MaxEnt Model Research Articles

Predicting the spatial distribution of three Ephedra species under climate change using the MaxEnt model

In the context of global warming, the habitats of Ephedra, including Ephedra sinica Stapf, Ephedra intermedia Schrenk ex Mey, and Ephedra equisetina Bunge, have been substantially threatened and deteriorated in recent years. Little is known about the potential geographic dynamics of economically renowned species, including those used in sand fixation and traditional Chinese medicine, under climate change. Therefore, evaluating their potential habitat and determining the crucial environmental variables affecting E. sinica, E. intermedia and E. equisetina under the driving force of global warming are extremely important. In this study, an optimized MaxEnt model in the kuenm package on the basis of occurrence records (a total of 103, 101 and 97 points for E. sinica, E. intermedia and E. equisetina, respectively) and 37 environmental factors were utilized to simulate the distribution of the three species. Two representative concentration pathways (SSP2.6 and SSP8.5) at 2041–2060 and 2061–2080, respectively, were used to establish a future distribution model of the three species. The results indicated that approximately 6.92 × 105 km2, 2.95 × 105 km2, and 11.5 × 105 km2 of suitable regions for E. sinica, E. intermedia and E. equisetina were obtained, which were mostly distributed in central and eastern Inner Mongolia, eastern and southern Gansu, and northern Xinjiang, respectively. Critical environmental variables, such as land cover and annual precipitation, were regarded as critical parameters for the three species. Future assessment revealed that over 60 % of the potential distribution area was affected, and the stability of E. sinica under the SSP8.5 scenario was the greatest. The spatial dynamic changes in suitable areas for E. intermedia were smaller than those for E. equisetina and E. sinica in the future. The comprehensive analysis revealed that the fluctuations in the distributions of the three Ephedra species under climate change are small and provide useful information for future conservation. Therefore, target conservation and management measures should be implemented in combination with the suitability thresholds of different environmental parameters. Our results provide useful recommendations for the current and future protection of Ephedra populations.

Predicting global geographical distribution and latitudinal suitability gradient for light brown apple moth

Epiphyas postvittana, commonly known as the light brown apple moth (LBAM), is native to Australia and has a restricted global distribution. Its polyphagous nature and the recent surge in interceptions have emphasized the need for focused risk assessments to guide effective measures to curb the entry of this pest into new countries. This study aimed to perform a detailed global invasion risk assessment using an optimized MaxEnt model that incorporated 19 bioclimatic variables and elevation. The predictive outcomes underscored the significance of key variables, specifically the minimum temperature of the coldest month (bio6), precipitation of the driest month (bio14), and precipitation of the coldest quarter (bio19), in shaping the potential geographical distribution of LBAM. Regions beyond the existing range, including the southeastern United States, southern Brazil, eastern Argentina, Uruguay, southern Chile, and various Western European countries, were identified as susceptible to invasion and establishment by LBAM. An increase in suitability was observed above 45°N and 40°S under future climate scenario. With respect to climate change, LBAM would expand its potential range in Western Europe and the United States, especially under SSP5-8.5, in the 2050s. An upward trend in the latitudinal suitability gradient for LBAM in mid-high latitude areas implies that amid changing climate conditions, LBAM may find favorable habitats in these regions. For countries and regions with invasion risk, it is imperative to implement corresponding inspections and quarantine measures to thwart the introduction of LBAM, particularly in countries with established trade ties with invaded regions.

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.

Southern Africa's Great Escarpment as an amphitheater of climate-driven diversification and a buffer against future climate change in bats.

Hosting 1460 plant and 126 vertebrate endemic species, the Great Escarpment (hereafter, Escarpment) forms a semi-circular "amphitheater" of mountains girdling southern Africa from arid west to temperate east. Since arid and temperate biota are usually studied separately, earlier studies overlooked the biogeographical importance of the Escarpment as a whole. Bats disperse more widely than other mammalian taxa, with related species and intraspecific lineages occupying both arid and temperate highlands of the Escarpment, providing an excellent model to address this knowledge gap. We investigated patterns of speciation and micro-endemism from modeled past, present, and future distributions in six clades of southern African bats from three families (Rhinolophidae, Cistugidae, and Vespertilionidae) having different crown ages (Pleistocene to Miocene) and biome affiliations (temperate to arid). We estimated mtDNA relaxed clock dates of key divergence events across the six clades in relation both to biogeographical features and patterns of phenotypic variation in crania, bacula and echolocation calls. In horseshoe bats (Rhinolophidae), both the western and eastern "arms" of the Escarpment have facilitated dispersals from the Afrotropics into southern Africa. Pleistocene and pre-Pleistocene "species pumps" and temperate refugia explained observed patterns of speciation, intraspecific divergence and, in two cases, mtDNA introgression. The Maloti-Drakensberg is a center of micro-endemism for bats, housing three newly described or undescribed species. Vicariance across biogeographic barriers gave rise to 29 micro-endemic species and intraspecific lineages whose distributions were congruent with those identified in other phytogeographic and zoogeographic studies. Although Köppen-Geiger climate models predict a widespread replacement of current temperate ecosystems in southern Africa by tropical or arid ecosystems by 2070-2100, future climate Maxent models for 13 bat species (all but one of those analyzed above) showed minimal range changes in temperate species from the eastern Escarpment by 2070, possibly due to the buffering effect of mountains to climate change.

