Annual Precipitation Research Articles

Macro- and micro-geographic genetic variation in early fitness traits in populations of maritime pine (Pinus pinaster Ait.).

Assessing adaptive genetic variation and its spatial distribution is crucial to conserve forest genetic resources and manage species' adaptive potential. Macro-environmental gradients commonly exert divergent selective pressures that enhance adaptive genetic divergence among populations. Steep micro-environmental variation might also result in adaptive divergence at finer spatial scales, even under high gene flow, but it is unclear how often this is the case. Here, we assess genetic variation in early fitness traits among distant and nearby maritime pine (Pinus pinaster Ait.) populations, to investigate climatic factors associated with trait divergence, and to examine trait integration during seedling establishment. Open pollinated seeds were collected from seven population pairs across the European species distribution, with paired populations spatially close (between <1 km up to 21 km) but environmentally divergent. Seeds were sown in semi-natural conditions at three environmentally contrasting sites, where we monitored seedling emergence, growth and survival. At large spatial scales, we found significant genetic divergence among populations in all studied traits, with certain traits exhibiting association with temperature and precipitation gradients. Significant trait divergence was also detected between pairs of nearby populations. Besides, we found consistent trait correlations across experimental sites, notably heavier seeds and earlier seedling emergence were both associated with higher seedling survival and fitness over two years in all experimental conditions. We identified mean annual temperature and precipitation seasonality as potential drivers of P. pinaster population divergence in the studied early-life traits. Populations genetically diverge also at local spatial scales, potentially suggesting that divergent natural selection can override gene flow along local-scale ecological gradients. These results suggest the species exhibits substantial adaptive potential that has allowed it to survive and evolve under contrasting environmental conditions.

Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China

Identifying alterations in the local-scale statistical distributions of daily temperature indices and precipitation is crucial for adapting to climate change and combating desertification in arid regions. This study focuses on Ningxia, a representative dryland area in China, where we investigated changes in the statistical distributions of daily temperature and precipitation across ten weather stations over two 30-year periods: 1991–2020 compared to 1961–1990. From 1961 to 2020, annual air temperature indices (mean, maximum, and minimum) exhibited a significant upward trend at all ten weather stations (P < 0.05). Specifically, when comparing the decades of 1961–1970 with those of 2001–2020, annual air temperature indices increased by 1.2–2.2 °C for annual air temperatures, by 0.91–1.97 °C for maximum air temperature, and by 1.26–3.28 °C for minimum air temperature; these corresponded to rates of increase ranging from 0.20–0.40 °C per decade for mean air temperatures, from 0.15–0.33 °C per decade for maximum air temperatures, and from 0.21–0.55 °C per decade for minimum air temperatures, respectively during this period. The observed differences in the statistical distributions of daily temperature indices between the two periods were statistically significant (P < 0.05), indicating an increase rate of approximately 1 °C during the period from 1991 to 2020 relative to that from 1961 to 1990 across various weather stations. In contrast, there were no significant increasing or decreasing trends in annual precipitation (P > 0.05). Similarly, the changes in statistical distributions of daily precipitation also did not reach a significance level (P > 0.05) when comparing data from both periods within these stations. Furthermore, the correlation coefficients between daily temperature indices and daily precipitation have decreased across these stations during the later period compared to earlier years. These findings suggest that while there has been a notable shift toward higher values in the statistical distribution of daily temperature indices—indicating rising temperatures—the corresponding distribution patterns for daily precipitation have remained relatively stable between these time frames. Consequently, this research confirmed an escalation in localized drought conditions within certain dryland areas despite broader regional trends toward humidification observed over recent decades; thus implying an increased risk of desertification as aridity intensifies.

