Search Results (561)

Accidents between vehicles and wildlife (WVCs) represent a complex management challenge, requiring integrated strategies that balance biodiversity conservation with public security and socio-ecological resilience. However, existing GIS hotspot analyses often identify spatial patterns without quantifying the structural landscape drivers that compel animal–road interactions. This study aims to identify “ecological traps” through an integrated landscape diagnostic framework combining Kernel Density Estimation (KDE) for statistical hotspot identification and landscape metrics (FRAGSTATS) for structural diagnosis, using the wild boar (Sus scrofa) as a focal species. An exploratory case analysis of high-collision locations was conducted, utilizing a high-quality dataset of 161 precisely georeferenced incidents recorded between 2015 and 2020 within the most critical municipalities of the Province of Avellino (Southern Italy). Results highlight two primary hotspots: the Guardia Lombardi-Conza corridor and the Avellino Nord-Pratola Serra axis. Quantitative analysis reveals that 39.1% of incidents occurred in non-irrigated arable lands and 19.9% in broad-leaved forests, with 52.8% of events situated within 500 m of river systems, which function as primary ecological movement corridors. Furthermore, fragmentation indices (Patch Density, Edge Density) were significantly higher in these focus areas, confirming that habitat isolation and the loss of core patches force animals to traverse infrastructure. These findings underscore the urgency of evidence-based spatial planning, offering a methodological framework with potential applicability for prioritizing mitigation actions, such as ecological corridors and intelligent signaling, to enhance the resilience of socio-ecological systems. This framework provides a scalable model for sustainable land management, ensuring that biodiversity conservation is integrated into long-term infrastructure governance. Full article

Soil salinisation and periodic flooding jointly shape coastal wetland ecosystems, but how coexisting halophytes differ in their rhizosphere bacterial communities under these conditions remains insufficiently understood. Here, we investigated rhizosphere bacterial communities associated with Suaeda glauca Bunge and Tamarix chinensis Lour. in saline–alkaline habitats of the Yellow River Delta, China. Twenty quadrat-level rhizosphere samples were collected across four plant–habitat groups, and near-full-length 16S rRNA gene amplicons were sequenced using Pacific Biosciences single-molecule real-time sequencing. Our analysis revealed that hydrological habitat and plant identity together contributed to differences in rhizosphere bacterial community composition. Across the dataset, 2325 bacterial operational taxonomic units were identified. T. chinensis showed higher Shannon and Gini–Simpson diversity, whereas richness patterns depended on habitat and the metric examined. Meanwhile, exploratory genus-level association networks revealed host- and habitat-dependent differences in node number, network density and average degree. PICRUSt2-based functional prediction suggested contrasting predicted functional response patterns: the S. glauca rhizosphere showed 19 significantly altered predicted pathways between flooded and non-flooded habitats, whereas the T. chinensis rhizosphere showed no significant pathway shifts after multiple-testing correction. These findings suggest that coexisting halophytes are associated with divergent rhizosphere bacterial community patterns under saline–alkaline and flooding-associated habitat conditions. Full article

Understanding habitat requirements across spatial scales is important for conserving declining migratory species such as the Cerulean warbler. This study integrates data from several forms of remote sensing platforms: automated recording units, airborne LiDAR, and satellite remote sensing, to characterize the habitat of Cerulean warbler detection sites across central Indiana. We also modeled the suitable habitat of the species across the contiguous United States. Automated recording units were deployed across four forest types, and automated classification was used to derive species detections, which were manually validated. Structural variables derived within 25 m and 50 m buffers included LiDAR-based canopy height metrics, vertical vegetation distribution, foliage height diversity, and satellite-derived Enhanced Vegetation Index (EVI). Cerulean warbler detections were identified at 14 of the 57 acoustic sensor locations. Habitat characteristics at detection and non-detection sites were compared using univariate statistical tests and logistic regression models. Habitat associations varied with spatial scale. At 25 m, detection sites had significantly lower vegetation cover within the 5–10 m height stratum, which was also the highest-ranked candidate predictor, whereas EVI was the highest-ranked predictor at 50 m. At the broad scale, occurrence records from the Global Biodiversity Information Facility (GBIF) were integrated with climatic, topographic, land-cover, and anthropogenic variables within a MaxEnt modeling framework. The model showed moderate predictive performance and identified land cover as a key predictor of habitat suitability, with deciduous forests showing the highest probability of occurrence. This study highlights how multi-modal sensor data can be integrated for biodiversity monitoring and habitat assessment. Full article