Remote sensing monitoring and potential distribution analysis of Spartina alterniflora in coastal zone of Guangxi.

In recent years, the continuous expansion of Spartina alterniflora (S. alterniflora) has caused serious damage to coastal wetland ecosystem. Mapping the coverage of S. alterniflora by remote sensing and analyzing its growth pattern pose great importance in controlling the expansion and maintaining the biodiversity of coastal wetlands in Guangxi. This study aimed to use harmonic regression to fit time series data of vegetation indices based on Landsat images, and the phenological features were extracted as the input of random forest model to distinguish S. alterniflora in coastal zone of Guangxi from 2009 to 2020. The influence of natural environmental factors on the distribution of S. alterniflora was evaluated by Maxent model, and the potential distribution was analyzed. The results showed that: (1) Based on the time series data of characteristic indices fitted by harmonic regression, the extraction of phenological features of S. alterniflora identification effect exhibited high accuracy (in the result of 2009, Overall Accuracy [OA] = 97.33%, Kappa = 0.95). (2) During 2009-2020, the S. alterniflora in coastal zone of Guangxi kept proliferating and expanding from east to west. The total area of S. alterniflora continued to increase while the growth rate showed a trend that increased first and then decreased. (3) The Maxent model shows good accuracy in simulating the habitat of S. alterniflora, with a potential distribution area of 14,303.39 hm2. The findings will be beneficial to the understanding of dynamic changes of S. alterniflora in coastal zone of Guangxi and provide a scientific reference for other coastal wetland studies on S. alterniflora expansion.

Predicting the Potential Risk Area of the Invasive Plant Galinsoga parviflora in Tibet Using the MaxEnt Model

The Tibetan plateau, with complex and diverse ecosystems, is an important ecological security barrier to China. However, climate change and the spread of invasive plant species have imperiled the once pristine and diverse ecosystem of the region. To prevent the further spread and control of invasive plants, it is important to delineate the potential distribution patterns of alien invasive plants at the regional scale across Tibet and understand their responses to climate change. Galinsoga parviflora Cav., a member of the family Asteraceae, is an annual herbaceous plant distributed globally as an invasive weed and possesses characteristics that make it highly invasive, such as a strong ability to proliferate and disperse. The species is also known to have an allelopathic effect. There has been no report on the spatial distribution of G. parviflora in Tibet. Using field survey data, we investigated the risk of G. parviflora invasion and its impacts on the ecological safety of Tibet. We employed the MaxEnt model using the R language and SPSS software to optimize and select model parameters and data. We acquired various environmental variables along with current and future climate change scenarios (two carbon emission scenarios, SSP126 and SSP585, for the years 2050 and 2090) to predict the geographic distribution and potential risk areas in Tibet that G. parviflora can invade. The MaxEnt model accurately predicted the distribution of G. parviflora in Tibet with an average AUC of 0.985. The most suitable environmental conditions in which G. parviflora performed the best in Tibet included a mean annual temperature of 6.2–10.0 °C and an elevation range of 2672–3744 m above sea level. Our results indicate that low precipitation during the coldest quarter of the year (mean temperature −2–3 °C) was the most important variable predicting G. parviflora distribution. The results also showed that the species was hardly found when precipitation in the coldest quarter exceeded 155 mm. The current potential invasion risk areas for G. parviflora included the river valleys of central, southeastern, and eastern Tibet. With future climate change scenarios (i.e., SSP126, SSP585), the suitable habitats for G. parviflora distribution will likely shift to northwest regions from the southeast. Particularly under the highest carbon emission scenario (i.e., SSP585), the potential risk area expands more rapidly, and the center of distribution shifts to northwest regions. These findings provide useful information about the current and future changes in G. parviflora distribution in Tibet, which is crucial for the comprehensive and proactive management and control of G. parviflora under future climate change.

