Search Results (445)

The Maceió Metropolitan Region (MMR) has undergone significant changes due to public policies that promote urban growth. This has intensified environmental impacts, adversely affecting local communities. The Remote Sensing Ecological Index (RSEI), a remote sensing-based metric, was used to evaluate ecosystem quality. The study assessed annual ecosystem quality in the MMR, Alagoas, using RSEI values from MODIS data spanning 2000 to March 2024/2025. To ensure data quality and reliable results, all MODIS data underwent rigorous quality control, including the exclusion of pixels affected by cloud cover, shadows, and missing values. Only data points meeting established MODIS quality assurance standards were used. Annual RSEI values varied considerably, from 0.449 in 2005 to 0.636 in 2014. Most areas in the MMR are classified as moderate quality (0.4 < RSEI < 0.6), particularly in central and eastern sectors. The lowest-quality regions (0 < RSEI < 0.4) are concentrated in the east—including Maceió, the hub city—and the west, largely due to high population density. The Sen-Slope Estimator and trend analysis revealed significant trends in the hub city, with positive trends in the northeast. Urban expansion has led to the loss of native vegetation, including sugarcane fields and remnants of the Atlantic Forest. The Pettitt test identified a structural change in 2018, likely linked to environmental violations related to the Braskem petrochemical industry and salt extraction in Maceió. Full article

Misiones, Argentina, holds one of the largest remnants of the Atlantic Forest, with almost 1.4 million hectares of native forest, representing a critical landscape for sustainable biodiversity conservation. However, connectivity across this ecoregion is increasingly threatened by habitat conversion, landscape fragmentation, and poaching pressures that extend beyond protected area boundaries, undermining long-term sustainability of wildlife populations. Using conservation detection dogs, we located, collected, and genetically confirmed 198 scats belonging to four game species: 20 lowland tapir (Tapirus terrestris), 72 white-lipped peccary (Tayassu pecari), 55 collared peccary (Pecari tajacu), and 51 Azara’s agouti (Dasyprocta azarae). Analyses examining species-specific habitat associations emphasized the importance of extending inference beyond point locations to encompass species’ home ranges, with native forest consistently identified as a key component of habitat use. The high prevalence of scats in mosaics of human-modified habitats outside protected areas, especially along their borders, underscores the importance of managing these areas as part of a broader sustainable landscape matrix. While native forest fragments outside of protected areas may serve as important refugia supporting species persistence, their contribution to sustainable management depends on reducing poaching pressure across these landscapes. There is an urgent need to expand antipoaching efforts beyond protected areas and across the Atlantic Forest in the Green Corridor of Misiones while preventing ongoing deforestation and the expansion of monoculture plantations. Achieving sustainable wildlife management in this region will require integrated strategies that promote sustainable land use, conservation planning, and rural development. Full article

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas. Full article

Accurate forecasts of tropical cyclone (TC) track and intensity with a sufficient lead time are critical for disaster preparedness and risk mitigation. Traditional numerical weather prediction models, while fundamental to operational forecasting, often exhibit systematic errors due to limitations in observations, physical parameterizations, and model resolution. In recent years, machine learning (ML) and deep learning (DL) approaches have emerged as promising data-driven alternatives for improving TC forecasts. This study presents a comparative evaluation of six ML and DL models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN)—for forecasting TC track and intensity in the North Atlantic basin. The models are trained using the National Hurricane Center’s (NHC) HURDAT2 best-track dataset for storms from 1990 to 2019 and evaluated on an independent test set from the 2020 season. Model performance is compared across all models and benchmarked against the 2020 mean Decay-SHIFOR5 intensity error, CLIPER5 track errors, and the NHC official forecast (OFCL) errors. Forecast skill is assessed using mean absolute error (MAE) with 95% bootstrap confidence intervals and the coefficient of determination ($R^2$) across lead times of 6, 12, 18, 24, 48, and 72 h. The results show that: (1) several ML and DL models achieve intensity forecast performance that is broadly comparable in magnitude to the 2020 mean OFCL benchmarks, with an average error reduction of 5–11% at the 24 h lead time; (2) among the ML models, XGBoost and CatBoost slightly outperform LightGBM and RF in accuracy, while LightGBM demonstrates the highest computational efficiency; and (3) among the DL models, CNNs outperform ANNs in predictive accuracy and intensity forecasting efficiency, while ANNs exhibit lower computational cost for track forecast. Bootstrap confidence intervals indicate relatively low variability in model errors, supporting the statistical stability of the results within the 2020 season. However, these results reflect within-season variability and do not necessarily generalize across different years or climatological conditions. Overall, the findings demonstrate the potential of ML/DL-based approaches to complement existing operational forecast systems and enhance TC track and intensity forecasting in the North Atlantic basin. Full article

