Predicting the Potential Distribution of Rare and Endangered Emmenopterys henryi in China Under Climate Change.

Climate change has a pivotal impact on the potential distribution of endangered and relic tree species. Probably due to unrepresentative sampling and single algorithm, at present, there are different views on the potential range of the endangered tree Emmenopterys henryi, endemic to China. Here, we first collated 612 occurrence records and 22 environmental variables including climate, topography, and soil. Combined the Biomod2 with MaxEnt, we then predicted its past, current, and future potential suitable area in China, and determined the key factors influencing its distribution. The ensemble model results showed that the main environmental variables affecting this species were the minimum temperature of the coldest month (BIO6), precipitation of warmest quarter (BIO18), and temperature seasonality (BIO4). Its current potential distribution area was 176.53 × 104 km2, mainly concentrated in eastern, central, and southwestern China. Collectively, the suitable area of E. henryi would averagely decrease by 3.90% in all 16 future scenarios, with its centroid largely migrating northeastward. Our findings indicate that the endangered E. henryi covered 18 provinces in China, having a larger area than known. Moreover, climate change may have an adverse effect on its potential distribution. In addition, the ensemble model can produce more effective prediction outcomes than MaxEnt for such endemic tree species with large environmental range. We recommend increasing sample representativeness by analyzing the completeness properties of sample coverage, and simultaneously selecting appropriate algorithms to ensure the reliability of distribution prediction for endangered and relict tree species.

Potential adaptive habitats for the narrowly distributed and rare bamboo species Chimonobambusa tumidissinoda J. R. Xue & T. P. Yi ex Ohrnb. under future climate change in China.

The global climate change has resulted in substantial modifications to the distribution patterns of narrowly distributed species across different time periods, leading to an imminent threat to the survival of some vulnerable species. Chimonobambusa tumidissinoda J. R. Xue & T. P. Yi ex Ohrnb., a bamboo species endemic to the transition zone from the Yunnan-Guizhou Plateau to the Sichuan Basin with high economic and ecological value, exhibits a limited range and rarity. Utilizing eight environmental variables and 56 occurrence records, we employed the MaxEnt model to predict the potential distribution range of C. tumidissinoda under current and future climate scenarios. The findings revealed that precipitation of the driest month (Bio14), elevation and isothermality (Bio3) were the crucial factors determining the species' distribution, accounting for 31.24%, 28.27% and 17.24% of data variability, respectively. The distribution centroid of C. tumidissinoda is anticipated to shift towards higher latitudes in response to future climate change, and the projected habitat suitability is expected to expand under ssp245 and ssp585 scenarios while remaining unchanged or contracting under the ssp126 scenario. Despite these expansions, the suitable habitats remain limited, with the largest being approximately 2.08 × 104 km2, indicating a significant threat to its survival. Our study provides insights into the adaptive responses of C. tumidissinoda to climate change, enriching scientific knowledge for developing effective conservation measurements as well as sustainable utilization.

Functional traits of ecosystem engineers as predictors of associated fauna.

The ongoing combination of global warming and increased anthropogenic pressure is causing latitudinal shifts in marine species, potentially impacting community composition, local richness, and marine trophic webs. This study investigates the factors influencing the distribution and diversity of intertidal seaweed and associated peracarid communities, including their functional traits, and explores various facets of beta diversity (taxonomic and functional). We hypothesize that: 1) abiotic factors such as temperature and anthropogenic pressure significantly influence seaweed distribution and diversity shifts, and 2) changes in seaweed functional diversity have an impact on the diversity and functioning of its associated peracarid communities. The sampling was conducted along a wide latitudinal gradient in the NE Atlantic (27°N - 65°N), encompassing three distinct ecoregions: Northern European coasts, the Iberian Peninsula, and Macaronesia. The identified seaweed and peracarid species were classified functionally, and taxonomic and functional diversity were analysed on a large geographic scale. The northern region exhibited large brown canopy seaweeds and epibiotic isopods, while Macaronesia featured small red, highly branched, and calcareous crust seaweeds with burrower and tube-building tanaids. The Iberian Peninsula acted as a transitional zone, showcasing a mix of green, red, and brown seaweeds, along with Amphipoda peracarids found across all ecoregions. Our findings underscore the impact of geographic distance on total beta diversity, revealing distinct seaweed and peracarid communities across spatial gradients. Environmental variables, particularly pH and maximum sea surface temperature, emerged as significant factors influencing beta diversity patterns of seaweeds, indicating the potential impact of acidification and heat waves on community composition. In addition, seaweed functional traits were shown to be significant in shaping the diversity and abundance of associated peracarid assemblages, impacting both taxonomic and functional beta diversity. These findings provide crucial insights into the factors influencing the biogeography and biodiversity dynamics of intertidal seaweeds and associated peracarids, offering essential implications for conservation and management strategies amid ongoing environmental changes.

