Prediction of suitable regions of wild tomato provides insights on domesticated tomato cultivation in China.

Climate change is one of the biggest challenges to the world at present. Tomato is also suffered from devastating yield loss due to climate change. The domesticated tomato (Solanum lycopersicum) is presumed to be originated from the wild tomato (S. pimpinellifolium). In this study, we compared the climate data of S. pimpinellifollium with the domesticated tomato, predicted the suitable regions of S. pimpinellifollium in China using MaxEnt model and assessed their tolerance to drought stress. We found that the predicted suitable regions of wild tomato are highly consistent with the current cultivated regions of domesticated tomato, suggesting that the habitat demand of domesticated tomato descended largely from its ancestor, hence the habitat information of wild tomato could provide a reference for tomato cultivation. We further predicted suitable regions of wild tomato in the future in China. Finally, we found that while average drought tolerance between wild and domesticated tomato accessions shows no difference, tolerance levels among wild tomato accessions exhibit higher variation, which could be used for future breeding to improve drought resistance. To summarize, our study shows that suitable regions of wild tomato provide insights into domesticated tomato cultivation in China.

Potential distribution and ecological impacts of Acmella radicans (Jacquin) R.K. Jansen (a new Yunnan invasive species record) in China.

BACKGROUND ACMELLA RADICANS: (Jacquin) R.K. Jansen is a new invasive species record for Yunnan Province, China. Native to Central America, it has also been recently recorded invading other parts of Asia. To prevent this weed from becoming a serious issue, an assessment of its ecological impacts and potential distribution is needed. We predicted the potential distribution of A. radicans in China using the MaxEnt model and its ecological impacts on local plant communities and soil nutrients were explored. RESULTS: Simulated training using model parameters produced an area under curve value of 0.974, providing a high degree of confidence in model predictions. Environmental variables with the greatest predictive power were precipitation of wettest month, isothermality, topsoil TEB (total exchangeable bases), and precipitation seasonality, with a cumulative contribution of more than 72.70% and a cumulative permutation importance of more than 69.20%. The predicted potential suitable area of A. radicans in China is concentrated in the southern region. Projected areas of A. radicans ranked as high and moderately suitable comprised 5425 and 26,338 km2, accounting for 0.06 and 0.27% of the Chinese mainland area, respectively. Over the 5 years of monitoring, the population density of A. radicans increased while at the same time the population density and importance values of most other plant species declined markedly. Community species richness, diversity, and evenness values significantly declined. Soil organic matter, total N, total P, available N, and available P concentrations decreased significantly with increasing plant cover of A. radicans, whereas pH, total K and available K increased. CONCLUSION: Our study was the first to show that A. radicans is predicted to expand its range in China and may profoundly affect plant communities, species diversity, and the soil environment. Early warning and monitoring of A. radicans must be pursued with greater vigilance in southern China to prevent its further spread.

Conservation genetics and potential geographic distribution modeling of Corybas taliensis, a small 'sky Island' orchid species in China.

BACKGROUND: Corybas taliensis is an endemic species of sky islands in China. Its habitat is fragile and unstable, and it is likely that the species is threatened. However, it is difficult to determine the conservation priority or unit without knowing the genetic background and the overall distribution of this species. In this study, we used double digest restriction-site associated DNA-sequencing (ddRAD-seq) to investigate the conservation genomics of C. taliensis. At the same time, we modeled the extent of suitable habitat for C. taliensis in present and future (2030 and 2090) habitat using the maximum-entropy (MaxEnt) model. RESULTS: The results suggested that the related C. fanjingshanensis belongs to C. taliensis and should not be considered a separate species. All the sampling locations were divided into three genetic groups: the Sichuan & Guizhou population (SG population), the Hengduan Mountains population (HD population) and Himalayan population (HM population), and we found that there was complex gene flow between the sampling locations of HD population. MT was distinct genetically from the other sampling locations due to the unique environment in Motuo. The genetic diversity (π, He) of C. taliensis was relatively high, but its contemporary effective population size (Ne) was small. C. taliensis might be currently affected by inbreeding depression, although its large population density may be able to reduce the effect of this. The predicted areas of suitable habitat currently found in higher mountains will not change significantly in the future, and these suitable habitats are predicted to spread to other higher mountains under future climate change. However, suitable habitat in relatively low altitude areas may disappear in the future. This suggests that C. taliensis will be caught in a 'summit trap' in low altitude areas, however, in contrast, the high altitude of the Himalaya and the Hengduan Mountains are predicted to act as 'biological refuges' for C. taliensis in the future. CONCLUSIONS: These results not only provide a new understanding of the genetic background and potential resource distribution of C. taliensis, but also lay the foundation for its conservation and management.

