Impact of climate factors on future distributions of Paeonia ostii across China estimated by MaxEnt

Impacts of Climate Change on the Habitat Suitability and Natural Product Accumulation of the Medicinal Plant Sophora alopecuroides L. Based on the MaxEnt Model

Sophora alopecuroides L., a perennial herb in the arid and semi-arid regions of northwest China, has the ecological functions of windbreaking and sand fixation and high medicinal value. In recent years, global warming and human activities have led to changes in suitable habitats for S. alopecuroides, which may affect the accumulation of natural products. In this study, MaxEnt 3.4 and ArcGIS 10.4 software were used to predict the distribution of potentially suitable habitats for S. alopecuroides in China under climate change. Furthermore, the geographical distribution of S. alopecuroides as affected by human activities, the differences in the content of natural products of S. alopecuroides between different suitable habitats, and the correlation between natural products and environmental factors were analyzed. The results showed that suitable habitats for S. alopecuroides were projected to expand in the future, and the major environmental factors were temperature (Bio1), rainfall (Bio18), and soil pH (pH). When Bio1, Bio18, and pH were 8.4283 °C, 7.1968 mm, and 9.9331, respectively, the distribution probability (P) of S. alopecuroides was the highest. After adding a human activity factor, the accuracy of the model prediction results was improved, and the area of suitable habitats was greatly reduced, showing a fragmented pattern. Meanwhile, habitat suitability had a specific effect on the content of natural products in S. alopecuroides. Specifically, the content of natural products in S. alopecuroides in wild habitats was higher than that in artificial cultivation, and highly suitable habitats showed higher contents than those in non-highly suitable habitats. The contents of total alkaloids and total flavonoids were positively correlated with human activities and negatively correlated with land use types. Among them, total alkaloids were negatively correlated with aspect, and total flavonoids were positively correlated with aspect. In addition, it is suggested that Xinjiang should be the priority planting area for S. alopecuroides in China, and priority should be given to protection measures in the Alashan area. Overall, this study provides an important foundation for the determination of priority planting areas and resource protection for S. alopecuroides.

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.

Geographic distribution and impacts of climate change on the suitable habitats of Rhamnus utilis Decne in China

BACKGROUND

Rhamnus utilis Decne (Rhamnaceae) is an ecologically and economically important tree species. The growing market demands and recent anthropogenic impacts to R. utilis forests has negatively impacted its populations severely. However, little is known about the potential distribution of this species and environmental factors that affect habitat suitability for this species. By using 219 occurrence records along with 51 environmental factors, present and future suitable habitats were estimated for R. utilis using Maxent modeling; the important environmental factors affecting its distribution were analyzed.

RESULTS

January water vapor pressure, normalized difference vegetation index, mean diurnal range, and precipitation of the warmest quarter represented the critical factors explaining the environmental requirements of R. utilis. The potential habitat of R. utilis included most provinces from central to southeast China. Under the climate change scenario SSP 245, Maxent predicted a cumulative loss of ca. 0.73 × 105 km2 in suitable habitat for R. utilis during 2041-2060 while an increase of ca. 0.65 × 105 km2 occurred during 2081-2100. Furthermore, under this climate change scenario, the suitable habitat will geographically expand to higher elevations.

CONCLUSIONS

The findings of our study provide a foundation for targeted conservation efforts and inform future research on R. utilis. By considering the identified environmental factors and anticipating the potential impacts of climate change, conservation strategies can be developed to preserve and restore suitable habitats for R. utilis. Protecting this species is not only crucial for maintaining biodiversity but also for sustaining the economic benefits associated with its ecological services.

Assessing the suitability and dynamics of three medicinal Sambucus species in China under current and future climate scenarios