Environmental drivers of mesozooplankton dynamics in the Munroe Island, adjacent to Ashtamudi Estuary, India.

The response of mesozooplankton is critical in assessing the health of an estuarine ecosystem. Reports on the spatial and temporal dynamics of mesozooplankton community in estuarine and backwaters of the Southern parts of India are scanty. In this scenario, we appraised the community structure of mesozooplankton and their spatio-temporal dynamics based on various multivariate statistical assessments. A total of 31 taxa were identified and the abundance was principally dominated by Copepoda followed by Luciferidae during three sampling seasons. The most abundant species were: Paracalanus parvus parvus, Pseudodiaptomus aurivillii, Temora stylifera, and Pseudodiaptomus serricaudatus. Canonical correspondence analysis and Spearman's correlation coefficients underlined that salinity, turbidity, conductivity, temperature, dissolved oxygen, chlorophyll a (Chl-a), and nutrients were the principal environmental variables strongly linked with mesozooplankton dynamics in Munroe Island. The highest abundance of mesozooplankton was recorded in MoN (monsoon), followed by PrM (pre-monsoon), and PoM (post-monsoon). Hierarchical clustering confirmed that the grouping of sampling stations is based on the estuarine and freshwater influences on mesozooplankton abundance. During the entire investigation, various ecological indices were observed in good condition. Moreover, the optimum environmental conditions during the PoM season are marked with the highest indices values. Overall, multivariate investigations undoubtedly proved the suitability of mesozooplankton communities as potential bioindicators for spatial and seasonal ecological assessments. Our investigation emphasizes the high assemblages of mesozooplankton and their responses to various environmental variables and highlights the significance of long-term ecological monitoring in a threatened ecosystem like Munroe Island.

Non-Linear Response of Alpha and Beta Diversity of Taxonomic and Functional Groups of Phytoplankton to Environmental Factors in Subtropical Reservoirs.

Exploring the response of the diversity of phytoplankton species and functional groups to environmental variables is extremely important in maintaining biodiversity in aquatic ecosystems. Although there were more taxonomic units at the species level than at the functional group level, it remained unclear whether species diversity was more sensitive than functional group diversity to environmental variables. In this study, taxonomic composition and alpha-beta diversity of phytoplankton were investigated in 23 subtropical reservoirs located in the Han River Basin in South China during wet and dry seasons. Structural Equation Modelling (SEM) and Generalized Dissimilarity Modelling (GDM) were employed to validate the response of phytoplankton species and functional group alpha-beta diversities to environmental variables. The results indicated that the community compositions of phytoplankton in eutrophic reservoirs were similar between wet and dry seasons, while there were distinct differences for community composition in oligotrophic-mesotrophic reservoirs between the two seasons. Across all reservoirs, there were no significant differences in alpha and beta diversities of species and functional groups between wet and dry seasons. The SEM and GDM results revealed that total phosphorus was the primary driving factor influencing alpha and beta diversities of species and functional groups in the 23 reservoirs. Meanwhile, the non-linear results of species beta diversity were stronger than the non-linear results of functional group beta diversity, indicating that phytoplankton species exhibited a higher explanatory power in responding to environmental changes compared to that of functional groups. Compared to that of species beta diversity, the response of functional group beta diversity to environmental variables was significantly lower in the dry season. These research findings lead to re-evaluating the common practice relating to the use of phytoplankton functional groups to assess environmental conditions, which may overlook the explanatory power of subtle changes at the species level, especially during periods of habitat diversification in the dry season.