Unravelling the role of tropical cyclones in shaping present species distributions.

Driven by climate change, tropical cyclones (TCs) are predicted to change in intensity and frequency through time. Given these forecasted changes, developing an understanding of how TCs impact insular wildlife is of heightened importance. Previous work has shown that extreme weather events may shape species distributions more strongly than climatic averages; however, given the coarse spatial and temporal scales at which TC data are often reported, the influence of TCs on species distributions has yet to be explored. Using TC data from the National Hurricane Center, we developed spatially and temporally explicit species distribution models (SDMs) to examine the role of TCs in shaping present-day distributions of Puerto Rico's 10 Anolis lizard species. We created six predictor variables to represent the intensity and frequency of TCs. For each occurrence of a species, we calculated these variables for TCs that came within 500 km of the center of Puerto Rico and occurred within the 1-year window prior to when that occurrence was recorded. We also included predictor variables related to landcover, climate, topography, canopy cover and geology. We used random forests to assess model performance and variable importance in models with and without TC variables. We found that the inclusion of TC variables improved model performance for the majority of Puerto Rico's 10 anole species. The magnitude of the improvement varied by species, with generalist species that occur throughout the island experiencing the greatest improvements in model performance. Range-restricted species experienced small, almost negligible, improvements but also had more predictive models both with and without the inclusion of TC variables compared to generalist species. Our findings suggest that incorporating data on TCs into SDMs may be important for modeling insular species that are prone to experiencing these types of extreme weather events.

Epidemiological characterization and risk assessment of rabbit haemorrhagic disease virus 2 (RHDV2/b/GI.2) in the world.

A novel variant of rabbit haemorrhagic disease virus, designated RHDV2/b/GI.2, was first discovered in France in 2010. Subsequently, RHDV2 rapidly spread to Africa, North America, Australia, and Asia. RHDV2 outbreaks have resulted in significant economic losses in the global rabbit industry and disrupted the balance of natural ecosystems. Our study investigated the seasonal characteristics of RHDV2 outbreaks using seasonal indices. RHDV2 is prone to causing significant outbreaks within domestic and wild rabbit populations during the spring season and is more likely to induce outbreaks within wild rabbit populations during late autumn in the Southern Hemisphere. Furthermore, based on outbreak data for domestic and wild rabbits and environmental variables, our study established two MaxEnt models to explore the relationship between RHDV2 outbreaks and the environmental factors and conducted outbreak risk predictions for RHDV2 in global domestic and wild rabbit populations. Both models demonstrated good predictive performance, with AUC values of 0.960 and 0.974, respectively. Road density, isothermality, and population density were identified as important variables in the outbreak of RHDV2 in domestic rabbits, while road density, normalized difference vegetation index, and mean annual solar radiation were considered key variables in the outbreak of RHDV2 in wild rabbits. The environmental factors associated with RHDV2 outbreaks identified in our study and the outbreak risk prediction maps generated in our study will aid in the formulation of appropriate RHDV2 control measures to reduce the risk of morbidity in domestic and wild rabbits.

What explains the high island endemicity of Philippine Rafflesia? A species distribution modeling analysis of three threatened parasitic plant species and their hosts.