Climate change exerts profound influences on the ecological environments on a global scale, leading to habitat destruction and altering distribution patterns for numerous plant species. Traditional Chinese medicinal plants, such as those belonging to the Sambucus genus, have been extensively utilized for several centuries to treat fractures, rheumatism, and inflammation. However, our understanding of their geographic distribution and climatic adaptation within China still needs to be improved. In this study, we screened the optimal predictive model (random forest model) to predict the potential suitable distribution of three Sambucus species (Sambucus adnata, Sambucus javanica, and Sambucus williamsii) across China under both current and future climate scenarios. Moreover, we identified key climate factors that influence their potential distributions. Our findings revealed that S. adnata and S. javanica are predominantly shaped by temperature seasonality and mean diurnal range, respectively, whereas S. williamsii is significantly affected by the precipitation of the wettest month. Currently, S. williamsii is primarily distributed in north and central south China (covering 9.57 × 105 km2), S. javanica is prevalent in the south and east regions (covering 6.41×105 km2), and S. adnata predominantly thrives in the southwest China (covering 1.99×105 km2). Under future climate change scenarios, it is anticipated that S. adnata may migrate to higher latitudes while S. javanica may shift to lower latitudes. However, potentially suitable areas for S. williamsii may contract under certain scenarios for the years 2050 and 2090, with an expansion trend under the SSP585 scenario for the year 2090. Our study emphasizes the importance of climatic variables in influencing the potential geographic distribution of Sambucus species. These findings provide valuable theoretical insights for the preservation, cultivation, and utilization of Sambucus medicinal plant resources in the context of ongoing climate change.

Assessment of suitable cultivation region for Pepino (Solanum muricatum) under different climatic conditions using the MaxEnt model and adaptability in the Qinghai-Tibet plateau

Pepino (Solanum muricatum), a member of the Solanaceae family originating from South America, is cultivated globally. However, the cultivation range and suitable habitat of Pepino have not been extensively studied, which hampers the further development of its cultivation industry. Therefore, we aimed at enrich and expand the planting scope of Pepino. Currently, the main cultivation areas of Pepino in China are the Yunnan-Guizhou Plateau and the Loess Plateau, where the altitude is above 1000 m. In this study, ArcGIS combined with the MaxEnt model was used for prediction, whose area under curve value was 0.949. The main climatic factors affecting the distribution of Pepino are temperature seasonality, annual means temperature, mean temperature of the coldest quarter, elevation, isothermality, and the climate factors, and their cumulative contribution rate of 87.6%. Pepino's main potential distribution areas are located in Yunnan-Guizhou Plateau, Yunnan Province, Hexi Corridor of Loess Plateau, and low altitude areas of Qinghai-Tibet Plateau. The main distribution ranges from 1000 to 2000 m above sea level, and the total suitable area accounts for 20.09% of China's total land area. The prediction results reveal an expanded potential area for Pepino, with no significant migration in the central region of the main potential distribution area by 2050 and 2070. No studies have been conducted on the open-area cultivation of Pepino in northern China. Our findings revealed that the yield and quality in the four experimental sites and final actual cultivation conditions were consistent with the predicted results of MaxEnt. The yiel d per plant in Xunhua and Minhe was significantly different from that in Xining, which was low, and that in Minhe was the highest. Overall, the fruit quality in the Xining region was the lowest among the three regions, which was related to the climatic differences in each region. These results align with the predicted outcomes, indicating that Xining is the least suitable area. Further, these data verify the accuracy of the prediction results. The climate data of the four regions were analyzed simultaneously to elucidate the influence of different climate conditions on the growth of Pepino. Our findings are of considerable significance for introducing characteristic horticultural crops in the Qinghai-Tibet Plateau and using the MaxEnt model to predict the cultivation range of crops.

Integrating human footprint with ensemble modelling identifies priority habitats for conservation: a case study in the distributional range of Arnebia euchroma, a vulnerable species

Climate change-driven rapid alteration of ecosystems globally is further complicated by growing anthropogenic pressures, especially in the ecologically sensitive mountainous regions. However, these two major drivers of change have largely been considered separately in species distribution models, thus compromising their reliability. Here, we integrated ensemble modelling with the human pressure index for predicting distribution and mapping priority regions across a whole range of occurrences for vulnerable species, Arnebia euchroma. Our results identified 3.08% of the study area as 'highly suitable', 2.45% as 'moderately suitable', and 94.45% as 'not suitable' or 'least suitable'. Compared to current climatic conditions, future RCP scenarios of 2050 and 2070 showed a significant loss in habitat suitability and a slight shift in the distribution pattern of the target species. By excluding the high-pressure areas of the human footprint from the predicted suitable habitats, we were able to identify the unique areas (70% of the predicted suitable area) that need special attention for conservation and restoration. Such models, if well implemented, may play a pivotal role in achieving the effective targets under the aegis of the current UN decade on ecological restoration (2021-2030) in accordance with SDG 15.4.