Mangrove prop root epifaunal communities support coastal biodiversity by providing habitat and refuge for associated sessile species and contributing to larval supply. Yet, regional surveys across latitudinal gradients in southeast Florida remain scarce. Understanding how these communities vary spatially and temporally is essential for forecasting biodiversity responses to environmental change. This study tested how macrobenthic assemblage composition and beta diversity on red mangrove prop roots vary from Key West to Melbourne, Florida. Presence-absence data from 32 sites in four zones were analyzed using beta diversity partitioning, indicator species analysis, and temporal turnover metrics. A strong biogeographic boundary near Palm Beach County delineated a species-rich southern pool, formed by the Florida Keys and Miami-Dade and characterized by tropical sponges. Of the 41 indicator species, 34 were associated with these two southern zones, while neither northern zone had exclusive indicators. Northern beta diversity was dominated by nested subsets of a larger species pool rather than species replacement. Over the three years, only Broward-Palm Beach gained species, while the other three zones lost species, though this was not statistically significant. These findings highlight the ecological significance of the potential biogeographic ecotones and provide a baseline study from which to gain insight into how climate-driven mangrove range expansion may impact coastal biodiversity and ecosystem resilience. Full article

Musk deer are forest-dwelling artiodactyls of high conservation value, but their wild populations remain under severe conservation pressure due to poaching driven by the demand for natural musk, together with habitat fragmentation and habitat loss. Efficient non-invasive image-based monitoring is therefore important for musk deer conservation; however, fine-grained detection of musk deer and visually similar artiodactyls in ecological images remains difficult because of background camouflage, vegetation occlusion, and high inter-class similarity. In this study, a fine-grained wildlife image dataset was constructed, and an RT-DETR-based framework, termed DSCF-DET, was proposed for automated detection in complex natural scenes. DSCF-DET integrates three task-oriented modules: DRPBlock for receptive-field-aware feature extraction, SASTE for sparse spatial encoding, and CBAFusion for cross-level feature fusion. On the constructed dataset, DSCF-DET achieved 91.4% precision, 86.2% recall, 88.7% F1-score, and 86.4% mAP50. Compared with RT-DETR-r18, it improved these metrics by 8.6, 12.1, 10.5, and 12.4 percentage points, respectively, while maintaining moderate model complexity. Visualization results showed more target-focused feature responses and reduced background-related activations. Cross-dataset experiments on an independent public wildlife dataset further suggested potential applicability to broader wildlife detection scenarios. These results indicate that DSCF-DET provides a computationally balanced approach for ecological image screening and intelligent musk deer monitoring. Full article