Temporal-Spatial Fluctuations of a Phytoplankton Community and Their Association with Environmental Variables Based on Classification and Regression Tree in a Shallow Temperate Mountain River.

The effects of environmental factors on phytoplankton are not simply positive or negative but complex and dependent on the combination of their concentrations in a fluctuating environment. Traditional statistical methods may miss some of the complex interactions between the environment and phytoplankton. In this study, the temporal-spatial fluctuations of phytoplankton diversity and abundance were investigated in a shallow temperate mountain river. The machine learning method classification and regression tree (CART) was used to explore the effects of environmental variables on the phytoplankton community. The results showed that both phytoplankton species diversity and abundance varied fiercely due to environmental fluctuation. Microcystis aeruginosa, Amphiprora sp., Anabaena oscillarioides, and Gymnodinium sp. were the dominant species. The CART analysis indicated that dissolved oxygen, oxidation-reduction potential, total nitrogen (TN), total phosphorus (TP), and water temperature (WT) explained 36.00%, 13.81%, 11.35%, 9.96%, and 8.80%, respectively, of phytoplankton diversity variance. Phytoplankton abundance was mainly affected by TN, WT, and TP, with variance explanations of 39.40%, 15.70%, and 14.09%, respectively. Most environmental factors had a complex influence on phytoplankton diversity and abundance: their effects were positive under some conditions but negative under other combinations. The results and methodology in this study are important in quantitatively understanding and exploring aquatic ecosystems.

Exploring the distribution and habitat preferences of Polytrichaceae (Bryophyta) in Tibet, China.

The Qinghai-Tibet Plateau stands as one of the most ecologically fragile and biodiversity-rich regions globally. Understanding the distribution of different taxa and their relationship with environmental factors is crucial for effective conservation and sustainable management. Polytrichaceae, a significant bryophyte family widely distributed in Tibet, displays distinct structural, morphological, and phylogenetic traits compared to other mosses. Despite its importance, the distribution of Polytrichaceae in Tibet and its correlation with environmental factors have yet to be explored. In this study, we used an optimized Maximum Entropy (MaxEnt) model to explore the potential suitable habitats of Polytrichaceae in Tibet, aiming to clarify their geographic distribution pattern as well as the key environmental influence factors. The model had high accuracy with an average Area Under the Curve (AUC) of 0.933 and True Skill Statistics (TSS) value of 0.789. The results showed that the potential suitability habitats of Polytrichaceae were mainly located in southeastern Tibet, and the low suitable, moderately suitable, and highly suitable habitats accounted for 12.53 %, 6.84 %, and 3.31 % of the total area of Tibet respectively. Unsuitable habitats were mainly located in northwestern Tibet, accounting for about 77.32 %. In Tibet, temperature factors (Mean Temperature of Coldest Quarter (Bio11) and Annual Mean Temperature (Bio1)) played a pivotal role in determining the potential suitable habitats for Polytrichaceae, and elevation, precipitation, and vegetation coverage also had an important influence. The family preferred warm, moist and densely vegetated habitats in Tibet. This study enriched our ecological understanding of bryophyte ecology in this region and provided data-driven support for biodiversity conservation and ecosystem management in Tibet.

Edaphoclimatic variation and harvest seasonality as determining factors of multidimensional quality in avocado cv. hass grown in the tropics.

The increase in cultivated areas in tropical zones such as Colombia for avocado cv. Hass and the lack of knowledge on edaphoclimatic relationships with factors associated with quality led to the present research. The aim of this research was to establish the relationship of soil, climatic, spatial factors (plot location), and harvest seasonality (principal and transitory) with the multidimensional quality of avocado cv. Hass planted under tropical conditions. This research was carried out on eight farms located in three producing subregions. Soil, environmental and harvest data were recorded for three years (2015-2017) in each plot. Avocado fruit samples were used to determine the parameters of macronutrient, fatty acids, minerals, and vitamin E. Descriptive, inferential statistics, multivariate analysis, effect size, second-order exponential model, and causal relationships were used to determine variables associated with soil, climate, harvest seasonality, and spatial location, and to determine quality parameters. The results established a relationship between nutritional quality and the origin region. Similarly, it was possible to identify parameters associated with differential quality with a robust statistical methodology to propose origin as a differentiating factor for quality. This study provided useful information for the value chain that selected the best areas for avocado crops according to market expectations and nutritional quality criteria.

