Popillia japonica is an invasive beetle first detected in Northern Italy in 2014 and considered as one of the most dangerous pests for crops due to the economic, environmental, and social costs by European phytosanitary authorities. Here we develop a process-based model for quantifying the invasion dynamics of P. japonica with the goal to elaborate risk maps for the design of surveillance and control strategies. The proposed spatially heterogeneous model captures the different behaviors of both adults, which drive the invasion through flight and short-distance human-mediated transport, and ovo-larval stages, which affect demography due to their susceptibility to environmental conditions. Both processes are biologically informed through variables that are known to influence population dynamics, namely the number of summer days, affecting the flight ability of the adults, and the environmental suitability for larval survival. The simulated presence of adult beetles is then contrasted to pest observation data at the municipality scale from Northern Italy and Switzerland from the initial invasion to December 2023. Results reveal that the ongoing outbreak is occurring in one of the most favorable areas for the beetle, the Po Valley, which favors rapid spread and can sustain high population densities. Mountain regions emerge as obstacles to the progression of the invasion, with the Alps, excluding the valleys, being an almost insurmountable defense, whereas the Apennines only slow down the southward advancement without stopping it altogether. The projected speed of invasion, within and outside the outbreak area, ranges between 0 and almost 20 km/year and it is modulated by environmental conditions. The predicted speed of invasion at continental level constitutes an actionable tool for invasion management, since it provides phytosanitary services with a critical evidence base for optimizing the geometry and extent of delimitation and containment areas.
Increasing vapor pressure deficit (VPD) alters ecosystem carbon and water exchange, but agroecosystem water use efficiency (WUE) may respond nonlinearly to rising atmospheric evaporative demand. The VPD levels at which these nonlinear transitions occur, their spatial consistency, and the factors associated with spatial variation in breakpoint magnitude remain poorly understood. We therefore examined nonlinear WUE responses to increasing VPD and assessed how breakpoint magnitude varied across croplands in relation to environmental and agricultural characteristics. Using multisource observations from 111 sub-basins of the Songhua River Basin during 2002–2024 and independent eddy covariance observations from cropland sites, we identified a nonlinear WUE–VPD relationship. The first derivative of the generalized additive model fit indicated a point of maximum WUE sensitivity to VPD at approximately 0.27 kPa, whereas piecewise linear fitting identified the primary basin-scale breakpoint at approximately 0.41 kPa. Repeated subsampling indicated that the basin-scale breakpoint was robust to sampling variation, while local breakpoint estimates varied across sub-basins. Statistically supported breakpoints were detected in 59 of the 111 sub-basins, and site-level screening retained breakpoint estimates for 20 of the 28 evaluated cropland flux sites. The mean breakpoint estimate across the 20 retained flux sites was approximately 0.43 kPa. Sub-basin breakpoint magnitude was negatively associated with leaf area index, soil bulk density, and mean growing-season precipitation total, and positively associated with C4 crop proportion. Above the basin-scale breakpoint, the sensitivity of gross primary productivity to VPD increased significantly, whereas the sensitivity of evapotranspiration showed no significant change, indicating an asymmetric carbon–water response. At the retained flux sites, higher Bowen ratios and lower evaporative fractions above the site-specific breakpoints were consistent with a relative shift in energy partitioning toward sensible heat. The approximately 0.41-kPa breakpoint characterizes the basin-scale WUE response to increasing VPD but does not represent a universal local threshold. Spatial variation in breakpoint magnitude was associated with climate, vegetation structure, soil properties, and crop composition, although the observational design precludes direct causal attribution to individual management factors. These findings clarify spatial variation in nonlinear agroecosystem WUE responses to increasing VPD and underscore the importance of local environmental context when interpreting carbon–water responses to atmospheric drying.