Wetlands have been noted to provide us with a wide range of ecosystem services—climate, water quality, flood regulation, and habitat benefits among others. The evidence that supports these service benefits is fairly well documented in the literature. However, it is often scattered across studies, metrics, and valuation methods, making it difficult for planners and landscape architects to use in physical projects and plans. The empirical result of this difficulty is that wetlands are often overlooked and under-utilized as part of broader ecosystem and land based planning solution sets. This paper addresses this usability deficiency using an ecosystem accounting framework that translates wetland science into spatially specific design and planning metrics. The framework follows the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA EA), an international statistical framework adopted by the UN to measure the environment’s contribution to the economy and human well-being. In our study, wetland extents in the state of Illinois are mapped on a statewide 30 m × 30 m grid. Individual wetland system conditions are estimated from floristic quality using a generalized additive model trained on 244 wetland sites that are part of the long-term Critical Trends Assessment Program (CTAP) at the Illinois Department of Natural Resources (IDNR). A floristic condition scalar, $w\left(x\right)$, provides a screening-level measure of ecological condition. It is applied only to a nonmonetary habitat-condition account and is not used to scale the monetary service accounts. Climate regulation, water purification, and flood regulation are quantified through service-specific physical models and reported as carbon-price and replacement-cost proxies. Under the central scenarios, these three monetary proxy accounts produce a combined subtotal of USD 1408.6 million per year. The annualized surface-storage replacement-cost scenario accounts for USD 1048.5 million, load-gated nitrogen-removal replacement cost for USD 338.0 million, and the climate-regulation carbon-price proxy for USD 22.1 million. These estimates are planning proxies rather than observed market benefits or realized avoided damages. The separate habitat-condition account totals 198,280 condition-weighted hectares, with a statewide mean $w\left(x\right)$ of 0.502, and is not added to the monetary subtotal. Climate performance varies across wetland types: methane emissions cause some emergent wetland categories to function as net greenhouse-gas sources under the central assumptions, while other categories remain net sinks. A protection-gap analysis shows that Tier 4 wetlands contain 70.1% of the classified vegetated-wetland area and 67.0% of the condition-weighted habitat area within the classified domain. Broadly, the framework demonstrates how ecosystem accounting can translate fragmented wetland evidence into consistent spatial metrics for planning. The resulting layers support statewide screening, comparison among wetland categories, conservation and restoration screening, and protection-gap analysis while preserving the distinction between monetary service proxies and nonmonetary ecological condition. Full article

Municipal records of human–wildlife conflict are widely available but remain underused for identifying conflict-prone urban interfaces and supporting preventive management. This study compared reported human–wild boar (Sus scrofa) conflict patterns and the land-use composition of high-density report zones in Olsztyn, Poland, in 2010 and 2020. Municipal records of wild boar presence, complaints, and interventions were matched to street-network features and integrated with harmonized land-use data in a geographic information system. Kernel density estimation delineated high-density report zones, Getis–Ord Gi* analysis characterized street-segment clustering, and land-use overlay and descriptive compositional metrics quantified differences between years. High-density zones shifted from an extensive, peripheral configuration in 2010 to a markedly more compact pattern integrated into the urban fabric in 2020. Proportional shares increased for transport (+12.64 percentage points), residential (+10.12), and recreational areas (+2.40), while industrial and wasteland areas (−13.13), grasslands (−6.33), and forests (−4.85) decreased. These findings indicate a reorganization of reported conflict geography, not changes in wild boar abundance, habitat preference, movement behavior, or synurbization. Routinely collected municipal records can support targeted monitoring, traffic-risk mitigation, waste management, green-space planning, and preventive urban wildlife management. Full article

Understanding how land-use change affects habitat fragmentation and connectivity is essential for assessing landscape degradation and conservation effectiveness in protected areas globally. It is particularly acute in tropical protected areas where anthropogenic pressures are intensifying. This study investigated long-term landscape dynamics, forest fragmentation, and structural connectivity in the Bena Mulumbu Hunting Domain, a Category VI protected area located in the Miombo woodland region of southeastern Democratic Republic of the Congo. Landsat imagery acquired in 1995, 2005, 2015, and 2025 was classified using the Random Forest algorithm into six land-cover classes (Miombo woodland, savanna, agricultural land, mining areas, built-up/bare land, and water bodies) to quantify land-cover changes over 30 years. Landscape composition was assessed using the percentage of landscape (PLAND), Shannon diversity metrics, and transition analyses. At the same time, fragmentation and structural connectivity of Miombo woodland were evaluated along an edge-to-core gradient (0–2 km, 2–4 km, 4–6 km, and >6 km) using landscape metrics. Results showed that savanna remained the dominant land-cover type throughout the study period. However, the landscape underwent progressive reorganization characterized by recurrent transitions among Miombo woodland, savanna, and agricultural land, leading to increased spatial heterogeneity. Fragmentation analyses revealed significant spatial differences in total core area among zones (Kruskal–Wallis: H = 8.12, p = 0.044); however, after normalization by zone area, no consistent edge-to-core gradient was observed for core habitat proportion, indicating that raw differences primarily reflect zone size rather than a systematic ecological gradient. Despite increasing fragmentation, structural connectivity remained high across the hunting domain. The CONNECT index increased significantly from the edge toward the core zone (p = 0.003), highlighting better-connected forest networks in interior sectors. These findings suggest that the Bena Mulumbu Hunting Domain is experiencing an intermediate stage of landscape transformation, where forest fragmentation is evident but has not yet resulted in widespread connectivity loss. Maintaining existing forest cores and connectivity corridors should therefore be prioritized to prevent further degradation of ecological integrity. These findings challenge the assumption that landscape degradation in protected tropical Miombo woodlands necessarily follows a simple edge-to-core gradient. Full article