The Atlantic Forest is a highly diverse biome that is under constant pressure due to human action, resulting in habitat fragmentation and intensifying edge effects, affecting biodiversity. The aim was to study the edge effect and influence of biotic and abiotic parameters on Calliphoridae and Mesembrinellidae communities in Três Picos State Park. Two traps baited using beef liver were placed at each site (n = 5) across 1000 m from the edge toward the interior of the forest, with vegetal characterization at each point. Collections occurred between June 2021 and May 2023, encompassing each season twice. The dipterans were identified taxonomically using a stereoscope microscope with the aid of taxonomic keys, totaling 5476 specimens. Dipteran abundance and species composition were primarily influenced by seasonal variation, while the distance from the forest edge or vegetation structure showed no effect. Abundance peaked during warmer periods, and temperature showed a positive effect on overall dipteran abundance. No species showed a strong association with specific seasons or distance along the edge–interior gradient. These results indicate that, in a relatively continuous and well-preserved forest remnant, edge effects do not lead to significant species loss, and climatic seasonality shapes patterns of dominance and abundance. Our findings highlight the ecological stability of the studied conservation unit and support the use of Calliphoridae and Mesembrinellidae as effective bioindicators. Understanding how dipteran assemblages respond to seasonal and edge-related gradients contributes to the development of cost-effective biomonitoring tools for tropical forest conservation. Full article

Anomalopsychidae Flint, 1981, is a small family of caddisflies comprising two genera: the monotypic Anomalopsyche Flint, 1967, and Contulma Flint, 1969, including 31 described species grouped into the cranifer and spinosa species groups. The genus Contulma is distributed across Costa Rica, the Andes, and the mountainous areas of Brazil and Chile, with six species recorded in Brazil, primarily from the Atlantic Forest biome in the southeastern region. In this study, we describe and illustrate a new species of Contulma from the Cerrado biome of São Paulo State, representing the first record of the genus in this biome. Male specimens were collected using Malaise traps in a stream within a protected area. The new species is distinguished by the presence of both a strongly sclerotized dorsomesal process and a strongly dorsolateral process on tergum IX and by an extremely deep cleft in the posteromesal process of sternum IX, dividing it into two narrow, digitated lobes. Based on its unique combination of characters, the new species cannot be placed unambiguously in either of the species groups. Consequently, C. assisensis sp. nov. is here treated as incertae sedis within Contulma. With this addition, seven species of Contulma are now known from Brazil, most of which are recorded from the Atlantic Forest (6 spp.), especially in the mountainous areas of southeastern Brazil. The discovery of this new species in the Cerrado highlights the underestimated diversity of the genus in Brazil and underscores the importance of regional taxonomic studies for addressing biogeographic and diversity knowledge gaps. The identification key provided enables the differentiation of the seven Contulma species now known from Brazil. Full article

