Archaeal communities change responding to anthropogenic and natural treatments of freeze-thawed soils.

The coverage of accumulated snow plays a significant role in inducing changes in both microbial activity and environmental factors within freeze-thaw soil systems. This study aimed to analyze the impact of snow cover on the dynamics of archeal communities in freeze-thaw soil. Furthermore, it seeks to investigate the role of fertilization in freeze-thaw soil. Four treatments were established based on snow cover and fertilization:No snow and no fertilizer (CK-N), snow cover without fertilizer (X-N), fertilizer without snow cover (T-N), and both fertilizer and snow cover (T-X). The research findings indicated that after snow cover treatment, the carbon, nitrogen, and phosphorus content in freeze-thaw soil exhibit periodic fluctuations. Snow covered effectively altered the community composition of bacteria and archaea in the soil, with a greater impact on archaeal communities than on bacterial communities. Snow covered improves the stability of archaeal communities in freeze-thaw soil. Additionally, the arrival of snow also enhanced the correlation between archaea and environmental factors, with the key archaeal phyla involved being Nanoarchaeota and Crenarchaeota. Further research showed that the application of organic fertilizers also had some impact on freeze-thaw soil, but this impact was smaller compared to snow cover. In summary, the arrival of snow could alter the archaeal community and protect nutrient elements in freeze-thaw soil, reducing their loss, and its effect is more pronounced compared to the application of organic fertilizers.

Using interview surveys and multispecies occupancy models to inform vertebrate conservation.

Species distribution data are an essential biodiversity variable requiring robust monitoring to inform wildlife conservation. Yet, such data remain inherently sparse because of the logistical challenges of monitoring biodiversity across broad geographic extents. Surveys of people knowledgeable about the occurrence of wildlife provide an opportunity to evaluate species distributions and the ecology of wildlife communities across large spatial scales. We analyzed detection histories of 30 vertebrate species across the Western Ghats biodiversity hotspot in India, obtained from a large-scale interview survey of 2318 people who live and work in the forests of this region. We developed a multispecies occupancy model that simultaneously corrected for false-negative (non-detection) and false-positive (misidentification) errors that interview surveys can be prone to. Using this model, we integrated data across species in composite analyses of the responses of functional species groups (based on disturbance tolerance, diet, and body mass traits) to spatial variation in environmental variables, protection, and anthropogenic pressures. We observed a positive association between forest cover and the occurrence of species with low tolerance of human disturbance. Protected areas were associated with higher occurrence for species across different functional groups compared with unprotected lands. We also observed the occurrence of species with low disturbance tolerance, herbivores, and large-bodied species was negatively associated with developmental pressures, such as human settlements, energy production and mining, and demographic pressures, such as biological resource extraction. For the conservation of threatened vertebrates, our work underscores the importance of maintaining forest cover and reducing deforestation within and outside protected areas, respectively. In addition, mitigating a suite of pervasive human pressures is also crucial for wildlife conservation in one of the world's most densely populated biodiversity hotspots.

Uso de Encuestas y Modelos de Ocupación Multiespecies para Orientar la Conservación de Vertebrados Resumen Los datos de distribución de especies son una variable esencial de la biodiversidad que requieren de un monitoreo sólido para orientar la conservación de la fauna. Aun así, dichos datos permanecen inherentemente escasos debido a los obstáculos logísticos del monitoreo de la biodiversidad a lo largo de extensiones geográficas generalizadas. Las encuestas realizadas a personas conocedoras de la incidencia de fauna proporcionan una oportunidad para evaluar la distribución de las especies y la ecología de las comunidades de fauna en escalas espaciales grandes. Analizamos las historias de detección de 30 especies de vertebrados en los Ghats Occidentales de la India obtenidos a partir de una encuesta a gran escala realizada por entrevistas a 2318 personas que viven y trabajan en los bosques de esta región. Desarrollamos un modelo de ocupación multiespecies que corrigió simultáneamente los errores falsos negativos (no detección) y los falsos positivos (identificación correcta) a los que están propensos las encuestas por entrevista. Con este modelo, integramos los datos de todas las especies a un análisis compuesto de las respuestas de los grupos funcionales de especies (con base en la tolerancia a la perturbación, dieta y características de masa corporal) para la variación espacial en las variables ambientales, protección y presiones antropogénicas. Observamos una asociación positiva entre la incidencia de especies con la baja tolerancia a la perturbación humana y a la cobertura forestal. Las áreas protegidas estuvieron asociadas con una incidencia mayor para las especies ubicadas en diferentes grupos funcionales comparadas con las áreas desprotegidas. También observamos que la incidencia de especies con una tolerancia baja a las perturbaciones, herbívoros y especies de mayor tamaño estaba asociada negativamente con las presiones de desarrollo, como los asentamientos humanos, la producción de energías y minería, y las presiones demográficas, como la extracción de recursos biológicos. Para la conservación de vertebrados amenazados, nuestro trabajo hace hincapié en la importancia de mantener la cobertura forestal y reducir la deforestación dentro y fuera de las áreas protegidas, respectivamente. Además, la mitigación de un conjunto de presiones humanas dominantes también es crucial para la conservación de la naturaleza en uno de los puntos calientes de biodiversidad con una de las mayores densidades poblacionales del mundo.

