Search Results (412)

Cross-ecosystem transmission of plant pathogens from crops to natural forests is increasingly recognized as a key factor in disease emergence and biodiversity loss. Agricultural systems serve as major sources of inoculum, with landscape interfaces—such as crop–forest edges, riparian zones, abandoned orchards, and nursery–wildland transitions—acting as active epidemiological gateways. Biological vectors, abiotic dispersal, and human activities collectively enable pathogen movement across these boundaries. Host-range expansion, recombination, and hybridization allow pathogens to infect both cultivated and wild hosts, leading to generalist and recombinant lineages that survive across diverse habitats. In natural ecosystems, such introductions can alter community composition, decrease resilience, and intensify the impacts of climate-driven stress. Advances in molecular diagnostics, genomic surveillance, environmental DNA, and remote sensing–GIS (Geographic Information System) approaches now enable high-resolution detection of pathogen flow across landscapes. Incorporating these tools into interface-focused monitoring frameworks offers a pathway to earlier detection, better risk assessment, and more effective mitigation. A One Health, landscape-based approach that treats agro–wild interfaces as key control points is essential for reducing spillover risk and safeguarding both agricultural productivity and the health of natural forest ecosystems. Full article

Understanding the variations in twig and leaf morphologies and photosynthetic physiological characteristics of riparian forest plants in heterogeneous habitats is of great significance for revealing their phenotypic plasticity mechanisms and ecological adaptation strategies. In this study, the riparian forest plant Periploca sepium Bunge was selected as the research object. According to the canopy light environment experienced by the P. sepium population, three habitats were established: under-canopy, gap, and full-sun areas. We studied the twig and leaf morphological and photosynthetic characteristics of P. sepium under heterogeneous light environments, as well as the relationships between these two aspects. Plants in the under-canopy area developed long and thick twigs with few large leaves, coupled with high actual photosynthetic efficiency of photosystem II (Y(II)) and low non-photochemical quenching (NPQ), whereas those in the full-sun area exhibited the opposite covariation strategy. Significant correlations between twig and leaf morphologies and photosynthetic physiological characteristics were found across all habitats. The coordinated variations in twig and leaf morphologies and photosynthetic physiology of P. sepium embody a resource investment trade-off strategy that plants have evolved through long-term adaptation to heterogeneous light environments. Full article

Forest cover dynamics strongly influence ecological integrity and resource sustainability, particularly in ecotonal landscapes, where vegetation is highly sensitive to climate variability, long-term climate change, and anthropogenic disturbances. This study examined Forest Land (FL), representing all areas of dense, canopy-forming woody vegetation with forest-like structure, aggregated from SANLC classes, in relation to eight other land cover classes across three periods: 1990–2014, 2014–2022, and 1990–2022. The study used South African National Land Cover datasets and the TerrSet–LiberaGIS Land Change Modeller to quantify changes in magnitude, direction, and source–sink relationships. Analyses included post-classification comparison to determine spatial changes, transition matrices to identify land-cover conversions, and asymmetric gain–loss metrics to reveal sources and sinks of forest change. The result shows that between 1990 and 2014, forests remained marginal and fragmented in the eastern central part of the study area, while shrubland increased from 40.4% to 60.2% at the expense of grasslands, cultivated land, bare land, wetlands, and forest land. From 2014 to 2022, FL regeneration was pronouncedly increased from 2% to 6%, especially along riparian corridors and reservoir margins, coinciding with shrubland decline (99.3%) and grassland recovery (261.2%). Over the entire 1990–2022 period, FL increased from 2.4% to 6% expanding into bare land, cultivated land, grassland, shrubland, and wetlands. Asymmetric analysis indicated that forests acted as a sink during the first period but as a source of ecological resilience in the second and final. These findings demonstrate strong vegetation feedback to hydrological and anthropogenic drivers. Overall, the findings underscore the potential for forest recovery to enhance biodiversity, ecosystem services, carbon storage, and hydrological regulation, while identifying priority areas for riparian conservation and integrated catchment management. Full article

