthropogenic disturbance were recorded. Data were statistically analyzed using generalized linear models and species accumulation curves to demonstrate that small patches were disproportionately important to the total biodiversity, and that 35-50% of the species in large patches were supported on small patches. Patch size, isolation, and habitat quality interactively had great effect on species richness (p < 0.01), and well-connected patches revealed a richer and more functional habitat. Additionally, there are indicator species that were only present in the small and high-quality patches, therefore emphasizing the special conservation interest. Network analyses revealed these patches to increase the landscape connectivity, in the form of stepping-stones that promote species dispersal and genetic exchange. Based on the findings, it is noted that managing small patches out of management plans may jeopardise the long-term conservation objectives especially to species that are vulnerable to fragmentation. The work comes to a conclusion that the conservation planning and protected area management using small habitat patches can help to maximize the persistence of biodiversity. The future studies are advised to consider time dynamics, species specific reaction and the contribution of small patches in reducing the impacts of climate changes. In general, in this study, the ecological and strategic importance of small pieces of habitat as a means of supporting biodiversity in fragmented areas of protection is highlighted.
Integrating Energetic Landscape Models and GPS Telemetry to Forecast Migration Route Changes in Large Mammals
Climate change, habitat fragmentation, and human activities are progressively posing a threat to the biodiversity of migration paths of large mammals. The proposed research combines the models of energetic landscapes to achieve the prediction of the changes in the migration pathways of species, including African elephants and mule deer, utilizing the GPS telemetry data. The model is able to measure the energy cost of movement in different habitats by taking environmental factors into account, such as terrain, weather, and availability of forage, and forecast future changes in migration under climate conditions (RCP 4.5 and RCP 8.5). The model has been tested through the cross-validation method and has been effective in predicting 85% overlap with observed paths of migration. The expected changes imply that there will be a 15 % increase in the northward migration routes by 2030-2050, which will be mainly caused by the diminishing of forage and the heat stress. The paper also reveals the major energy hotspots, including the rivers and cities, which are migration obstacles. The implication of these findings is enormous in the conservation of wildlife, especially in the restoration of migration patterns, as well as the design of dynamic protected areas that respond to changing climatic conditions of the future. Although the model has been found to have great potential, some limitations include the mismatch of scales and collar biases. Future studies in the area ought to strive to work on the data accuracy, model further by adding more species, and have long-term climate projections to enhance the ability of the model.
Predator Foraging Plasticity and Its Role in Top-Down Control Within Human-Modified Landscapes Using High-Resolution Biologging
The anthropogenic alters scenery such as urban and agricultural environments, which pose special predator challenges and require them to adapt the foraging activities according to the changing environmental conditions. The paper conducts a study based on the importance of predator foraging plasticity in top-down control in these landscapes, where high-resolution biologging technologies are used to trace predator movement and foraging patterns. After 2 years of monitoring GPS-collared predators, fox, coyotes, and hawks, in fragmented urban and rural environments, the predators were observed. The foraging behavior, movement patterns, and prey choices were examined to determine the effect of the landscape structure on predator prey interactions in these altered conditions. The findings revealed a significant distinction in foraging behavior between urban and rural predators. There was 25% greater foraging efficiency in urban predators (p < 0.05), as the predators spent less time foraging, possibly because there is anthropogenic food like garbage and domestic animals. Conversely, the foraging patterns of rural predators were less diverse and depended on wild fauna more indicating poorer foraging success. The range of urban predators was also wider in terms of territory, which is related to the increased availability of resources in human landscapes. Also, the research discovered that urban predators had a much more intense overall top-down influence on prey populations with a 30 % decrease in prey abundance (F = 4.78, p < 0.01) than 15 % decrease in rural settings. This shows the increased regulatory effect of predators in urban ecosystems, where anthropogenic resources favor higher predator densities and foraging success. These results indicate the significance of predator foraging plasticity in the ecological balance in anthropogenic landscapes. Predators can also help regulate prey populations through top-down control, adapting to conditions across varied environments, to avoid ecological disruption. The study indicates that predator foraging behavior is significant in the management and conservation of wildlife in anthropogenic habitats. The future studies need to focus on the effects of long-term predator plasticity on trophic cascades and ecosystem services, especially in quickly urbanizing regions.
Multispecies Evaluation of Habitat Restoration Effectiveness Using Before-After Control Impact Study Designs
Habitat restoration is one of the central conservation policies that will help reverse the trend of environmental degradation and support biodiversity. This paper measures the success of habitat restoration through a multispecies design, which involves the Before-After-Control-Impact (BACI) study design to test the response of the species in a restored forest ecosystem. Eight species were grouped within the region that was surveyed, which were herbivores (wild boar, spotted deer), carnivores (Bengal tiger, fishing cat), pollinators (honeybees), and small mammals (Indian field mouse, palm squirrel). Findings indicated a significant enhancement of the abundance of species, especially the wild boar, honeybees, and the small mammals, and a substantial improvement in habitat utilization of the restored riparian areas and the increased regions of grassland. But the Bengal tiger did not show many changes, and this may imply that apex predators might need more habitat remedies. The restoration process resulted in greater species richness and diversity of the affected regions, which supported the idea that habitat restoration is a part of the ecological recovery of a broader scope. Regardless of such constructive results, there are still issues to cope with the needs of large carnivores specifically and guarantee the success of the restoration process in the long run. This research demonstrates the significance of multispecies surveillance and long-term evaluation in measuring the results of the restoration process and determining the future conservation practices. The results highlight the importance of species-specific restoration response in light of ecological functions and habitat needs of different species.
Extreme Environmental Events as Ecological Reset Mechanisms in Animal Populations Under Drought, Fire, and Heatwave Disturbances
Climate change is driving extreme events, such as droughts, wildfires, and heatwaves, that affect ecosystems and animal populations. This paper explores how these disturbances are components of the ecological reset, shaping animal behavior, population dynamics, and community structure. The information was gathered through long-term field observations, satellite imagery, and ecological modeling to understand how these events affected different animal species in both temperate and tropical ecosystems. These findings indicate that droughts and heatwaves decrease the availability of food and water. Still, the regeneration of habitats and species-richness through wildfires follows, due to the elimination of invasive vegetation and the formation of new niches. Statistical tests showed that rapid rebounds in a species after a disturbance are caused by decreased competition, whereas habitat loss leads to long-term declines in other species. As an illustration, the population of small mammals rose by 30 % in places that had just been cleared by fire in overgrown vegetation (F = 3.91, p < 0.05). The results are important in that bring out the duality of extreme events as destructive and regenerative. The research summarizes that although extreme environmental events are harmful in the short run, serve as agents of ecological reset, allowing the establishment of new species assemblages and the reestablishment of ecological stability. Research in the future should be aimed at knowing the long-term ecological impacts of these events, especially on species adaptation, genetic diversity, and ecosystem services. Also, resilience to such disturbances should be included in conservation and habitat management strategies to ensure that future climate extremes do not affect the ecosystems.
AI-Based Approaches for Evaluating Animal Health and Forecasting the Consequences of Habitat Degradation
The rapid rate of environmental changes in the world requires shifting towards the use of state-of-the-art, data-driven ecological methodology. The proposed research study investigates the adoption of Artificial Intelligence (AI) as the main instrument for assessing animals' health and predicting the environmental consequences of habitat degradation. With the natural landscape experiencing unprecedented stresses due to urbanization, climate change, and pollution, real-time monitoring of species becomes an essential element of conservation biology. The review includes a wide range of AI applications, i.e., deep learning-based algorithms to identify the disease in livestock, and computer-based vision to monitor the behavioral changes in wildlife in fragmented forests. Using the multi-dimensional data, such as acoustic sensors, satellite images, and genetic markers, AI gives an overview of the animal-environment nexus. The findings prove that AI-based forecasting models are capable of depleting habitat suitability much more accurately than conventional statistical procedures, and conservation interventions can be implemented beforehand. Also, the article talks about how 5G-powered networks and data analytics can be used to establish a smooth exchange of data between field sensors and policy-making structures. This study has reached the conclusion that Eco-Intelligence is not a luxury in a technology application but a mandatory requirement in the name of sustainable development. By closing the gap between raw information about the environment and the practical management actions, AI will allow researchers to reduce the aftermath of habitat loss and improve the well-being of both wild and domestic animals. In the end, this study gives a detailed roadmap on how the application of intelligent systems can be used to protect biodiversity and also provide resiliency of global ecosystems in a world that is changing rapidly.
The Impact of Climate Change on Social Networks in Cooperative Breeding Species Across Disturbance Gradients
Climate change has significant impacts on animal populations, especially on species with complex social structures, such as cooperative breeders. These species are dependent on stable social networks to increase reproductive success and survival, and individuals cooperate in activities such as food provisioning, territory defense, and offspring care. But growing climate variability, including droughts, fires and heatwaves, is interfering with these networks by altering the availability of habitats, food resources and environmental conditions. The article examines the impact of climate change on social relationships in cooperatively-breeding species in varying disturbance gradients. A combination of field observations, social network analysis, and modeling of climate data enabled us to evaluate changes in social interactions, breeding success, and group cohesion in species such as the superb fairy-wren (Malurus cyaneus) and the African wild dog (Lycaon pictus). The climate disturbances increase, the social networks become less stable in terms of reproductive success. To cite an example, breeding success of wild dogs was reduced by a factor of 25 in high and low disturbance areas (F = 4.72, p < 0.05). Individuals in poorly bonded social groups were stressed and less cooperative. These results show how cooperative breeders are susceptible to climate change and how the social structures help conserve them. The paper will conclude with recommendations for specific conservation measures, such as habitat rehabilitation, reduced human-wildlife conflict, and social network resilience, to mitigate the impacts of climate change on cooperative breeders.
Edge Aware Restoration Planning to Mitigate Canal Induced Wildlife Habitat Fragmentation in Agricultural Regions
The paper explores the utility of edge-sensitive approaches to restoration in reducing wildlife habitat fragmentation in agricultural areas caused by canals. Canals have been found to fragment wildlife movement and connectivity, leading to a decrease in population and degradation of ecosystems due to habitat fragmentation. Old-fashioned restoration techniques are likely to ignore the importance of boundaries between habitats and the anthropogenic landscape. The proposed research will address a novel solution that will be based on the restoration of edges, including riparian buffers and wildlife corridors, to enhance habitat connectedness. There were three scenarios of restoration, and various buffer widths and vegetation types were considered, i.e., 20 meters of native grasses and 100 meters of mixed vegetation. The findings indicated that the bigger and more complicated restoration activities enhanced the connectivity of the habitats and the movement of the wildlife greatly. Scenario 3, which had a buffer of 100 meters and a variety of vegetation, showed an 80% growth in habitat connectivity and a 75% enhancement in the movement of wildlife. Small mammals, birds, and other species have shown greater abundance and better movement in restored regions, highlighting the significance of connectivity in the survival of species. The paper also contrasted edge-sensitive techniques and conventional small-scale restoration techniques, and specifically, the better outcomes of larger and landscape-based interventions. Nevertheless, some obstacles, including land use tensions, financial limitations, and long-term monitoring, were faced. The findings hint at how future restoration should be concerned with the methods of edge-aware strategies to be incorporated into the agricultural management practice and partner with the local stakeholders. Future studies are required to maximize the restoration method using other species and scenery and to assess the long-term effects of such approaches. The paper offers a model of how to manage the issue of canal-induced fragmentation and gives advice on how to enhance biodiversity and ecosystem connectivity in agricultural landscapes.