Anthropogenic protection alters the microbiome in intertidal mangrove wetlands in Hainan Island.

Intertidal mangrove wetlands are of great economic and ecological importance. The regular influence of tides has led to the microbial communities in these wetlands differing significantly from those in other habitats. In this study, we investigated the microbiomes of the two largest mangrove wetlands in Hainan Island, China, which have different levels of anthropogenic protection. Soil samples were collected from the root zone of 13 mangrove species. The microbial composition, including key functional groups, was assessed using Illumina sequencing. Bioinformatics analysis showed that there was a significant difference in the microbiomes between the protected Bamen Bay and the unprotected Dongzhai Bay. The overall microbiome was assigned into 78 phyla and Proteobacteria was the most abundant phylum at both sites. In the protected wetland, there were fewer marine-related microbial communities, such as sulfate-reducing bacteria, and more terrestrial-related communities, such as Verrucomicrobia methanotrophs. We also observed distinct microbial compositions among the different mangrove species at the protected site. Our data suggest that the different microbiomes of the two mangrove wetlands are the result of a complex interaction of the different environmental variables at the two sites.

Spatial variation of phytoplankton community structure in Daya Bay, China.

Daya Bay is one of the largest and most important gulfs in the southern coast of China, in the northern part of the South China Sea. The phylogenetic diversity and spatial distribution of phytoplankton from the Daya Bay surface water and the relationship with the in situ water environment were investigated by the clone library of the large subunit of ribulose-1, 5-bisphosphate carboxylase (rbcL) gene. The dominant species of phytoplankton were diatoms and eustigmatophytes, which accounted for 81.9 % of all the clones of the rbcL genes. Prymnesiophytes were widely spread and wide varieties lived in Daya Bay, whereas the quantity was limited. The community structure of phytoplankton was shaped by pH and salinity and the concentration of silicate, phosphorus and nitrite. The phytoplankton biomass was significantly positively affected by phosphorus and nitrite but negatively by salinity and pH. Therefore, the phytoplankton distribution and biomass from Daya Bay were doubly affected by anthropic activities and natural factors.

Modeling habitat suitability of Dorema ammoniacum D Don. in the rangelands of central Iran.

The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0-30 and 30-60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.

Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse.

The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

Meta-analytic and MaxEnt model prediction, the distribution of the Bactrocera minax (Diptera: Tephritidae), in China under changing climate.

The Tephritidae family causes damage to fruits in tropical and subtropical regions around the world, with Bactrocera minax Enderlein (Diptera: Tephritidae) widely distributed in China, causing severe economic damage to Chinese citrus. Currently, preventing the rapid spread of B. minax remains an effective strategy to control it as the climate continues to warm in the future. In this context, it is crucial to understand the potential geographic range of B. minax under climate change. We used meta-analysis to assess the survival of Tephritidae insects under temperature stress. We also used the maximum entropy (MaxEnt) model to predict the suitable regions and migration trajectories of B. minax in China under current and future climatic conditions. Through comprehensive analysis of the experimental data, we found that the survival rate of Tephritidae insects in the suitable temperature range showed an increasing trend with the increase in warming extent. Using the MaxEnt model, we observed that the highly suitable area, as well as the moderately suitable area of B. minax, were expanding in all 3 future climate scenarios, with the distribution moving toward the high latitude region and the coastal region of China. Our results also indicate that temperature and precipitation contribute more to the model in the current year. Combining multiexperiment data, our study demonstrates that the potential distribution of B. minax in China will expand under future climate warming scenarios, and these predictions will provide important information for monitoring B. minax and informing managers in developing control strategies.

Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning.

Mapping vegetation formation types in large areas is crucial for ecological and environmental studies. However, this is still challenging to distinguish similar vegetation formation types using existing predictive vegetation mapping methods, based on commonly used environmental variables and remote sensing spectral data, especially when there are not enough training samples. To solve this issue, we proposed a predictive vegetation mapping method by integrating an advanced machine learning algorithm and knowledge in an early coarse-scale vegetation map (VMK). First, we implemented classification using the random forest algorithm by integrating the early vegetation map as an auxiliary feature (VMF). Then, we determined the rationality of classified vegetation types and distinguished the confusing types, respectively, based on the knowledge of the spatial distributions and hierarchies of vegetation. Finally, we replaced each recognized unreasonable vegetation type with its corresponding reasonable vegetation type. We implemented the new method in upstream of the Yellow River based on GaoFen-1 satellite images and other environmental variables (i.e., topographical and climate variables). Results showed that the overall accuracy using the VMK method ranged from 67.7 % to 76.8 %, which was 10.9 % to 13.4 % and 3.2 % to 6.6 %, respectively, higher than that of the method without the early vegetation map (NVM) and the VMF method, based on cross-validation with 20 % to 60 % random training samples. The spatial details of the vegetation map using the VMK method were also more reasonable compared to the NVM and VMF methods. These results indicated that the VMK method can distinctly improve the mapping accuracy at the vegetation formation level by integrating knowledge of existing vegetation maps. The proposed method can largely reduce the requirements on the number of field samples, which is especially important for alpine mountains and arctic region, where collecting training samples is more difficult due to the harsh natural environment.

Novel insight into the feed conversion ratio in laying hens and construction of its prediction model.

Feed efficiency (FE) is an important economic factor in poultry production, and feed conversion ratio (FCR) is one of the most widely used measures of FE. Factors associated with FCR include genetics, the environment, and other factors. However, the mechanisms responsible for FCR in chickens are still less well appreciated. In this study, we examined the pattern changes of FCR, then delved into understanding the mechanisms behind these variations from both genetic and environmental perspectives. Most interestingly, the FCR at the front section of henhouse exhibited the lowest value. Further investigation revealed that laying rate in the high FCR (HFCR) group was lower than that in the low FCR (LFCR) group (P < 0.05). Cortisol, total antioxidant capacity (TAOC), and IgG levels in the LFCR group were significantly lower than those in the HFCR group (P < 0.05), while BUN level was significantly higher than that in the HFCR group (P < 0.05). We identified a total of 67 and 10 differentially expressed genes (DEGs) associated with FCR in ovarian and small intestine tissues, respectively. Functional enrichment analysis of DEGs revealed that they might affect FCR by modulating genes associated with salivary secretion, ferroptosis, and mineral absorption. Moreover, values for relative humidity (RH), air velocity (AV), PM2.5, ammonia (NH3), and carbon dioxide (CO2) in the LFCR group were significantly lower than those in the HFCR group (P < 0.05). Conversely, value for light intensity (LI) in the LFCR group was significantly higher than that in the HFCR group (P < 0.05). Correlation analysis revealed a positive correlation between FCR and RH, AV, PM2.5, NH3, and CO2, and a negative correlation with LI. Finally, the FCR prediction model was successfully constructed based on multiple environmental variables using the random forest algorithm, providing a valuable tool for predicting FCR in chickens.

Potential impact of climate change on the distribution of Capricornis milneedwardsii, a vulnerable mammal in China.

