Conservation Decisions Research Articles

An Approach for Wild Animal Detection in Videos by using Deep Learning

Due to land availability decreases and rapid urbanisation many wild animals near the forest areas comes out for water and food. Wild animal detection strives to address the critical issue of manual surveillance challenges faced by forest officers and conservationists in vast natural habitats. Traditional methods, such as periodic surveys and camera traps, have proven inadequate in providing real-time data and comprehensive coverage, impeding effective conservation efforts. Moreover, budget constraints and the absence of automation further exacerbate these challenges. In response, our project proposes an innovative solution that seamlessly integrates deep learning and instant messaging technologies, fostering affordable and continuous surveillance. By harnessing edge computing and freely available messaging channels, our system aims to significantly enhance real-time visibility and data-driven decision-making in conservation. The primary goal is to empower conservationists with timely and actionable information for informed decision-making. Through the deployment of advanced deep learning algorithms, our system can recognize and track wildlife activity, triggering instant notifications to forest officers and relevant stakeholders via instant messaging platforms.

Temporal and interspecific comparisons reveal shifts in the distributions of two Florida skunk species

AbstractUnderstanding the distribution of wildlife species is key to their effective conservation and management. Comparing distributions of similar species can provide valuable insights regarding range overlap and how overlap changes over time. We estimated the geographic distributions of 2 skunk species, the eastern spotted skunk (Spilogale putorius) and the striped skunk (Mephitis mephitis), in Florida, USA, for historical (1997–2002) and contemporary (2017–2022) periods using opportunistic presence data (e.g., public sightings, camera trap records, live trap records). For each species, we produced range models at 2 levels: a generalized boundary (range extent) using a concave hull and a more detailed delineation (occupied range) using kernel density estimation. We expected spotted skunk range to decline (as it has elsewhere), striped skunk range to remain stable, and both species' ranges to have a relatively high geographic overlap. The contemporary models show spotted skunk range encompassing 26% of Florida land area, primarily in southern and coastal Florida, and striped skunk range encompassing 55% of Florida land area, primarily in northern and interior Florida. Range size for spotted skunk declined by approximately 30% between periods, although range expansion occurred in some localized areas. Range size for striped skunk was stable over time, with some localized contractions and expansions. Considerable temporal shifts occurred in skunk distribution, where only 34% of spotted skunk and 54% of striped skunk occupied range remained spatially constant between periods. Divergent geographic use between the 2 species increased over time, yielding a low range overlap of 23% between their contemporary occupied ranges. Our study fills a spatial data gap in skunk research, can inform state‐ or species‐level conservation decisions for spotted skunk, and supports the need for further research on habitat requirements of skunks in Florida.

Local conservation action requires ethical investments in global digital equity

Summary Satellite remote sensing is vital for monitoring anthropogenic changes and for alerting us to escalating environmental threats. With recent technological advances, a variety of satellite-based monitoring systems are available to aid conservation practitioners. Yet, documented knowledge of who uses near-real-time satellite-based monitoring and how these technologies are applied to inform conservation decisions is sparse. Through an online survey and semi-structured interviews, we explored how developers and users leverage conservation early-warning and alert systems (CEASs) for enhanced conservation decisions. Some 167 developers and users of near-real-time fire and forest monitoring systems from 40 countries participated in this study. Globally, respondents used 66 unique CEASs. The most common applications were for education and awareness, fire/disaster management and law enforcement. Respondents primarily used CEASs to enforce land-use policies and deter illegal activities, and they perceived these tools as underutilized for incentivizing policy compliance or conservation. Respondents experienced inequities regarding system access, exposure and ability to act upon alert information. More investments in capacity-building, resources and action plans are needed to better link information to action. Implementing recommendations from this research can help us to increase the accessibility and inclusivity of CEAS applications to unlock their powerful capabilities for achieving conservation goals.