Integrating Human Behavioral Change into Conservation Models to Improve Wildlife Protection Success
Wildlife preservation has always been premised on ecological and enforcement-based systems, and in most cases, human activities have been viewed as external forces of the system instead of living components. This has curtailed the effectiveness of conservation interventions in the long run, especially where non-compliance, unlawful exploitation, and human-wildlife conflict continue to exist. Here, this paper will analyze the role of including human behavioral change into conservation models in improving the success of wildlife protection by making behavior endogenous and adaptive within the socio-ecological systems. A comprehensive conceptual framework is created linking the ecological system conditions to human behavioral force and intervention policy by adaptive feedback. By comparing and contrasting the secondary-data-based approach, the performance of conservation using traditional ecological-only models is compared and contrasted to the performance of the models using behavior-integrated models based on ecological, social, and economic measures. The analysis shows that behavioral dynamics in conservation models get much better results. Behavior-based models eliminate poaching problems by nearly half and also illegal logging reduce cases by 60.0%, but community compliance is doubled to 78%. These gains in behavior are linked to considerable ecological profits, such as the growth of the population of species increasing 2.1% to 6.4% yearly and a decline in indices of habitat degradation of over 50%. The proportional change in wildlife population, managed according to the behavior-integrated models, rises by 1,200 to 1,830 individuals in five years, compared to the population growth of 1,200 to 1,275 individuals under classic models, which is an approximate 43.5 % higher population growth. The cost-efficiency analysis also shows that behavior-based interventions are better in terms of conservation results at a reduced marginal cost per unit population change. On the whole, the results indicate that the explicit consideration of human behavioral change in conservation models is an effective way to enhance the effectiveness of the role of wildlife protection. The recommended framework offers an ecologically, socially, and longer-lasting foundation to design conservation strategies, which are scalable and flexible.
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Employing Machine Learning to Monitor Endangered Species and Assess the Impact of Habitat Fragmentation
A major problem of the biodiversity is habitat fragmentation, which causes the extinction of endangered species due to a disturbed ecosystem and decreased interconnectivity. Conventional monitoring techniques are often limited on a scalability and precision especially in remote or the large regions. This paper will attempt to compare the application of machine learning (ML) algorithms to track endangered species and determine how habitat fragmentation affects their populations. To analyze satellite images and field data on the distribution of the species and the fragmentation of their habitats, the study used supervised learning models such as Convolutional Neural Networks (CNN) and Random Forests (RF). The ML models have been trained on the data which includes wildlife sightings, environmental factors, and the fragmentation indices. The predominant results have shown that the ML-based models were more effective than the conventional approaches to detecting species in fragmented habitats, and CNN has shown the highest detection rate (85) in categorizing habitat patches and detecting the endangered species. Also, determined that habitat fragmentation is negatively correlated with species diversity and increasing habitat fragmentation (p = 0.01), which indicates a strong necessity in conservation of fragmented ecosystems. This research reveals the possible benefits of machine learning to the improvement of biodiversity monitoring, as well as provides useful information concerning the conservation management, especially in areas with habitat fragmentation. It has been found that the combination of sophisticated ML tools with wildlife surveillance could help to create highly efficient data-driven decisions in saving endangered species.
Animal Movement Decisions and Impact on the Effectiveness of Static Protected Area Boundaries
The focal point of conservation of biodiversity is the so-called protected areas (PA), although the quality of the latter is usually measured with the help of the fixed boundaries that fail to capture the dynamics of animal movement. The research formulates the relationship between animal movement choices and the functional efficacy of the boundary of protection areas through a combination of spatial movement trends and quantitative measures of conservation. Visualization of movement showed that there were a high degree of boundary permeability and a significant amount of access to adjacent unprotected landscapes, which was marked by concentrated edge use and a high frequency of boundary crossings. Resource availability was found to be high in the PAs at 0.74, and the strength of enforcement was found to be high at 0.81; the anthropogenic disturbance was higher in the surrounding areas at 0.67 than in the PAs at 0.22. The probability of mortality or conflict was higher, 0.08, in the reserves, and 0.27 in the open, and the probability of survival was lower, 0.92, within the reserves and 0.71 outside the reserves. Movement drivers were analyzed and revealed that resource scarcity explained 34.2 % of the boundary-crossing movements, then seasonal climate variability, which was 21.5 %, and then social behavior, which was 16.8 %. These findings show that the benefits of conservation offered by stationary safeguards are diminished by the absence of accounting for the movement of animals beyond the boundaries of reserves. The research indicates that movement-based assessments are essential in assessing the effectiveness of a protected area and the results of conservation of mobile species.
Trait-Based Predictions of Mammal Adaptation to Urban Environments Under Accelerating Climate and Land Use Change
The two are rapid urbanization and climate change, which pose a threat to biodiversity in Sub-Saharan Africa. Accra is a megacity in the Coastal Savannah Ecological Zone of Ghana that has seen an 800 % expansion of built-up regions during the past 30 years, with devastating consequences of habitat fracture. This paper uses a trait-based approach to forecast the way accelerating land-use transformation and changing bioclimatic situations sieve populations of native small mammals. Based on a mixture of Capture-Mark-Recapture of Urban Green Areas (UGAs), i.e., botanical gardens and remnant forest fragments, the study assesses the prediction ability of morphological, ecological, and life-history features on the predictability of an area. The results of Multi-Species Occupancy Models (MSOM) and Phylogenetic Generalized Least Squares (PGLS) provide some statistical insights in that the interaction between the body mass and dietary breadth explains the most variance in the urban occupancy. Findings show that there is a tremendous change in the composition of the communities through the filtering of the environment. The urban matrix is dominated by species that have fast life cycles, are generalists, and have high behavioral plasticity, especially Mastomys erythroleucus and Crocidura olivieri, which have a statistically significantly higher likelihood of survival. On the other hand, habitat specialists such as Grammomys poensis have a steep decrease in occupancy as patch size decreases below a critical number of 1.5 hectares. Morphometric analysis also indicates that urban populations usually have decreased body mass than rural conspecifics, with the Urban Heat Island (UHI) effect having a strong negative relationship with physiological state. These results show that there is a tendency towards biotic homogenization with a few anthropophilic winners substituting a wide variety of native assemblages. The study offers a critical evidence-based literature on the notion of wildlife-friendly urban planning, particularly that its connectivity is crucial in the conservation of operational diversity in thriving urban cities in the tropics.
Nocturnal Light and Sound Pollution and Combined Effects on Urban Wildlife Communication
The process of urbanization has altered the natural sensory landscapes with anthropogenic persistent noise and artificial night light (ALAN), which has increasing consequences for wildlife communication. Although the independent action of these stressors is well characterized, the joint action on the animal communication of different taxa is not adequately measured. The research paper is a synthesis of the available evidence based on a standardized cross-taxon analytical tool to assess the combined effects of nocturnal noise and ALAN on wildlife communication. Normalized effectiveness indices (01) of birds, bats, amphibians, and insects were used to measure communication performance that included various communication elements such as call rate, timing stability, signal clarity, and synchronization. Effectiveness in communication was maximum when there was a reference (0.88) and minimum when there were isolated noise (0.63) and isolated ALAN (0.59). The greatest decrease was when it was in noise and ALAN exposure, when the effectiveness was reduced to 0.31, which is more than 60 % of that in the reference conditions. Combined exposure showed that amphibians (0.33) and insects (0.29) had low communication indices compared to birds (0.40) and bats (0.51). Temporal communication elements were also the most impacted across the taxa, with timing stability and synchronization exhibiting lower indices than call rate or signal clarity. The findings suggest that nocturnal noise and ALAN have a synergistic effect on the communication of wildlife, and that the issue of multisensory conservation and planning of biodiversity in cities must be considered simultaneously.
Harnessing Genetic Algorithms to Optimize Wildlife Conservation and Ecosystem Management Practices
Biodiversity conservation and proper management of the ecosystem are very important in ensuring the health and sustainability of Earth. The conventional ways of managing wildlife are however limited because of the intricacy and uncertainty of the ecological systems. Optimization of conservation strategies is more urgent than ever before since the ecosystems and wildlife are facing mounting pressure due to both anthropogenic activities and climate change. This paper discusses how genetic algorithms (GAs) can be used to streamline wildlife preservation and management of ecosystems. In particular, the study intends to show how GAs may be used to find the most favorable conservation policies that incorporate ecological goals like species conservation, habitat connectivity, and resource distribution and reduces costs. In the strategy that employ, and develop a GA structure where conservation solutions are coded as individuals in a population. The fitness function analyzes these solutions against several ecological criteria and thus directs the algorithm in a series of improvements. The model was used to simulate the situation of wildlife management based on the real data on the distribution of species and the characteristics of the ecosystem. The findings suggest that GAs have the capacity of detecting conservation strategies that have high ecological benefit which are far more cost-effective than the conventional approaches. The solutions offered maximized the conservation of the biodiversity, increased connectivity of habitats and made the ecosystem more resilient. This paper highlights how genetic algorithms can be an effective instrument of informed decision-making in wildlife conservation, and ecosystem management, as a way to proceed in the fight against environmental issues on a global scale.