Climate change significantly impacted on the survival, development, distribution, and abundance of living organisms. The Chinese serow Capricornis milneedwardsii, known as the "four unlike," is a Class II nationally protected species in China. In this study, we predicted the geographical suitability of C. milneedwardsii under current and future climatic conditions using MaxEnt. The model simulations resulted in area under the receiver operating characteristic curve (AUC) values above 0.9 for both current and future climate scenarios, indicating the excellent performance, high accuracy, and credibility of the MaxEnt model. The results also showed that annual precipitation (Bio12), slope, elevation, and mean temperature of wettest quarter (Bio8) were the key environmental variables affecting the distribution of C. milneedwardsii, with contributions of 31.2%, 26.4%, 11%, and 10.3%, respectively. The moderately and highly suitable habitats were mainly located in the moist area of China, with a total area of 34.56 × 104 and 16.61 × 104 km2, respectively. Under future climate change scenarios, the areas of suitability of C. milneedwardsii showed an increasing trend. The geometric center of the total suitable habitats of C. milneedwardsii would show the trend of northwest expansion and southeast contraction. These findings could provide a theoretical reference for the protection of C. milneedwardsii in the future.

Predicting the potential geographical distribution of Zingiber striolatum Diels (Zingiberaceae), a medicine food homology plant in China.

Zingiber striolatum Diels is a unique medicine food homology plant native to China. In recent years, due to severe habitat destruction, studying the impact of climate change on the distribution of wild resources is of great significance for the ecological conservation and artificial cultivation of Z. striolatum. This study collected 141 valid species distribution records, and 37 environmental variables, and projected two future climate scenarios (SSP126 and SSP585) for two periods (2050s and 2090s). By employing Pearson analysis, Maximum Entropy Model (MaxEnt), and Geographic Information System (ArcGIS), we predicted the potential suitable habitats for Z. striolatum under present and future climates, as well as identified the dominant environmental variables influencing its distribution. The results indicated that the MaxEnt model performed well (AUC > 0.9) with high accuracy and reliability. The dominant environmental factors included Precipitation of driest quarter (39.0 ~ 473.8 mm), Precipitation of wettest quarter (593.2 ~ 1269.4 mm), Temperature annual range (9.8 ~ 28.6℃), and Mean diurnal range (6.5 ~ 9.6℃). The highly suitable areas for Z. striolatum were mainly distributed in western and southern Yunnan, northern and western Guangxi, Guangdong, Fujian, and central Hainan. Under future climate change, the centroid of the total suitable area for Z. striolatum is projected to shift towards the southwest (Yungui Plateau) at higher elevations.

Soil carbon sequestration potential of cultivated lands and its controlling factors in China.

Understanding soil organic carbon (SOC) stocks and carbon sequestration potential in cultivated lands can have significant benefit for mitigating climate change and emission reduction. However, there is currently a lack of spatially explicit information on this topic in China, and our understanding of the factors that influence both saturated SOC level (SOCS) and soil organic carbon density (SOCD) remains limited. This study predicted SOCS and SOCD of cultivated lands across mainland China based on point SOC measurements, and mapped its spatial distribution using environmental variables as predictors. Based on the differentiation between SOCS and SOCD, the soil organic carbon sequestration potentials (SOCP) of cultivated land were calculated. Boosted regression trees (BRT), random forest (RF), and support vector machine (SVM) were evaluated as prediction models, and the RF model presented the best performance in predicting SOCS and SOCD based on 10-fold cross-validation. A total of 991 topsoil (0-20 cm) SOC measurements and 12 environmental variables explaining topography, climate, organism, soil properties, and human activity were used as predictors in the model. Both SOCS and SOCD suggested higher SOC levels in northeast China and lower levels in central China. The cultivated lands in China had the potential to sequester about 2.13 ± 0.96 kg m-2 (3.25 Pg) SOC in the top 20 cm soil depth. Northeastern China had the largest SOCP followed by Northern China, and Southwestern China had the lowest SOCP. The primary environmental variables that affected the spatial variation of SOCS were mean annual temperature, followed by clay content and normalized difference vegetation index (NDVI). The assessment and mapping of SOCP in China's cultivated lands holds significance importance as it can provide valuable insights to policymakers and researchers about SOCP, and aid in formulating climate change mitigation strategies.

Environmental forcing of phytoplankton carbon-to-diversity ratio and carbon-to-chlorophyll ratio: A case study in Jiaozhou Bay, the Yellow Sea.

