环境•农林最新文献

Anthropogenic activities have a profound impact on ecosystems, threatening species and contributing to increased extinction rates. Among them are some Leporidae species, for which ecological data remains insufficient to support effective conservation. This study aimed to monitor the status of the volcano rabbit (Romerolagus diazi), eastern cottontail (Sylvilagus floridanus), and Mexican cottontail (Sylvilagus cunicularius) in 2018 in southern Mexico City, using citizen science tools. The field research involved recording fecal pellets and habitat characteristics along 1 km transects. Vegetation changes over the past 20 years were analyzed using the Standardized Precipitation Index (SPI), Enhanced Vegetation Index (EVI), and Standard Anomaly Index (SAI). Among 2,026 quadrants (~ 8,000 km²), the volcano rabbit, eastern cottontail, and Mexican cottontail were detected in 3.25%, 8.09%, and 12.37% of sampling points, respectively. Canonical correspondence analysis revealed that mature tree density harmed the presence of the volcano rabbit. At the same time, cottontails appeared to be more resilient to habitat changes. Despite reforestation efforts, SPI, EVI, and SAI data indicated an increase in dryness over time. Intensive planting has transformed alpine grasslands, a key habitat for the volcano rabbit, into densely forested areas. Citizen science is an effective tool for monitoring certain species of leporids, such as the volcano rabbit, and provides valuable insights for conservation strategies. Future interventions should prioritize the preservation of alpine grasslands to ensure their long-term survival.

Thermal degradation alters hair’s molecular structure, influencing its protein, lipid, and disulphide components, which serve as indicators for forensic analysis. The present study investigates the effect of thermal treatment on the chemical composition of human hair and explores the potential of ATR-FTIR spectroscopy combined with machine learning for forensic sex determination. ATR-FTIR spectroscopy was employed to analyze untreated and thermally treated hair strands collected from 50 male and 50 female participants aged 18–30 years. The resulting spectral data were subjected to multivariate analysis using PLS-DA, SVM, and KNN models to classify the samples based on sex and thermal treatment status. Thermal exposure caused distinct alterations in the key spectral bands, especially those associated with proteins (Amide I, II, III), lipids (C-H stretching), and disulfides (S-S stretching), indicating structural denaturation, bond cleavage, and oxidative modifications. Furthermore, the application of multivariate models PLS-DA, SVM, and KNN, on ATR-FTIR spectral data proved highly effective in classifying hair samples by sex and thermal treatment status. All three models achived 100% accuracy, precision, recall and F1-scores, effectively distinguishing between thermally treated and untreated samples by sex In conclusion, ATR-FTIR, coupled with advanced machine learning models, offers a powerful, non-destructive tool for assessing thermal damage, characterising hair composition determining sex, offering significant potential applications in forensic investigations involving burnt hair samples.

Oriental beech (Fagus orientalis Lipsky) is an ecologically and economically significant species, covering 8.5% of Türkiye’s total forest area. However, climate change threatens its distribution due to increasing temperatures and decreasing precipitation. This study integrates geospatial informatics and ensemble modeling (EM) to predict the potential geographic distribution (PGD) of F. orientalis under future climate scenarios using Biomod2 within the ShinyBIOMOD framework. An EM model has been developed from six models [Generalized Boosting Model (GBM), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Generalized Additive Model (GAM), Artificial Neural Networks (ANN), and Maximum Entropy (MaxEnt)] using 76 validated occurrence records and 19 environmental predictors. Model validation achieved high predictive accuracy (AUC = 0.96, TSS = 0.85). Spatial projections for SSP2-45 and SSP5-85 scenarios indicate significant shifts in PGD. Notably, high-suitability habitats will decline under SSP2-45 but expand under SSP5-85. Bio2 [Mean Diurnal Range (mean of monthly (maximum temperature – minimum temperature))] and Bio4 [Seasonal temperature fluctuation (temperature seasonality (standard deviation x 100))] emerged as the dominant drivers of distribution changes. Based on geospatial analyses, F. orientalis is expected to migrate to higher altitudes in the Black Sea region and expand into southern and inner Türkiye. This shift reflects a broader trend of temperate forest adaptation to climate change. This study underscores the power of ensemble modeling for ecological forecasting and conservation planning, demonstrating the value of computational tools in assessing climate-driven species distribution changes. The findings contribute to predictive modeling for biodiversity conservation and ecosystem management.