Deciphering the microbial succession and color formation mechanism of "green-covering and red-heart" Guanyin Tuqu.

"Green-covering and red-heart" Guanyin Tuqu (GRTQ), as a type of special fermentation starter, is characterized by the "green-covering" formed on the surface of Guanyin Tuqu (SQ) and the "red-heart" in the center of Guanyin Tuqu (CQ). However, the mechanisms that promote temporal succession in the GRTQ microbial ecology and the formation of "green-covering and red-heart" characteristics remain unclear. Herein, we correlated the temporal profiles of microbial community succession with the main environmental variables (temperature, moisture, and acidity) and spatial position (center and surface) in GRTQ throughout fermentation. According to the results of high-throughput sequencing and culture-dependent methods, the microbial communities in the CQ and SQ demonstrated functional complementarity. For instance, the bacterial richness index of the CQ was greater than that of SQ, and the fungal richness index of the SQ was greater than that of CQ at the later stage of fermentation. Furthermore, Saccharomycopsis, Saccharomyces, Aspergillus, Monascus, Lactobacillus, Bacillus, Rhodanobacter, and Chitinophaga were identified as the dominant microorganisms in the center, while the surface was represented by Saccharomycopsis, Aspergillus, Monascus, Lactobacillus, Acetobacter, and Weissella. By revealing the physiological characteristics of core microorganisms at different spatial positions of GRTQ, such as Aspergillus clavatus and Monascus purpureus, as well as their interactions with environmental factors, we elucidated the color formation mechanism behind the phenomenon of "green" outside and "red" inside. This study provides fundamental information support for optimizing the production process of GRTQ.

Leaf carbon, nitrogen, and phosphorus ecological stoichiometry of grassland ecosystems along 2,600-m altitude gradients at the Northern slope of the Tianshan Mountains.

Ecological stoichiometry of terrestrial ecosystems has been a hot issue in current research, with intense focus on the proportional relationships of nutritional elements within plants and between plants and their environment. To clarify these relationships along continuous environmental gradients is essential for a more comprehensive understanding how plants adapt to a changing environment. In arid regions, the varying plant and soil types along altitude gradients offer a unique opportunity to examine the vertical spectrum of plant and soil ecological stoichiometry. In this study, the northern slope of the Tianshan Mountains was selected as the study area to explore the carbon (C), nitrogen (N), and phosphorus (P) ecological stoichiometric characteristics of herbaceous plants along 900-m-3,500-m altitude gradients. We also investigated the variation of ecological stoichiometric characteristics among different grassland types. The results indicated that the mean C, N, and P in leaf of grassland were 342.95 g·kg-1-557.73 g·kg-1, 6.02 g·kg-1-20.97 g·kg-1, and 0.71 g·kg-1-3.14 g·kg-1, respectively. There was no significant change in leaf carbon content along the elevation gradient, and the highest and lowest leaf C concentrations were in the upland meadow and the semidesert grasslands. Both N and P concentrations obtained their highest value in the meadow steppe. The P concentration gradually increased in desert and semidesert grasslands and reached the highest value in the meadow steppe, and then decreased to the lowest value in the upland meadow and subsequently increased in the alpine meadow. The ranges of the C:N ratio, C:P ratio, and N:P ratio were 16.36-155.53, 109.36-786.52, and 2.58-17.34, respectively. Due to fluctuations in the P concentration, the C:P ratio and N:P ratio reached the lowest value in the meadow steppe and obtained their highest value in the upland meadow. Redundancy analysis showed that temperature was the dominant factor affecting the C, N, and P ecological stoichiometry of herbaceous plants, followed by soil organic carbon, mean annual precipitation, soil pH, and soil electrical conductivity. Corresponding results could enhance predictive models of nutrient cycling and ecosystem responses to climate change, particularly in arid and semiarid regions.

Environmental DNA Reveals the Impact of Submarine Groundwater Discharge on the Spatial Variability of Coastal Fish Diversity.