The relationships between phytoplankton carbon (C) biomass and diversity (i.e., C-to-H' ratio) and chlorophyll a (i.e., C-to-Chl a ratio) are good indicators of marine ecosystem functioning and stability. Here we conducted four cruises spanning 2 years in Jiaozhou Bay to explore the dynamics of C-to-H' and C-to-Chl a ratios. The results showed that the phytoplankton C biomass and diversity were dominated by diatoms, followed by dinoflagellates. The average C-to-H' ratio ranged from 84.10 to 912.17, with high values occurring in the northern region of the bay. In contrast, the average C-to-Chl a ratio ranged between 15.55 and 89.47, and high values primarily appeared in the northern or northeastern part of the bay. In addition, the redundancy analysis showed that temperature and phosphate (DIP) were significantly correlated with both ratios in most cases, indicating that temperature and DIP may be key factors affecting the dynamics of C-to-H' and C-to-Chl a ratios.

Predicting the Potential Distribution of Oxalis debilis Kunth, an Invasive Species in China with a Maximum Entropy Model.

Oxalis debilis Kunth, an invasive plant native to South America, has already spread extensively throughout various regions in China including West China, East China, Central China, and South China. It poses a certain degree of damage to the local ecosystem and demonstrates significant invasive potential. Utilizing distribution information along with environmental variables such as bioclimate, soil factors, elevation, and UV-B radiation, the MaxEnt model combined with ArcGIS was employed to forecast the potential distribution of O. debilis in China. The ROC curve was employed to assess the accuracy of the model, while the jackknife test was utilized to identify dominant environmental variables and determine their optimal values. The simulated AUC value was 0.946 ± 0.004, and the predicted results exhibited a remarkable concordance with the actual outcomes, thereby indicating that the Maxent model demonstrated a high level of confidence in its predictive capabilities. The potential distribution of O. debilis in China spanned 18,914,237 km2, accounting for 19.70% of the total land area. This distribution was primarily observed in East, Central, and South China, with Guangdong, Guangxi, and Guizhou being identified as highly suitable habitats for O. debilis. Furthermore, it was observed that the distribution of O. debilis is primarily influenced by environmental variables such as the precipitation of the driest month, the monthly diurnal range, the mean temperature of the wettest quarter, and the isothermality. The findings can serve as a valuable point of reference for the prevention and monitoring of O. debilis spread, thereby contributing to the protection of China's agricultural, forestry, and ecological environments. It is imperative to acknowledge the hazards associated with O. debilis, closely monitor its invasion, and prevent uncontrolled dissemination.

Spatial analysis of the occurrence of the western conifer seed bug Leptoglossus occidentalis (Heteroptera: Coreidae) in Europe based on multiple environmental variables.

The western conifer seed bug (WCSB) Leptoglossus occidentalis (Heidemann) (Heteroptera: Coreidae) is a pest insect that causes significant losses of coniferous trees worldwide. In this study, we sought to project the potential distribution of the WCSB based on dual CLIMEX modeling and random forest (RF) analysis to obtain basic data for WCSB monitoring strategies. The CLIMEX model, a semimechanistic niche model that responds to climate-based environmental parameters, is a species distribution model that focuses on regional climatic suitability. Given that this model can be used to select areas that are likely to reflect the climatically favorable spread of species, which we initially used CLIMEX to evaluate the potential distribution of the WCSB. The RF algorithm was used to predict the potential occurrence of WCSB and to evaluate the relative importance of environmental variables for WCSB occurrence. Using the RF model, land cover was found to be the most important variable for classifying the presence/pseudo-absence of the WCSB, with an accuracy of 77.1%. Climatic suitability for the WCSB was predicted to be 2.4-fold higher in Southern Europe than in Western Europe, and the WCSB was predicted to occur primarily near coniferous forests. Given that CLIMEX and RF analyses yielded different prediction results, using the findings of both models may compensate for the shortcomings of these models when used independently. Consequently, to ensure greater prediction reliability, we believe that it would be beneficial to base predictions on the combined potential distribution data obtained using both modeling approaches.

Suitable overwintering habitat simulation and the drivers of their population expansion for Siberian cranes (Grus leucogeranus) in China.