Divergent responses of forest canopy height to environmental conditions across China

As major components of terrestrial ecosystems, forest ecosystems play an important role in sequestering carbon and hence mitigating climate change. Canopy height is a crucial factor characterizing the structure and function of forest ecosystems, yet the driving mechanism of forest canopy height receives less attentions in China. Here, we utilize the satellite-based forest canopy height product with several environmental and climate factors (e.g. forest age, temperature, etc.) to delineate the spatial distributions of forest canopy height and its drivers in China at 1 km spatial resolution during the period of 2014 to 2018. The random forest is employed for identifying the dominant factors at province level, while Shapley additive explanations (SHAP) analysis is further incorporated at pixel-level to dig into the specific contributions of each driver. The results show that forest age primarily dominates the spatial distributions of forest canopy height across different forest ecosystems of China, followed by mean annual precipitation, soil type, and aspect. SHAP analysis further indicates that other factors, such as soil moisture and wind speed, also play critical roles to shape the spatial patterns of forest canopy height in China, which could not be revealed from province-level random forest analyses. Such results emphasize the priority of incorporating SHAP analysis with random forest to advance our understanding of forest canopy height distributions and benefit future projections. Our study highlights the necessity to characterize the spatial heterogeneity of forest canopy height, which is critical for accurate estimations of forest and even terrestrial carbon sink in China, facilitating the achievement of the goal of “carbon peak in 2030 and carbon neutrality in 2060”.

Impact of Natural and Human Factors on Dryland Vegetation in Eurasia from 2003 to 2022.

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors' influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10-3 yr-1 (p < 0.01), 1.30 g C m-2 yr-2 (p < 0.01) and 1.00 × 10-3 Wm-2μm-1sr-1yr-1 (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia.

MaxEnt modeling of Klebsiella pneumoniae: predicting future distribution and evaluating the risk for public health

Klebsiella pneumoniae is a Gram-negative bacterium known for causing infections such as pneumonia, urinary tract infections, and bloodstream infections in humans. Its increasing drug resistance poses a significant threat. Understanding the effects of climate change on this bacterium is crucial to managing its impacts effectively. In this study, we employed MaxEnt modeling to explore the current and future distribution of K. pneumoniae under different climate change scenarios for 2050 and 2070. Our models utilized 280 valid occurrence points and 19 bioclimatic variables, with Mean Annual Temperature and Mean Annual Precipitation being the most influential variables. The models demonstrated high accuracy, with an AUC of 0.889. Our findings revealed that K. pneumoniae has a cosmopolitan distribution, with the highest suitability observed in Southeastern Asia, Central Africa, and Southern Europe. Future predictions indicated an apparent expansion of its range across almost all continents, particularly in the RCP 8.5 2070 scenario. Results have crucial implications for epidemiology, as they enable the identification of current and potential future disease hotspots and geographical patterns. This knowledge can inform the development of effective infection control strategies. Furthermore, it provides valuable insights for decision-makers and policymakers in allocating resources efficiently for medical control and prevention strategies.

Modeling spatiotemporal distribution of yellow rust wheat pathogen using machine learning algorithms: Insights from environmental assessment

The yellow rust pathogen (Puccinia striiformis Westend) poses a significant threat to wheat production in the world, necessitating a comprehensive understanding of its spatiotemporal distribution and the influence of climatic factors. In this study, we employed an ensemble of four prominent machine learning algorithms to assess the impact of various environmental and remote sensing variables on the spread of yellow rust at a national scale. Our analysis incorporated 55 climatic parameters, including monthly temperature, precipitation, solar radiation, and wind speed. The results demonstrated that the RF algorithm yielded robust predictions, with a Receiver Operator Characteristic (ROC) of 0.916 and True Skill Statistic (TSS) of 0.748. Furthermore, the study identified key influencing variables for wheat disease modeling, such as annual precipitation, temperature seasonality, and isothermality. Projections based on the model indicate a potential decrease in disease spread by 2050 in specific regions. The findings underscore the efficacy of ensemble modeling in predicting the spatiotemporal distribution of yellow rust on a large scale, offering valuable insights for the development of robust agricultural management strategies in the face of evolving climate conditions.

Increased precipitation has not enhanced the carbon sequestration of afforestation in Northwest China

Concerns have been raised about the sustainability of large-scale afforestation in semi-arid regions due to potential water constraints. This study investigated whether increased humidity in the semi-arid regions of northwest China could sustain the continued expansion of afforestation efforts. Using multi-source remote sensing data, we found that between 2012 to 2020, annual cumulative precipitation increased by 2.5 millimeters per year, while annual average carbon sequestration in afforested areas declined by 0.002 kilograms of carbon per square meter per year, indicating asynchronous trends. This disparity was primarily attributed to the trade-off between vegetation photosynthesis and transpiration in response to external water conditions, which led to a decline in the water use efficiency of afforested vegetation. The effect of water use efficiency on carbon sequestration was driven by gross primary productivity, rather than evapotranspiration. These findings underscore the importance of targeted afforestation in semi-arid regions, considering local water resource sustainability.