Submarine groundwater discharge (SGD) has recently been recognized as an influential factor in coastal ecosystems; however, little research has been conducted on its effects on coastal fish diversity. To investigate the relationship between SGD and fish diversity, we conducted a survey at the coastal island scale using the environmental DNA (eDNA) method. Our findings indicate that fish species richness and functional richness peak at stations with high SGD. Environmental variables, such as salinity, dissolved inorganic nitrogen (DIN) concentration, and SGD, significantly influence fish diversity. Carnivore fish richness was negatively correlated with salinity, while planktivore fish richness was positively correlated. Additionally, SGD and DIN concentrations were found to be crucial in shaping omnivorous and pelagic communities, respectively. This study highlights the role of SGD in enhancing nutrient conditions favorable for diverse fish communities and demonstrates the effectiveness of eDNA metabarcoding for rapid marine biodiversity assessment. These findings provide valuable insights for coastal ecosystem monitoring and management.

Environmental variables improve the accuracy of remote sensing estimation of soil organic carbon content.

Accurately and quickly estimating the soil organic carbon (SOC) content is crucial in the monitoring of global carbon. Environmental variables play a significant role in improving the accuracy of the SOC content estimation model. This study focuses on modeling methodologies and environmental variables, which significantly influence the SOC content estimation model. The modeling methods used in this research comprise multiple linear regression (MLR), partial least squares regression (PLSR), random forest, and support vector machines (SVM). The analyzed environmental variables include terrain, climate, soil, and vegetation cover factors. The original spectral reflectance (OSR) of Landsat 5 TM images and the spectral reflectivity after the derivative processing were combined with the above environmental variables to estimate SOC content. The results showed that: (1) The SOC content can be efficiently estimated using the OSR of Landsat 5 TM, however, the derived processing method cannot significantly improve the estimation accuracy. (2) Environmental variables can effectively improve the accuracy of SOC content estimation, with climate and soil factors producing the most significant improvements. (3) Machine learning modeling methods provide better estimation accuracy than MLR and PLSR, especially the SVM model which has the highest accuracy. According to our observations, the best estimation model in the study area was the "OSR + SVM" model (R2 = 0.9590, RMSE = 13.9887, MAE = 10.8075), which considered four environmental factors. This study highlights the significance of environmental variables in monitoring SOC content, offering insights for more precise future SOC assessments. It also provides crucial data support for soil health monitoring and sustainable agricultural development in the study area.

Forecasting daily total pollen concentrations on a global scale.

BACKGROUND: There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. METHODS: The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. RESULTS: The best pollen forecasts include Mexico City (R2(DL_7) ≈ .7), and Santiago (R2(DL_7) ≈ .8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈ .4) and Seoul (R2(DL_7) ≈ .1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28-100 cm depth, and past soil temperature in 0-7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. CONCLUSIONS: This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in mitigating climate change, there are very few studies that have assessed the overall spatial distribution of cover crops and tillage practices based on the farm's pedoclimatic and topographic characteristics. Hence, the primary objective of this study was to use multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota (SD). We used a machine learning classifier trained with in situ field samples and environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map the conservation agriculture practices. Our classification accuracies (>80%) indicate that the employed satellite spectral indices and environmental variables could successfully detect the presence of cover crops and the tillage intensity in the study region. Our analysis revealed that 4% of the corn (Zea mays) and soybean (Glycine max) fields in eastern SD had a cover crop during either the fall of 2022 or the spring of 2023. We also found that environmental factors, specifically seasonal precipitation, growing degree days, and surface texture, significantly impacted the use of conservation practices. The methods developed through this research may provide a viable means for tracking and documenting farmers' agricultural management techniques. Our study contributes to developing a measurement, reporting, and verification (MRV) solution that could help used to monitor various climate-smart agricultural practices.

Predicting the Impact of Climate Change on the Future Distribution of Paederus fuscipes Curtis, 1826, in China Based on the MaxEnt Model.