PREMISE: Rafflesia are rare holoparasitic plants. In the Philippines, all but one species are found only on single islands. This study aimed to better understand the factors contributing to this distributional pattern. Specifically, we sought to determine whether narrow environmental tolerances of host and/or parasite species might explain their island endemicity. METHODS: We used Maxent species distribution modeling to identify areas with suitable habitat for R. lagascae, R. lobata, and R. speciosa and their Tetrastigma host species. These analyses were carried out for current climate conditions and two future climate change scenarios. RESULTS: Although species distribution models indicated suitable environmental conditions for the Tetrastigma host species in many parts of the Philippines, considerably fewer areas were inferred to have suitable conditions for the three Rafflesia species. Some of these areas are on islands from which they have not been reported. All three species will face significant threats as a result of climate change. CONCLUSIONS: Our results suggest that limited inter-island dispersibility and/or specific environmental requirements are likely responsible for the current pattern of island endemicity of the three Rafflesia species, rather than environmental requirements of their Tetrastigma host species.

Disentangling the biotic and abiotic drivers of bird-building collisions in a tropical Asian city with ecological niche modeling.

Bird collisions with buildings are responsible for a large number of bird deaths in cities around the world, yet they remain poorly studied outside North America. We conducted one of the first citywide fine-scale and landscape-scale analyses of bird-building collisions in Asia and used maximum entropy modeling (as commonly applied to species distribution modeling) in a novel way to assess the drivers of bird-building collisions in the tropical city-state of Singapore. We combined 7 years of community science observations with publicly available building and remote sensing data. Drivers of bird-building collisions varied among taxa. Some migratory taxa had a higher relative collision risk that was linked to areas with high building densities and high levels of nocturnal blue light pollution. Nonmigratory taxa had a higher collision risk in areas near forest cover. Projecting our results onto official long-term land-use plans, we predicted that future increases in bird-building collision risk stemmed from increases in blue light pollution and encroachment of buildings into forested areas and identified 6 potential collision hotspots linked to future developments. Our results suggest that bird-building collision mitigation measures need to account for the different drivers of collision for resident and migratory species and show that combining community science and ecological modeling can be a powerful approach for analyzing bird-building collision data.

Modelos de nicho ecológico para esclarecer los causantes bióticos y abióticos de las colisiones entre aves y edificios en una ciudad tropical asiática Resumen Las colisiones entre aves y edificios son causa de un gran número de muertes en todas las ciudades del mundo, y aun así se estudian muy poco fuera de América del Norte. Realizamos uno de los primeros análisis a escala fina y a escala de paisaje en una ciudad asiática y usamos el modelo de entropía máxima (como se aplica con frecuencia a los modelos de distribución de especies) de manera novedosa para analizar los causantes de estas colisiones en Singapur, una ciudad­estado tropical. Combinamos siete años de observaciones de ciencia comunitaria con los datos públicos de teledetección y construcción. Los causantes de las colisiones entre aves y edificios variaron entre taxones. Algunos taxones migratorios tuvieron un riesgo de colisión relativamente más alto relacionado con áreas de alta densidad de edificios y niveles elevados de contaminación lumínica de luz azul nocturna. Los taxones no migratorios tuvieron un riesgo de colisión más elevado en las áreas cercanas a la cobertura forestal. Con la proyección de nuestros resultados sobre los planes oficiales de uso de suelo a largo plazo, pronosticamos que el incremento en el futuro de colisiones entre aves y edificios vendrá del incremento en la contaminación de luz azul y la invasión de edificios en las áreas forestales; también identificamos seis potenciales puntos calientes de colisión relacionados a futuros desarrollos inmobiliarios. Nuestros resultados sugieren que para mitigar estas colisiones se necesita considerar los diferentes causantes de dichas colisiones para las especies migratorias y residentes y también muestran que la combinación de la ciencia comunitaria y los modelos ecológicos puede ser una estrategia poderosa para analizar los datos de colisiones entre aves y edificios.

Update of the geographic range of Humboldt's squirrel monkeys (Saimiri cassiquiarensis cassiquiarensis Lesson, 1840) using a model-based approach.

Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model-based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO, as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation.