Multi-scale assessment of high-resolution reanalysis precipitation fields over Italy

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, whereas multiple products showed an underestimation of precipitations across the North-West coast, the Apennines, and Southern Italy. Finally, a comparison with a time-consistent observational dataset (UniMi/ISAC-CNR) revealed a non-stable deviation from observations in the annual precipitation cumulate of the reanalysis products analyzed. This should be taken into account when interpreting precipitation trends over Italy.

Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction

Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R2 close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.

Cat out of the bag: Coastal and Gangetic plains outside protected areas require focus for fishing cat conservation in southern West Bengal, India

State of West Bengal is the last stronghold for fishing cats in the country. Although Fishing cats has been mentioned as a vulnerable wetland species, few works have been conducted on their distribution pattern, habitat or ecology. Our study uses camera traps in various urban and rural wetland areas of southern West Bengal spanning more than 67,000 km2 to identify their presence. Out of 38 potential habitats, 24 regions came up with evidence of these cats spanning through the eight districts of southern West Bengal. We used presence absence data to fit an ensemble model to delineate suitable habitat for the species in the southern West Bengal. Results indicated areas less than 30 m of elevation and enough annual precipitation that functions healthy hydrological cycle in the areas of lower Gangetic plains to be most suitable for fishing cats. High mortality of the species due to retaliation and roadkill is a known fact for the species in the state. Hence, we also demarcated conservation priority zones for the species in the southern West Bengal which suggested agricultural lands (51.02 %), wetlands (21.22 %), vegetation cover (18.6 %) with populistic tourism spots in urban patches of southern West Bengal need to have focus from the concerned authority to save the remaining habitats. Accordingly, we further identified potential roadkill zones over state and national highways within the suitable habitat of the southern West Bengal.

Aerosol size distribution properties associated with cold-air outbreaks in the Norwegian Arctic

Abstract. The aerosol particles serving as cloud condensation and ice nuclei contribute to key cloud processes associated with cold-air outbreak (CAO) events but are poorly constrained in climate models due to sparse observations. Here we retrieve aerosol number size distribution modes from measurements at Andenes, Norway, during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) and at Zeppelin Observatory, approximately 1000 km upwind from Andenes at Svalbard. During CAO events at Andenes, the sea-spray-mode number concentration is correlated with strong over-ocean winds with a mean of 8±4 cm−3 that is 71 % higher than during non-CAO conditions. Additionally, during CAO events at Andenes, the mean Hoppel minimum diameter is 6 nm smaller than during non-CAO conditions, though the estimated supersaturation is lower, and the mean number concentration of particles that likely activated in-cloud is 109±61 cm−3 with no statistically significant difference from the non-CAO mean of 99±66 cm−3. For CAO trajectories between Zeppelin Observatory and Andenes, the upwind-to-downwind change in number concentration is the largest for the accumulation mode with a mean decrease of 93±95 cm−3, likely attributable primarily to precipitation scavenging. These characteristic properties of aerosol number size distributions during CAO events provide guidance for evaluating CAO aerosol–cloud interaction processes in models.

Revealing the optimal strategy for tree plantations on controlling soil erosion in China: A meta-analysis

Tree plantation has increased globally, driven by a desire to supply multiple ecosystem services including soil erosion control. However, how to plant trees can more effectively mitigate soil erosion still lacks systematic investigation. Therefore, we conducted a meta-analysis consisting of 412 paired observations from 80 studies to rigorously compare the effects of tree plantations on soil erosion control across different plantation approaches in China. The results showed that plantation age and ground cover type were the primary factors influencing the efficiency of tree plantations on runoff and sediment reduction. Mature plantations (>20 years) showed more efficiency in runoff (80.9 %) and sediment (97.2 %) reduction than young plantations (<10 years). The benefit of tree plantations with grass cover on runoff reduction was 70 %, while the capacity of plantations without ground cover was not significant. In addition, tree plantations in areas with steep slope, low mean annual precipitation, high soil sand content, and low soil organic carbon had better benefits on soil erosion control. Loess Plateau and Karst areas suffer the most severe water erosion in China, with distinct environmental conditions. This paper further revealed the targeted plantation strategy for these two regions: tree plantations in Karst areas should focus on the lithophyticity and drought tolerance of tree species, in the Loess Plateau should consider the trade-off between water consumption and soil erosion control.