Phytochemical Analysis and Habitat Suitability Mapping of Cardiocrinum cordatum (Thunb.) Makino Collected at Chiburijima, Oki Islands, Japan

Cardiocrinum cordatum, known as ubayuri in Japan, has antihypertensive properties and has been shown to inhibit angiotensin-converting enzyme (ACE), which contributes to the production of angiotensin II, a hypotensive substance in the renin−angiotensin system. C. cordatum has been the subject of various studies as a useful plant and is applied as a functional food. Due to the limited distribution, loss of natural habitat by frequent natural disasters, and environmental conditions, the chemical content and biological activity of C. cordatum have been drastically affected. Obtaining a stable supply of Cardiocrinu cordatum material with high biological activity is still a challenge. Understanding the native habitat environment and suitable cultivation sites could help in solving this issue. Therefore, in the current study we investigated the effect of environmental parameters on the hypertensive and antioxidant activities of C. cordatum collected at Chiburijima, Oki Islands, Shimane Prefecture, Japan. We also predicted the habitat suitability of C. cordatum using a geographic information system (GIS) and MaxEnt model with various conditioning factors, including the topographic, soil, environmental, and climatic factors of the study area. A total of 37 individual plant samples along with soil data were collected for this study. In vitro assays of ACE inhibitory and antioxidant activity were conducted on the collected samples. The results show that plants at 14 out of 37 sites had very strong ACE inhibitory activity (IC50 < 1 mg mL−1). However, the collected plants showed no signs of strong antioxidant activity. Statistical analysis using analysis of variance (ANOVA) showed that BIO05 (F value = 2.93, p < 0.05), nitrate−nitrogen (F value = 2.46, p < 0.05), and silt (F value = 3.443, p < 0.05) significantly affected ACE inhibitory activity. On the other hand, organic carbon content (F value = 10.986, p < 0.01) was found to significantly affect antioxidant activity. The final habitat suitability map shows 3.3% very high and 6.8% high suitability regions, and samples with ACE inhibition activity were located within these regions. It is recommended further investigations and studies are conducted on C. cordatum in these locations. The prediction suitability model showed accuracy with AUC-ROC of 96.7% for the study area.

Impacts of environment and human activity on grid-scale land cropping suitability and optimization of planting structure, measured based on the MaxEnt model

Making full use of agricultural resource endowment, determining the planting suitability of areas for different crops according to the environment and human activities, and optimizing planting structure are important ways to ensure stable increases in crop yield and improve food production capacity. Taking Songhua River Basin (SRB) as an example, this study used geographic distribution information on different crops and the Maximum Entropy (MaxEnt) model to determine the degree of suitability of land in SRB for cropping, and to optimize the layout of crop planting structure. The results showed that the main factors affecting land suitability for different crops, with a combined contribution >80%, were population density, Distance from road to cultivated land, normalized difference vegetation index, and total phosphorus. Under the joint influence of the environment and human activity, the total unsuitable area of the four crops became much more extensive, with the unsuitable area of soybean being the largest (173 thousand km²) and the smallest for wheat (128 thousand km²). The highly suitable area was largest for wheat (2 thousand km²), while the other three crops were less than 2 thousand km². Suitable distribution areas for all four crops were mainly located in the center of the basin (Songnen Plain) and in a wedge in the northeast corner (Sanjiang Plain). The relationships between different crops and environment and human activities revealed that crop suitability distribution is mainly determined by human activities, rather than the environment. These results provide a scientific basis for optimizing crop layout and improving the planting system, ensuring the security of food production.

Decline in the suitable habitat of dominant Abies species in response to climate change in the Hindu Kush Himalayan region: insights from species distribution modelling