Potential planting regions of Pterocarpus santalinus (Fabaceae) under current and future climate in China based on MaxEnt modeling.

This study modeled the habitat distribution of Pterocarpus santalinus, a valuable rosewood species, across China under current and future climate scenarios (SSPs126, SSPs245, and SSPs585) using MaxEnt. Our findings reveal that the current suitable habitat, spanning approximately 409,600 km2, is primarily located in the central and southern parts of Guangdong, Guangxi, Fujian, and Yunnan, as well as in the Hainan provinces, along with the coastal regions of Taiwan, and the Sichuan-Chongqing border. The habitat's distribution is significantly influenced by climatic factors such as temperature seasonality (bio4), mean temperature of the wettest quarter (bio8), annual mean temperature (bio1), and annual precipitation (bio12), while terrain and soil factors play a lesser role. Under future climate scenarios, the suitable habitat for P. santalinus is projected to expand, with a northeastward shift in its distribution center. This research not only sheds light on the geoecological characteristics and geographical distribution of P. santalinus in China but also offers a scientific basis for planning its cultivation areas and enhancing cultivation efficiency under changing climate conditions.

Adaptive divergence, historical population dynamics, and simulation of suitable distributions for Picea Meyeri and P. Mongolica at the whole-genome level

The taxonomic classification of Picea meyeri and P. mongolica has long been controversial. To investigate the genetic relatedness, evolutionary history, and population history dynamics of these species, genotyping-by-sequencing (GBS) technology was utilized to acquire whole-genome single nucleotide polymorphism (SNP) markers, which were subsequently used to assess population structure, population dynamics, and adaptive differentiation. Phylogenetic and population structural analyses at the genomic level indicated that although the ancestor of P. mongolica was a hybrid of P. meyeri and P. koraiensis, P. mongolica is an independent Picea species. Additionally, P. mongolica is more closely related to P. meyeri than to P. koraiensis, which is consistent with its geographic distribution. There were up to eight instances of interspecific and intraspecific gene flow between P. meyeri and P. mongolica. The P. meyeri and P. mongolica effective population sizes generally decreased, and Maxent modeling revealed that from the Last Glacial Maximum (LGM) to the present, their habitat areas decreased initially and then increased. However, under future climate scenarios, the habitat areas of both species were projected to decrease, especially under high-emission scenarios, which would place P. mongolica at risk of extinction and in urgent need of protection. Local adaptation has promoted differentiation between P. meyeri and P. mongolica. Genotype‒environment association analysis revealed 96,543 SNPs associated with environmental factors, mainly related to plant adaptations to moisture and temperature. Selective sweeps revealed that the selected genes among P. meyeri, P. mongolica and P. koraiensis are primarily associated in vascular plants with flowering, fruit development, and stress resistance. This research enhances our understanding of Picea species classification and provides a basis for future genetic improvement and species conservation efforts.

Prediction of Suitable Habitat of Alien Invasive Plant Ambrosia trifida in Northeast China under Various Climatic Scenarios

Ambrosia trifida is an invasive alien plant species, which has very high reproductive and environmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants and destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of A. trifida with climate change before implementing scientific control measures. Based on 106 A. trifida distribution data and 14 points of environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained using the MaxEnt (version 3.4.1) model optimized by Kuenm package, and thus the potential suitable areas of A. trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050, 2070) were predicted. The changes of A. trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of A. trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of A. trifida.

Predicted Distribution of Locoweed Oxytropis glabra in China under Climate Change

The research on the significant toxic weed Oxytropis glabra, which adversely affects the grazing industry and the ecological integrity of natural grasslands in the arid and semi-arid regions of northern China, aims to delineate its potential distribution amidst changing climate conditions. This analysis involves both current conditions (1970–2000) and future projections (2050s and 2070s) under four climate scenarios using an R-optimized MaxEnt model. The results indicate that the distribution of O. glabra was primarily influenced by the temperature of the coldest quarter (bio11, ranging from −12.04 to −0.07 °C), precipitation of the coldest quarter (bio19, 0 to 15.17 mm), and precipitation of the warmest quarter (bio18, 0 to 269.50 mm). Currently, the weed predominantly occupies parts of Xinjiang, Inner Mongolia, Gansu, Qinghai, Ningxia, and Tibet. Projections indicate that, across four future climate scenarios, the area of suitable habitats for O. glabra is expected to expand and shift toward higher latitudes and elevations. The research provides valuable information and a theoretical foundation for the management of O. glabra, alongside advancing grassland ecological research and grazing practices.