Evolutionary Rescue in Urban Fauna and Genomic Signatures of Adaptation to Chronic Environmental Stress
Urbanization has resulted in chronic stressful conditions on wildlife, marked by constant stressors such as pollution, habitat fragmentation, urban heat islands, noise, artificial light, and changed resource supply. Although these conditions tend to reduce the population, there are still certain urban fauna, which means that there are adaptive evolutionary mechanisms. This paper is a synthesis of existing information on evolutionary rescue in the case of urban fauna, particularly looking at the genomic signatures of adaptation that can help populations to endure the long-term stresses of the urban environment. An organized literature review of evolutionary ecology and urban genomics was carried out, incorporating conceptual frameworks, genomic and regulation-based information, and exemplary case studies of larger animal groups. There was a focus on the connection between chronic stressors in urban areas and adaptive genomic reactions and the population-scale outcomes. Synthesis reveals that evolutionary rescue among the urban fauna is assisted by coordinated genomic adaptations of genomic stress response, detoxification, immune regulation, metabolism, behavior, and gene regulation. The genomic signatures of allele frequency changes, regulatory changes, and adaptive divergence are always related to the characteristics that can be seen to make individuals become tolerant of urban stress. Theoretical studies have shown that in the event of sustained moderate selection, evolutionary rescue is most probable when the standing genetic variation is sufficient and there is demographic stability. Urban setting functions as a selective filter, which causes the adaptation of heredity, not only acclimation in the short term. The contribution of genomic evidence to urban ecological studies enhances the ability to predict species resilience and conservation interventions that increase the adaptive capacity and long-term survival of species in more urbanized environments.
Neural Network Models of Anti-predator Behavior Thresholds in Birds Exposed to Variable Human Disturbance Regimes
The anthropogenic perturbation is gradually defining the avian behavioral scenarios, thus the need to move past the models of cognitive stasis to those that exemplify cognitive plasticity. This paper will apply the Artificial Neural Networks (ANNs) to simulate changes in the anti-predator decision-making threshold of bird populations under varying human disturbance regimes. The correlation between the frequency of encountering human beings and Flight Initiation Distance (FID) was studied by simulating 1,000 generations of a feed-forward neural architecture. It has been shown via simulations that birds in High-Frequency or High-Predictability regimes (urban analogues) developed much greater tolerance thresholds, a reduction in FID on average of 64% (p < 0.001) compared to the situation in low-disturbance environments. The neural weight analysis shows that the process of habituation is caused by a sensory filtering layer, which makes the system less responsive to non-lethal stimuli (which occur frequently). Conversely, in Stochastic/Low-Frequency regimes (rural analogues), the preferred neural configuration was risk-averse, meaning the Fids of it were 1.8 times higher than the optimal Fids to ensure that a false negative incurs a high penalty. Statistical information shows that there is a trade-off between the effectiveness of foraging and survival, with the population in highly disturbed areas showing a 12% gain of net energy acquisition by lowering the number of unwarranted flights of False Alarm, even though the potential risk of predation rose by 2.5%. The sensitivity test run proves that the energetic state is the major modulator of the neural thresholds, and the starvation risk in this case prevails over the learned habituation in cases where the body fat reserves decline to less than 15 % of the total body mass. Such results not only offer a mechanistic basis for predicting avian displacement but also imply that conservation buffer zones need to be scaled based on a certain cognitive adaptation state of local populations, and not based on predetermined species-wide scales.
Climate-Smart Wildlife Corridor Design Using Dispersal and Social Learning Models for Enhanced Species Movement
Wildlife corridors serve critical roles in species connectivity in fragmented landscapes by hindering the ecosystem, and their conventional forms fail to account for behavioral processes and climate effects. This paper combines both the dispersal and social learning models and climate data in developing better wildlife corridor designs. The movement of species across both landscapes with and without social learning in both favorable and unfavorable climate conditions was simulated using an agent-based model. The design of the corridors was also based on the dispersal, social learning, and climate projections to evaluate their effects on connectivity and patch occupancy. The analysis incorporated empirical evidence on the GPS telemetry, field observations, and climate forecasts. It was found that there was a significant increase in connectivity and patch occupancy when behavioral and climate factors were combined. Dispersal + Social Learning + Climate model had a 91% connectivity index and 85% patch occupancy, which was in contrast to 65% and 58% of the Dispersal only model. Designs that were climate-informed were able to preserve connectivity of up to 90 % in future climate conditions. The inclusion of dispersal, social learning, and climate data in the wildlife corridor design plays a crucial role in improving the ecological connectivity, resilience, and efficiency of the species movement. The paper shows the significance of adopting behavioral ecology and climate-smart plans in conservation planning. The research in the future should aim at perfecting these models in various species and landscapes.
Synergizing Ecological Modelling and Genetic Conservation Strategies for Species Reintroduction and Habitat Restoration
Reintroduction and restoration of habitats are popular methods of restoring threatened animal species and reintroduction is a success that has not been consistently successful because of the lack of integration of ecological and genetic factors. This work will bring forward a combined framework that will integrate ecological modelling and genetic conservation approaches to aid in the evidence-based animal reintroduction planning. An assessment of habitat suitability and landscape connectivity was done among candidate reintroduction sites, and values of Habitat Suitability Index (HSI) were 0.48 to 0.82, and a landscape connectivity index of 0.31-0.76. Genetic analyses of candidate source populations showed that there was an anticipated heterozygosity range of 0.54-0.71 and inbreeding coefficients of 0.04-0.18, which is a wide range of variation in genetic viability. A combination of ecological and genetic measures gave composite scores of viabilities between 0.32 and 0.84 among site-population interactions. The scenarios that incorporated high habitat and connectivity with genetically strong source populations were always found to score high viability (>0.75), and the ones that incorporated fragmented habitats and genetically weak populations were found to score low viability (<0.50). The results show that ecological suitability and genetic quality are not sufficient factors to guarantee the success of reintroduction. The suggested framework offers a clear and quantitative methodology for setting priorities in reintroduction sites, source population selection, and habitat restoration to increase long-term population survival.
Energetic Costs of Foraging in Degraded Seascapes and Long-Term Impacts on Marine Populations
Marine seascape degradation alters habitat structure and resource distribution, potentially imposing strong energetic constraints on marine organisms. This study aimed to quantify how seascape degradation influences foraging energetics, metabolic performance, and energy balance, and to evaluate how individual energetic constraints scale up to affect population-level survival. The research was done at the marine locations along coastal regions with a gradient of seascape degradation. Habitat condition was measured based on a composite degradation index that involved habitat complexity, prey density, turbidity, and nutrient load. Two marine species representing contrasting trophic roles were examined. Field energy expenditure was estimated using tri-axial accelerometry corrected to oxygen consumption, whereas standard and active metabolic rates were measured using respirometry. Telemetry, accelerometry, and prey calorimetry were used to measure foraging behavior, energy intake, and net energy gain. The relationship between degradation, the energetics, and the survival was measured using linear mixed-effects and generalized linear models, and the model performance was measured using the R2, RMSE, and MAE. Habitat degradation was linked with drastic changes in habitat complexity and prey density, and a rise in turbidity and nutrient load. The degradation elevated the metabolic rates strongly and reduced the aerobic scope (p < 0.001). In poor habitats, the amount of daily energy expenditure rose significantly, with the foraging expense consuming up to 51.9% of total energy expenditure. High degradation caused a more than 60% decline in net energy gain by predators and 70% by consumers. The model fits were good (R2= 0.740.81) and the probability of survival grew nonlinearly with individual energy reserves, varying between 0.42 and 0.91 in the 25th percentile of energy reserves. The destruction of seascapes enhances the cost of energy, lowers foraging efficiencies, and limits demographic survival. These results demonstrate the role of energetic balance as a central mechanistic relationship between habitat degradation and population-level responses with significant implications for marine conservation and habitat restoration.
Citizen Science Enhanced Monitoring of Phenological Mismatches in Migratory Species Along Latitudinal Gradients
Climate change is quickly changing the time synchrony between migrant species and the seasonal periodicities of their habitats. Greece's complex topography, featuring over 3,000 islands and steep mountain ranges, produces distinct phenological signals (e.g., in the European roller Coracias garrulus and other trans-Saharan raptors). This paper uses more than 25,000 citizen science checklists (2010 2025) on platforms such as eBird and iNaturalist to observe the arrival of 18 indicator species. The study uses a combination of these records and satellite-derived Normalized Difference Vegetation Index (NDVI) to measure the progression of the so-called Green Wave, moving southwards (Peloponnese) to northwards (Rhodope Mountains) respectively. Generalized Additive Models (GAMs) statistical analysis demonstrates important changes in the migration phenology. Findings show that despite the average of 0.42 days of advance in the yearly beginning of spring (green-up), the long-range migrants have merely changed arrival dates by 0.18 days/year, which leads to an increasing potential timing of the mismatch. This level of asynchrony is strongest at higher latitudes (> 40oN) with the breeding thermal range being smaller. Regression models indicate that there is a significant relationship (R2 = 0.68, p < 0.01) between warmer mean temperatures in April and a greater magnitude of mismatch. These results indicate that northern Greece has been found to have the most severe thermal delays, which can negatively affect the survival of fledglings because of the loss of food coincidence. The research highlights the strength of citizen science in supplying high-resolution spatiotemporal data required to monitor ecological changes over complicated landscapes. The findings suggest that conservation of stopover sites in the Aegean flyway should be prioritized in order to counter the cost of the increasingly complex phenological gaps of fitness.
Mapping Behavioural Landscapes of Fear in Agroecosystems and Impact on Agricultural Sustainability
Agroecosystems are multifaceted socio-ecological systems in the interaction of wildlife, humans, and agricultural activities, which determine spatial and temporal risk perception landscapes, called landscapes of fear. This paper set out to map behavioural terrains of fear in agroecosystems and evaluate their relevance to the sustainability of agriculture. In particular, it assessed spatial distributions of natural and human-induced risk factors, measured animal behavior responses, and interconnected fear-mediated behavior with the outcomes of ecosystem services. The experiment had been performed in mixed-use landscapes that included intensive croplands, pastures, hedges, and remnant vegetation. Camera traps, direct observations, and GPS telemetry were used to collect behavioral observations of herbivores, omnivores, and insectivorous species. Risk factors such as predator distribution, the anisotropy of the environment, the level of human activity, the concentration of infrastructure, as well as the exposure to pesticides, were standardized and spatially interpolated to create continuum risk surfaces. The Landscape of Fear Index (LoFI) was calculated by integrating behavioral responses, and relationships with sustainability indicators (crop damage, pest suppression, pollination, and yield variability) were analyzed using generalized linear mixed models (GLMMs), spatial regression, and multivariate analyses (PCA/RDA). Findings showed that there was a significant level of spatial heterogeneity of perceived risk. The highest LoFI was observed in intensive croplands (0.78 ± 0.14), and the lowest in remnant vegetation (0.22 ± 0.06), indicating the perceived threat in the human dominion area. Behavioral response was related to risk gradients, with a lower frequency of visitation, foraging time, and higher vigilance and avoidance in high-risk zones. GLMM analyses indicated a negative relationship between crop damage and LoFI (β = −0.63, p < 0.001), pest suppressive activity (β = 0.41, p < 0.001), and pollination activity (β = 0.27, p = 0.001) was maximal at intermediate levels of fear. Multivariate analyses confirmed anthropogenic pressures, and LoFI explained substantial variance in behavioral and ecosystem service metrics.