Rapid human population growth accelerates biodiversity loss through urban habitat fragmentation, yet ecologically informed urban planning can mitigate these effects. This study evaluates whether and how vegetation characteristics, as captured by Earth observation data varies across forest habitats in a small Mediterranean city in Italy. The Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) for the Functional Urban Area of Campobasso were derived from multitemporal Landsat 8 imagery (2020–2023) acquired during the growing season and combined with elevation data to account for topographic gradients. Different forest habitats were identified using the regional coeval Carta della Natura (Map of Nature) and were sampled by a random stratified strategy yielding more than 900,000 observations. A linear mixed-effects model was used to model NDVI as a function of NDMI, LST, elevation, and habitat type, while accounting for temporal and spatial dependencies. The model explained a large proportion of NDVI variability (marginal R² = 0.75; conditional R² = 0.85), with NDMI emerging as the strongest predictor, followed by weaker effects of LST and elevation. Habitat differences were also evident: oak-dominated forests (i.e., Quercus frainetto, Q. cerris, and Q. pubescens dominated habitats) exhibited the highest NDVI values, while coniferous plantations (i.e., Pinus nigra dominated habitat) had the lowest; forests dominated by Robinia pseudoacacia and riparian Salix alba showed intermediate vegetation greenness values. These results highlight the ecological importance of oak forests in Mediterranean urban landscapes and demonstrate the value of satellite-based monitoring for capturing habitat variability. The reproducible workflow applied here provides a scalable tool to support habitat conservation and planning in urban environments, also accounting for impending climate change scenarios. Full article

Soil organic carbon (SOC) plays a crucial role in climate change mitigation by regulating atmospheric CO₂ and maintaining ecosystem balance; however, its stability is influenced by land use in anthropized areas such as the tropical Andes. This study developed a dynamic compartmental model based on ordinary differential equations to simulate carbon fluxes among litter, humus, and microbial biomass under four land uses in the Las-Piedras River basin (Popayán, Colombia): riparian forest (RF), ecological restoration (ER), natural-regeneration (NR), and livestock (LS). The model includes two decomposition rate constants: k₁, for the transformation of fresh organic matter, and k₂, for the turnover of humified organic matter. It was calibrated using field data on soil physicochemical and biological properties, as well as carbon inputs and outputs. The results showed clear differences in SOC dynamics among land uses: RF had the highest SOC stocks (148.7 Mg ha⁻¹) and microbial biomass, while LS showed the lowest values and the greatest deviation due to compaction and low residue input. The humus fraction remained the most stable pool (k₂ ≈ 10⁻⁴ month⁻¹), confirming its recalcitrant nature. Overall, the model reproduced SOC behavior accurately (MAE = 0.01–0.30 Mg ha⁻¹) and provides a framework for improving soil carbon management in mountain ecosystems. Full article

Individual tree segmentation (ITS) is essential for forest inventory, health assessment, carbon accounting, and evaluating restoration efforts. Populus euphratica, a widely distributed desert riparian tree species found along the inland rivers of Central Asia, presents challenges for accurately identifying individual trees and conducting forest inventories due to its complex stand structure and overlapping crowns. To determine the most effective ITS approach for P. euphratica, we benchmarked six commonly used tree segmentation approaches for terrestrial laser scanning (TLS) data: canopy height model segmentation (CHMS), point cloud segmentation (PCS), comparative shortest-path algorithm (CSP), stem location seed point segmentation (SPS), deep-learning trunk-based segmentation (TBS), and leaf–wood separation-based segmentation (LWS). All methods followed a unified preprocessing and tuning protocol. We evaluated these methods based on tree-count accuracy, crown delineation, and structural attributes such as tree height (H), diameter at breast height (DBH), and crown diameter (CD). The results indicated that the TBS and LWS methods performed the best, achieving a mean tree-count accuracy of 98%, while the CHMS method averaged only 46%. These two methods provide the basic branch structure within the tree crown, reducing the likelihood of incorrect segmentation. Validation against field-measured values for H, DBH, and CD showed that both the TBS and LWS methods achieved accuracies exceeding 80% (RMSE = 0.8 m), 86% (RMSE = 0.02 m), and 73% (RMSE = 0.7 m), respectively. For TLS data in P. euphratica desert riparian forests, these two methods provide the most reliable results, facilitating rapid plot-scale inventory and monitoring. These findings establish a practical basis for conducting high-accuracy inventories of Euphrates poplar desert riparian forests. Full article