Hydraulic traits exert greater limitations on tree-level maximum sap flux density than photosynthetic ability: Global evidence

Transpiration is a key process that couples the land-atmosphere exchange of water and carbon, and its maximum water transport ability affects plant productivity. Functional traits significantly influence the maximum transpiration rate; however, which factor plays the dominant role remains unknown. SAPFLUXNET dataset, which includes sap flux density of diverse species worldwide, provides fundamental data to test the importance of photosynthetic and hydraulic traits on maximum tree-level sap flux density (Js_max). Here, we investigated variations in Js_max of 2194 trees across 129 species using data from the SAPFLUXNET dataset, and analysed the relationship of Js_max with photosynthetic and hydraulic traits. Our results indicated that Js_max was positively correlated with photosynthetic traits at both leaf and tree level. Regarding hydraulic traits, Js_max was positively related to xylem hydraulic conductivity (Ks), leaf-specific hydraulic conductivity (Kl), xylem pressure inducing 50 % loss of hydraulic conductivity (P50), xylem vessel diameter (Vdia), and leaf-to-sapwood area ratio (AlAs). Random forest model showed that 87 % of the variability in Js_max can be explained by functional traits, and hydraulic traits (e.g., P50 and sapwood area, As) exerted larger effects on Js_max than photosynthetic traits. Moreover, trees with a lower sapwood area or depth could increase their sap flux density to compensate for the reduced whole-tree transpiration. Js_max of the angiosperms was significantly higher than that of the gymnosperms. Mean annual total precipitation (MAP) were positively related to Js_max with a weak correlation coefficient. Furthermore, Js_max showed a significant phylogenetic signal with Blomberg's K below 0.2. Overall, tree species with acquisitive resource economics or more efficient hydraulic systems show higher water transport capacity, and the efficiency of xylem hydraulic system rather than the demand for carbon uptake predominantly determines water transport capacity.

Shift from legacy to emerging per- and polyfluoroalkyl substances for watershed management along the coast of China

Per- and polyfluoroalkyl substances and their short-chain alternatives have attracted world-wide attention due to their widespread presence and persistence in the environment. However, the sources, environmental fate, and driving forces of PFAS in coastal ecosystems remain poorly understood. In this study, the spatial distribution, source apportionment, and driving mechanisms of PFAS were investigated through a comprehensive analysis of water samples collected along the China's coastline. The concentrations of Σ25PFAS in water samples followed a general pattern, with higher levels observed in northern coastal zones of China than the south, ranging from 0.72 to 1872.21 ng L−1. PFOA and PFBA were dominant. Emerging short-chain PFAS, such as PFBS, PFBA, F-53B and GenX, were frequently detected, with detection rates of 97%, 99%, 95% and 77%, respectively. This indicated a shift in coastal PFAS contamination from legacy compounds to emerging short-chain alternatives. Source apportionment using the Positive Matrix Factorization model identified key contributors to PFAS pollution, including textile production, volatile precursors, precious metal industries, aqueous film-forming foam, metal-plating, electrochemical fluorination, and fluoropolymer manufacturing. Additionally, PFAS concentrations were significantly positively correlated with cultivated land, urban area, and wastewater discharge, while negatively correlated with annual precipitation and woodland coverage (p < 0.05). Socio-economic development was identified as a major driver of PFAS emissions, while the hydrological factors and vegetation coverage can significantly enhance watershed resilience against PFAS pollution.