Comparative Resilience of Soil Seed Banks in Restored Wildlife Corridors and Remnant Forests Under Recurrent Disturbance
This paper will look at the resilience of soil seed banks in reinstated wildlife corridors and remnant forests during repeated disturbances, with emphasis on the importance of seed banks in ecosystem restoration. The vegetation recovery after disturbances cannot take place without soil seed banks, and the robustness of these seed banks in disturbed landscapes is not well comprehended. The aim was to compare the seed bank density, species richness, diversity, and functional composition of restored wildlife corridors and the remnant forests. The experiment was done in a discontinuous forest environment, having paired restored corridors and remnant forest patches. The samples of the soil and the seed bank properties were measured using a germination experiment under controlled conditions. The frequency and intensity of disturbance were described using historical data and field observations. Statistical analysis revealed significant differences between the two habitats. Seed bank density in restored corridors was significantly lower (1,420 ± 160 seeds m⁻²) than in remnant forests (2,870 ± 210 seeds m⁻²; p < 0.01). Species richness in corridors (12.4 ± 1.1 species) was significantly lower than in remnant forests (21.7 ± 1.6 species; p < 0.01). Shannon-Wiener diversity (H′) and evenness (J′) were also significantly reduced in corridors (H′ = 1.83 ± 0.12; J′ = 0.62 ± 0.05) compared to remnant forests (H′ = 2.71 ± 0.15; J′ = 0.78 ± 0.04). Resistance–recovery indices showed greater resilience in remnant forests (Rᴅ = 0.87 ± 0.05; Rᴴ′ = 0.91 ± 0.04) than in restored corridors (Rᴅ = 0.58 ± 0.07; Rᴴ′ = 0.63 ± 0.06). Regression analysis indicated that there was a significant negative correlation between disturbance intensity and seed bank properties, especially in corridors. These results underline the importance of considering the ecological processes in the ground when restoring the areas.
Consequences of Cultural Memory Loss in Migratory Ungulates Due to Selective Removal of Knowledge-Bearing Individuals
Migratory ungulates depend on social knowledge to negotiate a complex environment, and the disappearance of experienced individuals may interfere with cultural memory, efficiency in migration, and population stability. The purpose of the study was to measure the ecological and behavioral effects of the selective removal of knowledge-bearing individuals on the performance of migration, memory of culture, and demographics. The used a composite Experience Index based on age, migratory consistency, and social centrality to identify knowledge-bearing people. Migration performance was measured based on the metrics of route fidelity and navigational efficiency, cultural memory based on the Cultural Retention Index across successive generations, and population consequences by population growth rate (λ) in a stage-structured demographic model. The analyses were made by statistical techniques of generalized linear mixed models using bootstrapped confidence intervals to consider repeated measurements and hierarchy. Findings show that knowledge-targeted removal had a disproportionate impact on high-EI people (66.1%), in contrast to 25.5% when removal was random and 36.4% when it was age-biased. Under focused removal, migration performance dropped drastically, route fidelity fell from 0.82 ± 0.03 to 0.54 ± 0.06, and navigational efficiency fell from 0.87 ± 0.04 to 0.69 ± 0.06. CRI decreased between 0.63 and 0.41 in three generations, and the mean migratory deviation had risen by 38%. The rate of population growth (λ) decreased 1.04 ± 0.02 to 0.96 ± 0.03 with knowledge-targeted removal, which is a sign of lower reproductive success and high mortality. Such findings show that knowledge-bearing individuals are one of the key nodes in migratory networks. To retain migration fidelity, cultural continuity, and population resilience, conservation strategies must focus on the protection of the socially experienced individuals. Other interventions that can be addressed in future studies to reduce cultural memory loss include guided reintroduction or social learning facilitation.
Predicting Animal Foraging Behaviour Under Climate Variability Using Advanced Ecological Simulation Models
The changing animal foraging behavior due to climate variability caused by global environmental change has significant implications for the persistence of the population and stability in the ecosystem. The non-stationary and stochastic climatic forcing makes it difficult to predict these responses. The given research builds a hybrid model, agent-based and machine learning (ABM-ML), in order to simulate and predict the foraging behavior of animals in a climate fluctuation scenario. Animal agents are a heterogeneous set of behavioral characteristics that control movement, patch choice, sensitivity to risk, and energetic state, and the environment as a spatially explicit climate-driven resource field. In order to enhance predictive accuracy in varying environmental conditions, machine learning is employed to calibrate important behavioral parameters with the use of empirical movement and foraging data. Benchmark dataset validation of the model with migratory birds and marine predators shows high levels of performance with net energy intake coefficients of determination of 0.82 and patch residence time coefficients of determination of 0.76. The models used to predict the future climate show considerable drops in the foraging efficiency by the middle of the century and the average falls of about 18 percent under the RCP 4.5 and up to 37 percent under the RCP 8.5. Findings also indicate that behaviorally flexible foragers are more affected by efficiency losses to a disproportionate degree, which implies that plasticity can make them more vulnerable to severe climate variability. The proposed model will offer an assessment instrument of climate effects on foraging behavior that is scalable and transparent, and suggests ecological tipping points and adaptive conservation and management strategies.
Wild-Domestic Interface Dynamics and Disease Feedbacks at Expanding Livestock-Wildlife Frontiers
Livestock production in wildlife areas has further boosted the degree of interaction between wildlife and human beings, thus forming hotspots of cross-species diseases. The dynamics of interaction between livestock and wildlife, environmental drivers, and disease feedback are examined in this study in frontier landscapes that are experiencing land-use change. Using a retrospective longitudinal design, secondary datasets from GPS tracking, camera traps, epidemiological reports, and environmental and socio-economic sources were integrated. Livestock and wildlife contact rates, disease prevalence, and environmental covariates were analyzed across seasons and land-use types. Multi-host susceptible infected–recovered (SIR) models incorporating behavioral feedback were used to quantify cross species transmission and evaluate system-level disease dynamics. Results indicate strong spatial and temporal heterogeneity in interactions, with the highest contact rates in fragmented rangelands (dry season mean 8.7 contacts/day) and agricultural frontier zones (7.9 contacts/day). The prevalence of diseases was higher in dry seasons, the seroprevalence of livestock and wildlife increased from 9.8% to 18.6%, and wildlife from 7.3% to 14.9%. The greatest prevalence was seen in hosts in high-contact interface areas (livestock near wildlife, 22.4%; wildlife in high-density livestock areas, 19.7%). The proportion of new infections being due to cross-species transmission was 34 - 47%, and the elasticity of feedback (E = 0.62-0.71) indicated a reinforcement of behavioral responses that perpetuated transmission. When interspecific pathways were added, simulated prevalence matched empirical data (R2 = 0.68 0.74), and system-level R 0 was greater than the epidemic threshold (1.34). This paper illustrates that interface disease dynamics are the result of the interplay of land-use change, seasonal resource dynamics, and behavioral feedback. To achieve effective management, landscape-scale approaches that mitigate livestock health, wildlife protection, and habitat connectivity are required to minimize spillover of zoonotic diseases and ecosystem resilience.
Bayesian Meta-Analysis of Conservation Interventions for Endangered Species with Sparse Outcome Data
The major objective of the paper was to perform Bayesian meta-analysis to assess the effectiveness of conservation interventions targeting endangered species, given situations with sparse data. Particularly, the study aimed at comparing the efficacy of some conservation measures, including habitat restoration, species translocation, captive breeding, and anti-poaching measures, to serve as an improvement of species survival and conservation of biodiversity. Web of Science, Scopus, and Google Scholar were selected as systematic literature search platforms that identified the studies that reported quantitative results of conservation interventions. A total of 20 empirical studies and 112 effect sizes were selected after inspection of the studies and the application of the inclusion criteria. A Bayesian random-effects meta-analysis was used in the study to bring prior knowledge to explain the uncertainties and heterogeneity across the studies. This model enabled the synthesis of thin data that generated more valid estimates of intervention effectiveness. The Bayesian meta-analysis showed that the overall effect size is moderate, 0.48 (95% credible interval: 0.26-0.69), which shows that conservation intervention tends to enhance population-level response. The positive impacts were the most consistent with habitat restoration and measures based on policies, with means of 0.62 and 0.55, respectively. Interventions, such as species translocation and captive breeding, however, had a more mixed result. The heterogeneity was significant (I² = 64.3%, τ² = 0.21), and the effectiveness of the interventions depends on the situation. This experiment is quite convincing about the fact that conservation interventions have a beneficial effect on the recovery of endangered species, and the efficacy of the intervention is dependent on the kind of intervention and the situation. The findings have demonstrated the importance of the adaptive and context-sensitive approaches, as well as the need to conduct additional research to be able to maximize practices like species translocation and captive breeding. Continued investment in habitat restoration and policy-based measures is essential for long-term biodiversity conservation.
Advanced Ecological Network Models for Understanding Animal Social Dynamics and Enhancing Habitat Connectivity
Nature reserves and protected areas are essential for biodiversity conservation, but habitat fragmentation poses a significant challenge, interfering with species movement and ecosystem function. This paper will evaluate how ecological network models can be used to augment connectedness in the broken Sundarbans Mangrove Forest (India and Bangladesh), which forms a crucial habitat for particular species such as the Bengal tiger and the Olive Ridley sea turtle. These species, which occupy large territories and engage in migration, are especially susceptible to fragmentation, and the number of Bengal tigers has dropped by around 50 % in the past ten years due to habitat loss. In the conservation corridors model linking the conservation areas, it will be discussed how the corridors can alleviate the effects of fragmentation, promote natural movement, reduce inbreeding risks, and increase genetic diversity. The properly designed corridors may enhance the movement of species by a factor of 30 to enhance gene flow and stability of populations. The paper also explores the effects of these corridors on social behaviors, foraging, and reproduction, which are fundamental to species survival. It employs the best ecological network modelling to determine the major corridors and the efficacy of connectivity plans. The results emphasize the role of the protection areas in ensuring the sustainability of the ecosystem services, including flood control, carbon sequestration, and enabling adaptation to climate change by providing secure migration pathways. This study gives practical results on how to enhance the habitat connectivity in the Sundarbans, which can be used as practical solutions on how biodiversity conservation can be improved, as well as informing any future policies on landscape management.