The Central Valley of California provides critical wintering habitat for Sandhill Cranes (Antigone canadensis), which rely on wetlands, grasslands, and grain crops to meet their energetic needs. However, temporary row crops that support Sandhill Cranes and other wintering birds are ostensibly being replaced by permanent woody crops, which offer little value for wetland and grassland-dependent species. To better understand how landcover changes may be affecting habitat availability for these wintering cranes, we analyzed landcover trends within priority crane wintering areas from 2008 to 2023. We employed a mixed-methods approach that allowed us to describe both linear and non-linear trends over time and across regions. Our findings indicate a significant decrease in landcover types suitable as crane habitat over the 16-year period ($\tau$ = −0.90, p < 0.001), with an average annual decline of approximately −1.15 ± 0.21% (B± 95% CI). The best-fit trendline showed that habitat suitability in priority wintering areas decreased from over 81% in 2008 to under 65% in 2023. Specifically, grasslands, rice fields, and alfalfa acreage declined across priority wintering areas, while woody landcover—including orchards, vineyards, and riparian forest breaks—increased significantly ($\tau$ = 0.88, p < 0.001; B = 1.14 ± 0.20%). These landscape-level changes may constrain the regional carrying capacity for Sandhill Cranes and reduce their overall resilience. Full article

Functioning as a critical ecotone between terrestrial and aquatic ecosystems, riparian zones exhibit soil enzyme activities that serve as key biomarkers of their nutrient cycling processes. However, despite considerable focus on riparian soil properties, the dynamics and underlying drivers of these enzymatic activities are not yet fully characterized. To this end, soils were systematically sampled across varying widths and depths from three representative riparian zones to quantify the driving forces of physicochemical properties on enzyme activity dynamics. The results showed that the soil enzyme activity was highest in the forest riparian zone and lowest in the farmland riparian zone, with average enzyme activities of 37.95 (μmol·g⁻¹·h⁻¹) and 26.85 (μmol·g⁻¹·h⁻¹), respectively. The width of the riparian zone changes the spatial distribution of soil enzyme activity. The soil enzyme activity is higher in the land edge area far from the river (profile-1) and lower in the water edge area near the river (profile-4), with average enzyme activities of 47.4384 (μmol·g⁻¹·h⁻¹) and 17.0017 (μmol·g⁻¹·h⁻¹), respectively. Moreover, soil water content (SWC) has a strong impact on enzyme activity changes. The increase in soil depth reduces soil enzyme activity, with enzyme activity in the 0–20 cm soil layer being 1.5 times higher than in the 20–50 cm soil layer. Meanwhile, the primary factors influencing changes in soil enzyme activity have gradually shifted from total nitrogen (TN), nitrate nitrogen (NO₃-N), and soil organic carbon (SOC) to the sole control of SOC. Research has shown that human influence strongly interferes with soil enzyme activity in riparian zones. The width of the riparian zone and soil depth serve as key drivers of the spatial distribution of soil enzyme activity by modulating soil environmental factors. The patterns revealed in this study indicate that maintaining appropriate riparian zone width and reducing anthropogenic disturbances can enhance nutrient cycling dynamics at the micro-scale by increasing soil enzyme activity. This process is crucial for strengthening the riparian zone’s macro-level ecosystem services, particularly by effectively enhancing its capacity to sequester and transform nutrients like nitrogen and phosphorus from agricultural nonpoint sources, thereby safeguarding downstream water quality. Consequently, soil enzyme activity serves as a key indicator, providing essential scientific basis for assessing riparian health and guiding ecological restoration efforts. Full article

