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Questions Species distribution models (SDMs) based on habitat suitability and niche quantification are powerful tools in vegetation science. Recent findings suggest that they could be applied at the landscape scale as vegetation conservation tools, but that some environmental dimensions (e.g., climate) need to be considered at larger scales. What is the importance of applying hierarchical SDMs combining information from different scales to ensure consistent local vegetation management decisions? Study Site Mainland Spain and Biosphere Reserve of Sierra del Rincón (central Spain). Methods We generated SDMs for five tree species at the regional scale (mainland Spain) using climatic variables plus presence/absence data from the Spanish National Forest Inventory; and at the landscape scale (Sierra del Rincón Biosphere Reserve) using local environmental variables plus locally gathered vegetation presence/absence data. Predictions of both regional and landscape models were combined at the landscape scale following two different hierarchical approaches. The four resulting predictions were compared with correlation coefficients and independently evaluated with the AUC statistic and data collected in the study area. Results The regional SDMs depict suitable climatic conditions for the tree species, while the landscape SDMs capture important local ecological drivers that influence habitat suitability at finer scales. Expectedly, the regional SDMs predict larger suitable areas than the landscape SDMs. The predictions from the hierarchical approaches are reliable and provide on average better results than non‐hierarchical ones. Conclusions SDMs can be valuable tools for local plant conservation programs. We present examples of the applicability of a hierarchical modeling approach and conceptual and methodological solutions related to the use of these tools in local vegetation conservation programs. For example, we show that landscape SDMs could be used to determine the current distribution of endangered plant species, while a hierarchical approach would be better suited to define areas to re‐vegetate within a local restoration program. Species distribution models are powerful tools in vegetation science. Here, we highlight the importance of applying hierarchical SDMs combining information from different scales to ensure consistent local vegetation management decisions. We present examples of the applicability of a hierarchical modeling approach and conceptual and methodological solutions related to the use of these tools in local vegetation conservation programs.

Accurate identification of the spatiotemporal characteristics and spatial matching of ecosystem services (ESs) supply and demand, as well as determination of the factors influencing the ESs supply-demand relationship, is of great significance for controlling the design of regional ecological protection policies and sustainable management. Unfortunately, the comprehensive characteristics of changes in ESs supply and demand, as well as their driving mechanisms, after large-scale vegetation restoration in the Shaanxi section of the Yellow River basin (SYRB) are still unclear. This study, conducted in the SYRB, employed specialized models to assess water yield, soil conservation, and carbon fixation on both the supply and demand sides after vegetation restoration in 2000 and 2023. Subsequently, the spatiotemporal heterogeneity of the supply-demand matching of ESs was explored by constructing the supply-demand ratio index. Finally, using the optimal parameter Geodetector, the influencing factors of the ESs supply-demand matching relationship in the SYRB were further identified. The results showed that the water yield in the SYRB was generally in a deficit, indicating insufficient supply throughout the study period, but this deficit state improved over time. In 2023, the counties with insufficient water yield supply were primarily located in the northern part of the SYRB and the Guanzhong Plain. Soil conservation reached a fundamental reversal from a “general deficit” to an “overall surplus.” In 2023, the counties with insufficient soil conservation supply were mainly located in the northern part of the SYRB. In contrast, the supply-demand relationship of carbon fixation in the vast majority of countries deteriorated. In 2023, the counties with insufficient carbon fixation supply were primarily located in the northern part of the SYRB and the Guanzhong Plain. Economic density, vegetation coverage, and population density were identified as the key factors in monitoring the water yield supply-demand matching relationship. Precipitation, slope, and population density were the main controlling factors for the soil conservation supply-demand matching relationship. Economic density, forest and grassland percentage, and population density were identified as the key factors shaping the carbon fixation supply-demand matching relationship. This study clarified the supply-demand relationship and driving mechanisms for key ESs in the SYRB, thereby providing a theoretical basis for the comprehensive management of regional ecosystems.