Reliable predictions of future distribution ranges of ecologically important species in response to climate change are required for developing effective management strategies. Here we used an ensemble modelling approach to predict the distribution of three important species of Abies namely, Abies pindrow, Abies spectabilis and Abies densa in the Hindu Kush Himalayan region under the current and two shared socioeconomic pathways (SSP245 and SSP585) and time periods of 2050 and 2090s. A correlative ensemble model using presence/absence data of the three Abies species and 22 environmental variables, including 19 bioclimatic variables and 3 topographic variables, from known distributions was built to predict the potential current and future distribution of these species. The individual models used to build the final ensemble performed well and provided reliable results for both the current and future distribution of all three species. For A. pindrow, precipitation of the driest month (Bio14) was the most important environmental variable with 83.3% contribution to model output while temperature seasonality (Bio4) and annual mean diurnal range (Bio2) were the most important variables for A. spectabilis and A. densa with 48.4% and 46.1% contribution to final model output, respectively. Under current climatic conditions, the ensemble models projected a total suitable habitat of about 433,003 km², 790,837 km² and 676,918 km² for A. pindrow, A. spectabilis and A. densa, respectively, which is approximately 10.36%, 18.91% and 16.91% of the total area of Hindu Kush Himalayan region. Projections of habitat suitability under future climate scenarios for all the shared socioeconomic pathways showed a reduction in potentially suitable habitats with a maximum overall loss of approximately 14% of the total suitable area of A. pindrow under SSP 8.5 by 2090. A decline in total suitable habitat is predicted to be 9.6% in A. spectabilis by 2090 under the SSP585 scenario while in A. densa 6.67% loss in the suitable area is expected by 2050 under the SSP585 scenario. Furthermore, there is no elevational change predicted in the case of A. pindrow while A. spectabilis is expected to show an upward shift by about 29 m per decade and A. densa is showing a downward shift at a rate of 11 m per decade. The results are interesting, and intriguing given the occurrence of these species across the Hindu Kush Himalayan region. Thus, our study underscores the need for consideration of unexpected responses of species to climate change and formulation of strategies for better forest management and conservation of important conifer species, such as A. pindrow, A. spectabilis and A. densa.

Prediction of the potential geographical distribution of Betula platyphylla Suk. in China under climate change scenarios

Climate is a dominant factor affecting the potential geographical distribution of species. Understanding the impact of climate change on the potential geographic distribution of species, which is of great significance to the exploitation, utilization, and protection of resources, as well as ecologically sustainable development. Betula platyphylla Suk. is one of the most widely distributed temperate deciduous tree species in East Asia and has important economic and ecological value. Based on 231 species distribution data points of Betula platyphylla Suk. in China and 37 bioclimatic, soil, and topography variables (with correlation coefficients < 0.75), the potential geographical distribution pattern of Betula platyphylla Suk. under Representative Concentration Pathway (RCP) climate change scenarios at present and in the 2050s and 2070s was predicted using the MaxEnt model. We analyzed the main environmental variables affecting the distribution and change of suitable areas and compared the scope and change of suitable areas under different climate scenarios. This study found: (1) At present, the main suitable area for Betula platyphylla Suk. extends from northeastern to southwestern China, with the periphery area showing fragmented distribution. (2) Annual precipitation, precipitation of the warmest quarter, mean temperature of the warmest quarter, annual mean temperature, and precipitation of the driest month are the dominant environmental variables that affect the potential geographical distribution of Betula platyphylla Suk. (3) The suitable area for Betula platyphylla Suk. is expected to expand under global warming scenarios. In recent years, due to the impact of diseases and insect infestation, and environmental damage, the natural Betula platyphylla Suk. forest in China has gradually narrowed. This study accurately predicted the potential geographical distribution of Betula platyphylla Suk. under current and future climate change scenarios, which can provide the scientific basis for the cultivation, management, and sustainable utilization of Betula platyphylla Suk. resources.

Effects of climate change on the distribution of wild Akebia trifoliata

Understanding the impacts and constraints of climate change on the geographical distribution of wild Akebia trifoliata is crucial for its sustainable management and economic development as a medicinal material or fruit. In this study, according to the first-hand information obtained from field investigation, the distribution and response to climate change of A. trifoliata were studied by the MaxEnt model and ArcGIS. The genetic diversity and population structure of 21 natural populations of A. trifoliata were studied by simple sequence repeat (SSR) markers. The results showed that the most important bioclimatic variable limiting the distribution of A. trifoliata was the Mean Temperature of Coldest Quarter (bio11). Under the scenarios SSP1-2.6 and SSP2-4.5, the suitable area of A. trifoliata in the world will remain stable, and the suitable area will increase significantly under the scenarios of SSP3-7.0 and SSP5-8.5. Under the current climate scenario, the suitable growth regions of A. trifoliata in China were 79.9-122.7°E and 21.5-37.5°N. Under the four emission scenarios in the future, the geometric center of the suitable distribution regions of Akebia trifoliata in China will move to the north. The clustering results of 21 populations of A. trifoliata analyzed by SSR markers showed that they had a trend of evolution from south to north.