Delineating the Area for Sustainable Cultivation of Morinda officinalis Based on the MaxEnt Model

Delineating the Area for Sustainable Cultivation of Morinda officinalis Based on the MaxEnt Model

Predictive modeling of green water availability: The role of annual plants as an ecological indicator in dryland ecosystems

Green water is primarily associated with the appearance of annual plants and plays a significant role in biomass production in both arid and semi-arid ecosystems. Herein, we aim to estimate the optimal threshold for determining the presence or absence of annual plants and use them as an ecological indicator to assess potential green water areas in Kuwait as a case study. We integrate remote sensing techniques and MaxEnt modeling. The AUC for the annual plant distribution with all examined factors is 0.847, and the standard deviation is 0.050. The results demonstrated that potential locations with high levels of green water cover <20% of the country. The annual plant distribution was significantly correlated with several types of perennial plants, maximum temperature, precipitation, and sandy soils. It was also found that annual plants are controlled by the spring and winter temperature decline and the timing of precipitation occurrence, especially the pattern and amount of rainfall received in November. Sandy loam and loam soils were found to be ideal for annual plants, although land depressions and soil types are crucial factors in determining annual plant distribution. Additionally, annual plants enhanced the growth of several perennial communities. To reiterate, our study's model helped to comprehend the significance of annual plants as an ecological indicator in sustaining soil moisture over a prolonged period, as well as factors controlling the distribution of annual plants. The developed model and indicators could support decision-makers in determining appropriate locations with adequate levels of green water for revegetation planning in arid landscapes.

Predicting the potential distribution of Dendrolimus punctatus and its host Pinus massoniana in China under climate change conditions

IntroductionDendrolimus punctatus, a major pest endemic to the native Pinus massoniana forests in China, displays major outbreak characteristics and causes severe destructiveness. In the context of global climate change, this study aims to investigate the effects of climatic variations on the distribution of D. punctatus and its host, P. massoniana. MethodsWe predict their potential suitable distribution areas in the future, thereby offering a theoretical basis for monitoring and controlling D. punctatus, as well as conserving P. massoniana forest resources. By utilizing existing distribution data on D. punctatus and P. massoniana, coupled with relevant climatic variables, this study employs an optimized maximum entropy (MaxEnt) model for predictions. With feature combinations set as linear and product (LP) and the regularization multiplier at 0.1, the model strikes an optimal balance between complexity and accuracy.ResultsThe results indicate that the primary climatic factors influencing the distribution of D. punctatus and P. massoniana include the minimum temperature of the coldest month, annual temperature range, and annual precipitation. Under the influence of climate change, the distribution areas of P. massoniana and its pests exhibit a high degree of similarity, primarily concentrated in the region south of the Qinling−Huaihe line in China. In various climate scenarios, the suitable habitat areas for these two species may expand to varying degrees, exhibiting a tendency to shift toward higher latitude regions. Particularly under the high emission scenario (SSP5-8.5), D. punctatus is projected to expand northwards at the fastest rate. DiscussionBy 2050, its migration direction is expected to closely align with that of P. massoniana, indicating that the pine forests will continue to be affected by the pest. These findings provide crucial empirical references for region-specific prevention of D. punctatus infestations and for the rational utilization and management of P. massoniana resources.

Potential suitability areas of Sitobion miscanthi in China based on the MaxEnt model: Implications for management

Wheat is a crucial grain crop in China, but its yield has been severely reduced by a number of pests. Identifying the pest's distribution range is vital for its effective control. Therefore, based on distribution data and environmental variables, the MaxEnt model was used herein to predict the suitability areas for habitation by Sitobion miscanthi in China now and in the future (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The results showed that terrain roughness was the most important environmental variable in determining the distribution of S. miscanthi. Furthermore, the current suitability areas reached an area of 1.91 × 106 km2, mainly distributed in southern, eastern, and northeastern China, such as Hebei, Shandong, and Anhui provinces. Under future environmental conditions, the suitability areas for its habitation are expected to primarily expand in the northwest, especially in Xinjiang and Inner Mongolia, and move northwest. Moreover, the high suitability areas for habitation by this pest showed a trend of continuous increase, indicating a high probability of outbreaks. The findings on the suitability areas for S. miscanthi to inhabit at present and in the future, as determined here, are conducive to the implementation of management and control measures to enhance wheat yield and ensure food security in China.