As climate change accelerates, ecologically fragile regions are increasingly exposed to compound extremes. Agricultural intensification may perturb land–atmosphere feedback processes and exacerbate drought–heatwave coupling, yet the land-use thresholds governing non-linear risk amplification remain poorly constrained. This study focuses on a representative area in the agro-pastoral ecotone of northern China, the Horqin Sandy Land Ecological Conservation Area, to quantify cultivated-land-proportion thresholds for compound drought and heatwave onset. A space-for-time substitution was applied to 50 grid cells across five land-use patterns (cultivated land: 6.4
Abstract Background Although the Southeastern United States (SE US) has been identified as a major terrestrial carbon sink, the balance of CO 2 and CH 4 fluxes from its wetlands remains poorly constrained due to strong environmental heterogeneity and limited regional-scale assessments. In this study, we used observations from 24 wetland sites located in the SE US, including freshwater and salt marshes, to train machine learning models (Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, and Partial Least Squares Regression) to predict monthly mean gross primary productivity (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE), and methane (CH 4 ) fluxes. Results Model performance measured in r 2 for GPP ranged from 0.60 to 0.70, for RECO ranged from 0.44 to 0.56, for NEE ranged from 0.45 to 0.56, and for CH 4 flux ranged from 0.33 to 0.37. Random Forest and XGBoost were the most consistent algorithms between fluxes. Feature importance analyses of these two algorithms indicated that CO 2 flux predictions were primarily associated with vegetation and climatic variables, whereas CH 4 predictions were more strongly associated with soil and hydrological properties. Using a performance-weighted multi-model ensemble (MME), we scaled up the predictions for the entire SE US from 2000 to 2020. Spatial patterns of productivity and respiration showed flux hotspots in the Everglades, the Mississippi River Valley, and the Gulf and East Coast. We also identified hotspots of CH 4 emissions in coastal North Carolina. Slightly lower CH 4 fluxes were observed between 2000–2007 in comparison to 2008–2020. Mean annual wetland carbon fluxes were 280 ± 38 Tg CO 2 -C for GPP, 233 ± 51 Tg CO 2 -C for RECO, − 20 ± 18 Tg CO 2 -C for NEE, and 5.7 ± 1.7 Tg CH 4 -C for CH 4 . Conclusions Our results provide regional estimates of CO 2 and CH 4 fluxes that improve carbon budget assessments and support future wetland management in the SE US. By identifying the spatial and temporal variability of carbon uptake and emissions across the region, this study provides a foundation for improving regional carbon accounting and informing wetland conservation and management strategies.
Abstract Background Montane forest ecosystems are critical for biodiversity conservation, yet baseline data on butterfly diversity in many Ethiopian highlands remain lacking. This study provides the first baseline inventory of butterfly diversity in Hamericho Mountain Forest, a threatened ecotourism landscape in central Ethiopia. The objectives were to: (1) provide a baseline inventory of butterfly species, (2) compare butterfly diversity and community composition across three vegetation zones (forest, shrubland, and grassland), and (3) identify local habitat characteristics associated with butterfly diversity patterns. Methods Butterflies were surveyed across three purposively selected vegetation zones: a forest site (lower elevation, 2315–2471 m), a shrubland site (mid elevation, 2651–2804 m), and a grassland site (higher elevation, 2902–2981 m). Standardized Pollard walk transects were used over 28 consecutive days. Species richness was compared using rarefaction. Community composition was analyzed using non-metric multidimensional scaling (NMDS) and analysis of similarity (ANOSIM). Generalized linear models (GLMs) were used to assess associations between habitat characteristics and butterfly metrics. Indicator species analysis identified species associated with specific vegetation zones. Results A total of 32 butterfly species (2037 individuals) from five families were recorded. The forest site had the highest species richness, followed by shrubland, while the grassland site had the lowest. Each vegetation zone supported a distinct butterfly community (ANOSIM: R = 0.595, p = 0.002). Herb cover showed the strongest negative association with butterfly richness and diversity, while canopy cover was positively associated with Shannon diversity. GLM results showed that neither site nor herb cover was significant due to collinearity. Seven indicator species were identified, with Charaxes brutus and Charaxes kirki as forest specialists, and Colias electo as a shrubland-grassland generalist. Conclusion This study provides a baseline butterfly inventory for Hamericho Mountain. All three vegetation zones support unique assemblages and should be considered for conservation. Future research should sample at overlapping elevations to separate vegetation from elevation effects, extend sampling across seasons, and directly measure tourism impacts.