Fatigue Life of Pre-Cut Seam Asphalt Mixture Composite Beams: A Combined Study of Fatigue Damage Evolution and Reflective Cracking Extension

This study investigated the impact of reflective cracking on the fatigue performance of asphalt pavements after milling and resurfacing under various conditions. Fatigue life was assessed through four-point flexural fatigue tests, while the crack extension pattern of composite beams was analyzed by digital image correlation (DIC) at both macroscopic and microscopic scales. Evaluation parameters such as stress ratios, immersion time, porosity, and types of viscous oils were assessed. A fatigue life prediction model of composite beams was established, accounting for the combined influence of these factors. To enhance the accuracy of determining composite beam failure, the critical fatigue damage was calculated by defining the damage variable in terms of the dynamic modulus. A nonlinear fatigue damage model was proposed, incorporating this critical damage under the combined influence of various factors. Additionally, a modified logistic function model was developed to describe the relationship between crack extension and failure life under different stress ratios, porosities, and viscous layer oil conditions. It was found that the modulus decay curves and the crack extension curves intersected at different stress levels as the life ratio increased. At the intersection, the modulus ratios were consistently around 0.55, marking the transition of the specimen from a stable to an unstable state. Beyond this point, the crack rapidly propagated, leading to a sharp reduction in the modulus until the specimen ultimately failed. Our results provide a basis for timing and conservation decisions.

Multi-Scale Mapping and Analysis of Broadleaf Species Distribution Using Remotely Piloted Aircraft and Satellite Imagery

Tree species mapping from the individual crown to landscape scales provides crucial information on the diversity and richness of forest ecosystems, supporting major conservation decisions under ongoing climate change. With the emergence of Remote Piloted Aircraft (RPA), high spatial resolution datasets can be obtained and analyzed to inherently improve the current understanding of broadleaf tree species distribution. The utility of RPA for mapping broadleaf species at broader scales using satellite data needs to be explored. This study investigates the use of RPA RGB imagery captured during peak fall foliage to leverage coloration commonly exhibited by different broadleaf tree species during phenology transition to delineate individual tree crowns and map species distribution. Initially, a two-step hybrid segmentation procedure was designed to delineate tree crowns for two broadleaf forests using RPA imagery collected during the fall season. With the tree crowns, a subsequent Object-based Random Forest (ORF) model was tested for classifying common and economically important broadleaf tree species groups. The classified map was further utilized to improve ground reference data for mapping species distribution at the stand and landscape scales using multispectral satellite imagery (1.4 m to 10 m). The results indicated an improvement in the overall accuracy of 0.13 (from 0.68 to 0.81) and a MICE metric of 0.14 (from 0.61 to 0.75) using reference samples derived from RPA data. The results of this preliminary study are promising in utilizing RPA for multi-scale mapping of broadleaf tree species effectively.

What Can Genome Sequence Data Reveal About Population Viability?

Biologists have long sought to understand the impacts of deleterious genetic variation on fitness and population viability. However, our understanding of these effects in the wild is incomplete, in part due to the rarity of sufficient genetic and demographic data needed to measure their impact. The genomics revolution is promising a potential solution by predicting the effects of deleterious genetic variants (genetic load) bioinformatically from genome sequences alone bypassing the need for costly demographic data. After a historical perspective on the theoretical and empirical basis of our understanding of the dynamics and fitness effects of deleterious genetic variation, we evaluate the potential for these new genomic measures of genetic load to predict population viability. We argue that current genomic analyses alone cannot reliably predict the effects of deleterious genetic variation on population growth, because these depend on demographic, ecological and genetic parameters that need more than just genome sequence data to be measured. Thus, while purely genomic analyses of genetic load promise to improve our understanding of the composition of the genetic load, they are currently of little use for evaluating population viability. Demographic data and ecological context remain crucial to our understanding of the consequences of deleterious genetic variation for population fitness. However, when combined with such demographic and ecological data, genomic information can offer important insights into genetic variation and inbreeding that are crucial for conservation decision making.

Assessing Engineering Art Heritage: Historical Lifting Devices in Poland

ABSTRACT Technical heritage, such as machinery, requires a different perspective than traditionally understood works of art. To protect the most valuable of them, heritage conservation offices need to find an answer to questions such as which ones should be protected, why these precisely, and to what extent? A procedure designed to assist heritage conservation authorities in making informed decisions and which serves to rationalize the conservation decision is the valuation of monuments. The analysis of the methods presented in the literature shows that there is still a need to address the issues related to engineering heritage, particularly devices, machines, and vehicles, whose conservation should encompass operational methods and operational properties. The research here aimed to develop the method of valuing historical lifting equipment, objectivize the assessment of heritage value represented by the examined devices, and research the possibility of their universalization to protect the heritage of engineering art.