Adapting Precision Livestock Monitoring Tools to Assess Wildlife Stress in Free-Ranging Populations
The integration of Precision Livestock Farming (PLF) technologies into the sphere of wildlife biology provides a revolutionary path for measuring the health and stress levels of free-ranging animals in the wild. Conventionally, the evaluation of stress in wild populations has been based on invasive sampling or haphazard observations that do not usually reflect the dynamic behaviour of physiological responses to sudden changes in the environment. The main issue of the study relates to the absence of non invasive, constant monitoring systems that may allow estimating the effects of climate change and human intrusion without increasing the suffering of animals. The research design is an interdisciplinary redesign of sensor technologies that have been used to monitor physiological and behavioral statuses of domestic ruminants in large-scale systems to monitor those of the wild. The innovations in accelerometry, biometrics, and digital control technology will be incorporated to develop a diversified platform for wildlife use. The analysis of the Five Freedoms in terms of the precision data helps the study to identify the specific biomarkers that can be translated between the domestic and the wild species. The findings suggest that high-fidelity data can be obtained using repurposed sensors, e.g., virtual fencing collars and biometric ear tags, with behavioral classification accuracies of (88-92.5%). Statistical comparison reveals that there is a significant correlation (r = 0.82 to 0.88) between thermal indications and physiological strain to detect small deviations due to stress, even before the onset of physical symptoms. In summary, this study has shown that a process of adaptation of the methods of PLF can offer a very strong data-driven roadmap to conservationists. The adoption of these accuracy instruments will mean that welfare interventions will be proactive, as opposed to reactive, to a large degree, improving the resilience of biodiversity in more disturbed natural ecosystems.
Adaptive Co-Management Frameworks for Protected Areas Integrating Local Livelihoods and Wildlife Conflict Mitigation
In human-dominated habitats, human-wildlife conflict is a challenge that is frequently observed to affect the existence of protected areas that serve as important in conserving biodiversity. This paper set out to advance and test the adaptive co-management framework, which combines local livelihoods in a way that supports conflict mitigation to improve socio-ecological resilience. The data were gathered under the mixed-method socio-ecological systems approach, through the various protected areas that differ in governance regimes, livelihood dependence, and conflict intensity. Human-wildlife conflict data were based on the incident records, participatory mapping, and household survey, and the ecological indicators such as habitat condition and fragmentation obtained using the multi-temporal Landsat and Sentinel. Composite indices of livelihood resilience and participation in governance included income diversity, adaptive capacity, institutional access, and stakeholder engagement. The findings indicated that strong co-managed sites had the lowest levels of conflict (HWCᵢ = 2.1 ± 0.3), highest resilience livelihood (LRIᵢ = 0.74 ± 0.05), and better habitat condition (VCI = 0.68 ± 0.04), and weakly managed sites had the highest levels of conflict (HWCᵢ = 5.2 ± 0.6), low resilience (0.39 ± 0.07). The involvement of governance positively affected livelihood resilience (β = 0.62, p < 0.001) and indirectly decreased the intensity of conflict (β = −0.48, p < 0.01), with adaptive learning having a further positive impact on institutional effectiveness (β = 0.55, p < 0.001). The researchers summarize the research by concluding that adaptive co-management, which provides a structured feedback-driven learning and livelihood assistance, can reduce HWC, enhance community resilience, and ecological performance all at the same time. Further research must aim at expanding the framework to various socio-ecological settings and implement technological assistance in monitoring and mitigation of proactive conflicts.
Leveraging Positive Human-Wildlife Interactions for Coexistence in Urban Regions Under Rapid Development
The recent rate of global urbanization, an unprecedented pace, the old paradigm of human-wildlife conflict is altering its form to a complex form of coexistence. The study is one of the solutions to the acute problem of maintaining biodiversity in the rapidly growing urban areas where the negative interaction processes are converted into positive ones through leveraging. This study takes a multidisciplinary approach, known as coupled human and natural systems approach, which involves synthesis of ecological information using social survey methods. The methodology measures the effectiveness of artificial intelligence-based monitoring and citizen science programs in urban fringe regions to support the design of something that is wildlife inclusive. The results have shown that the real-time detection systems of animals and the active participation of stakeholders reduce the friction level significantly and increase the psychological strength of the people living in urban areas. Also, findings shows that urban wildlife undergoes strong changes in evolution to the thickening habitats. These adaptive features can be exploited in a premeditated manner in the form of strategic habitat restoration and novel building designs. The research concludes that sustainable coexistence does not only consist of the lack of conflict but active integration of the urban environment with wildlife. Planners can also create resilient ecosystems in which the well-being of humans and the existence of species live in symbiosis by concentrating on positive leveraging. This study will act as a critical policy guide that will enable sustainability of the environment to be incorporated into the modern urban environment.
Employing Agent-Based Modeling to Investigate Wildlife Behavioral Responses and Ecosystem Functionality in Fragmented Environments
The​‍​‌‍​‍‌​‍​‌‍​‍‌ Congo Basin, a globally significant forest with immense biodiversity, is gradually losing its continuous habitat due to the cutting down of trees, in addition to forest clearance, illegal logging, farming, and road and bridge construction activities. Over the last several decades, the Congo Basin has had a staggering loss of forest cover, with a rough estimate of 10 million hectares of forest being cleared between the year 2000 and 2025. The fragmentation interferes with natural processes and poses substantial difficulties to animal populations. Therefore, it is important to know how animals adjust and behave in fragmented areas so as to be able to conserve this ecosystem. This is research that makes use of Agent-Based Modeling (ABM) to investigate the behavioral changes of the key species in the Congo Basin, such as the western lowland gorilla, forest elephant, okapi, and chimpanzee, and to determine the effects that fragmentation has on the ecosystem's functionality. The present study will utilize an Agent-Based Approach (ABA) to simulate how habitat fragmentation will impact the daily routine, foraging behaviour, social interaction, reproductive success, The initial results reveal that fragmentation causes the movement range of forest elephants to be limited by 40%, group cohesion in gorillas reduced by 25%, and that breeding success in okapi has drastically gone down due to their being isolated. Additionally, the conservation model explores the impact of such conservation measures as wildlife corridors and protected areas to mitigate the harmful effects of fragmentation. The outcome of this research will be instrumental in improving the understanding of the intricate relationship between the animals and fragmented habitats in the Congo Basin. This knowledge serves as the foundation for conservation policy, making and planning to protect the rich biodiversity of one of the most vital ecosystems on the planet while tackling the escalating problem of habitat ​‍​‌‍​‍‌​‍​‌‍​‍‌fragmentation.
Role of Agency and Control in Domestic Animals and Impact of Human Interaction on Welfare and Behaviour
Animal welfare thinking has been transformed towards a paradigm shift in thinking where it no longer remains on the alleviation of suffering, but rather goes into active enhancement of positive affective states by agency and control of the environment. This study researches and discusses a severe issue of limited autonomy in controlled settings. The study combines the Five Domains Model with recent developments in the field of human-animal-computer interaction (HACI) to determine the role of self-determination in psychological resilience using a multidisciplinary methodology. The findings suggest that the capacity to exercise choice, both in straightforward environmental adaptation processes and complicated interactions with other people, is one of the primary defenses against chronic stress and learned helplessness. Moreover, the results of the research demonstrate that the quality of human interaction is a two-sided sword: on one hand, positive reinforcement helps to implement the manifestation of natural agency, on the other, high-frequency and non-contingent human presence is a major behavioral pressure factor. The Agency-Control Nexus is discussed, and it is shown that the domestic animals, which have access to functional control, show a greater level of cognitive engagement and a lesser number of stereotypical behaviours. To sum up, the paper concludes by stating that modern husbandry is not only ethically but scientifically necessary to integrate the agency-based frameworks. The findings offer a blueprint to policymakers and practitioners to leave the traditional confinement models behind and choose rich conditions. In the finality, the study adds to the knowledge on the interdependencies between animals and their environment by revealing that real welfare is only attained when animals are given a chance to play an active role in their lives.
Precision Livestock Management Systems for Simultaneous Optimization of Welfare Health and Environmental Footprint
The livestock production systems have been under pressure to address the global demand and maintain high standards of animal welfare, control of diseases, and environmental sustainability. Traditional management is manual, based on observations and reactive intervention processes, which restrict the ability to identify and respond to stress and health abnormalities on time, lower productivity, and increase the environmental imprint. This research introduces a combined Precision Livestock Management System (PLMS), which can be used to optimize welfare, health, and the footprint of the environment. The suggested system will combine behavioral, physiological, and environmental sensors with a data acquisition and preprocessing layer, a multi-objective analytics and optimization engine, and a decision support module of adaptive farm interventions. The welfare indicators, such as the pattern of activity, regularity of feeding, and resting behavior, were observed continuously, whereas the physiological and behavioral indicators were examined to identify the initial signs of health disturbances. The assessment of energy consumption, emissions, and waste accumulation as environmental metrics was done simultaneously to assess the effect of management decisions. Findings indicate that the implementation of PLMS improved welfare indices by as much as 27 %, behavioral stress incidences by 38 %, disease identification rates by 25.8 %, and the average response time by 64 %against the traditional monitoring. There was also an improvement in environmental performance with a decrease in energy consumption, feed loss, and variability of emissions. These results demonstrate that the proposed PLMS can lead to coordinated, scalable, and ethical livestock improvements in terms of welfare, health, and environmental goals, resulting in a sustainable and pragmatic solution to contemporary livestock management.
Reevaluating Minimum Patch Size for Effective Conservation by Including Cumulative Value of Small Habitat Fragments
Conservation planning has been systematically ignoring small areas of habitat due to the long tradition of relying on the parameter of the minimum patch size, which is held to provide negligible ecological value. This study is aimed at busting that paradigm by reevaluating the usefulness of small habitat fragments, when summed together with regard to cumulative value, as well as the contribution of small habitat fragments to the world's biodiversity. In a synthesis and multi-scale landscape analysis as a critical review of recent ecological data, explain how the omission of small patches may negatively sabotage conservation goals by failing to safeguard islands with a rich diversity of species and vital forms of migration. The methodology will be directed by comparing the review of patch-size efficacy in a broad spectrum of ecosystems, such as the Ethiopian highlands and the Nepal tropical corridors. Test the effects of matrix quality and time dynamics on habitat value and demonstrate that small patches can often provide different sets of species that are not seen in large contiguous patches. It has also been found that in the majority of the endangered taxa, like the African lion, the giant panda, and other amphibian groups, small fragments act as stepping stones that guarantee landscape connectivity and sustainability of populations. Also, find out that frequently the cumulative biodiversity of a patch of small size is larger than that of a patch of the same size. Discover that the conservation policy must no longer be a size-based concept but a cumulative value-based concept. The method suggests that it is possible to preserve small pieces of land and restore them as one of the most important solutions in order to decrease the level of fragmentation and raise the stability of the ecosystems in anthropogenic landscapes.