Early-successional species show higher tolerance of drought than late-successional species across Europe

Climate change is exacerbating forest disturbances through more frequent and more intense droughts and fires, undermining their ability to recover from such disturbances. The response of fast-growing early-successional species to drought is poorly understood, despite their key role in ecological succession and their ability to enhance ecosystem resilience. Here, we compared the growth responses to drought events of three early-successional species (silver birch, black poplar, and Scots pine) with that of one late-successional species (European beech) across their natural distribution ranges in Europe. We used tree-ring widths of 6340 trees from 109 forest sites to establish species-specific tree-ring chronologies. We then used multiple linear regression to analyze which climatic or growth variables (pre-drought growth and growth during drought) best explained the tree responses to drought. Silver birch, Scots pine, and black poplar showed superior drought tolerance, with a slight, non-significant growth reduction under drought, whereas European beech showed a significant decrease in growth. The variables that influenced growth during and after the drought were species-specific. Annual precipitation and growth variables were key predictors of post-drought growth for Scots pine, black poplar, and European beech. Scots pine and silver birch grew better with increasing latitude, i.e., in Northern Europe than in Central Europe, while European beech and black poplar showed more growth at sites with high precipitation during the vegetation and dormant period, respectively. This study provides insights into the drought tolerance of early-successional species and highlights their ability to promote ecological succession and facilitate the transition to drought-resistant, late-successional forest ecosystems.

Miocene evolution of vegetation, climate, and elevation in the Wulan Basin of northeast Tibetan Plateau based on a CRACLE analysis of palynological assemblages

The uplift of the Tibetan Plateau (TP) during the Cenozoic has played a pivotal role in shaping global climate and vegetation evolution, however, understanding the uplift history of its various tectonic blocks remains complex. In this paper, we analyze palynological samples from the Miocene Xiayoushashan and Shangyoushashan formations in the Wulan Basin to explore the relationship between climate change and TP uplift. The analysis identified four distinct pollen zones: Zone I (18–15.3 Ma) characterized by Artemisia-Lycopodium-Aster-Polygonaceae; Zone II (15.3–12.7 Ma) dominated by Pinus-Picea-Aster-Artemisia; Zone III (12.7–11.7 Ma) featuring Lycopodium-Picea-Pinus-Polygonaceae; Zone IV (11.7–8.7 Ma) characterized by Picea-Chenopodiaceae-Lycopodium-Artemisia. Results indicate that the whole study interval was dominated by shrubs and grasses, interspersed with conifers and broad-leaved trees, exhibiting an altitudinally zonal pattern. Using the Coexistence Likelihood Estimation (CRACLE) method, we estimate a mean annual temperature (MAT) ranging from 6.0 to 14.7 °C and a mean annual precipitation (MAP) between 919.1 and 1612.4 mm during Miocene period. While minor climatic fluctuations occurred, the region experienced a cool-temperate and sub-humid climate significantly warmer and more humid conditions compared to the present. Based on MAT differences, the paleoelevation of the Wulan Basin during the Miocene was estimated to be c. 1655–2025 m, suggesting an uplift of c. 1275–1645 m since that time. These findings corroborate previous suggestions that the northeastern TP had not reached its current elevation prior to the Late Miocene.

Precipitation, solar radiation, and their interaction modify leaf hydraulic efficiency-safety trade-off across angiosperms at the global scale.

In theory, there is a trade-off between hydraulic efficiency and safety. However, the strength and direction of this trade-off at the leaf level are not consistent across studies, and habitat climate may impact this trade-off. We compiled a leaf hydraulic efficiency and safety dataset for 362 species from 81 sites world-wide, with 280 paired observations of both traits, and tested whether climate was associated with departure from the proposed trade-off. The leaf hydraulic efficiency-safety trade-off was weak (R2 = 0.144) at the global scale. Mean annual precipitation and solar radiation (SR) modified the trade-off. Species from dry and high SR habitats (e.g. desert and tropical savanna) were generally located above the trade-off line, indicating that these species tended to have higher leaf hydraulic safety and efficiency than species from wet habitats with low SR (e.g. subtropical monsoon forest and montane rainforest), which were located below the trade-off line. Leaves with high vein density, dry leaf mass per area, and osmotic regulation enhanced safety without compromising hydraulic efficiency. Variation in the hydraulic efficiency-safety trade-off at the leaf level likely facilitates plant survival in specific habitats and allows for a more nuanced view of leaf hydraulic adaption strategies at the global scale.