Consistency and flexibility of character in free-ranging male African elephants across time, age, and social contexts.

Post-dispersal male African elephants (Loxodonta africana) live within complex social networks. To quantify the consistency of male elephant character (or personality) within these networks, we employed behavioral repeatability analysis tools across social and environmental contexts. We collected behavioral data from thirty-four individually-identified male elephants at the same waterhole over five field seasons (2007-2011) in Etosha National Park, Namibia. Using repeatability models to assess ten behavioral categories, we found five behaviors (affiliation, aggression, dominance, self-directed anxious, and self-directed comfort) were consistent at the individual level. Some of these behaviors were also repeatable, depending on social context. In particular, the presence of younger males and a keystone male (i.e., the most dominant and socially-integrated individual during our study period) had the biggest impact on adult male behaviors. Surprisingly, the presence of elephants in musth had little impact. Finally, we found that younger individuals were more alike in their overall character profiles than older males, further supporting the hypothesis that male elephants develop unique, yet socially-flexible character types as they age. These results demonstrate that male elephants possess distinct character traits that are also behaviorally adaptable, depending on the social context. Overall, our research further highlights the complexity of male elephant individuality and social dynamics that might be leveraged to improve in-situ and ex-situ management and conservation decisions for the species.

Measuring genetic diversity across populations.

Diversity plays an important role in various domains, including conservation, whether it describes diversity within a population or diversity over a set of species. While various strategies for measuring among-species diversity have emerged (e.g. Phylogenetic Diversity (PD), Split System Diversity (SSD) and entropy-based methods), extensions to populations are rare. An understudied problem is how to assess the diversity of a collection of populations where each has its own internal diversity. Relying solely on measures that treat each population as a monomorphic lineage (like a species) can be misleading. To address this problem, we present four population-level diversity assessment approaches: Pooling, Averaging, Pairwise Differencing, and Fixing. These approaches can be used to extend any diversity measure that is primarily defined for a group of individuals to a collection of populations. We then apply the approaches to two measures of diversity that have been used in conservation-Heterozygosity (Het) and Split System Diversity (SSD)-across a dataset comprising SNP data for 50 anadromous Atlantic salmon populations. We investigate agreement and disagreement between these measures of diversity when used to identify optimal sets of populations for conservation, on both the observed data, and randomized and simulated datasets. The similarity and differences of the maximum-diversity sets as well as the pairwise correlations among our proposed measures emphasize the need to clearly define what aspects of biodiversity we aim to both measure and optimize, to ensure meaningful and effective conservation decisions.

The Africa Bird Data packages: African citizen science bird data in R

The African Bird Atlas Project (ABAP) and the Coordinated Waterbird Counts (CWAC) are the main citizen science bird monitoring programmes in southern Africa. ABAP data are collected by volunteers that compile bird lists over a grid of geographic areas known as pentads, resulting in millions of records that are key to studying bird distribution. In contrast, CWAC collects data on waterbird abundance at a selection of wetlands twice a year, offering important data on waterbird population dynamics. ABAP and CWAC data are extensively used in research. They also provide the evidence base for conservation decisions, site management and national reporting obligations against international agreements. The respective project websites provide distribution maps, summary statistics and basic data download features. However, a more advanced use of these data for modelling and their incorporation into analytical workflows requires flexible and direct access to the databases. The Africa Bird Data R packages presented here provide an important functionality to download and process data directly into the analytical software R. Apart from accessing the databases, there are functions that help with the analysis of these data by incorporating model covariates or formatting data for R packages that are popular for the analysis of this kind of data. Our objective is to make African bird data more accessible and easier to work with in order to unlock their full potential for research, and to inform management and conservation actions.

Prenatal Zika Virus Exposure Alters the Interaction Between Affective Processing and Decision-Making in Juvenile Rhesus Macaques (Macaca mulatta).