AI-Powered Habitat Suitability Models to Inform Migratory Species Protection and Ecological Restoration Efforts
Moving animals also play a critical role in the ecology and the preservation of biodiversity through their interdependence within ecosystems. Nevertheless, patterns of habitat loss, global warming, and human disturbances have disrupted migration pathways and diminished habitat quality, posing severe threats to species survival. This work presents a habitat suitability modeling framework powered by AI, designed to enhance the conservation of migratory species and to aid ecological restoration projects. The given strategy combines data on animal presence with crucial environmental factors, including climatic, land-use, vegetation cover, topographical, and human disturbance indicators. Using machine learning algorithms, intricate associations between species distributions and ecological conditions are captured, enabling more precise, spatially explicit predictions than traditional approaches. The produced suitability maps identify key habitats, including breeding sites, stopover areas, and migration routes, which are essential to the maintenance of migratory populations. The outcomes of model validation indicate strong predictive ability and reliability across a wide range of environmental conditions. The results reveal the practical use of AI in animal and environmental studies, providing important information for conservation planning, ranking habitat restoration priorities, and informing management decision-making. Altogether, the paper reveals the opportunities AI-based applications offer to enhance the security of migratory species and support ecologically sustainable shifts in the evolving environment.
Behavioural Early Warning Signals for Population Collapse Under Climate Extremes and Predicting Vulnerability in Species
Problem: The biodiversity of the world is today undergoing unprecedented pressure due to climate extremes, which cause sudden population explosions, which the conventional monitoring mechanisms tend to predict. Traditional Early Warning Signals (EWS) are usually based on numerical abundance information, which is usually reflected until a population reaches a critical tipping point. This introduces a lag in detection, which makes conservation efforts reactive and not proactive. Methodology: The study hypothesizes a new predictive model by combining behaviors measures including foraging efficiency, timing of reproduction, and migratory changes as the key antecedents of collapse. Using a combination of evolutionary genomics information and physiological thresholds, examine the sensitivity of behavioral plasticity in a variety of taxa, that is, land vertebrates, freshwater fish, and pollinators. Particularly investigate the change in steady states to tipping points by modelling the effects of extreme thermal events on life-history strategies and dynamics of human-wildlife conflicts. Findings: The discussion shows that EWS behavioral are a more direct diagnostic measure compared to mortality rates. As an example, temperatures that cause sterilization or forging failure will reach higher threshold temperatures much sooner than lethal temperatures, providing more time to intervene. Discover that behavior change in community stability is the earliest quantifiable answer to climate-induced range change in both seasonal and desert biomes. Moreover, the findings show that ecosystem cascading failures in the loss of these types of behavioral functions include decreased seed dispersal and disturbed pest interactions in agricultural landscapes. Conclusion: The incorporation of behavioral modeling into ecological forecasting greatly increases the precision of vulnerability predictions. This study offers a reliable predictive curve of wildlife control, which moves the emphasis from recording extinction to wildlife prevention by identifying behavioral indicators in their early stage.
Sensor Based Early Warning Indicators of Welfare Compromise in Extensively Managed Ruminant Production Systems
The large-scale ruminant production systems, including grazing-based cattle, sheep, and goat management, are part of global food security with major welfare issues as a result of environmental stressors, nutritional deficiency, and other threats that are inherent in open-range management. The conventional welfare assessment techniques of such systems are not sufficient in many cases because of the magnitude and distance of the operations, and it is not easy to identify welfare compromise starting in time. The proposed study hypothesizes the implementation of an early warning system based on sensors to examine and determine the welfare compromise in high-managed ruminants by using continuous and non-invasive data recording. The system combines behavioral, physiological, and environmental data of wearable sensors, which can be activity, rumination, and body temperature sensors, and GPS and environmental sensors, to get a cohesive picture of the state of health and well being of an animal. Early deviation of the system from the baseline welfare states can be identified, and timely interventions can be made to ensure that welfare does not deteriorate. The findings indicate that the system has a high detection accuracy (maximum of 94.6 %) and sensitivity (92.8 %) to the early indicators that include reduced grazing, abnormal activity, and an increased body temperature, with a lead time ranging from 12 to 48 hours prior to the onset of visible symptoms. The research paper has pointed out the resilience of the system in a wide range of environments, especially during heat stress and resource-constrained settings. This system can provide a viable, scalable solution to enhance the welfare management and sustainability of large ruminant systems by coupling large-scale environmental adaptation with welfare surveillance. The next step in future activity will be to perfect sensor fusion methods, develop greater ecological integration, and research policy frameworks to allow greater implementation of sensor-based welfare monitoring to facilitate sustainable ruminant management and climate resilience.
Socially Transmitted Migration Routes and Role as Conservation Assets in A Shifting Climate Envelope
Migration routes play a vital role in species survival by providing key resources such as food, breeding areas, and favourable climates. Climate change, however, is disrupting these routes, posing a significant threat to biodiversity. The paper explores the socially transmitted migration routes (STMRs), in which migration knowledge is learned socially, and how it is resilient in response to climate change. Together with the climate models, this study simulates the impact of mild, moderate, and extreme climate change conditions on the migration paths using long-term tracking data of migratory birds and terrestrial mammals. The findings indicate that the species that depend on STMRs are more resilient than those that depend on genetically inherited pathways. Particularly, species with STMRs had greater viability of their migration routes, with some species, such as migratory birds, having 95% viability in a moderate climate change but reducing to 55% in harsh environments, and other species, taking genetically inherited routes, having a viability that decreases 90% to 40%. Besides, conservation measures like the establishment of protected migration corridors and improved social learning networks were modelled and led to the increase of migration routes' viability by 20 % and species resilience by 30 %. These results indicate the importance of social learning in climate change adaptation and the imperative of conserving migration routes, which are socially transmitted as a component of conservation measures. The paper stresses that future conservation plans should consider adaptive management and real-time monitoring in order to conserve these valuable corridors as the climate keeps evolving very fast.
Behavioral Enrichment Protocols to Enhance Positive Welfare States in Working Dogs in Urban Service Roles
Urban service working dogs, e.g., police, search and rescue, and therapy dogs, experience considerable stress because of their work-related stress and pressure. The paper is based on the introduction of behavioral enrichment procedures to improve the welfare of such dogs, including both emotional and physical conditions. The enrichment programs consisted of cognitive stimulation, exercise, and sensory interaction, which were specific to the requirements of the working dogs in the urban environment. The controlled experimental design was used, involving an experimental group (enrichment) and a control group (continuing regular routines), over a period of 12 weeks. Behavioral observation, cortisol, and heart rate variability were the key welfare indicators measured before and after the intervention. The findings indicated that the experimental group had a considerable decrease in stress-related behaviors (22%), aggressive behaviors (40%), and positive behaviors (31%). There were also physiological improvements, with the cortisol level reduced by 22 % and the heart rate variability rose by 18%. These results show that enrichment protocols can significantly improve the emotional and behavioral well-being of working dogs and increase their performance and general welfare. The article indicates that the introduction of well-organized enrichment programs into the daily life of urban service dogs can result in long-term enhancements of welfare, and the consequences of these optimizations of both health and work outcomes are expected. Nevertheless, more studies are required to understand the long-term effects and to improve the enrichment techniques to be used in a broader application in working dogs in a wider range of functions.
AI-Assisted Behaviour Tracking to Quantify Adaptation Windows in Species Following Natural Disasters
The consequences of the natural disasters on wildlife populations are enormous, as they usually cause alterations of the behavior, the places where such species live, and their existence. To preserve the species, it is essential to understand how they react to such things by identifying the adaptation time periods so that they could be preserved. The behavior-monitoring systems used in this paper are AI based in measuring adaptation windows among natural species that are exposed to disasters. The algorithms of machine learning, including deep learning models, were employed in this study to classify numerous behavioral examples and predictive analytics to approximate the behavioral adaptations that take place and when they would take place by examining post-disaster behavior in a sample of species. The research study utilized over 5,000 hours of animal tracking data acquired in different ecological regions that were severely affected by a natural calamity that occurred recently. Within the framework of the statistical analysis, survival analysis, and regression model construction, one of the measurable changes in the activity patterns and habitat preferences of the species in the first three months after the disaster was observed. The rates of adaptation were diverse with herbivores adapting more rapidly (mean = 28.5 days) than carnivores (mean = 48.7 days). These findings underscore the relevance of species-specific conservation plans in order to endorse species-specific recovery actions. The paper finds that the use of AI-assisted behavior monitoring is a powerful tool in the quantification and understanding of the processes of wildlife adaptation to environmental stressors, which should be applied in the future to improve post-disaster recovery interventions and in policy making in wildlife management.
Simulating Urbanization Impacts on Species Adaptation and the Role of Green Infrastructure in Ecosystem Health
One of the significant drivers of environmental transformation is accelerating urbanization, which alters habitats and disrupts the processes of animal adaptation and ecosystem function. This analysis role-plays the effects of urbanization on species adaptation and the importance of green infrastructure in maintaining the ecosystem's well-being. The model was a spatially explicit simulation that quantified the dynamics of animal populations, adaptive responses, and survival across urban-intensity gradients. The significant stressors considered are habitat fragmentation, reduced landscape connectivity, and changes in microclimatic conditions associated with the urban environment. The results of the simulation indicate that in urbanized landscapes, generalist species and more dispersively able species have greater adaptive success, while habitat specialists have increased vulnerability and decreased population. Green infrastructure (urban green spaces, ecological corridors, green roofs, riparian buffers, etc.) plays a significant role in improving habitat quality and connectivity, boosting population survival and genetic interactions. The other positive role of Green infrastructure is that it moderates urban heat effects and supports four trophic interactions. The results highlight the need to incorporate ecologically conscious green infrastructure into the urban planning process to reduce biodiversity loss. The study presents a functional approach to understanding how animals adapt to urban ecosystems and offers a realistic perspective on how to enhance resilience, nature, and sustainable urban landscapes through nature-based solutions.