Saimiri cassiquiarensis cassiquiarensis (Cebidae) é uma subespécie de primata com ampla distribuição na região amazônica do Brasil, Colômbia e Venezuela. No entanto, os limites de sua distribuição geográfica permanecem mal definidos. Este estudo apresenta novas localidades de ocorrência para essa subespécie e atualiza sua distribuição usando 140 registros de ocorrência compilados com base na literatura, espécimes depositados em coleções científicas e novos registros de campo para produzir mapas de distribuição baseados em modelos. Após a limpeza do nosso banco de dados, atualizamos a extensão de ocorrência da subespécie, que foi usada na calibração do modelo. Em seguida, modelamos a área de distribuição da subespécie usando um algoritmo de entropia máxima (MaxEnt). O modelo final foi ajustado usando um limiar fixo e revisamos esse polígono com base em barreiras geográficas conhecidas e na distribuição de congêneres parapátricas. Nosso modelo sugere que a espécie é fortemente associada a áreas planas, com temperaturas diárias consistentemente altas. Propomos modificações em todos os limites da área de distribuição e estimamos que 3% da área de ocupação (AOO, conforme definida pela IUCN) da subespécie já foi perdida devido ao desmatamento, resultando em uma área de distribuição atual de 224,469 km2. Também estimamos que 54% de sua AOO encontra­se atualmente coberta por áreas protegidas. Com base nesses resultados, consideramos que a subespécie está apropriadamente classificada como Pouco Preocupante, pois ocupa uma área extensa, que é relativamente bem coberta por áreas protegidas e atualmente apresenta baixas taxas de desmatamento.

Predicting the current and future suitable distribution range of Trilocha varians (Walker, 1855) (Lepidoptera: Bombycidae) in China.

Trilocha varians is one of the major pests of Ficus spp. Based on 19 bioclimatic variables provided by the Worldclim, our study analysed the suitable distribution areas of T. varians under current and future climate changes (SSP1-2.6, SSP2-4.5, SSP5-8.5) for two periods (the 2050s and 2090s) using the maximum entropy algorithm (MaxEnt) model. Key environmental variables affecting the geographic distribution of T. varians were also identified, and the changes in the area of suitable range under current and future climate changes were compared. The results showed that the key environmental variables affecting the distribution of T. varians were temperature and precipitation, comprising annual mean temperature (bio1), temperature seasonality (standard deviation × 100) (bio4), precipitation of driest month (bio14), and precipitation of driest quarter (bio17). Under the current climatic conditions, the suitable distribution area of T. varians is within the range of 92°13'E-122°08'E, 18°17'N-31°55'N. The current high, medium, and low suitable areas for T. varians predicted by the MaxEnt model are 14.00 × 104, 21.50 × 104, and 71.95 × 104 km2, of which the high suitable areas are mainly distributed in southern Guangdong, southwestern Guangxi, western Taiwan, Hong Kong, and Hainan. Under different future climatic conditions, some of the high, medium, and low suitability zones for T. varians increased and some decreased, but the mass centre did not migrate significantly. The Pearl River Basin is predicted to remain the main distribution area of T. varians.

Considering climate change impact on the global potential geographical distribution of the invasive Argentine ant and little fire ant.

The Argentine ant (Linepithema humile) and the little fire ant (Wasmannia auropunctata) are among the top 100 invasive alien species globally, causing significant ecological and economic harm. Therefore, it is crucial to study their potential geographic distribution worldwide. This study aimed to predict their global distribution under current and future climate conditions. We used distribution data from various sources, including CABI, GBIF, and PIAKey, and key climate variables selected from 19 environmental factors to model their potential geographic distribution using MaxEnt. The AUC values were 0.925 and 0.937 for L. humile and W. auropunctata, respectively, indicating good predictive performance. Suitable areas for L. humile were mainly in southern North America, northern South America, Europe, central Asia, southern Oceania, and parts of Africa, while W. auropunctata suitable areas were mostly in southern North America, most of South America, a small part of Europe, southern Asia, central Africa, and some parts of Oceania. Under climate change scenario, suitable areas for L. humile increased, while highly suitable areas for W. auropunctata decreased. The top four countries with the largest areas of overlapping suitable habitat under current climate were Brazil, China, Australia, and Argentina, while under future SSP585 climate scenario, the top four countries were Brazil, China, Indonesia, and Argentina. Some countries, such as Estonia and Finland, will see an overlapping adaptation area under climate change. In conclusion, this study provides insight into controlling the spread and harm of L. humile and W. auropunctata.