Many challenges during pregnancy can disrupt fetal development and have varying consequences on the subsequent psychological development of infants. Notably, exposure to infectious pathogens during fetal development, such as those encountered in viral pandemics, has been associated with persistent developmental consequences on infants' brains and behavior. However, the underlying mechanisms and the degree to which neural plasticity over infancy may accommodate fetal insults remain unclear. To address this gap, we investigated the interaction between affective processing and decision-making in a cohort of rhesus monkey juveniles exposed to Zika virus (ZIKV) during fetal development, a pathogen known to profoundly disrupt central nervous system development. Ten juveniles exposed to ZIKV during their fetal development and nine procedure-matched controls (CONs) completed a judgment bias task with and without a negative mood induction. Although ZIKV exposure did not impact the monkeys' decision-making processes during the task or the magnitude of their behavioral responses to the mood induction procedure, it did alter the influence of mood induction on decision-making. Although CON monkeys exhibited significantly more conservative decision-making following negative mood induction, the decision-making of Zika-exposed monkeys remained consistent among conditions. These findings suggest that fetal exposure to ZIKV impacts the neural systems involved in integrating affective and cognitive information, with potential long-term implications for learning, memory, and emotion regulation.

Integrating UAV LiDAR and multispectral data to assess forest status and map disturbance severity in a West African forest patch

Unmanned aerial vehicle (UAV) technologies have emerged as promising tools to improve forest ecosystem assessments. These technologies offer high-resolution data that can significantly enhance evaluations of forest structure, condition, and disturbance severity. UAV sensors such as LiDAR and multispectral provide complementary information about forest attributes, capturing structural and spectral details, yet their integration for comprehensive forest assessment remains understudied. In this paper, we explored the potential of combining UAV LiDAR and multispectral data to assess the disturbance severity of a West African forest patch (Benin). We developed an integrated disturbance index (IDI) that fuses structural properties from LiDAR data and spectral characteristics from multispectral vegetation indices through principal component analysis (PCA). This allowed us to delineate low (> 0.65), medium (0.35–0.65), and high (< 0.35) forest disturbance levels. We applied the IDI to the 560-ha Ewe-Adakplame relict forest in Benin, West Africa, and achieved 95 % overall accuracy in disturbance detection, outperforming both LiDAR-only (80 %) and multispectral-only (75 %) approaches. The IDI revealed that 23 % of the forest area has experienced low disturbance, while 28 % and 49 % face medium and high disturbance levels, respectively. These findings highlight that more than three-quarters of this relict forest is under considerable stress, underscoring the urgent need for tailored conservation strategies to strengthen forest resilience. This method's ability to differentiate disturbance levels can inform resource allocation, prioritize conservation efforts, and guide the development of site-specific management plans. The integration of UAV LiDAR and multispectral data demonstrated here has potential for application across diverse tropical forest patches, providing an effective means to monitor forest health, assess disturbance severity, and support data-driven decision-making in forest conservation and sustainable management.

Utilization of Cloud Computing for Water Quality Monitoring in the Northern Waters of Aceh

Abstract Managing marine geographic information systems is crucial, especially with climate change and the rise of spatial big data. Cloud-based geospatial systems are essential for stakeholders needing quick decision-making in marine conservation. This study examines the capabilities of Google Earth Engine (GEE) in analyzing sea surface quality in the northern waters of Aceh (NWA). The results show GEE can access remote sensing datasets and reanalysis models to map temperature, salinity, and chlorophyll-a. Annual trends reveal lower salinity near the Malacca Strait and higher levels in the Indian Ocean and Andaman Sea. GEE supports proactive coastal ecosystem management, emphasizing the role of geospatial technology in marine conservation and civil engineering for climate-resilient coastal development.

Levels and Spatial Patterns of Effective Population Sizes in the Southern Damselfly (Coenagrion mercuriale): On the Need to Carefully Interpret Single‐Point and Temporal Estimations to Set Conservation Guidelines