Understanding how environmental heterogeneity shapes animal movement is a central goal of movement ecology, yet movement metrics derived from GPS data often integrate multiple behavioural states, complicating ecological interpretation. We analysed 30,323 GPS observations from six juvenile white storks (Ciconia ciconia) to disentangle the effects of habitat and behavioural state on movement speed and altitude above ground level. Behaviour was classified into resting, local movement, and flight based on speed thresholds and incorporated into linear mixed-effects models alongside habitat. Behavioural state was the dominant determinant of movement metrics. Relative to resting, flight increased speed by approximately 43 km h⁻¹ and altitude above ground level by approximately 566 m, whereas habitat effects were significant but substantially smaller. Differences in movement metrics among habitats were largely explained by variation in the distribution of behavioural states, with cropland dominated by resting behaviour and water associated primarily with flight. Sensitivity analyses based on alternative behavioural thresholds, leave-one-individual-out models, flight-only analyses, and alternative treatments of altitude above ground level produced consistent results. These findings demonstrate that habitat-associated variation in movement metrics is primarily mediated by behavioural state rather than representing a direct consequence of habitat alone. Our results highlight the importance of explicitly accounting for behavioural heterogeneity when linking movement to environmental conditions and provide a refined interpretation of the energy landscape framework. Full article

Research on insect biodiversity in arid and semi-arid agroecosystems of Central Asia remains limited, hindering the development of effective insect conservation strategies and integrated pest management (IPM) programs. This study examined insect community structure, diversity, and seasonal dynamics across three habitat types in Kekedala City, in the autonomous region of Ili Kazakh, Xinjiang, China. Solar-powered ultraviolet LED light traps (350–420 nm) were used for sampling from April to August 2024. A total of 63,090 individuals of 83 species, 57 families and 72 genera of insects were collected. Diversity indices calculated using the complete species dataset showed highest diversity in the riparian corridor (L3: H′ = 3.42, D = 0.962, S = 62), intermediate diversity in the agro-horticultural garden (L2: H′ = 3.18, D = 0.948, S = 54), and lowest diversity in the xerophytic shrubland (L1: H′ = 2.97, D = 0.935, S = 47). A supplementary analysis targeted a shared core assemblage of 17 species, defined as taxa captured at all three sampling sites with cumulative total abundances ranging from 50 to 100 individuals. This subset exhibited near-uniform diversity metrics across habitats (H′ = 2.83, D = 0.941, Pielou’s evenness J′ ≈ 1.0), a pattern driven by the prevalence of habitat generalists rather than capturing site-specific full community diversity. Two abundant pest species, Helicoverpa armigera (Hübner, 1808) (cotton bollworm, 5209 individuals, 8.3% of total) and Oryctes rhinoceros (Linnaeus, 1758) (rhinoceros beetle, 5206 individuals, 8.2% of total), both peaked in abundance in July–August; O. rhinoceros is associated with local ornamental and date palm plantings in the study area. In total, 15 major pest species were recorded. Mitochondrial cytochrome c oxidase subunit I (COI) DNA barcoding confirmed two new national insect records for China: Stethoconus pyri (Mella, 1861) (Hemiptera: Anthocoridae) and Pinacoplus didymogramma (Hampson, 1907) (Lepidoptera: Noctuidae). This study provides the first comprehensive baseline inventory of the insect fauna of Kekedala City. Descriptive ordination analyses suggest that habitat vegetation complexity may exert a stronger filtering effect on insect assemblage composition than the relatively small seasonal variation in temperature and humidity observed during the sampling period. As only a single trap was deployed within each habitat with no within-site replication, all cross-habitat community patterns reported herein are strictly descriptive and require validation via replicated field sampling in future research. Full article