Thermal Inequity and Microhabitat Heterogeneity and Impact on Vulnerability Within Animal Social Groups
Microhabitat heterogeneity causes thermal inequality, which is beginning to influence how animals respond to environmental stress in social groups, but the nature of its effects on intra-group vulnerability remains poorly understood. Although social living is considered a form of defense mechanism against climatic extremes, an unequal distribution of thermally favourable microhabitats can create inequality among group members, shaping behaviour, exposure to climatic extremes, and resilience. The paper will examine the effects of fine-scale thermal gradients on the vulnerability of animal groupings to pattern disruption. The research methodology is an integrative observational method that combines microhabitat temperature profiling and group-level behavioural evaluation across heterogeneous environments. Microclimatic observations were recorded in areas of frequently used groups, whereas positioning, movement, and activity assignment among individuals were also studied to get insight into dissimilar thermal exposure. Vulnerability was assessed by shifts in activity levels, spatial displacement within the group, and temporal changes in social cohesion during periods of high thermal stress. Important observations suggest that microhabitat heterogeneity generates high levels of thermal inequity among social groups. People sitting in open or peripheral areas felt 20-35% warmer than those sitting in the shade or in central positions. About 50% of the individual thermal exposure difference could be explained by microhabitat organization rather than by general ambient conditions. At high temperatures, groups showed a 15-25% decrease in coordinated activity, accompanied by increased fragmentation, as people vied to occupy scarce thermally favorable zones. Such trends indicate that who benefits under thermal stress in the presence of microhabitat strongly depends on the availability of the microhabitat. The results indicate that social grouping does not equally reduce climate vulnerability and, under heterogeneous thermal conditions, can exacerbate inequalities within groups. In the context of species responses to a warmer climate, it is crucial to recognize thermal inequity at both microhabitat and social position scales. These views are beneficial to ecological studies and conservation approaches that should be used to improve resilience in socially organized animal populations.
Social Network Analysis of Human Wildlife Encounters to Predict Conflict Hotspots in Peri-Urban Interfaces
The growing human-wildlife interaction at the peri-urban interface is rapidly increasing due to urban sprawl, habitat fragmentation, and altered patterns of wildlife dispersal, leading to frequent conflicts that threaten human health and wildlife conservation. Common traditional methods of conflict assessment are typically based on spatial incident mapping, which does not account for the social and interactional processes that drive conflict formation. The present research offers a new social network analysis (SNA)-powered method to simulate the human-wildlife interactions in the form of interaction networks and forecast the conflict hotspots in the peri-urban environment. Nodes represent human communities, wildlife species, land-use features, and encounter events that are interconnected such that the frequency of interaction, the proximity, as well as the co-occurrence of the events are maintained in the shape of the edges. Degree centrality, betweenness centrality, clustering coefficient, and community modularity are used as network measures that characterize the actors with the highest level of influence, the most critical interactive channels, and high-risk interface areas. To detect seasonal and behavioral changes in movement and human activity patterns, a temporal network is evolved. The suggested framework combines social network indicators with the spatial features to produce proactive conflict hotspot maps, which are more sensitive in comparison with the traditional density-based approaches. Experimental analysis shows that the hotspots derived by SNA better correspond to recurrent conflict events, and therefore, it is possible to identify potential risk areas early. The results emphasize the role of interaction structure, but not the role of instances in conflict development. The study will add a data-driven, scalable methodology for proactive management of human-wildlife conflicts to facilitate evidence-based planning, mitigation strategies, and coexistence based conservation policies in fast-urbanizing peri-urban settings.
Integrating Animal Culture into Red List Assessments and Recovery Planning for Migratory Species to Enhance Conservation Strategies
Assessment of animal culture in the Red List and recovery planning is essential in developing stronger conservation measures for migratory species. Migration patterns, breeding locations, and foraging patterns are an important part of a species' life, yet they are usually not considered when focusing on conventional conservation. This paper will discuss the possibility of including animal culture in Red List assessments of migratory species to enhance conservation outcomes. This study evaluated cultural characteristics in a sample of migratory birds and marine mammals based on long-term tracking data, which shows the importance of socially transmitted knowledge (STK) in species migration and adaptation to changing environments. Statistical analyses, including survival analysis and regression models, showed that species that used STK were more resilient to environmental stressors, including habitat loss and climate change, than those that used genetic migration patterns. The most important findings were that the culturally transmitted migration routes were found to be 20 % more resilient to habitat disruptions than genetically inherited migration routes. Also, cultural behaviors are incorporated in the recovery planning, which contributed towards a 30% increase in the percentage of successful conservation intervention. This is crucial because these findings underscore the need to ensure cultural elements are considered in conservation efforts to ensure the complexity of the needs of migratory species is met. The application of cultural behaviors to Red List assessments can result in more effective and sustainable conservation efforts through tailored conservation efforts to species specific survival strategies.
Remote Sensing Innovations for Tracking Wildlife Populations and Monitoring Critical Habitat Conservation
Remote Sensing technologies have become effective methods for monitoring animal populations and critical habitats in large, inaccessible areas. This paper discusses new technologies in satellite imagery, uncrewed aerial vehicles, LiDAR, and machine learning for wildlife population estimation and habitat protection. The main purpose is to assess the roles of multi-source remote sensing data in enhancing the accuracy, efficiency, and scalability of wildlife monitoring relative to conventional field-based procedures. Multispectral and hyperspectral high-resolution images are combined with LiDAR derived structural metrics to determine species distribution, population density, and habitat quality. Sophisticated image classification, object detection, and change-detection systems are used to examine temporal and spatial changes in ecosystems. These findings illustrate the significant advances in wildlife presence detection, habitat fragmentation mapping, and conservation hotspot identification, while minimizing human disturbance. Remote sensing-based monitoring allows near-real-time evaluation of ecological change and supports proactive conservation planning and policy formulation. On the whole, the results outline the opportunities of remote sensing solutions to optimize biodiversity conservation, to ease sustainable management of wild animals and to reinforce the decision-making patterns towards the safeguarding of essential habitats in the face of growing environmental demands.
Physiological and Behavioural Plasticity of Arctic Marine Vertebrates Facing Rapid Sea Ice Habitat Transformation
The rate of Arctic Warming has intensified the meltdown of the sea ice and basically altered the makeup, size, and seasonal trends of the marine ecosystems. Sea ice is also strongly linked to arctic oceanic vertebrates, including mammals, seabirds, and fish, to forage, breed, migrate, and evade predation, and therefore are very vulnerable to such modifications. The paper is a review of the available knowledge regarding the physiological and behavioral plasticity that allows the Arctic marine vertebrates to cope with the rapid alteration in sea ice environments. This study considers major physiological transitions, such as metabolic plasticity, thermoregulatory responses, endocrinology of stress, energy redistribution to favor survival in novel thermal environments, and prey dispersion. At the same time, it also studies the behaviour changes, including a shift in timing and route of migration, and diving and foraging behaviour, a change in preference and reliance on pelagic or coastal systems as ice-related niches are exploited. Though plasticity may provide a temporary cushion against environmental change, this study demonstrates its limits, especially when the melting rate of sea ice is more than the capacity of acclimating or when behavioral change increases the cost of acclimating and the number of human-wildlife interactions. The adaptive potential depends on species-specific differences in life history, trophic position, and sea ice dependence, which causes uneven tax-specific resilience. Another aspect that the paper addresses is the long-term sustainability, in terms of the vertebrate populations of the sea, of the Arctic marine ecosystems that endure cumulative stressors, i.e., ocean warming, prey redistribution, and anthropogenic disturbance. Physiological and behavioral plasticity are vital technologies and constraints essential to promoting predictive systems and to sustain conservation and ecosystem-led management of a swifter-shifting Arctic seascape.
Biodiversity Triage and Role of Behavioural Plasticity in Conservation Prioritization for Species at Risk
Accelerating environmental changes, habitat fragmentation, climate shifts, and human activities threaten global biodiversity, requiring triage frameworks to prioritize conservation efforts for species at risk. Behavioural plasticity, the ability to adjust behaviours flexibly to changing conditions, serves as an overlooked factor in these frameworks. Existing methods emphasize fixed traits like population size and threat levels, neglecting adaptive capacities that support survival in modified environments. This study explores the role of behavioural plasticity in improved triage. Twelve mammal species vulnerable in the fragmented habitats of South Asia, including tiger reserves and dry forests near Bengaluru, were chosen. Field methods included camera traps, direct observations, and tracking records to measure plasticity in foraging patterns, habitat choices, and social structures (over 4,500 observations). Plasticity was scored according to differences in responses to environmental gradients, e.g., resource availability and levels of disturbance. A triage framework merged these scores with standard risk assessments (threat status, population trends) and habitat quality evaluations through comparative analysis. Findings demonstrate that species with high behavioural plasticity, exemplified by Bengal tigers' adaptable ranging and diet shifts, received lower triage priority. Such flexibility reduced projected extinction risks by 25-40% in scenarios of moderate habitat loss. Species with low plasticity, including certain herbivores, were more susceptible. These outcomes make plasticity an important priority criterion. Incorporating behavioural plasticity into triage facilitates habitat preservation and resource allocation and also bridges ecology and animal behaviour studies. This approach supports sustainable wildlife management and aligns with broader environmental goals in dynamic landscapes. Traits over time is due to the drastic change in the prices of inputs and outputs.
Multi-Agent System Simulations for Human-Wildlife Conflict Resolution and Ecosystem Restoration Planning
As habitat fragmentation and land-use changes increase alongside increased human activities, human wildlife conflict has risen and is currently posing dire consequences for biodiversity conservation and ecosystem stability. This paper presents an analysis, prediction, and mitigation of human-wildlife conflicts using a multi-agent system (MAS) simulation framework to facilitate ecosystem restoration planning. It is founded on the assumption that human beings, wildlife, and environmental units are represented as adaptive autonomous agents with adaptive behaviors as well as decision-making capabilities. The primary objective is to measure the impacts of agent interactions, movement, and resource competition patterns on the prevalence of conflict and habitat recovery. The spatial land-use, ecological properties of species, and patterns of human activity within an agent-based simulation environment form the methodology for experimenting with scenarios combining diverse management strategies. The simulation outcomes suggest that, with coordinated interventions (such as habitat corridors, buffer zones, and controlled land-use practices), the frequency of conflicts can be significantly reduced and the connectivity and success of ecosystems and restoration efforts can be enhanced. The findings highlight the application of MAS simulations in the decision-making process of conservation planning, and they help to avoid conflicts and sustainably manage ecosystems. Overall, the article highlights that agent-based modeling can align ecological dynamics with human preferences in problematic socio-ecological settings.