Identifying Potential Planting Sites for Three Non-Native Plants to Be Used for Soil Rehabilitation in the Tula Watershed

The Tula watershed in Mexico, located in a semiarid and sub-humid climate zone, is experiencing intensive population growth, the expansion of mining concessions for construction materials, and agricultural and urban development, resulting in the degradation of soils and vegetation and a greater demand on natural resources. The aims of this study were to evaluate the survival rates and identify potential habitats within the Tula watershed for planting three non-native forage species (Atriplex canescens, Cynodon dactylon, and Leucaena collinsii) using the Kaplan-Meier estimator and the MaxEnt model with the purpose of rehabilitating degraded soils via agroforestry systems. There were 19 edaphoclimatic variables used and the occurrences of three species, obtained from the GBIF, MEXU, and SNIB databases. The models generated with MaxEnt were very accurate (area under the curve [AUC] ≥ 0.7). The species Atriplex canescens and Cynodon dactylon showed areas of potential planting sites (>0.4) and high survival rates (80% and 92%, respectively). The species Leucaena collinsii presented areas with lower potential planting (<0.4) but registered the greater survival rate (100%). The results provide a solid basis to evaluate the survival rates of forage species within potential planting sites in the Tula watershed using agroforestry systems to rehabilitate degraded soils.

Predicting Dynamics of the Potential Breeding Habitat of Larus saundersi by MaxEnt Model under Changing Land-Use Conditions in Wetland Nature Reserve of Liaohe Estuary, China

Identifying waterfowl habitat suitability under changing environments, especially land-use change, is crucial to make waterfowl habitat conservation planning. We took Wetland Nature Reserve of Liaohe Estuary, the largest breeding area of Saunders’s Gulls (Larus saundersi) in the world, as our study area, generated land-use-type maps through interpretation of satellite images from four different years (1988, 2000, 2009, 2017), and predicted the potential breeding habitat of Saunders’s Gulls by MaxEnt model based on the land-use map, along with other environmental variables (NDVI, distance to roads and artificial facilities, distance to rivers and water bodies, DEM and distance to shoreline) for the four years, respectively. The models were evaluated using the area under the curve (AUC). We analyzed the changes of the breeding habitat from 1988 to 2017 and utilized RDA to explore the relationships among the changes of suitable habitat of Larus saundersi and the dynamics of land uses. Our results showed that the most suitable habitat decreased by 1286.46 ha during 1988-2009 and increased by 363.51 ha from 2009 to 2017. The suitable habitat decreased by 582.48 ha from 1988 to 2009 and then increased to 1848.96 ha in 2017, while the unsuitable habitat increased by 2793.87 ha during 1988–2009 and then decreased by 178.83 ha from 2009 to 2017. We also found that land use, distance to the coastline, distance to artificial facilities, distance to rivers, distance to roads, and NDVI had certain degrees of impact on the Larus saundersi distribution. The contribution of land use ranged from 16.4% to 40.3%, distance to coastline from 34.7% to 48.0%, distance to artificial facilities from 5.9% to 11.1%, distance to rivers from 5.5% to 11.0%, distance to roads from 3.9% to 12.5%, and NDVI from 0.3% to 6.3%. The change in suitable habitat of Larus saundersi has a positive relationship with the change of seepweed marsh. Human-induced changes in seepweed marsh and coastline position are the main factors influencing the potential breeding habitat of Saunders’s Gulls. We suggest strict conservation of seepweed marsh and implementation of habitat management practices to better protect Saunders’ Gull’s breeding habitat.