Central and northern Kazakhstan contain a substantial but incompletely synthesised component of the country’s Apiaceae flora. We combined targeted field surveys conducted in 2023–2025, taxonomic revision of 1243 herbarium specimens, and a presence–absence matrix for six historical floristic units. The taxonomic audit reconciled accepted names and synonyms, documented three entries without unit-confirmed incidence, and produced an analytical dataset of 79 taxa in 38 genera. Recorded richness ranged from 33 taxa in Kokshetau to 54 in the Western Uplands. Fourteen taxa occurred in all six units, and 21 were recorded in only one unit. Pairwise Jaccard similarity ranged from 0.333 to 0.683, and species replacement contributed 82% of the mean pairwise dissimilarity. The Western Uplands had the highest within-study range-rarity richness, whereas Ulytau had the largest local contribution to beta diversity. The broader 81-taxon ecological checklist was dominated by perennial herbs and xerophytes and documented medicinal, fodder, aromatic, food, melliferous, and ornamental uses. These metrics are conditional on the six-unit incidence matrix: they do not estimate local abundance, population size, national rarity, endemism, threat status, or harvest capacity. The principal contribution is therefore a voucher-informed, nomenclaturally harmonised regional baseline that identifies records and habitats requiring georeferenced verification and population-level follow-up. Full article

Climate change is altering the potential distribution of many butterfly species, but climatic suitability alone may be insufficient to sustain the long-term viability of host-dependent butterflies. For papilionid butterflies, larval feeding and development depend on specific host plants; however, the extent to which host plant availability modifies projected suitable habitats under climate change remains unclear. Here, we first selected three threatened papilionids in China: Luehdorfia chinensis Leech, Teinopalpus imperialis Hope, and Troides aeacus C. & R. Felder (Lepidoptera: Papilionidae). Then we constructed two sets of ensemble distribution models: an environment-only model (Env-only) and a host-inclusive model (Env+Host), and compared the resulting projections for each species under current and future climate scenarios (the 2050s, 2070s, and 2090s under SSP1–2.6, SSP2–4.5, and SSP5–8.5). Landscape pattern metrics were further employed to assess changes in the spatial structure of suitable habitats. In the Env-only models, precipitation-related variables were identified as the primary factors constraining the potential distributions of all three species. After it was incorporated, host plant availability became the most important predictor for L. chinensis (41.54%) and T. aeacus (51.53%). In contrast, precipitation of the warmest quarter remained the dominant predictor for T. imperialis (36.00%). Compared with the Env-only models, the Env+Host models forecast smaller suitable habitats for all three species under all scenarios. T. imperialis exhibited the largest host-related contraction under SSP5–8.5 2090s (68.44%). Additionally, landscape pattern metrics indicated increased habitat fragmentation for L. chinensis and T. imperialis, but enhanced spatial continuity for T. aeacus. Overall, the three species have pronounced interspecific differences in their responses to the incorporation of host plant availability. These findings suggest that biotic factors should be incorporated into suitable habitat assessments for threatened papilionids in China. Accordingly, conservation strategies should be tailored to their host specificity, climate-driven distribution, and habitat requirements. Full article