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.

Using proxy data and vegetation modelling to predict past, current and future distributional shifts of Butea monosperma, a marker of land degradation in India

Extensive deviations in spatio-temporal social and environmental dynamics currently alter the health of ecosystems and the services they provide. Detecting the causes that contribute to the distribution of a natural forest species capable of restoring the lost ecosystem function and productivity will aid in determining better food security, livelihoods and provision of ecosystem goods and services. We modelled the spatial range of Butea monosperma (B. monosperma) under past, that is, Last Glacial Maximum (LGM), Middle Holocene (MH), current and future (2070) climatic scenarios with Maximum Entropy (MaxEnt) trained on present-day occurrences. We identified areas of suitable habitats for which the estimation of habitat stability is predicted in all the models at different times. To validate the inferred suitable habitat, we tested the model by the current occurrence and fossil pollen data of B. monosperma. Our distribution models agree with the fossil pollen records for the MH (4,500–7,000 yr BP) and predict the prevalence of B. monosperma covering 84.22% of the Indian subcontinent with maximum habitat stability in western and southwestern India (10.95%). The widespread potential distribution of the plant species during the LGM supports the presence of the last remnants of tropical dry deciduous forest in the region. However, a decline in habitat suitability (62.84%) is predicted under current and future climatic scenarios with maximum stability (0.90%–3.09%) along the Western Ghats, Nilgiri hills, Gir range in the western India and north-eastern region covering the Assam Valley and foothills of Tripura and Mizo hills. Temperature seasonality (33.6%) measured in terms of variable contribution in MaxEnt model significantly affects the distribution shift of B. monosperma, along with annual precipitation (22.8%) and annual mean temperature (16.2%). Model results provide evidence of habitat reduction and identify the stability hotspots for B. monosperma for its conservation and establishment of land management policies mainly for the dry tropics.

Modeling climate change impacts under future CCM3 scenario on sorghum (Sorghum bicolor) as an drought resilient crop in tropical arid Lombok Island, Indonesia

Abstract. Wibowo AA, Meylani V. 2024. Modeling climate change impacts under future CCM3 scenario on sorghum (Sorghum bicolor) as an drought resilient crop in tropical arid Lombok Island, Indonesia. Intl J Trop Drylands 8: 35-43. The arid ecosystems and drought conditions exacerbated by climate change and rising CO2 levels necessitate the identification of alternative drought-tolerant crops. Sorghum bicolor L. has emerged as a promising option due to its resilience to drought. However, there is dearth of information regarding its future potential distribution, particularly in arid regions like Lombok Island, Indonesia, where sorghum is being considered as a viable alternative to ensure food security. This study employs Maximum Entropy (MaxEnt) modeling, incorporating environmental and bioclimatic variables, along with the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3) scenario reflecting doubled CO2 levels, to model the future potential distribution of S. bicolor. The model projects a total suitable habitat area of 1,875 km2, constituting 39.56% of Lombok Island’s land area. Notably, very high-suitability areas of 175 km2, and high-suitability areas of 200 km2 encompass 3.69% and 4.22% of the island’s territory, respectively, predominantly concentrated in the southern region of the island and characterized by low precipitation and high temperatures, particularly at altitudes ranging from 0 to 1,000 meters. The model's performance, evaluated using the Area Under the Curve (AUC), yields a score of 0.725, indicating a good level of accuracy. Key factors influencing sorghum distribution include annual precipitation (68.69%), isothermality (9.56%), temperature seasonality (9.56%), precipitation seasonality (8.69%), and annual mean temperature (3.47%). The CCM3 model forecasts an expansion of sorghum distribution toward the north, occupying approximately 6.25% of Lombok's total area. These findings highlight sorghum’s adaptability and resilience to future climate changes, positioning it as a valuable resource for sustainable agriculture in arid environments.