Paederus fuscipes Curtis, 1826, belongs to the Coleoptera order, Staphylinidae family, and Paederus genus (Fabricius, 1775). It has a wide distribution and strong invasive and environmental adaptation capabilities. As a predatory natural enemy of agricultural and forestry pests, understanding its suitable habitat is crucial for the control of other pests. This study, for the first time, uses the MaxEnt model and ArcGIS software, combining known distribution information of P. fuscipes and climate environmental factors to predict the current and future suitable habitat distribution of this insect. The key environmental variables affecting the distribution of P. fuscipes have been identified as mean diurnal range (mean of monthly (max temp-min temp)) (bio2), isothermality (Bio2/Bio7) (*100) (bio3), minimum temperature of the coldest month (bio6), temperature annual range (bio5-bio6) (bio7), mean temperature of the driest quarter (bio9), mean temperature of the coldest quarter (bio11), precipitation of the wettest month (bio13), precipitation of the driest month (bio14), and precipitation seasonality (coefficient of variation) (bio15). The highly suitable areas for P. fuscipes in China are mainly distributed in the hilly regions of Shandong, the North China Plain, and the middle and lower reaches of the Yangtze River Plain, with a total suitable area of 118.96 × 104 km2, accounting for 12.35% of China's total area. According to future climate change scenarios, it is predicted that the area of highly and lowly suitable regions will significantly decrease, while moderately suitable regions will increase (except for the 2090s, SSP2-4.5 scenario). These research findings provide important theoretical support for pest control and ecological conservation applications.

Assessing Reptile Conservation Status under Global Climate Change.

Global climate change drives variations in species distribution patterns and affects biodiversity, potentially increasing the risk of species extinction. Investigating the potential distribution range of species under future global climate change is crucial for biodiversity conservation and ecosystem management. In this study, we collected distributional data for 5282 reptile species to assess their conservation status based on distributional ranges using species distribution models. Our predictions indicate that the potential distribution ranges for over half of these species are projected to decrease under different scenarios. Under future scenarios with relatively low carbon emissions, the increase in the number of threatened reptiles is significantly lower, highlighting the importance of human efforts. Surprisingly, we identified some endangered species that are projected to expand their distribution ranges, underscoring the potential positive effects of climate change on some special species. Our findings emphasize the increased extinction risk faced by reptile species due to climate change and highlight the urgent need to mitigate the effects of habitat degradation and human activities on their potential distribution in the future.

Environmental DNA metabarcoding reveals diverse phytoplankton assemblages and potentially harmful algal distribution along the urban coasts of Türkiye.

Marine phytoplankton are widely used to monitor the state of the water column due to their rapid changes in response to environmental conditions. In this study, we aimed to investigate the coastal phytoplankton assemblages, including bloom-forming species using high-throughput sequencing of 18S rRNA genes targeting the V4 region and their relationship with environmental variables along the Istanbul coasts of the Sea of Marmara. A total of 118 genera belonging to six phyla were detected. Among them, Dinoflagellata (36) and Bacillariophyta (26) were represented with the highest number of genera. According to the relative abundance of DNA reads, the most abundant taxa were Dinoflagellata_phylum (18.1%), Emiliania (8.4%), Biecheleria (8.4), and Noctiluca (8.1%). The ANOSIM test showed that there was a significant temporal difference in the assemblages, while the driving environmental factors were pH, water temperature, and salinity. According to the TRIX index, the trophic state of the coasts was highly mesotrophic and eutrophic. In addition, 45 bloom-forming and HAB taxa were detected and two species of Noctiluca and Emiliania, which frequently cause blooms in the area, were recorded in high abundance. Our results provide insight into the phytoplankton assemblages along the urbanized coastlines by analysing the V4 region of 18S rRNA. This data can support future studies that use both traditional methods and metabarcoding, employing various primers and targeting different genes and regions.

Distribution model and prediction of the tree fern Alsophila costularis Baker (Cyatheaceae) in China.