To gain a more comprehensive understanding of habitat preferences, potential wintering area distribution and drivers of population expansion of Grus leucogeranus, we selected 70 geographical distribution points and 11 environmental variables in its wintering period from 2015-2022. We modeled suitable habitat patterns for G. leucogeranus in China using MaxEnt model, and analyzed the relationship between suitable habitat distribution and environmental factors. The results showed that the dominant factors affecting the overwintering distribution of G. leucogeranus were distance to mudflats, elevation, average precipitation in February, distance to water sources, minimum temperature in December, and land use type, with a cumulative contribution rate of 94.6%. The wintering ground of G. leucogeranus in China was mainly distributed in the middle and lower reaches of the Yangtze Plain and the Yellow River Delta of the North China Plain. In these regions, the area of high-, medium- and low-suitability habitat were 17685, 60787 and 60747 km2, respectively. A total of 40 protected areas had been established in the high-suitability wintering range of G. leucogeranus in China, whereas 12 high-suitability wetlands such as Qili Lake in Anhui, Liangzi Lake in Hubei and Chenjia Lake in Jiangxi were still unprotected. The wintering grounds had shown a trend of expansion to the northeast and southeast since 2015. Considering the large-scale habitat shifts of G. leucogeranus in recent years and the frequent new wintering records in various places, we suggested that the shortage of food resources in natural habitats was the main factor driving the expansion of G. leucogeranus' wintering range. To protect G. leucogeranus more effectively, we should strengthen the restoration of natural habitats and the management of farmland habitats.

Shrimp community structure and influence of environmental variables in the East China Sea and Yellow Sea in spring.

Based on data collected from research vessel cruises performed in May 2020 off the East China Sea (ECS) and the southern Yellow Sea (YS) (26°30'-35°00' N, 120°30'-127°00' E), we analyzed the shrimp community and its relationships with environmental variables by using index of relative importance, biodiversity indices, and multivariate techniques. A total of 29 species were recorded, belonging to 11 families and 19 genera. The dominant species were Metapenaeopsis longirostris, Leptochela gracilis, Solenocera melantho, Crangon hakodatei, Parapenaeus fissuroides, Plesionika izumiae, and Trachypenaeus curvirostris, which together accounted for 82.9% of the total biomass and 90.8% of the total abundance of shrimps. Results of Cluster and NMDS analyses showed that three groups were identified for the shrimp community in the ECS and YS in spring, including group A (inshore of northern ECS and YS group), group B (offshore of northern ECS group) and group C (southern ECS group). ANOSIM and SIMPER analysis showed significant differences between group A and B, gourp A and C, and group B and C, with the dissimilarity of 92.2%, 95.8% and 91.6%, respectively. The typical species were T. curvirostris, C. hakodatei, L. gracilis and Palaemon gravieri in group A, and S. melantho in group B, and M. longirostris, P. fissuroides, P. izumiae and Solenocera alticarinata in group C. Significant differences were also detected in biomass, diversity index, species richness index and evenness index among groups, with significantly greater values in group C than those in A and B. Environmental variables and the substrate also displayed significant differences among groups. Results of canonical correspondence analysis showed that bottom temperature, bottom salinity, depth, and the substrate were the main environmental variables affecting spatial structure of shrimp community. Water mass characteristics and substrate type had important influences on the distribution of shrimp community in the ECS and YS in spring.

Spatio-temporal patterns of zooplankton community in the Yellow River estuary: Effects of seasonal variability and water-sediment regulation.