Infectious diseases are rapidly emerging and many are increasing in incidence across the globe. Processes of land‐use change, notably habitat loss and fragmentation, have been widely implicated in the emergence and spread of zoonoses such as Lyme disease, yet evidence remains equivocal. Here, we discuss and apply an innovative approach from the social sciences; instrumental variables, which seeks to tease out causality from observational data. Using this approach, we revisit the effect of forest fragmentation on Lyme disease incidence, focusing on human interaction with fragmented landscapes. Although human interaction with infected ticks is of clear and fundamental importance to human disease incidence, human activities that influence exposure have been largely overlooked in ecology literature. Using county‐level land‐use and Lyme disease incidence data for ~800 counties from the northeastern United States over the span of a decade, we illustrate (a) that human interaction with fragmented forest landscapes reliably predicts Lyme disease incidence, while ecological measures of forest fragmentation alone are unreliable predictors and (b) that identifying the effect of forest fragmentation on human disease entails addressing the feedback between Lyme disease risk and human decisions to avoid interaction with high‐risk landscapes. Synthesis and applications. Our innovative approach and novel results help to clarify the equivocal literature on the effects of forest fragmentation on Lyme disease and illustrate the key role that human behaviour may be playing in the ecology of Lyme disease in North America. Accounting for human activity and behaviour in the ecology of disease more broadly may result in improved understanding of both the ecological drivers of disease, as well as actionable intervention strategies to reduce disease burden in a changing world. For example, our model results indicate that forest fragmentation by human settlement increases Lyme disease incidence, which has practical implications for land‐use policy aimed at disease reduction. Specifically, our model suggests land‐use regulations that reduce parcel size would be an actionable approach to reduce Lyme disease transmission for policymakers concerned about increasing Lyme disease incidence in the northeastern United States. Our innovative approach and novel results help to clarify the equivocal literature on the effects of forest fragmentation on Lyme disease and illustrate the key role that human behaviour may be playing in the ecology of Lyme disease in North America. Accounting for human activity and behaviour in the ecology of disease more broadly may result in improved understanding of both the ecological drivers of disease, as well as actionable intervention strategies to reduce disease burden in a changing world. For example, our model results indicate that forest fragmentation by human settlement increases Lyme disease incidence, which has practical implications for land‐use policy aimed at disease reduction. Specifically, our model suggests land‐use regulations that reduce parcel size would be an actionable approach to reduce Lyme disease transmission for policymakers concerned about increasing Lyme disease incidence in the northeastern United States.

Context Recent large and high-severity wildfires have burned vast areas of coniferous forests throughout Western North America. These burned landscapes are recovering amid increasingly frequent climate extremes, such as drought. We need to understand how post-fire climate extremes and other ecological drivers (such as fire impacts) influence patterns and trends of coniferous recovery. Objectives We worked at a landscape scale (> 400,000 hectares) to investigate the association between distinct post-fire forest recovery and ecological drivers in dry sub-boreal forests. We created structural recovery groups distinct in patterns and trends of coniferous cover and density and then modeled their association with ecological drivers. Methods We used Landsat time-series data to identify unique spectral recovery, which we grouped based on post-fire regrowth and stocking estimates. Remotely Piloted Aircraft light detection and ranging (lidar) provided structural estimates 5–21 years post-fire. We modeled the association between structural recovery groups and ecological drivers with random forests. For each category of drivers (site conditions, climate, climate anomalies, pre-fire composition, and fire impacts), we used individual models to identify important drivers. We then incorporated the most important drivers in a global model to highlight the drivers that were important across categories. Results Initial spectral trends indicated longer-term differences in structural forest recovery. Climate anomalies (such as post-fire extremes in temperature and precipitation) and pre-fire basal area best predicted observed structural groupings—abnormally cold and dry summers after the fire were associated with slow conifer establishment. Comparatively, areas with a higher pre-fire basal area maintained a mixed canopy of deciduous and coniferous stems. Conclusions At a landscape scale, post-fire climate conditions best predicted structural forest recovery, suggesting management plans should be adaptable to the conditions experienced post fire.

Untangling the multiple drivers that affect biodiversity along elevation gradients is crucial for predicting the consequences of climate change on mountain ecosystems. However, the distribution patterns of microorganisms along elevation gradients have not yet been clarified, in particular when associated with strong changes in dominant species. Five typical vegetation types (i.e., coniferous forests, meadow grasslands, alpine shrubs, alpine meadows, and sparse vegetation of limestone flats) from contrasting vegetation belts were selected to explore the influence of elevation gradients on soil microbial communities. The results showed that Actinobacteriota and Proteobacteria were the dominant bacterial phyla. Ascomycota and Basidiomycota were the prevalent fungal phyla. Soil bacterial alpha diversity increased with increasing elevation, while soil fungal alpha diversity showed an obvious mid-elevation pattern. The beta diversity of the bacterial and fungal communities reflected a clear spatial niche-differentiation, and indicated that herbaceous plants affected soil bacterial communities while shrubs preferred soil fungal communities. A correlation analysis showed that environmental factors had different contributions to the composition and diversity of soil microbial communities. Soil bacteria were primarily affected by soil properties, whereas fungi were affected by vegetation. The research results can improve the prediction of soil microbial ecological processes and patterns related to elevation, and provide a theoretical basis for maintaining the sustainable development of soil microbial biodiversity under the background of global change.