This study examines how assessments of coastal extreme sea levels depend on the separability and reconstructability of the astronomical tide in hourly sea-level records. Using a global tide-gauge network, it proposes an ensemble-learning correction framework that integrates a physical-baseline threshold with multi-criteria consistency testing to determine whether machine-learning enhancement is genuinely effective across stations and time windows. The analysis uses hourly records from 528 UHSLC tide gauges, with 31-day short sequences used to reconstruct 180-day sea-level variability. Taking the physical tidal model as the baseline, residuals are corrected using Extremely Randomized Trees, Random Forest, and Gradient Boosting. To avoid false improvement driven solely by error reduction, a hierarchical decision framework is established. Baseline model quality is first screened using NSE and the coefficient of determination, after which mathematical artefacts are identified through diagnostics of peak suppression and variance shrinkage. A five-level classification is then derived from the convergent evidence of twelve performance metrics and four statistical significance tests. The results show a consistent global pattern across all three algorithms. Approximately 57% of stations meet the criterion for genuine improvement, whereas about 42% are associated with an unreliable physical baseline, indicating that the dominant source of failure arises not from the ensemble-learning algorithms themselves, but from spatially varying limitations in the underlying physical baseline. Spatially, the credibility of machine-learning correction is strongly conditioned by baseline quality: stations with effective correction are more continuous along the eastern North Atlantic and European coasts, whereas stations with ineffective correction are more concentrated in the Gulf of Mexico, the Caribbean, and the marginal seas and archipelagic regions of the western Pacific. These results indicate that the observed spatial heterogeneity primarily reflects geographically varying physical and dynamical conditions that control baseline reliability and residual learnability, rather than a standalone difference in the intrinsic capability of ensemble learning itself. Full article

This study investigates the influence of parametric uncertainty in the van Genuchten–Mualem (VGM) model on hydrological simulations and proposes a deterministic, soil-focused calibration strategy within the MOHID-Land model. The approach was applied to the Pedro do Rio watershed to quantify the impact of VGM parameters, typically estimated via pedotransfer functions, on streamflow performance and to reduce uncertainty through targeted calibration. A one-at-a-time sensitivity analysis using the 95% Prediction Uncertainty (95PPU) metric identified the saturated water content (θ_s) and pore-size distribution (n) as the most influential parameters. Calibration scenarios adjusting these parameters, especially Scenario S45 (+30% θ_s, +20% n), significantly improved model performance, increasing the Nash–Sutcliffe Efficiency (NSE) from 0.20 to 0.66 on a daily scale and to 0.80 on a monthly scale during the validation period. Subsequent hydrodynamic refinements raised the daily NSE to 0.72, while monthly performance remained unchanged. The results underscore that soil parameter uncertainty plays a central role in long-term water balance representation, while hydrodynamic parameters primarily influence short-term dynamics in steep, responsive basins. Overall, the proposed strategy provides a computationally efficient alternative to fully automatic calibration methods, delivering robust performance while maintaining physical consistency, particularly in data-scarce environments. Full article

Hurricanes routinely damage forests across the North Atlantic Basin, yet efforts to characterize their impacts have had mixed subregional success. To elucidate these challenges, this study analyzed pre- and post-hurricane land surface phenology (LSP) for 44 moderate and strong hurricanes over 22 years using the Enhanced Vegetation Index (EVI). We statistically grouped storms based on their long-term climate attributes, then compared subregional impacts with wind speed and land cover. After accounting for wind speed, responses differed among the six subregions. The Southeast U.S. showed declines in EVI for the first winter and first year post storm, but this response was weak or absent elsewhere. The Central America region declined in the first winter but not in the subsequent growing season, while four other regions showed no increased impact with wind speed in either season. We then examined six category 4 hurricanes using a forest mask. In dry areas, drought-sensitive vegetation explained weak responses, whereas in the humid tropics, rapid refoliation or sprouting was common. These factors complicate optical remote sensing assessments. Rapid evaluations can mistake defoliation for more substantial damage, and delayed assessments can confuse EVI recovery with structural recovery. Results underscore the need for ecologically tailored monitoring approaches. Full article

Euterpe edulis, commonly known as juçara, is a palm tree native to the Brazilian Atlantic Forest whose purple fruits are rich in phenolic compounds associated with high antioxidant activity. Juçara pulp is traditionally produced under predominantly artisanal conditions, which limits its shelf life and commercial stability, making drying a relevant preservation strategy. This study investigated the drying of juçara pulp in a forced-air circulation oven at 45, 65, and 85 °C under different drying times. Classical drying models were fitted to the experimental moisture data. Higher temperatures accelerated moisture removal, with the sample dried at 85 °C reaching a powdered state within 60 min at approximately 10% moisture. Drying at 65 °C for 100 min reduced moisture to 5.30%, while drying at 45 °C for 180 min resulted in a moisture content of 6.62%. Total phenolic content decreased as a function of temperature and drying time. Among the evaluated conditions, drying at 65 °C for 100 min provided a favorable balance between efficient dehydration and phenolic retention, maintaining 12.38 mg gallic acid equivalents g⁻¹ (dry basis), corresponding to approximately 55% of the initial content. Full article