Assessing the Impact of Climate Change on Potential Distribution of Meconopsis punicea and Its Influence on Ecosystem Services Supply in the Southeastern Margin of Qinghai-Tibet Plateau

Meconopsis punicea is an iconic ornamental and medicinal plant whose natural habitat has degraded under global climate change, posing a serious threat to the future survival of the species. Therefore, it is critical to analyze the influence of climate change on possible distribution of M. punicea for conservation and sustainable utilization of this species. In this study, we used MaxEnt ecological niche modeling to predict the potential distribution of M. punicea under current and future climate scenarios in the southeastern margin region of Qinghai-Tibet Plateau. Model projections under current climate show that 16.8% of the study area is suitable habitat for Meconopsis. However, future projections indicate a sharp decline in potential habitat for 2050 and 2070 climate change scenarios. Soil type was the most important environmental variable in determining the habitat suitability of M. punicea, with 27.75% contribution to model output. Temperature seasonality (16.41%), precipitation of warmest quarter (14.01%), and precipitation of wettest month (13.02%), precipitation seasonality (9.41%) and annual temperature range (9.24%) also made significant contributions to model output. The mean elevation of suitable habitat for distribution of M. punicea is also likely to shift upward in most future climate change scenarios. This study provides vital information for the protection and sustainable use of medicinal species like M. punicea in the context of global environmental change. Our findings can aid in developing rational, broad-scale adaptation strategies for conservation and management for ecosystem services, in light of future climate changes.

Effects of Climate Change on the Distribution of Akebia quinata

Akebia quinata, also known as chocolate vine, is a creeping woody vine which is used as Chinese herbal medicine, and found widely distributed in East Asia. At present, its wild resources are being constantly destroyed. This study aims to provide a theoretical basis for the resource protection of this plant species by analyzing the possible changes in its geographic distribution pattern and its response to climate factors. It is the first time maximum entropy modeling (MaxEnt) and ArcGIS software have been used to predict the distribution of A. quinata in the past, the present, and the future (four greenhouse gas emission scenarios, namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Through the prediction results, the impact of climate change on the distribution of A. quinata and the response of A. quinata to climate factors were analyzed. The results showed that the most significant climatic factor affecting the distribution pattern of A. quinata was the annual precipitation. At present, the suitable distribution regions of A. quinata are mainly in the temperate zone, and a few suitable distribution regions are in the tropical zone. The medium and high suitable regions are mainly located in East Asia, accounting for 51.1 and 81.7% of the worldwide medium and high suitable regions, respectively. The migration of the geometric center of the distribution regions of A. quinata in East Asia is mainly affected by the change of distribution regions in China, and the average migration rate of the geometric center in each climate scenario is positively correlated with the level of greenhouse gas emission scenario.

Modelling Potential Distribution of Snow Leopards in Pamir, Northern Pakistan: Implications for Human–Snow Leopard Conflicts

The snow leopard (Panthera uncia) is a cryptic and rare big cat inhabiting Asia’s remote and harsh elevated areas. Its population has decreased across the globe for various reasons, including human–snow leopard conflicts (HSCs). Understanding the snow leopard’s distribution range and habitat interactions with human/livestock is essential for understanding the ecological context in which HSCs occur and thus gives insights into how to mitigate HSCs. In this study, a MaxEnt model predicted the snow leopard’s potential distribution and analyzed the land use/cover to determine the habitat interactions of snow leopards with human/livestock in Karakoram–Pamir, northern Pakistan. The results indicated an excellent model performance for predicting the species’ potential distribution. The variables with higher contributions to the model were the mean diurnal temperature range (51.7%), annual temperature range (18.5%), aspect (14.2%), and land cover (6.9%). The model predicted approximately 10% of the study area as a highly suitable habitat for snow leopards. Appropriate areas included those at an altitude ranging from 2721 to 4825 m, with a mean elevation of 3796.9 ± 432 m, overlapping between suitable snow leopard habitats and human presence. The human encroachment (human settlements and agriculture) in suitable snow leopard habitat increased by 115% between 2008 and 2018. Increasing encroachment and a clear overlap between snow leopard suitable habitat and human activities, signs of growing competition between wildlife and human/livestock for limited rangeland resources, may have contributed to increasing HSCs. A sound land use plan is needed to minimize overlaps between suitable snow leopard habitat and human presence to mitigate HSCs in the long run.

Predicting the potential global distribution of Ageratina adenophora under current and future climate change scenarios

AIM

Invasive alien species (IAS) threaten ecosystems and humans worldwide, and future climate change may accelerate the expansion of IAS. Predicting the suitable areas of IAS can prevent their further expansion. Ageratina adenophora is an invasive weed over 30 countries in tropical and subtropical regions. However, the potential suitable areas of A. adenophora remain unclear along with its response to climate change. This study explored and mapped the current and future potential suitable areas of Ageratina adenophora.

LOCATION

Global.