Hainan Tropical Rainforest National Park is a prime example of a “continental island” tropical rainforest and holds significant value for biodiversity conservation. However, human activities have led to frequent changes in land use and increased habitat fragmentation within the park; a precise analysis of the underlying mechanisms is necessary for ecological restoration. Consequently, drawing upon land-use data from 2000 to 2020, this study coupled multi-dimensional fragmentation metrics (CFI, AFI, and SFI) with the XGBoost-SHAP framework to systematically unravel the spatiotemporal dynamics and underlying driving mechanisms of landscape fragmentation in Hainan Tropical Rainforest National Park. Our findings revealed that the spatial configuration of fragmentation predominantly propagated along river networks and transport corridors, accompanied by a fluctuating ‘decline–rise–decline’ temporal trajectory. Notably, the XGBoost-SHAP attribution highlighted a distinct temporal shift in the dominant drivers: fragmentation was primarily mitigated (negatively driven) by NDVI between 2000 and 2010 but was subsequently exacerbated (positively driven) by GDP growth from 2010 to 2020. The findings of this study provide data support and a scientific basis for ecosystem restoration and land use planning in Hainan Tropical Rainforest National Park. Full article

High-density rodent populations cause severe habitat degradation and ecological imbalance in natural grasslands through intense foraging and burrowing activities. However, dynamically monitoring these small mammals and assessing their large-scale damage using traditional ground surveys alone is challenging. In this study, we evaluated rodent damage severity in alpine meadows and typical steppe by proposing an integrated framework that combines ground, unmanned aerial vehicle (UAV), and satellite data. Using data from 36 plots per grassland type, we extracted a suite of ecological parameters, including aboveground biomass, vegetation cover, community height, rodent burrow density, and plant diversity metrics, to construct a plot-scale Rodent Damage Index ($RDI$). This $RDI$ was then linked with a satellite-derived Remote Sensing Ecological Index ($RSEI$) to model and map damage severity at the regional scale. Separate linear regression models were developed for the two grassland types. The alpine meadow model exhibited better model fit and predictive performance (fitting $R^2$ = 0.762, RMSE = 0.136; LOOCV $R^2$ = 0.729, RMSE = 0.145, 95% CI: 0.580–0.886) than the typical steppe model (fitting $R^2$ = 0.574, RMSE = 0.198; LOOCV $R^2$ = 0.478, RMSE = 0.214, 95% CI: 0.279–0.787), highlighting that the predictive relationship model performance differs significantly between grassland types. Our findings demonstrate that integrating multi-source, cross-scale spatial data is an effective approach for assessing rodent damage. Furthermore, these results indicate that rodent damage assessment should be grassland-type-specific to ensure accuracy and support targeted rodent damage management planning. Full article

Global biodiversity loss undermines ecological integrity and natural capital, yet many biodiversity–finance mechanisms still reward activities rather than verified ecological outcomes, reducing accountability and investor confidence. We introduce Verifiable Nature Units (VNUs), a standardised, outcome-based framework that approximates annual change in ecological integrity at 1 km² resolution. The framework combines an anthropogenic disturbance-based Habitat Intactness (HI) index, derived from remote sensing and spatial datasets, with an Indicator Species (IS) layer estimated through repeated on-site monitoring of selected indicator species across at least three functional guilds. Baselines, tolerance buffers, eligibility criteria, and independent verification requirements translate measured outcomes into investable units. In Majete Wildlife Reserve (711 km²), southern Malawi, HI remained consistently high (0.99) from 2018–2024, with localised declines linked to park infrastructure and extreme weather events. Occupancy analyses for six indicator species across four functional guilds showed high and stable park-level performance from 2021–2025, resulting in all 711 potential VNUs being eligible for issuance in 2024/5. The VNU is not an exhaustive measure of ecological integrity; it is a practical, auditable MRV approach for outcome-based biodiversity finance that can adapt as monitoring systems and ecological metrics mature. Full article