The temporal distribution of endophytic and exophytic insect guilds responds to host plant phenology in the Brazilian Savannah

Abstract Niche theory predicts high specialisation in species‐rich environments such as neotropics, where plant‐herbivore interactions are ubiquitous in all terrestrial systems. However, there is much to be explored on how different insect guilds respond to plant phenology in these specialised environments. Using a Fabaceae‐herbivore community as an ecological model, we evaluated how plant phenology affects the abundance and shapes the temporal distribution of exophytic (folivore chewers) and endophytic (seed chewers) insects. We tested the specialisation hypothesis, predicting insect species on only one or a few host plants, with a seasonal pattern of occurrence, many rare and few abundant species. Additionally, we analysed the relationship between plant phenology and environmental factors (temperature and precipitation). We conducted fieldwork with five species and 97 individuals of Fabaceae in Brazilian Savannah and performed an insect‐rearing study in the laboratory. We found an immediately synchronised relationship between endophytic insects and plant phenology in the dry season, whereas exophytic were throughout the year. Only six species from the exophytic guild, out of a total of 87 species for both guilds, fed on more than one host plant, showing a specialised network. Endophytic herbivores are more abundant, ephemeral and predictable than exophytic ones. We also found a negative relationship between fruiting and precipitation and a positive relationship between new leaves and temperature and precipitation. Our study shows that the temporal distribution of insect‐herbivore guilds responds to plant phenology. In a scenario of global warming with effects on the annual precipitation, endophytic insects are most vulnerable to local extinction due to temporal changes in plant phenology than the exophytic ones.

Geographical variation and genetic diversity of Parashorea chinensis germplasm resources.

Parashorea chinensis is a rare monodominant species in southwest China known for its production of high-quality timber, is facing decline due to its narrow distribution, human interference and habitat destruction. However, there are no reports on genetic diversity and geographical variation of phenotypic traits of P. chinensis. In this study, phenotypic characters and genetic diversity of 15 germplasms resources from five provenances in southwest China were investigated, and their relationships with geographical and environmental factors was discussed. Our results revealed a rich phenotypic diversity among the germplasms, with variation coefficients ranging from 3.63% to 45.49%. Among the studied germplasms, NP03 from Napo and ML02 from Mengla region exhibited superior phenotypic traits. Notably, NP03 also demonstrated the highest genetic diversity. Genetic differentiation analyses including genetic differentiation coefficient (0.6264) and gene flow (0.3736) illustrated that genetic variation was most prevalent among populations. Furthermore, redundancy analysis showed that temperature related factors (maximum air temperature, annual mean temperature and minimum air temperature) significantly affected phenotypic variation. Similarly, altitude, longitude, latitude, annual mean precipitation and the minimum air temperature significantly impacted the level of genetic diversity. The molecular variation of the natural population of P. chinensis followed a certain geographical pattern. Our finding indicated abundant phenotypic variation among P. chinensis germplasms. However, populations exhibited low levels of genetic diversity alongside high genetic differentiation, potentially contributing to the species' rarity. Based on our results, NP03 and ML02 germplasm could be used as the parents for breeding superior germplasm of P. chinensis. Overall, this study provides valuable insights into germplasm diversity and conservation, genetic improvement, and utilization of P. chinensis.

A decade of rain exclusion in a Mediterranean forest reveals trade-offs of leaf chemical defenses and drought legacy effects

Increasing aridity in the Mediterranean region will result in longer and recurrent drought. These changes could strongly modify plant defenses, endangering tree survival. We investigate the response of chemical defenses from central and specialized metabolism in Quercus pubescens Willd. to future Mediterranean drought using a long-term drought experiment in natura where trees have been submitted to amplified drought (~ –30% annual precipitation) since April 2012. We focused on leaf metabolites including chlorophylls and carotenoids (central metabolism) and flavonols (specialized metabolism). Measurements were performed in summer from 2016 to 2022. Amplified drought led to higher concentrations of total photosynthetic pigments over the 2016–2022 period. However, it also led to lower AZ/VAZ and flavonol concentrations. Additionally, chemical defenses of Q. pubescens responded to previous precipitation where low precipitation 1 year and/or 2 years preceding sampling was associated to low concentrations of VAZ, flavonol and high neoxanthin concentrations. Our study indicates that the decline of flavonol concentration under long-term drought is counterbalanced by a higher production of several central metabolites. Such results are potentially due to an adjustment in tree metabolism, highlighting the importance of performing long-term experimental studies in natura for assessing drought legacy effects and thus forest adaptation to climate change.