Environmental filtering and spatial structuring are important ecological processes for the generation and maintenance of biodiversity. However, the relative importance of these ecological drivers for multiple facets of diversity is still poorly understood in highland streams. Here, we examined the responses of three facets of stream macroinvertebrate alpha diversity to local environmental, landscape‐climate and spatial factors in a near‐pristine highland riverine ecosystem. Taxonomic (species richness, Shannon diversity, and evenness), functional (functional richness, evenness, divergence, and Rao's Quadratic entropy), and a proxy of phylogenetic alpha diversity (taxonomic distinctness and variation in taxonomic distinctness) were calculated for macroinvertebrate assemblages in 55 stream sites. Then Pearson correlation coefficient was used to explore congruence of indices within and across the three diversity facets. Finally, multiple linear regression models and variation partitioning were employed to identify the relative importance of different ecological drivers of biodiversity. We found most correlations between the diversity indices within the same facet, and between functional richness and species richness were relatively strong. The two phylogenetic diversity indices were quite independent from taxonomic diversity but correlated with functional diversity indices to some extent. Taxonomic and functional diversity were more strongly determined by environmental variables, while phylogenetic diversity was better explained by spatial factors. In terms of environmental variables, habitat‐scale variables describing habitat complexity and water physical features played the primary role in determining the diversity patterns of all three facets, whereas landscape factors appeared less influential. Our findings indicated that both environmental and spatial factors are important ecological drivers for biodiversity patterns of macroinvertebrates in Tibetan streams, although their relative importance was contingent on different facets of diversity. Such findings verified the complementary roles of taxonomic, functional and phylogenetic diversity, and highlighted the importance of comprehensively considering multiple ecological drivers for different facets of diversity in biodiversity assessment. Our findings showed that both environmental and spatial factors are important ecological drivers for biodiversity patterns of macroinvertebrate in Tibetan streams, although their relative importance was contingent on different facets of diversity. Such findings verified the complementary roles of taxonomic, functional and phylogenetic diversity, and highlight the importance of comprehensively considering multiple ecological drivers for different facets of diversity in biodiversity assessment.

Genomic GC (Guanine-Cytosine) content is a fundamental molecular trait linked with many key genomic features such as codon and amino acid use. Across bacteria, GC content is surprisingly diverse and has been studied for many decades; yet its evolution remains incompletely understood. Since it is difficult to observe GC content evolve on laboratory time scales, phylogenetic comparative approaches are instrumental; but this dimension is rarely studied systematically in the case of bacterial GC content. We applied phylogenetic comparative models to analyze GC content evolution in multiple bacterial groups across 2 major bacterial phyla. We find that GC content diversifies via a combination of gradual evolution and evolutionary “jumps.” Surprisingly, unlike prior reports that solely focused on reductions in GC, we found a comparable number of jumps with both increased and decreased GC content. Overall, many of the identified jumps occur in lineages beyond the well-studied peculiar examples of endosymbiotic and AT-rich marine bacteria and do not support the predicted role of oxygen dependence. Our analysis of rapid and large shifts in GC content thus identifies new clades and novel contexts to further understand the ecological and evolutionary drivers of this important genomic trait.

Peatland ecosystems are a highly effective long-term carbon sink. However, the CO 2 fluxes could be substantially altered by climate changes and the fate of carbon stored in these ecosystems is still uncertain. Currently, most studies concerning the carbon fluxes in peatlands were performed at high latitude sites, where these ecosystems are more widely distributed compared to temperate regions, where peatlands are less frequent and, in addition to climate pressure, increasingly threatened by human activities. However, the information we have on these peatlands is very scarce. To fill this knowledge gap, we studied CO 2 fluxes in an alpine peatland, through light and dark incubations. Using the natural variation in ecological conditions, we identified the main drivers of CO 2 fluxes, considering in particular their interactions and covariation. Ecosystem respiration and gross primary production were primarily stimulated by the lowering of the water table and the amount of photosynthetic radiation, respectively, whereas net ecosystem CO 2 exchange showed greater variation along the growing season. The influence on CO 2 fluxes of the interactions between the drivers investigated, including soil temperature and moisture as well as vegetation type and plant functional diversity, was found to be of pivotal importance. Finally, a substantial part of the variation in CO 2 emission and uptake processes was regulated by the joint variation of atmospheric and edaphic factors. To understand and predict the CO 2 dynamics of alpine peatlands, it is necessary to consider the interplays among ecological factors, especially in relation to the expected changes in climate and vegetation.