Climatic change is a challenge for plant conservation due to plants' limited dispersal abilities. The survival and sustainable development of plants directly depend on the availability of suitable habitats. In this study, we employed an optimized MaxEnt model to evaluate the relative contribution of each environmental variable and predict the suitable habitat for Alsophila costularis under past, current, and future periods, which is an endangered relict tree fern known as a living fossil. For the Last Glacial Maximum (LGM) and Mid-Holocene scenarios, we adopted two atmosphere-ocean general circulation models: CCSM4 and MIROC-ESM. The BCC-CSM2-MR model was used for future projections. The results revealed that temperature annual range (Bio7) contributed most to the model construction with an optimal range of 13.74-22.44°C. Species distribution modeling showed that current suitable areas were mainly located in most areas of Yunnan, most areas of Hainan, most areas of Taiwan, southeastern Tibet, southwestern Guizhou, western Guangxi, southern Sichuan, and southern Guangdong, with an area of 35.90 × 104 km2. The suitable habitat area expanded northward in Yunnan from the Last Interglacial to the LGM under the CCSM4 model, while a significant contraction toward southwestern Yunnan was found under the MIROC-ESM model. Furthermore, the potential distributions during the Mid-Holocene were more widespread in Yunnan compared to those under current period. It is predicted that in the future, the range will significantly expand to northern Yunnan and western Guizhou. Almost all centroids of suitable habitats were distributed in southeastern Yunnan under different periods. The stable areas were located in southwestern Yunnan in all scenarios. The simulation results could provide a theoretical basis for the formulation of reasonable conservation and management measures to mitigate the effects of future climate change for A. costularis.

Fecal thyroid hormone metabolites in wild ungulates: a mini-review.

This review aims to analyse the fluctuations of fecal thyroid hormone metabolites (FTMs) related to environmental and individual variables in different species of wild ungulates and provide a collection of assay methods. The great advantage of fecal sampling is being completely non-invasive. A systemic search was conducted from 2019 to 2024, using data sources PubMed, Scopus, Web of Science, and the World Wide Web, and ten studies were found on this topic. Three studies used the radioimmunoassay method for FTMs analysis, while the others used a less expensive enzyme-linked immunosorbent assay. Most of these papers validated the method for the species-specific matrix. Related to the studied variables, some authors analysed FTM fluctuations only concerning individual variables, and others in response to both. Temperature and fecal cortisol metabolites (FCMs) were the most studied environmental and individual variables, respectively. Since FTMs are an integrative measure of plasma thyroid hormones, the information obtained from a non-invasive-assay method regarding wild ungulate physiology is becoming of great interest to the scientific community.

Potential distribution of three invasive agricultural pests in China under climate change.

Invasive pests reduce biodiversity and ecosystem service functions, thereby leading to economic and also agricultural losses. Banana skipper (Erionota torus Evans), red palm weevil (Rhynchophorus ferrugineus), and coconut caterpillar (Opisina arenosella Walker) are invasive insect pests in the palm-growing regions and they have had serious consequences for the planting of bananas (Musa nana), palms (Trachycarpus fortune) and coconut (Cocos nucifera). Based on screened occurrence data, the present research utilized Maximum Entropy model (Maxent) to simulate the distribution dynamics of these three invasive insects in China, under current and future climate (2050s, 2070s, 2090s) in two shared socio-economic pathways (SSPs: 126 and 585) of the newly released coupled model intercomparison project phase6 (CMIP6). The results show that: (1) Under current and future climate conditions, all model groups exhibited an AUC value exceeding 0.92, which shows that the model prediction results are very good;(2) The suitable habitat area of E. torus Evans remains relatively stable with some expansion in the SSP126 of 2090s and some contraction in the SSP585 of 2090s. The suitable habitat area of R. ferrugineus showed an overall contraction, with substantial contraction in the SSP585 of 2090s.The suitable habitat area of O. arenosella has an overall expansion, with the most pronounced expansion in the SSP585 of 2070s; (3) The current centroid of suitable habitats for R. ferrugineus and E. torus Evans is located in Guangxi Province and wholely shift toward the south direction under future climate. The centroid of suitable habitats for O. arenosella is currently located in the northeastern maritime area of Hainan Province and will shift toward the north direction under future climate; (4) Temperature, precipitation and Human disturbance factors (Population density and Human influence index) were crucial variables for describing the distribution of the three species. For E. torus Evans in particular, percentage contributions of Population density was up to 31.4, which is only 0.1 different from ranked first Bio19 (Precipitation of the coldest quarter). The dynamics of habitats of these three species and the correlating driver factors proposed in this work provide essential insights into future spatial management of the three invasive insects in China. Our work is necessary and timely in identifying newly areas at high risk of expansion of the three invasive insects in the future, then suggesting strategic control measures to prevent their spread, and finally providing scientific evidence for the early prevention and rapid response to the three invasive insects.