Forecasting the future of Fall armyworm Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) in India using ecological niche model.

The Fall armyworm, Spodoptera frugiperda is the most notorious invasive pest species on maize, recently reported in India. The continuous spread of Fall armyworms to new ecological niches raises global concern. The current study is the first in India to forecast the suitability of a habitat for S. frugiperda using a maximum entropy algorithm. Predictions were made based on an analysis of the relationship between 109 occurrence records of S. frugiperda and pertinent historical, current, and predicted climatic data for the study area. The model indicated that S. frugiperda could thrive in different habitats under the current environmental circumstances, particularly in the west and south Indian states like Maharashtra, Tamil Nadu, and Karnataka. The model predicted that areas with higher latitudes, particularly in Uttar Pradesh, Odisha, West Bengal, and some portions of Telangana, Rajasthan, Chhattisgarh, and Madhya Pradesh, as well as some tracts of northeastern states like Assam and Arunachal Pradesh, would have highly climate-suitable conditions for S. frugiperda to occur in the future. The average AUC value was 0.852, which indicates excellent accuracy of the prediction. A Jackknife test of variables indicated that isothermality with the highest gain value was determining the potential geographic distribution of S. frugiperda. Our results will be useful for serving as an early warning tool to guide decision-making and prevent further spread toward new areas in India.

Predicting suitable areas for Metcalfa pruinosa (Hemiptera: Flatidae) under climate change and implications for management.

Climate change is a prominent factor reshaping the distribution of invasive species. Metcalfa pruinosa (Say 1830) (Hemiptera: Flatidae), native to North America, has invaded other continents and poses a serious threat to various agricultural crops and the human residential environment. Understanding the distribution of M. pruinosa based on climatic conditions is a critical first step to prevent its further invasion. Therefore, based on its occurrence records and associated environmental variables, a Maxent model was developed to predict suitable areas for this species in the present and future on a global scale. The model exhibited outstanding performance, with a mean area under the receiver operating characteristic curve and true skill statistic values of 0.9329 and 0.926, respectively. The model also indicated that annual precipitation (Bio12) and max temperature of the warmest month (Bio5) were the key environmental variables limiting the distribution of M. pruinosa. Moreover, the model revealed that the current suitable area is 1.01 × 107 km2 worldwide, with southern China, southern Europe, and the eastern United States predicted to be the primary and highly suitable areas in the latter 2 regions. This area is expected to increase under future climate scenarios, mainly in the northern direction. The study's findings contribute to our understanding of climate change's impact on M. pruinosa distribution, and they will aid governments in developing appropriate pest management strategies, including global monitoring and strict quarantine measures.

Simulation of mangrove suitable habitat in the Guangdong-Hong Kong-Macao Area under the background of climate change.

Climate change has resulted in great influence on the geographical distribution of species. Mangrove forests are one of the most precious ecosystems on the planet, yet they are being threatened by the habitat destruction and degradation under the situation of global warming. Seeking suitable areas for planting mangroves to tackle climate change has been gradually popular in ecological restoration. In this study, we applied the Maximum Entropy algorithm to assess the contribution of environmental factors on mangrove distribution, simulated mangrove suitable habitat for present and future (scenario of SSP245-2070s), and used kernel density analysis for identifying priority of mangrove reserve construction. Results indicate that mean diurnal range and elevation made the highest contribution on mangrove distribution. At present, the mangrove habitat suitability along the western coast of the Guangdong-Hong Kong-Macao Area (GHMA) was the highest while that along the eastern coast was the lowest. By 2070s, mangrove suitable areas would show a decreasing trend under SSP245 scenario. High suitable areas (HSAs) would change fastest and shift to northeast in the same direction as dominant environmental factors. For further mangrove restoration, it is advisable to select sites with high suitability density in the future but low reclamation density at present as prior mangrove reserves, and these sites distribute along the northeastern and northwestern coast of Zhanjiang, Yangjiang and Jiangmen, the Pearl River Estuary and Honghai Bay of Shanwei. Meanwhile, regions with lower suitability density but higher reclamation density could be listed as secondary mangrove reserves.