TAXA

Asteraceae A. adenophora (Spreng.) R.M.King & H.Rob. Commonly known as Crofton weed.

METHODS

Based on A. adenophora occurrence data and climate data, we predicted its suitable areas of this weed under current and future (four RCPs in 2050 and 2070) by MaxEnt model. We used ArcGIS 10.4 to explore the potential suitable area distribution characteristics of this weed and the "ecospat" package in R to analyze its altitudinal distribution changes.

RESULTS

The area under the curve (AUC) value (>0.9) and true skill statistics (TSS) value (>0.8) indicated excelled model performance. Among environment factors, mean temperature of coldest quarter contributed most to the model. Globally, the suitable areas for A. adenophora invasion decreased under climate change scenarios, although regional increases were observed, including in six biodiversity hotspot regions. The potential suitable areas of A. adenophora under climate change would expand in regions with higher elevation (3,000-3,500 m).

MAIN CONCLUSIONS

Mean temperature of coldest quarter was the most important variable influencing the potential suitable area of A. Adenophora. Under the background of a warming climate, the potential suitable area of A. adenophora will shrink globally but increase in six biodiversity hotspot regions. The potential suitable area of A. adenophora would expand at higher elevation (3,000-3,500 m) under climate change. Mountain ecosystems are of special concern as they are rich in biodiversity and sensitive to climate change, and increasing human activities provide more opportunities for IAS invasion.

Predicting Suitable Environments and Potential Occurrences for Cinnamomum camphora (Linn.) Presl

Global climate change has created a major threat to biodiversity. However, little is known about the habitat and distribution characteristics of Cinnamomum camphora (Linn.) Presl., an evergreen tree growing in tropical and subtropical Asia, as well as the factors influencing its distribution. The present study employed Maxent and a GARP to establish a potential distribution model for the target species based on 182 known occurrence sites and 17 environmental variables. The results indicate that Maxent performed better than GARP. The mean diurnal temperature range, annual precipitation, mean air temperature of driest quarter and sunshine duration in growing season were important environmental factors influencing the distribution of C. camphora and contributed 40.9%, 23.0%, 10.5%, and 7.2% to the variation in the model contribution, respectively. Based on the models, the subtropical and temperate regions of Eastern China, where the species has been recorded, had a high suitability for this species. Under each climate change scenario, the potential geographical distribution shifted farther north and toward a higher elevation. The predicted spatial and temporal distribution patterns of this species can provide guidance for the development strategies for forest management and species protection.

Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China

Pine wilt disease is a devastating forest disease caused by the pinewood nematode Bursaphelenchus xylophilus, which has been listed as the object of quarantine in China. Climate change influences species and may exacerbate the risk of forest diseases, such as the pine wilt disease. The maximum entropy (MaxEnt) model was used in this study to identify the current and potential distribution and habitat suitability of three pine species and B. xylophilus in China. Further, the potential distribution was modeled using the current (1970-2000) and the projected (2050 and 2070) climate data based on two representative concentration pathways (RCP 2.6 and RCP 8.5), and fairly robust prediction results were obtained. Our model identified that the area south of the Yangtze River in China was the most severely affected place by pine wilt disease, and the eastern foothills of the Tibetan Plateau acted as a geographical barrier to pest distribution. Bioclimatic variables related to temperature influenced pine trees' distribution, while those related to precipitation affected B. xylophilus's distribution. In the future, the suitable area of B. xylophilus will continue to increase; the shifts in the center of gravity of the suitable habitats of the three pine species and B. xylophilus will be different under climate change. The area ideal for pine trees will migrate slightly northward under RCP 8.5. The pine species will continue to face B. xylophilus threat in 2050 and 2070 under the two distinct climate change scenarios. Therefore, we should plan appropriate measures to prevent its expansion. Predicting the distribution of pine species and the impact of climate change on forest diseases is critical for controlling the pests according to local conditions. Thus, the MaxEnt model proposed in this study can be potentially used to forecast the species distribution and disease risks and provide guidance for the timely prevention and management of B. xylophilus.