Object Scarlet fever is an acute respiratory infectious disease that endangers public health and imposes a huge economic burden. In this paper, we systematically studied its spatial and temporal evolution and explore its potential ecological drivers. The goal of this research is to provide a reference for analysis based on surveillance data of scarlet fever and other acute respiratory infectious illnesses, and offer suggestions for prevention and control. Method This research is based on a spatiotemporal multivariate model (Endemic-Epidemic model). Firstly, we described the epidemiology status of the scarlet fever epidemic in Sichuan Province from 2016 to 2019. Secondly, we used spatial autocorrelation analysis to understand the spatial pattern. Thirdly, we applied the endemic-epidemic model to analyze the spatiotemporal dynamics by quantitatively decomposing cases into endemic, autoregressive, and spatiotemporal components. Finally, we explored potential ecological drivers that could influence the spread of scarlet fever. Results From 2016 to 2019, the incidence of scarlet fever in Sichuan Province varied much among cities. In terms of temporal distribution, there were 1–2 epidemic peaks per year, and they were mainly concentrated from April to June and October to December. In terms of transmission, the endemic and temporal spread were predominant. Our findings imply that the school holiday could help to reduce the spread of scarlet fever, and a standard increase in Gross Domestic Product (GDP) was associated with 2.6 folds contributions to the epidemic among cities. Conclusion Scarlet fever outbreaks are more susceptible to previous cases, as temporal spread accounted for major transmission in many areas in Sichuan Province. The school holidays and GDP can influence the spread of infectious diseases. Given that covariates could not fully explain heterogeneity, adding random effects was essential to improve accuracy. Paying attention to critical populations and hotspots, as well as understanding potential drivers, is recommended for acute respiratory infections such as scarlet fever. For example, our study reveals GDP is positively associated with spatial spread, indicating we should consider GDP as an important factor when analyzing the potential drivers of acute infectious disease.

Background Plants are constantly in need of adapting to different environmental conditions and responses in many ways. The response of plants is different between different species to the same environmental factors. It is therefore important to fulfill more about how plants respond and adapt. This study aimed to analyze four different plant species collected from two different habitats. It focused on examining the responses of these species based on the composition of phytoconstituents, measurements of antioxidant compounds, and the expression level of cellular proteins. Results Plant responses varied. Total phenolics varied in all plant species between different sites, as both Tamarix aphylla and Erodium glaucophyllum have high total phenolic in plant samples collected from Al Qalyubia and the revers was correct for Zygophyllum coccineum and Haloxylon salicornicum . The flavonoid is higher in samples collected from Al Qalyubia than from Al Suez in all plant species and the highest values recorded in samples of E. glaucophyllum (2.80 mg/g FW) and the lowest values recorded in Z. coccineum (0.19 and 0.173 mg/g FW). Correlation between plant Total phenols, Flavonoids, TAC, DPPH and soil analysis showed a significant negative correlation between DPPH and Total phenols. GC-MS analysis showed a remarkable variation in phytochemistry in plants from different location. The PCA analysis between soil and the GC-MS analysis and heatmap clustering correlation arranged plant in groups according to the similarity in phytoconstituents and soil, Haloxylon salicornicum showed the most distance between the samples from the same species and the short distance between samples of the same species was in Zygophyllum coccineum the data was matching with the TDS and EC analysis of soil samples. Genetic diversity was studied where total cellular proteins (TCPs) were extracted using 12% SDS-PAGE. The SDS-PAGE technique resolved clear and distinct protein bands ranging between 15 and 130 kDa. The results showed differential expression of multiple protein bands running at approximately 130, 70, 55, 50, 45, and 30 kDa in all studied samples, detected with various intensities. At the species-specific level, several unique protein bands were detected in some taxa but were barely detected or absent in others. Band scoring revealed a total of 38 protein bands with polymorphism percentage (P % = 73.68%) of 10 monomorphic and 28 polymorphic bands. The Euclidean distance tree revealed the differentiation of the eight samples into two main groups. Moreover, multivariate heatmap analysis was conducted and the results agreed with and affirmed the results of the cluster analysis. Conclusions It could be suggested that the effect of the ecological drivers (viz. EC, TDS, Ca, Mg, Na, and SO 4 ) on the metabolic activities, metabolomics of phytoconstituents, and the expression levels of cellular proteins influenced a differential behavior indicated through the results shown in the present study. This behavior could be linked and engaged with the protection of cellular metabolic activities and consistent protein expression against adverse climatic environmental conditions. This manuscript demonstrated the potential integration of ecological, physiological, and molecular analyses as a powerful strategy that can benefit different sectors of stockholders, including both academic and non-academic researchers, in sustaining the medicinal and economic significance of plant productivity.