Geospatial forest fire risk assessment and zoning by integrating MaxEnt in Gorkha District, Nepal

Forest fires are an imminent danger to natural forest ecosystems, and carrying out zoning studies and forest fire risk assessments are of great practical significance in steering fire prevention, minimizing fire incidents, and limiting the environmental consequences of fire. Using the Gorkha district of Nepal as a case study, this study used remotely sensed high-temperature fire data as the forest fire sample. Nine parameters related to topography, climatic conditions, vegetation, and human intervention were used as environmental variables affecting fire occurrence. Next, a MaxEnt forest fire risk assessment model was generated with GIS and R, which analysed the contribution, significance, and responses of environmental variables to the forest fire in Gorkha District. The findings demonstrate that (1) following a test of sample locations for forest fires, the MaxEnt model has excellent relevance and practicality when applied to fire risk assessment; (2) Out of 2747 fire points in the forest, only 110 Spatio-temporally independent fire points were used for the model building having high and normal confidence level. Regarding Area Under Curve (AUC) values, the training data yielded results of 0.875, while the test data produced acceptable results of 0.861 with a standard deviation of 0.0322; (3) the importance of climatic and Land Use Land Cover (LULC) variables to forest fire are 56.2 % and 32.9 %, respectively, and their contribution to forest fire are 32 % and 47.6 %, respectively. (4) There are numerous and intricate ways that environmental factors influence forest fires. The forest fire response curves to the nine chosen environmental variables are complex and nonlinear rather than linear; Maximum temperature of the warmest month (bio_5), Isothermality (bio_3), Precipitation of Driest Quarter (bio_17) and mean Diurnal Range (bio_2) bear a nonlinear positive link with the possibility of forest fires. In contrast, elevation, slope, temperature seasonality (bio_4), distance from the settlement, and LULC have a favorable stimulating response to the possibility of forest fires within an appropriate interval. (5) In Gorkha, there are geographical differences in the risk of forest fires. Only 12.83 % of the whole area is made up of areas at significantly high risk or above, compared to 87.17 % for high-risk and below.

Subtropical mangroves poleward shift to the Yangtze Estuary under different carbon emission scenarios

Mangroves are crucial plant communities in the coastal intertidal zones, providing various ecological services and supporting biodiversity. To identify the primary factors affecting the poleward shift of subtropical mangroves and the northernmost extent of subtropical mangroves under various carbon emission scenarios, the MaxEnt model was utilized with presence data of subtropical mangrove and environmental data under different carbon emission scenarios. Kandelia obovata, Avicennia marina, and Aegiceras corniculatum in China were included for modeling, utilizing 22 bioclimate variables to predict mangrove distributions, and kernel density analysis was further employed to identify conservation and afforestation hotspots for mangroves. The results indicated that the number of days below 10 °C per year is the major climatic factor shaping the distribution of subtropical native mangroves, while precipitation exerts a relatively lower contribution. Under the low and medium emissions scenarios (SSP126 and SSP245), the northern boundary of the distribution of K. obovata remains stable, while A. marina and A. corniculatum are projected to shift northward to Fuzhou, Fujian Province (26.1°N) from 24.9°N by 2100. Under higher emissions scenarios (SSP370 and SSP585), the suitable habitat of K. obovata is expected to shift north to the mouth of the Yangtze River (31.9°N) from 26.8°N, and A. marina and A. corniculatum are projected to move northward to the coastal area of Ningde, Fujian Province (26.8°N) by 2100. In conclusion, with the increase in carbon emissions, there is a significant northward migration observed in subtropical native mangroves (p < 0.01), and suitable areas for restoration in China have been identified, with the northernmost region located in Zhejiang Province. The spatial and temporal patterns of range change predicted in this study provide valuable information for conservation and afforestation strategies for these ecologically important species.

Modelling lantana camara invasion in the inkomati catchment in Mpumalanga, South Africa

Lantana Camara ranks among the most notorious and hazardous invasive species on the planet. There has been extensive research focusing on the remote sensing of Lantana Camara (L. Camara). However, the factors that facilitate its spread, especially in savanna rangelands, are not yet fully comprehended. The present research investigated the recent spatial distribution of L. Camara in the Inkomati catchment, Mpumalanga, South Africa. The research used the Random Forest classification algorithm, with Sentinel-2 remotely sensed data within Google Earth Engine. The study also modelled areas vulnerable to L. Camara invasion using the Maxent species distribution model. The study found that L. Camara covered approximately 34.86% of the entire study area, with a user's and a producer's accuracies of 91% and 84%, respectively. Additionally, elevation was identified as the most influential topographic variable on the species spatial distribution and invasion, while Topographic Wetness Index had the least influence. The model developed using topographic variables had the highest accuracy of 0.88. On the other hand, the predictive model that utilised Sentinel 2 bands had the least accuracy of 0.81. The red edge and NIR bands with Random Forest allowed for an accurate assessment of L. Camara distribution. The study therefore provides the basis for the control or further expansion of L. Camara species in the province.