Urban–rural mountainous regions face persistent challenges in reconciling ecological conservation, cultural heritage preservation, and recreational demands, all of which are vital to advancing social sustainability. This study develops an integrated corridor framework for the Qinling–Daba region that couples ecological, heritage, and recreational networks within a socially sustainable planning perspective. Ecological sources were identified using Morphological Spatial Pattern Analysis (MSPA) combined with connectivity indices (IIC, PC, dPC). Heritage and recreation resources were inventoried through field surveys and prioritized using the Analytic Hierarchy Process (AHP). Function-specific corridors were modelled with a Minimum Cumulative Resistance (MCR) approach, and the three networks were synthesized through GIS overlay and hotspot analysis. The results indicate that there are 19 ecological sources and 28 corridors, 34 heritage nodes and 41 corridors, and 29 recreation nodes and 50 corridors. The composite network comprises 69 key nodes and 141 segments, classified into four node categories and three corridor types. Derived planning directives include graded buffer zones, continuity of riparian and forest belts, remediation of breakpoints with wildlife-friendly crossings, and universal accessibility standards for high-demand sites. By aligning ecological integrity, cultural values, and equitable access, the proposed framework offers a reproducible pathway to integrate people and places through multifunctional corridors. Beyond regional application, this research provides transferable insights for socially sustainable governance of urban–rural built environments in mountainous territories, supporting the achievement of Sustainable Development Goal 11. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

The Lower Rio Grande Valley (LRGV) of Texas is a biodiversity hotspot due to its high alpha, beta, and gamma diversity and high regional endemism, which are at high risk of degradation. The region has lost 95% of its native thornforest habitat primarily due to agricultural and urban expansion. This study aims to evaluate the current vegetative structure and composition of restored thornforest sites located in the LRGV to identify restoration methods and site characteristics that affect forest restoration outcomes. Twelve restored thornforest sites were selected for this study that varied in time since restoration, patch size, degree of isolation, and method of restoration. Canopy, understory, and ground layer vegetation were evaluated at six survey points per restored site (n = 72), and 17 environmental variables were incorporated into univariate and multivariate analyses to identify factors influencing restored plant communities. Actively restored sites showed higher overall richness, abundance, and diversity than passively restored sites. More isolated patches had higher overall richness, abundance, and diversity, and longer times since restoration began increased richness and diversity. Higher abundances of Urochloa maxima, an invasive grass, altered community composition and reduced diversity in each forest layer and overall and reduced richness in the canopy and ground layers. Important considerations for thornforest restoration in the LRGV should include invasive grass prevalence; proximity to riparian and seasonal wetland habitats; landscape factors that influence water availability; and patch geography, including shape, size, and proximity to other forest patches. Full article

The desert riparian forest oasis, dominated by Populus euphratica and Tamarix chinensis, is an important barrier to protect the economic production and habitat of the Tarim River Basin. However, there is still a lack of high-precision spatial distribution data of desert ri-parian forest species below 10 m. The recently launched PlanetScope CubeSat constella-tion, which provides daily earth observation imagery with a resolution of 3 m, offers a highly favorable dataset for mapping the high-resolution distribution of P. euphratica and T. chinensis and an unprecedented opportunity to explore the optimal phenology window to distinguish between them. In this study, time-series PlanetScope images were first used to extract phenological metrics of P. euphratica, dividing the annual life cycle into four phenology windows: duration of leaf expansion (DLE), duration of leaf maturity (DLM), duration of leaf fall (DLF), and duration of the dormancy period (DDP). The random forest model was used to obtain the classification accuracy of 16 phenological window combinations. Results indicate that after gap filling of vegetation index time series, the identification accuracy for P. euphratica and T. chinensis exceeded 0.90. Among individual phenology windows, the DLE window exhibited the highest classification accuracy (average F1-score 0.87). Among the two phenology window combinations, the DLE-DLF and DLE-DLM windows have the highest classification accuracy (average F1-score 0.90). Among the three phenology window combinations, DLE-DLM-DLF displayed the highest classification accuracy (average F1-score 0.91). Nevertheless, the inclusion of features within the DDP window led to a decrease in accuracy by 1–2% points, which was unfavorable for discriminating tree species. Additionally, features observed during the phenology asynchrony period were found to be more valuable for distinguishing between tree species. Our findings highlight the potential of PlanetScope constellation imagery in tree species classification, offering guidance for selecting optimal image acquisition timing and identifying the most valuable images within time series data for future large-scale tree mapping. Full article