Biodiversity conservation and management of lake wetlands based on the spatiotemporal evolution patterns of crane habitats.

The typical lake wetlands in the middle and lower reaches of the Yangtze River are important wintering sites of cranes in China. The spatiotemporal evolution of crane populations and their habitats has great value in clarifying the pivotal role of regional lake wetlands in biodiversity conservation. Therefore, 2562 data points of four crane species were selected in this study. The data reflected the distributional position of the cranes over the period 2000-2020. Twelve surrounding environmental factors were selected to investigate the spatiotemporal evolution in the study area by using the MaxEnt model. The Jackknife method was used to identify the main environmental factors affecting the choice of crane habitats. The results indicated that: (1) Developed land in the study area increased by 42,795.81 hm2. The crane populations were mainly distributed in the farmland and mudflat, and their number decreased yearly. (2) From 2000 to 2020, the area of suitable crane habitat experienced an overall decrease. Specifically, the mid-suitable area dwindled by 6234.23 hm2, marking a substantial reduction of 52.05 %. Similarly, the most suitable area saw a decline of 786.41 hm2, representing a noteworthy decrease of 71.09 %. (3) The findings from the analysis of influencing factors revealed a dynamic pattern over the years. Habitat type, water density, and distance to water were the main influencing factors in the study area from 2000 to 2020. This study provides a new perspective on the conservation and structural habitat restoration of crane populations in the middle and lower reaches of the Yangtze River.

A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models.

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.

Evaluating effects of climate change on the spatial distribution of an atypical cavefish Onychostoma macrolepis.

Comprehending endangered species' spatial distribution in response to global climate change (GCC) is of great importance for formulating adaptive management, conservation, and restoration plans. However, it is regrettable that previous studies mainly focused on geoclimatic species, while neglected climate-sensitive subterranean taxa to a large extent, which clearly hampered the discovery of universal principles. In view of this, taking the endemic troglophile riverine fish Onychostoma macrolepis (Bleeker, 1871) as an example, we constructed a MaxEnt (maximum-entropy) model to predict how the spatial distribution of this endangered fish would respond to future climate changes (three Global Climate Models × two Shared Socio-economic Pathways × three future time nodes) based on painstakingly collected species occurrence data and a set of bioclimatic variables, including WorldClim and ENVIREM. Model results showed that variables related to temperature rather than precipitation were more important in determining the geographic distribution of this rare and endemic fish. In addition, the suitable areas and their distribution centroids of O. macrolepis would shrink (average: 20,901.75 km2) and move toward the northeast or northwest within the study area (i.e. China). Linking our results with this species' limited dispersion potential and unique habitat requirements (i.e. karst landform is essential), we thus recommended in situ conservation to protect this relict.

Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients.

Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10-30°S and 20-30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.

Predicting Quercus gilva distribution dynamics and its response to climate change induced by GHGs emission through MaxEnt modeling.