Oropouche fever (OF), caused by Oropouche virus (OROV), has expanded beyond its Amazonian range into Minas Gerais (MG), Brazil, raising concern about transmission in extra-Amazonian Atlantic Forest landscapes. Critical gaps persist regarding Culicoides vector communities, anthropophily, and climate-sensitive transmission risk in these newly affected regions. We conducted targeted entomological surveys outbreak-driven by human OF cases, standardized across five MG communities using CDC light traps and Protected Human Attraction (PHA) to characterize Culicoides composition. Females of Culicoides underwent RT-qPCR for OROV (n = 819) and physiological assessment (n = 312). We developed an entomological alert framework that integrates blood-fed abundance, minimum infection rate (MIR) upper confidence bounds, and environmental drivers (i.e., mean temperature, relative humidity and precipitation) via generalized additive mixed models, which explained 68% of the variability in Culicoides abundance and the alert index across communities. We collected 1171 Culicoides individuals representing five species (C. leopoldoi, C. paraensis, C. pusillus, C. foxi, and C. limai). C. leopoldoi (79.1%) and C. paraensis (20.3%) were the predominant species; notably, C. paraensis is recognized as the primary vector of OROV in the Americas. C. paraensis was documented for the first time in all five outbreak areas and dominated PHA captures (90%), suggesting anthropophily. Although no specimens tested OROV-positive (consistent with expected field infection rates of 0.01–1%), MIR upper bounds reached 132/1000 in low-sample settings and humidity and temperature strongly modulated abundance. This operational baseline and alert index transform virologically negative, sparse surveillance data into prioritized targets for intensified sampling and vector control during early, low-prevalence phases, when containment of OROV’s extra-Amazonian spread is still achievable. Full article

Anthurium (Araceae) is one of the most species-rich Neotropical genera, yet its infrageneric classification remains unresolved. This study tests the monophyly of the morphologically defined Anthurium sect. Pachyneurium diagnosed by rosulate habit, involute prefoliation, and absence of a collective vein with a focus on Brazilian species. Using target capture sequencing (Angiosperms353 probe set), we generated a phylogenomic dataset for 35 Anthurium species (18 from sect. Pachyneurium) and conducted maximum likelihood and coalescent-based analyses. Our results demonstrate that sect. Pachyneurium is not monophyletic as traditionally circumscribed. Brazilian species previously assigned to the section are recovered in three geographically structured and strongly supported lineages: Amazonian, Atlantic Forest, and Caatinga/Cerrado. The Atlantic Forest lineage is unexpectedly resolved as sister to A. coriaceum (sect. Urospadix), revealing an evolutionary relationship not predicted by morphology. Divergence-time estimates place the origin of crown Anthurium in the Paleocene (~62 Ma), with diversification of the Brazilian lineages occurring during the Miocene (20–3 Ma), coinciding with major geoclimatic events in South America. Our findings indicate that key diagnostic morphological characters are homoplastic and provide a phylogenomic framework for revising the infrageneric classification of Anthurium. By identifying evolutionarily distinct lineages, this study also contributes to prioritizing conservation efforts in threatened Neotropical biomes. Full article