Telecoupled Conservation and Role of Distant Urban Consumption in Shaping Habitat Loss Within Protected Areas
Western Ghats, which is a global biodiversity hot spot, is being impacted by land-use change, which has its beginnings way outside the administrative borders of Protected Areas (PAs) in the Western Ghats. Although local human activities have frequently been cited as having caused habitat degradation, this paper uses a telecoupling model to examine how remote urban consumption and infrastructural pressures by hubs like Bengaluru, Mumbai, and Coimbatore contribute to habitat degradation within and in the surrounding areas of these PAs. The study examines the socioeconomic and environmental processes or flows linking the urban centers with the montane forests. Cities have triggered an evocation of luxury commodities, such as coffee, rubber, and timber, which have resulted in the transformation of the zones of significant buffers into commercial plantations. Moreover, the demand for nature-based tourism and second-home real estate has triggered the rapid infrastructure build-up, which consequently leads to linear encroachment, such as roads and resorts, that dissect the essential wildlife corridors. Through the combination of multi-temporal satellite images and city economic indicators, the paper measures the relationship between the capital of the city and loss of forest cover in major areas such as the Nilgiris biosphere reserve. Results indicate that the conventional methods of conservation, namely fence-and-fine, are inadequate in countering telecoupled market forces. The findings demonstrate that distal urban dietary changes and energy demand determine the level of habitat integrity in the Western Ghats, and this demand enforces large scale hydroelectric projects in conserved landscapes. In an effort to alleviate these pressures, conservation policy would need to change towards telecoupled governance, rather than local-centric management. This involves the introduction of urban-financed Payment of Ecosystem Services (PES), tougher control of supply chains of Ghats-sourced foods, and obligatory green infrastructure of urban urban transporting routes. Finally, the sustainability of Western Ghats biodiversity would be determined by the ability to balance the ecological boundaries of the Protected Areas with the growing footprint of the urban middle class in India.
Multiscale Modeling of Pollinator Habitat Restoration Incorporating Taxon-Specific and Regional Conservation Priorities
Habitat loss, climate change, and agricultural escalation subject pollinator species, which are important to ecosystem services and biodiversity, to unprecedented threats. The habitat restoration aspect will be one of the crucial elements in reversing such trends, yet in conventional conservation approaches, the form of restoration ventures is, in most instances, not specific to the environmental conservation process. The suggested paper presents a novel approach to restoring pollinator habitats through multiscale models, including taxon-specific requirements and conservation priorities at the regional level. This study can map the framework that maximizes restoration of habitats in various scales, small (local) and large (landscapes), by the combination of ecological, environmental, and socio-economic information. The model considers the ecological requirements of pollinator taxa, such as bees, butterflies, and moths, and balances restoration priorities with local conservation priorities, including land-use policies, farm management, and urbanization patterns. The hybrid approach of spatially explicit data and machine learning algorithms is the primary method the study uses to estimate the success of restoration interventions across different environmental conditions. It is also a model that establishes the critical parameters of habitat, like floral resources, nest areas, and connection corridors, that are required for maintaining different populations of pollinators. Also, it considers stakeholder input to make the restoration plans relevant and practical to the context. The results show that transferring taxon-specific strategies to regional contexts is an effective means of enhancing the effectiveness of habitat restoration efforts. This will assist in conserving pollinators and help move toward a more sustainable path through optimal resource allocation, cost reduction, and ecological requirements. The paper illustrates the importance of adaptive and scalable models in the question of habitat restoration, and provides the policy makers and conservationists with the relevant tools to become a part of the particular and efficient action.
Combining Behavioral Ecology and Sustainability Modeling to Advance Wildlife Conservation and Environmental Health
Environmental health and wildlife conservation play an essential role in ensuring the ecosystems and biodiversity. Nevertheless, the combination of behavioral ecology and sustainability modeling has not been explored much in the development of these domains. The current paper brings a new strategy that considers behavioral ecology and sustainability modeling to enhance conservation of wildlife and environmental health. The paper has found the critical gaps in the existing conservation approaches and suggested a predictive framework to overcome the challenges. The study applies statistical modelling to examine the behavior pattern of wildlife in relation to environmental factors and thereafter incorporates models of sustainability to predict the ecological scenario in the long run. Statistical knowledge, such as regression data and predictive analytics, has demonstrated that predictions that are based on behavior can add 35 % of accuracy to conservation models to make resource allocation and policy implementation more effective. There is a detailed data gathering of various ecosystems, observations in the field, and ecological experiments. Findings show that behavioral responses of species to environmental stressors are predictable and quantifiable, which will result in more effective conservation interventions. The results of the model are compared with the real-world data of a number of wildlife reserves, which verifies the findings of the model and a clear correlation between predictive modeling and positive conservation effects. To sum up, the paper underlines the necessity to incorporate ecological data and sustainability frameworks to have stronger and more action-oriented conservation policies. The results indicate that the concept of behavioral ecology in environmental health models can lead to a substantial enhancement of the conservation practice, providing the policy-makers with data-driven information on how to make decisions more efficiently. Further studies are recommended to extend these models, which include more elements of the environment and behavior, to increase the generalizability and applicability of the suggested model.
Longitudinal Analysis of Domestic Animal Welfare Under Compound Climate Stressors in Smallholder Farming Systems
Problem: Compound climate stressors, such as extended droughts, extended heat waves, and changing populations of pests, are growing to threaten smallholder farming systems. Although the literature on the topic often focuses on the economic productivity, the direct biological and behavioral well-being of domestic animals in such a complex of environmental demands is a gap in knowledge that has the potential to be bridged. Such systems are inimitable because of the underdeveloped infrastructure and overdependence on the unpredictable natural resources. Methodology: This study uses longitudinal analysis on a multidisciplinary basis to evaluate the interactions between animals and the environment over a period. The study assesses the effectiveness of climate-smart strategies, including livestock diversification and integrated pest management, by combining the data from the different geographical locations. The approach focuses on animal-based indices, such as physiological stability and adaptive behavioral capacity, and not fiscal performance only. Results: It was found that animals in diversified systems that are integrated are much more resilient to heat stress than monocultural systems. Nevertheless, there is an ongoing weakness in changing communities of ticks and disease vectors caused by climate change. Other findings also point to a high association between farmer well-being and livestock health, implying that social stability is a precondition for animal welfare. Conclusion: The study determines that there are selective adaptive mechanisms that counter the adverse effects of climatic volatility on native species. It adds to the body of knowledge by giving practical information that can be used by policymakers to balance conservation agendas and animal husbandry. Finally, it is necessary to enhance a greater appreciation of these interdependencies to achieve global food security and animal welfare in the age of unparalleled environmental change.
Assessing Impact of Microclimate Variability on Wildlife Habitat Selection and Movement Patterns in Fragmented Forest Ecosystems
The variability of microclimate (change in temperature, humidity, and light) is very important in the choice of wildlife habitat and patterns of movement. In isolated forest ecosystems, the habitat edges, different vegetation types, and the nearness to human destabilization of the ecosystem cause microclimatic conditions, which cause serious changes in wildlife behavior. This paper examines the influence of variability in microclimates on the choice of habitat and movement in fragmented forest systems. Its primary goal was to evaluate the effect of temperature and humidity gradient on the movement and choice of habitat of wildlife species in various forest sections. The HOBO U30-NRC data loggers and Vaisala HMP60 sensors were used to observe the microclimate conditions, and the Lotek Biotracker GPS collars were installed on target species spotted deer (Axis axis), leopard (Panthera pardus), and elephant (Elephas maximus) to collect data. GPS telemetry data, camera trap data, and additional public video data were used to monitor wildlife movement. The research found that there was a high level of microclimate variation among forest patches, with cooler and more humid habitats preferring species such as the leopard, while species such as the spotted deer offered a wider variety of habitats. The presence of movement data suggested that wildlife tended to utilize corridors between larger areas of forest, and species exhibited variability in the size of their home ranges and the potential speed of movement. Statistical tests revealed that there were significant negative relationships between temperature and habitat suitability of leopards (r = -0.78, p < 0.01) and a moderate positive correlation with humidity (r = 0.65, p < 0.05). The results of this study demonstrate the need to pay attention to fine-scale microclimate data in conservation planning, especially the design of wildlife corridors and the improvement of habitat connectivity in partitioned ecosystems. The future studies need to be concerned with long-term observation to determine the effects of climate change on such microclimatic patterns.
Integrating Remote Sensing and Individual Based Models to Prioritize Rewilding Sites for Significant Carnivore Recovery
Trophic rewilding is based on the restoration of large carnivores, but locating possible sites where can be reintroduced is becoming more and more complex due to habitat fragmentation and human anthropogenic pressures. Conventional conservation planning applications are usually based on fixed suitability maps that do not recognize the dynamic, behavioral patterns of movement of predators in humanized landscapes. This study fills this gap by suggesting the combined use of high-resolution Remote Sensing (RS) and Individual-Based Models (IBMs) to rank rewilding locations by functional connectedness. Designed structural habitat features, including canopy architecture and heterogeneity of woody covers, using the multi-spectral satellite data to produce fine-scale landscape resistance surfaces. It was these surfaces that parameterized an IBM, which was used to simulate individual animal trajectories and dispersal success at varying environmental conditions. Find that the high resolution edge detection significantly enhances the determination of bottlenecks of movement, which are often ignored by traditional models and discovered that the shape of individual vegetation corridors and the behavioral response to landscape resistance by individuals are more important than the area of the total habitat in the persistence of the population. This methodology is helpful as it successfully fills the gap between spatial ecology and management in that it can be used to identify locations that not only accommodate occupancy, but also long-term dispersal. This combined RS-IBM solution can be a powerful, scalable conservationist tool, whereby rewilding can be done in landscape areas with the highest likelihood of ecological success and coexistence between humans and wildlife.
Assessment of Groundwater Quality in Gorgan Using the Groundwater Quality Index (GWQI)
Water quality is a fundamental factor that influences all dimensions of ecosystems as well as public health. Numerous researchers emphasize that the provision of safe drinking water, proper sanitation, and efficient management constitute the three essential pillars of community health. Accordingly, the present study aimed to evaluate the groundwater quality index (GWQI) of rural drinking water resources in Gorgan County. A total of 13 sampling stations under the supervision of the Rural Water and Wastewater Company were investigated over a three-year period (2016–2018) to identify temporal trends in groundwater quality. Water samples were analyzed at the laboratories of the Rural Water and Wastewater Company and Golestan Institute of Higher Education using standard analytical methods. The GWQI index was employed to compare groundwater quality across the studied villages. Results revealed that Kaloo village exhibited the lowest GWQI value (37.41, classified as excellent), whereas Ghal’eh Mahmoud village showed the highest (277.39, classified as very poor). Overall, 49% of the groundwater samples were categorized as excellent, 46% as good, and 5% as poor to very poor in terms of drinking suitability. The findings highlight that groundwater in Ghal’eh Mahmoud requires treatment with respect to several chemical parameters, including hardness, turbidity, chloride, and sulfate. Concentrations of iron and fluoride in all drinking water sources remained below permissible limits. Nevertheless, the observed increase in certain parameters in recent years suggests the potential infiltration of wastewater, likely associated with the north-to-south topographic gradient of the region. In recent years, the increase in certain parameters indicates the potential infiltration of wastewater, likely influenced by the north-to-south slope of the area. This issue requires more comprehensive investigations.