Quercus gilva, an evergreen tree species in Quercus section Cyclobalanopsis, is an ecologically and economically valuable species in subtropical regions of East Asia. Predicting the impact of climate change on potential distribution of Q. gilva can provide a scientific basis for the conservation and utilization of its genetic resources, as well as for afforestation. In this study, 74 distribution records of Q. gilva and nine climate variables were obtained after data collection and processing. Current climate data downloaded from WorldClim and future climate data predicted by four future climate scenarios (2040s SSP1-2.6, 2040s SSP5-8.5, 2060s SSP1-2.6, and 2060s SSP5-8.5) mainly based on greenhouse gases emissions of distribution sites were used in MaxEnt model with optimized parameters to predict distribution dynamics of Q. gilva and its response to climate change. The results showed that the predicted current distribution was consistent with natural distribution of Q. gilva, which was mainly located in Hunan, Jiangxi, Zhejiang, Fujian, Guizhou, and Taiwan provinces of China, as well as Japan and Jeju Island of South Korea. Under current climate conditions, precipitation factors played a more significant role than temperature factors on distribution of Q. gilva, and precipitation of driest quarter (BIO17) is the most important restriction factor for its current distribution (contribution rate of 57.35%). Under future climate conditions, mean temperature of driest quarter (BIO9) was the essential climate factor affecting future change in potential distribution of Q. gilva. As the degree of climatic anomaly increased in the future, the total area of predicted distribution of Q. gilva showed a shrinking trend (decreased by 12.24%-45.21%) and Q. gilva would migrate to high altitudes and latitudes. The research results illustrated potential distribution range and suitable climate conditions of Q. gilva, which can provide essential theoretical references for the conservation, development, and utilization of Q. gilva and other related species.

Evaluation of Environmental Factor Effects on the Polyphenol and Flavonoid Content in the Leaves of Chrysanthemum indicum L. and Its Habitat Suitability Prediction Mapping.

The leaves of Chrysanthemum indicum L. are known to have various bioactive compounds; however, industrial use is extremely limited. To overcome this situation by producing high-quality leaves with high bioactive content, this study examined the environmental factors affecting the phytochemical content and antioxidant activity using C. indicum leaves collected from 22 sites in Kochi Prefecture, Japan. Total phenolic and flavonoid content in the dry leaves ranged between 15.0 and 64.1 (mg gallic acid g-1) and 2.3 and 11.4 (mg quercetin g-1), while the antioxidant activity (EC50) of the 50% ethanol extracts ranged between 28.0 and 123.2 (µg mL-1) in 1,1-Diphenyl-2-picrylhydrazyl radical scavenging assay. Among the identified compounds, chlorogenic acid and 1,5-dicaffeoylquinic acid were the main constituents in C. indicum leaves. The antioxidant activity demonstrated a positive correlation with 1,5-dicaffeoylquinic acid (R2 = 0.62) and 3,5-dicaffeoylquinic acid (R2 = 0.77). The content of chlorogenic acid and dicaffeoylquinic acid isomers varied significantly according to the effects of exchangeable magnesium, cation exchange capacity, annual temperature, and precipitation, based on analysis of variance. The habitat suitability map using the geographical information system and the MaxEnt model predicted very high and high regions, comprising 3.2% and 10.1% of the total area, respectively. These findings could be used in future cultivation to produce high-quality leaves of C. indicum.

Predicting future climate change impacts on the potential distribution of the black howler monkey (Alouatta pigra): an endangered arboreal primate.

Climate change is one of the main factors affecting biodiversity worldwide at an alarming rate. In addition to increases in global extreme weather events, melting of polar ice caps, and subsequent sea level rise, climate change might shift the geographic distribution of species. In recent years, interest in understanding the effects of climate change on species distribution has increased, including species which depend greatly on forest cover for survival, such as strictly arboreal primates. Here, we generate a series of species distribution models (SDMs) to evaluate future projections under different climate change scenarios on the distribution of the black howler monkey (Alouatta pigra), an endemic endangered primate species. Using SDMs, we assessed current and future projections of their potential distribution for three Social Economic Paths (SSPs) for the years 2030, 2050, 2070, and 2090. Specifically, we found that precipitation seasonality (BIO15, 30.8%), isothermality (BIO3, 25.4%), and mean diurnal range (BIO2, 19.7.%) are the main factors affecting A. pigra distribution. The future climate change models suggested a decrease in the potential distribution of A. pigra by projected scenarios (from - 1.23 to - 12.66%). The highly suitable area was the most affected above all in the more pessimist scenario most likely related to habitat fragmentation. Our study provides new insights into the potential future distribution and suitable habitats of Alouatta pigra. Such information could be used by local communities, governments, and non-governmental organizations for conservation planning of this primate species.