Modelling the Effects of Climate Change on the Distribution of Endangered Cypripedium japonicum in China

Cypripedium japonicum is an endangered terrestrial orchid species with high ornamental and medicinal value. As global warming continues to intensify, the survival of C. japonicum will be further challenged. Understanding the impact of climate change on its potential distribution is of great significance to conserve this species. In this study, we established an ensemble species distribution model based on occurrence records of C. japonicum and 13 environmental variables to predict its potential distribution under current and future climatic conditions. The results show that the true skill statistic (TSS), Cohen’s kappa statistic (Kappa), and the area under the receiver operating characteristic curve (AUC) values of the ensemble model were 0.968, 0.906, and 0.995, respectively, providing more robust predictions. The key environmental variables affecting the distribution of C. japonicum were the precipitation in the warmest quarter (Bio18) and the mean temperature in the driest quarter (Bio9). Under future climatic conditions, the total suitable habitat of C. japonicum will increase slightly and tend to migrate northwestward, but the highly suitable areas will be severely lost. By 2070, the loss of its highly suitable habitat area will reach 57.69–72.24% under representative concentration pathway (RCP) 4.5 and 8.5 respectively, and the highly suitable habitats in Zhejiang and Anhui will almost disappear. It is noteworthy that the highly suitable habitat of C. japonicum has never crossed the Qinba mountainous area during the migration process of the suitable habitat to the northwest. Meanwhile, as the best-preserved area of highly suitable habitat for C. japonicum in the future, the Qinba mountainous area is of great significance to protect the wild germplasm resources of C. japonicum. In addition, we found that most of the changes predicted for 2070 will already be seen in 2050; the problem of climate change may be more urgent than it is believed.

Current Situation, Global Potential Distribution and Evolution of Six Almond Species in China

Almond resources are widely distributed in Central Asia; its distribution has not been studied in detail. Based on the first-hand data of field investigation, climate variables and chloroplast genome data, climatic characteristics of six almond species in China were analyzed, and the global distribution and evolutionary relationship were predicted. The six almond species are concentrated between 27.99°N and 60.47°N. Different almond species have different climatic characteristics. The climate of the almond species distribution has its characteristics, and the distribution of almond species was consistent with the fatty acid cluster analysis. All the test AUC (area under curve) values of MaxEnt model were larger than 0.92. The seven continents except for Antarctica contain suitable areas for the six almond species, and such areas account for approximately 8.08% of the total area of these six continents. Based on the analysis of chloroplast DNA and the distribution characteristics, the evolutionary relationship of the six almond species was proposed, which indicated that China was not the origin of almond. In this study, the construction of a phylogenetic tree based on the chloroplast genome and the characteristics of geographical distribution were constructed. The six almond species in China may have evolved from "Unknown almond species" through two routes. The MaxEnt model for each almond species provided satisfactory results. The prediction results can provide the important reference for Prunus dulcis cultivation, wild almond species development and protection.

Seed Proteomic Profiles of Three Paeonia Varieties and Evaluation of Peony Seed Protein as a Food Product

Peony (Paeonia) has high ornamental, edible, and medicinal values. In order to distinguish seeds varieties, describe the proteomic profiles correlated with stress tolerance, and evaluate peony seed protein (PSP) as a functional food product, we characterized the seed protein profiles of these three species and their glucosidase inhibition activities. Results showed that the intensity of protein bands in sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and specific protein ID (especially for specifically expressed proteins (SEPs)) was effective to distinguish these peony seed varieties. Proteomic analysis of the three species showed that P. ostii "Fengdan" has heat and pathogen tolerance-related proteins, while P. rockii has higher content of proteins related to cold resistance, which were all highly consistent with their adaptation of heat or cold habitat. Moreover, stress-related proteins were also accumulated in P. lactiflora Pall "Hangshao" seeds, showing its potential for stress resistance. Further protein analysis showed that the primary composition of PSP was albumin and globulin. And the solubility of PSP was good. Furthermore, PSP also showed high glucosidase inhibition activity, indicating that PSP might have some potential function for the remission of hyperglycemia. And P. ostii "Fengdan" seeds may be a better source for protein production than seeds of the other two species in terms of protein solubility and the content of total protein, albumin, and globulin. In addition, an optimal protocol of microwave-assisted alkali extraction was developed to produce PSP. In conclusion, the evaluated stress-related proteins in three peony seed species by proteomic analysis quite agreed with their adaptation of heat or cold stress; proteomics could also be a very useful tool for distinguishing species in the production; and peony seeds may be a good source for protein production.

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.

Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning

The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment.

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.