The Bełchatów Lignite Mine (BLM) in central Poland, one of Europe’s largest Neogene lignite deposits, provides key insights into palaeofloral evolution. Located in the Kleszczów Graben, the BLM consists of four distinct lithological units: subcoal, coal, clayey-coal, and clayey-sandy units. The study presents a palynological investigation of 31 samples from all units, identifying 78 sporomorph taxa, including 10 plant spores, 15 gymnosperm pollen, and 53 angiosperm pollen taxa. Pollen grains from angiosperms and gymnosperms were consistently observed in all samples, while plant spores were scarce. The analysis reveals three distinct palynological zones, reflecting shifts in vegetation. The first zone is characterized by swamp, riparian, and mixed mesophilous forests, dominated by Taxodium/Glyptostrobus, Ulmus, Carya, Engelhardia, Pterocarya, and Quercus. In the second zone, slightly cooler climatic conditions led to the decline of Taxodium/Glyptostrobus and Alnus, indicating a deterioration of swamp forests. The third zone marks a subsequent recovery of these forests. Palaeoclimatic interpretations indicate three phases: a subtropical-humid climate during the Early Miocene, fluctuating humidity in the late Early Miocene, and a transition to a warm-temperate and humid climate in the Late Miocene. Full article

Interspecific hybridization in forest trees is common and can have important implications for ecology, evolution, and the conservation of forest habitats. Hybridization often results in greater genetic diversity and opportunities for backcrossing with one or both parents, which may introduce novel genotypes that influence biodiversity and ecosystem processes. However, the extent of hybridization, direction of backcrossing, and overall survival and performance of hybrids is often poorly understood, leading to inaccurate assessments of the role hybrids may play in forest ecology and conservation. Here, we investigate interspecific hybridization and the extent and direction of backcrossing between two species, Populus fremontii (S. Watson) and P. angustifolia (E. James ex Torr.), which are broadly distributed along riparian corridors in the riparian ecosystems of the southwestern United States. Using molecular assays of six putative hybrid zones and a common garden trial we test the following: (1) whether putative hybrids show evidence of genetic intermediacy relative to the parent species; (2) if confirmed hybrids exhibit higher genetic diversity than either parent species; (3) the extent and direction of backcrossing (uni- or bi-directional) within each site; and (4) whether hybrid derivatives show evidence of higher survival and performance in an experimental common garden consisting of both parents and hybrids that were propagated from the six sites. Our results confirm genetic intermediacy in all six sites, but with varying degrees of backcrossing, genetic diversity, and structure. All six locations reveal extensive bidirectional backcrossing to both parent species, a result that contrasts with previous findings, which suggest that backcrossing is predominantly unidirectional between the two species. Results from our common garden trial indicate that hybrids do not have higher survival or out-perform the parent species, suggesting that heterosis may be limited in this system, or that long-term assessments beyond the duration of our field experiment may be required. Results from this study improve our understanding of the frequency of hybridization, and the associated backcrossing in this system, and provide land managers with information on how hybrids may be employed for the long-term preservation of riparian habitats undergoing rapid environmental change. Full article

Mapping forest tree species is vital for the habitat assessment, ecosystem services estimation, and implementation of European environmental policies such as the Habitats Directive. This study explores how repeated satellite observations over time, known as multitemporal data, can improve the mapping of tree species in riparian forests. Although many studies have shown that the use of multitemporal data improves tree species classification accuracies, there is a lack of research on how different multitemporal models perform compared to each other. We compared three multitemporal remote sensing approaches using Sentinel-2 imagery to map tree species within the Austrian riparian Natura 2000 site, Salzachauen. Seven tree species (five native and two non-native riparian species) were mapped using random forest models trained on a dataset of 444 validated tree samples. The three multitemporal approaches tested were: (i) multi-date image stacking, (ii) seasonal mean composites, and (iii) spectral–temporal metrics (STMs). The three approaches were compared to twenty single-date image classifications. The multitemporal models achieved 62 to 65% overall accuracy, while the median accuracy of single-date classification was 50% (SD = 6%). The seasonal model obtained the highest overall accuracy (65%), with F1 scores exceeding 73% for four individual species. However, differences among the three multitemporal approaches were not statistically significant. The mapping of native versus non-native riparian species achieved 92% accuracy. We evaluated misclassification patterns of individual species according to the two riparian forest habitats, 91E0* and 91F0, as defined in Annex I of the Habitats Directive. Most omission and commission errors occurred between species within the same habitat type. These findings underline the potential of translating tree species mapping to habitat-type classifications and the need to further explore the capabilities of satellite remote sensing to fill data gaps in Natura 2000 areas. Full article