ABSTRACTThe effective population size (Ne) is a key parameter in conservation and evolutionary biology, reflecting the strength of genetic drift and inbreeding. Although demographic estimations of Ne are logistically and time‐consuming, genetic methods have become more widely used due to increasing data availability. Nonetheless, accurately estimating Ne remains challenging, with few studies comparing Ne estimates across molecular markers types and estimators such as single‐sample methods based on linkage disequilibrium or sibship analyses versus methods based on temporal variance in allele frequencies. This study aims at bridging this gap by analysing single‐sample and temporally spaced populations in the southern damselfly (Coenagrion mercuriale), a bioindicator Odonata species of conservation concern found in southwestern Europe's freshwater stream networks. A total of 77 local populations were sampled from a semi‐urbanised area located in eastern France near Strasbourg city, yielding 2842 individuals that were genotyped with microsatellites and 958 of which were also genotyped for 2092 SNPs. Spatial genetic structure was stable over time, suggesting porosity between alternate‐year cohorts. When accounting for spatial genetic structure, single‐sample and temporal estimations of Ne were consistent for each set of molecular markers. Biologically meaningful results were obtained when the effect of migration was minimising by considering metapopulation Ne estimates based on the level of genetic differentiation and population boundaries. In terms of applied conservation and management, most depicted metapopulations displayed large Ne, indicating no immediate need for conservation measures to mitigate anthropogenic pressures, provided that a continuous suitable freshwater network is maintained. However, urbanisation negatively impacted Ne levels in populations close to Strasbourg city. Because Ne is used to inform conservation decisions, caution is crucial in interpreting Ne estimates, especially in continuously distributed populations undergoing migration. Altogether, our study highlights the challenge of obtaining robust Ne estimates and the necessity of careful interpretation to set relevant conservation guidelines.

Multiple Long-Term, Landscape-Scale Data Sets Reveal Intraspecific Spatial Variation in Temporal Trends for Bird Species.

Quantifying temporal changes in species occurrence has been a key part of ecology since its inception. We quantified multidecadal site occupancy trajectories for 18 bird species in four independent long-term, large-scale studies (571 sites, ~1000 km latitude) in Australia. We found evidence of a year × long-term study interaction in the best-fitting models for 14 of the 18 species analysed, with differences in the temporal trajectories of the same species in multiple studies consistent with non-stationarity. Non-stationarity patterns in occupancy were not related to the distance from a species niche centroid; species in locations further from their niche centroid did not demonstrate differing temporal trajectories to those closer to their niche centroid. Furthermore, temporal trajectories of species were not associated with climatic values for each study relative to their niche. Our findings demonstrate the need for multiple long-term studies across a species range, especially when tailoring conservation decisions for populations.

Freshwater fish in mid and northern German rivers – Long-term trends and associated species traits

To understand biodiversity change and support conservation decision-making, estimates of species' long-term population trends at regional and national scales are essential. However, such estimates are missing for many freshwater taxa, despite the diverse range of threats that they face. For this study, we mobilised monitoring data on riverine freshwater fish abundance collected across different regions of Germany. We applied generalized mixed effect models to estimate the population trends for 55 native species and 11 non-native species between 2004 and 2020. In addition, we used boosted regression trees to identify trait-based predictors of species trends and assessed their predictive ability. We found evidence of increasing abundance trends for 14 species and decreasing trends for 15 species; while the remaining species were mostly stable (26 species). Native species were more typically decreasing than increasing (14 vs 10 species); while non-native species were more often increasing (4 vs 1 species). Important traits associated with trends were maximum length, spawning temperature, and water quality tolerance, with small species, those spawning at high temperatures, and those preferring unpolluted waters, being most likely to have positive trends. Despite these associations, overall trait-based models showed limited power to predict trends in terms of direction as well as magnitude. Our results highlight the ongoing change in riverine fish communities and the importance of on-going species-level monitoring. The trait-based associations also indicate climate change and invasive species as important drivers of change in European freshwater rivers.

Stakeholder perceptions of mangrove ecosystem services across scales of conservation focus.

Conservation faces a chronic shortage of resources, including time, funding, mental capacity, and human capital. Efforts to make the expenditure of these resources more efficient should, therefore, support more equitable and effective conservation prioritization. To achieve this, it is necessary to ensure the integration of the knowledge and perceptions of local stakeholders into larger scale conservation decisions. We used fuzzy cognitive mental modeling to assess the perceptions of mangroves and the prioritization of ecosystem services across 3 groups of stakeholders: representatives from 3 coastal Fijian villages, the national office of an international nongovernmental organization (NGO), and the US office of that same NGO. We found different topologies and valuations among the resultant mental models, with the US NGO office having the most terms. However, when comparing models from local villages with the US NGO office, scale-dependent perceptions shifted, including the relative devaluation of locally important cultural valuations of mangroves. Despite these variations in perceptions, however, 3 key components of the mental models-women's livelihoods, men's livelihoods, and fisheries supplementation-all appeared as consistently important in multiple models, suggesting areas around which potential collaboration among stakeholders could be forged. By focusing on system-wide, rather than stakeholder-specific, optimal solutions within the system, new opportunities for collaboration may emerge. In doing so, these system-wide solutions may increase efficiency and collaboration. Moreover, we found that boundary-spanning organizations, such as the national-level conservation organizations, played a role in facilitating information transfer and mediating conservation goals in a culturally appropriate fashion. Finally, although the specific example used here is mangrove conservation, our methodologies and findings are broadly applicable across a variety of conservation scenarios.