Zooplankton community is ecological important because of its high sensitivity to environmental changes especially in estuarine areas. The Yellow River estuary (YRE) in China is the fifth biggest estuary in the world with significant seasonal characteristics and anthropogenic influence of Water-Sediment Regulation (WSR). This study investigated the spatio-temporal patterns of zooplankton in the YRE to explore the response of zooplankton to seasonal variation and WSR. Results suggested that the temporal patterns of zooplankton were mainly characterized by seasonal shift of dominant species. Hierarchical cluster analysis and non-metric multidimensional scaling determined summer, summer-autumn and winter-spring three zooplankton assemblages. Zooplankton spatial distributions represented seasonal consistency, in which the abundance generally showed a decreasing gradient from the river mouth to sea. WSR caused a high species replacement rate in July-August (80.36%) and a dramatic abundance decline from 4224.60 ind./m3 to 1541.10 ind./m3 with persistency and hysteresis effect. The high zooplankton abundance moved seaward in spatial distribution after WSR. Summer spatial pattern was determined with two and three zooplankton station assemblages, which was more clear after WSR. Redundancy analysis identified SSS, SST and transparency as important factors structuring zooplankton spatio-temporal patterns, in which SSS was the key one. The results provide a necessary reference for understanding the response of zooplankton community in estuarine areas to spontaneous changes and anthropogenic factors, and can help the protection of estuarine ecosystems and the formulation of hydrological regulatory policies.

[Potential geographical distribution of Rana hanluica in China under climate change]. / 气候变化下寒露林蛙在中国的潜在地理分布.

Global warming in the last few decades had strong impacts on biodiversity and geographi-cal distribution of different animal species worldwide, especially amphibians. Rana hanluica, a frog species endemic in China, is still classified as Least Concerned in the Red List of Threatened Species because few studies have been conducted on this species. To understand the survival of Rana hanluica population, we used maximum entropy models (MaxEnt) to analyze its distribution across regions under current climatic conditions based on 47 distribution records and 20 environmental factors. We investigated the changes in distribution of this species under different climate scenarios in China (2050s and 2070s). Finally, current and future suitable habitats for R. hanluica were mode-led, and the impacts of environmental factors in shaping its distribution were evaluated. The results showed that the prediction accuracy of the MaxEnt model was high, and AUC value of the receiver operating curve was 0.993. The total suitable habitat area for R. hanluica was 36.36×104 km2, mainly located in Hunan and Guizhou provinces in China. The major environmental factors influencing the geographic distribution of R. hanluica were precipitation of dryest month and altitude. Under the future climate scenario (2050 and 2070) with two representative concentration pathways (RCPs, SSP1-2.5, SSP5-8.5), the suitable habitat of R. hanluica was reduced in different degrees, resulting in a decreasing trend of the total suitable habitat area. The center of gravity in highly suitable habitat of R. hanluica shifted to high-latitude regions, with the core distribution area in Hunan Province.

Predicting the Suitable Geographical Distribution of Sinadoxa Corydalifolia under Different Climate Change Scenarios in the Three-River Region Using the MaxEnt Model.

Sinadoxa corydalifolia is a perennial grass with considerable academic value as a rare species owing to habitat destruction and a narrow distribution. However, its distribution remains unclear. In this study, we predicted the distribution of Sinadoxa corydalifolia in the three-river region (the source of the Yangtze River, Yellow River, and Lancang River) under the context of climate change using the maximum entropy (MaxEnt) model. Under the current climate scenario, the suitable distribution mainly occurred in Yushu County and Nangqian County. The suitable distribution area of Sinadoxa corydalifolia covered 3107 km2, accounting for 0.57% of the three-river region. The mean diurnal air temperature range (Bio2), temperature seasonality (Bio4), and mean air temperature of the driest quarter (Bio9) contributed the most to the distribution model for Sinadoxa corydalifolia, with a cumulative contribution of 81.4%. The highest suitability occurred when air temperature seasonality (Bio4) ranged from 6500 to 6900. The highest suitable mean air temperature of the driest quarter ranged from -5 to 0 °C. The highest suitable mean diurnal temperature (Bio2) ranged from 8.9 to 9.7 °C. In future (2041-2060) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: representative concentration pathway (RCP)26 (6171 km2) > RCP45 (6017 km2) > RCP80 (4238 km2) > RCP60 (2505 km2). In future (2061-2080) scenarios, the suitable distribution areas of Sinadoxa corydalifolia from high to low are as follows: RCP26 (18,299 km2) > RCP60 (11,977 km2) > RCP45 (10,354 km2) > RCP80 (7539 km2). In general, the suitable distribution will increase in the future. The distribution area of Sinadoxa corydalifolia will generally be larger under low CO2 concentrations than under high CO2 concentrations. This study will facilitate the development of appropriate conservation measures for Sinadoxa corydalifolia in the three-river region.