Forest and landscape restoration (FLR) represents a critical nexus of climate change mitigation, biodiversity conservation, and sustainable development. Despite substantial federal investments and commitments, empirical subnational research quantifying the relationships between governance structures, funding mechanisms, and restoration outcomes remains scarce, and integrated implementation frameworks bridging institutional, technical, and socio-economic dimensions are largely absent from the literature. This study presents a mixed-methods analysis of FLR implementation gaps across Maryland, Virginia, and West Virginia. Three Mid-Atlantic Appalachian states selected for their contrasting ecological conditions, governance structures, and restoration trajectories that collectively represent the heterogeneity of subnational restoration challenges. We examined 147 restoration projects (2019–2024), conducted 25 stakeholder interviews, and analyzed federal funding allocations ($428 million) through spatial and temporal frameworks. Our findings reveal five critical implementation barriers: (1) policy incoherence across federal–state–local jurisdictions creating 34% project delays; (2) chronic underfunding with 63% of projects receiving less than 60% of planned budgets; (3) technical capacity deficits affecting 71% of rural communities; (4) inadequate stakeholder engagement mechanisms reducing project sustainability by 45%; and (5) insufficient monitoring frameworks limiting adaptive management. We introduce an Integrated Restoration Implementation Framework (IRIF) that uniquely integrates policy coordination, sustainable financing, technical capacity building, and community engagement within a unified adaptive management cycle, operationalized through empirically derived thresholds, to guide evidence-based interventions. Quantitative analyses demonstrate that multi-stakeholder governance models increase restoration success rates by 2.3-fold (p < 0.001), while integrated funding mechanisms improve long-term sustainability by 67%. Theoretically, this study advances socio-ecological systems scholarship by providing empirical evidence that multi-scalar governance configurations and integrated stakeholder engagement mechanisms are principal determinants of restoration success, advancing the evidence base for adaptive governance approaches in complex federal systems. Our findings provide actionable intelligence for policymakers and practitioners, while underscoring that sustainable FLR in complex federal systems depends on coherent multi-level governance architectures coordinating institutional mandates, financial resources, technical capacity, and community agency across jurisdictional scales. Full article

Recent genomic phylogenies have generated new robust classifications of actinopterygian fishes, making possible greater nomenclatural stability, but genus-level classifications of groups like the diverse catfish subfamily Trichomycterinae are still unclear, containing ill-defined paraphyletic taxa. The focus of the present study is the Trichomycterus Lineage (TL), a clade with great morphological diversity, containing about 170 species widely distributed in South America, occurring in the most important biodiversity hotspots of the world, such as the Atlantic Forest, Cerrado, and the Tropical Andes. Most species are small, but at least one reaches about 400 mm of total length, being used as food and depicted in pre-Hispanic Andean ceramics. Based on a comparative morphological analysis, mainly using osteological characters, supported by concordant genomic phylogenies, a new classification at the genus level is here provided. Many morphological features delimiting TL genera seem to be related to ecological adaptations. Nine genera are here recognised of which five are new. Recognition of the new genera will allow easier descriptions of new species and consequently better biodiversity estimates. Full article

Roads impact medium- and large-sized mammal populations through both collisions and barrier effects. This study examined how landscape characteristics influence roadkill occurrences along the Dom Pedro I highway (SP-065), located in the Cantareira-Mantiqueira Corridor, São Paulo State, Brazil. The SP-065 crosses important remnants of the Brazilian Atlantic Forest, a global hotspot for biodiversity. Roadkill records were obtained from the Environmental Company of the State, and land use data were extracted from the MapBiomas platform. We analyzed seven landscape variables (in percentage): native forest, pasture, agriculture, forestry, urban areas, mosaic of uses, and water bodies, considering multiple spatial scales. Mammal species were grouped functionally by home range size and tolerance to anthropogenic environments. In total, 1418 roadkills of 24 species were recorded, including eight threatened species. Capybaras (Hydrochoerus hydrochaeris) were the most frequently killed species. Generalized linear models showed that, for Group G1 (small home range, common in human-modified areas), roadkills were positively associated with native forest and pasture, and negatively with mosaic landscapes. For Group G3 (large home range, tolerant to anthropogenic areas), agriculture had a positive effect, especially within a 3000 m radius. For Group G5 (capybara), roadkills increased with pasture and agriculture, while mosaic uses had a negative effect. Since pasture and agriculture were frequently linked to higher roadkill rates, environmental impact assessments should consider these land-use types when planning mitigation actions. Ultimately, responsibility for roadkill extends beyond highway managers to rural landowners and local governments, as land-use patterns around roads strongly influence mammal movement and mortality. Full article