Designing a sustainable-resilient vaccine cold chain network in uncertain environments

In recent years, outbreaks of diseases have been prevalent, significantly impacting human’s work, life and social economy. Vaccination is widely seen as the most promising way to fight against most of the epidemics. However, building a sustainable-resilient vaccine cold chain network is a complex planning problem, which may face various challenges, such as low-temperature transportation and storage, uncertain environments, and waste management. To address these challenges, a distributionally robust vaccine cold chain network design model is established. Using Wasserstein ambiguity set to manage uncertainties, the Wasserstein distributionally robust optimization (WDRO) model can be transformed into a computationally tractable form. A case study on influenza vaccines in Clalit reveals that the proposed WDRO model can yield a robust solution, incurring a small robust price. Conservative decision makers can choose a slightly larger Wasserstein ambiguty set to enhance the supply chain resilience at the cost of reducing economic and environmental benefits.

Assessing Landscape Fragmentation Dynamics with Fourier Transforms

Abstract Quantifying landscape fragmentation is crucial for understanding its impact on biodiversity. Within landscape ecology, there is an ongoing effort to create metrics that systematically characterize landscape patterns and predict their effects on various species. In this study, we utilized Fourier transforms to measure landscape fragmentation in urban and heterogeneous landscapes over periods of 24 and 12 years, respectively. Analyzing Landsat images from 1986, 1999, and 2010, we focused on the first principal component (PC1) to evaluate changes in fragmentation over time. Our analysis revealed a significant increase in fragmentation in urban areas from 1986 to 2010, with a particularly noticeable rise between 1999 and 2010. Specifically, the average Fourier spectrum decreased slightly from 16.0 to 15.9 throughout the study period, indicating increased fragmentation at both urban and landscape levels. The sensitivity analysis further showed that removing 6,000 cells from the original image caused the mean Fourier spectrum to drop from 16.0 to 15.5, demonstrating the method’s effectiveness in detecting subtle land cover changes. Our findings demonstrated that Fourier analysis effectively detects subtle landscape changes, particularly in larger study areas and over longer time intervals. However, it lacks detailed information on specific types or quantities of altered land cover. To address this limitation, integrating Fourier analysis with land cover classification datasets could enhance understanding of landscape dynamics and support more informed conservation and management decisions.

Most Random-Encounter-Model Density Estimates in Camera-Based Predator-Prey Studies Are Unreliable.

Identifying population-level relationships between predators and their prey is often predicated on having reliable population estimates. Camera-trapping is effective for surveying terrestrial wildlife, but many species lack individually unique natural markings that are required for most abundance and density estimation methods. Analytical approaches have been developed for producing population estimates from camera-trap surveys of unmarked wildlife; however, most unmarked approaches have strict assumptions that can be cryptically violated by survey design characteristics, practitioner choice of input values, or species behavior and ecology. Using multi-year datasets from populations of an unmarked predator and its co-occurring unmarked prey, we evaluated the consequences of violating two requirements of the random encounter model (REM), one of the first developed unmarked methods. We also performed a systematic review of published REM studies, with an emphasis on predator-prey ecology studies. Empirical data analysis confirmed findings of recent research that using detections from non-randomly placed cameras (e.g., on trails) and/or borrowing movement velocity (day range) values caused volatility in density estimates. Notably, placing cameras strategically to detect the predator, as is often required to obtain sufficient sample sizes, resulted in substantial density estimate inflation for both the predator and prey species. Systematic review revealed that 91% of REM density estimates in published predator-prey ecology studies were obtained using camera-trap data or velocity values that did not meet REM requirements. We suggest considerable caution making conservation or management decisions using REM density estimates from predator-prey ecology studies.