Maximum Entropy Algorithm Research Articles

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.

Spatial Modelling Habitat Suitability of Javan Langur (<i>Trachypithecus auratus</i> É. Geoffroy Saint-Hilaire, 1812) in Bromo Tengger Semeru National Park (TNBTS), East Java

Javan Langur (T. auratus) is well-known as one of endemic primates from Java, Bali and Lombok Islands. The activities of land clearing, vegetation conversing, wild hunting and illegal wildlife trading are the main causes of the extinction of the Javan Langur. It can be used as an important issue for conservation action by making prediction maps of suitable habitat potential, especially for species facing a high risk of extinction in the wild. We were documenting an information about potential habitat for Javan langur using spatial suitability model in order to provide rigorous information as the basis for conservation activities of Javan langur in TNBTS. We used Landsat-8 TM image and geospatial data to support analysis as a representative of environmental parameters in order to develop the habitat model. We were using maximum entropy (MaxEnt) algorithm refers to Javan langur presence or absence. The results showed that the suitability of the Javan langur habitat in TNBTS has an excellent model accuracy level with an AUC (Area Under the Receiver Operating Characteristics) value of 0.964 and a standard deviation of 0.961. Parameters with the highest response values here are elevation, NDVI (Normalised Difference Vegetation Index) and temperatures.

Climate change impacts on dengue transmission areas in Espírito Santo state, Brazil.

Espírito Santo state, in Brazil, is a dengue-endemic region predicted to suffer from an increase in temperature and drought due to climate change, which could affect the areas with active dengue virus transmission. The study objective was modeling climatic factors and climate change effects in zones suitable for dengue virus transmission in Espírito Santo state, Brazil. Data on dengue reports from 2022 were used to determine climatic variables related to spatial distribution. The climate change projections were generated for the 2030s, 2050s, 2070s, and 2090s for three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5 and SSP5-8.5. A maximum entropy algorithm was used to construct the three models and projections, and the results were used to calculate the ensemble mean. Isothermality, the maximum temperature of the warmest month, precipitation of the wettest month, precipitation of the warmest quarter, and annual precipitation impacted the model. Projections indicated a change in areas suitable for dengue virus transmission, varying from -30.44% in the 2070s (SSP1-2.6) to +13.07% in the 2070s (SSP5-8.5) compared to 2022. The coastal regions were consistently suitable in all scenarios. Urbanized and highly populated areas were predicted to persist with active dengue transmission in Espírito Santo state, posing challenges for public health response.

Unveiling of climate change-driven decline of suitable habitat for Himalayan bumblebees

Insect pollinators, especially bumblebees are rapidly declining from their natural habitat in the mountain and temperate regions of the world due to climate change and other anthropogenic activities. We still lack reliable information about the current and future habitat conditions of bumblebees in the Himalaya. In this study, we used the maximum entropy algorithm for SDM to look at current and future (in 2050 and 2070) suitable habitats for bumblebees in the Himalaya. We found that the habitat conditions in the Himalayan mountain range do not have a very promising future as suitable habitat for most species will decrease over the next 50 years. By 2050, less than 10% of the Himalayan area will remain a suitable habitat for about 72% of species, and by 2070 this number will be raised to 75%. During this time period, the existing suitable habitat of bumblebees will be declined but some species will find new suitable habitat which clearly indicates possibility of habitat range shift by Himalayan bumblebees. Overall, about 15% of the Himalayan region is currently highly suitable for bumblebees, which should be considered as priority areas for the conservation of these pollinators. Since suitable habitats for bumblebees lie between several countries, nations that share international borders in the Himalayan region should have international agreements for comprehensive pollinator diversity conservation to protect these indispensable ecosystem service providers.

PRELIMINARY MODEL OF POTENTIAL AREAS FOR RESTORATION FOR THE INTER-ANDEAN VALLEY OF CAUCA RIVER (COLOMBIA, SOUTH AMERICA) BASED ON HABITAT SUITABILITY MODELS

Tropical dry forests (TDF) are highly susceptible to land degradation. The inter-Andean Valley of the Cauca River (IVCR) has the most fragmented Colombian dry forests, and their restoration is essential. Here, potential areas for restoration were identified using a habitat suitability modeling (HSM) approach. TDF vascular plants and bioclimatic predictors were used. Species were selected based on threatened status, endemism, and priority level for conservation. Two sets of predictors were chosen using Variance Inflation Factor (VIF) and Principal Component Analysis (PCA). Then, with a maximum entropy algorithm, PCA and VIF models were projected for the selected species. These models were evaluated via true skill statistics (TSS) and area under the curve (AUC) statistical metrics. Models with good performance (TSS, AUC, standard deviation, variance) were ensembled, and a preliminary model where areas with suitable bioclimatic conditions for the selected species were generated. Results show that nearly 45 % of the IVCR has suitable conditions for the selected species. Although potential conflicts may arise in areas under permanent or semipermanent crops which represent more than 80 % of the IVCR, cropland mosaics, and natural and seminatural land covers might provide alternative solutions to reduce the land-use conflict. The potential areas for restoration identified in this study may provide a comprehensive framework for environmental impact and regional risk assessments related to the current land use and land cover change dynamics. Also, they may provide relevant information for designing landscape restoration programs as an adaptive strategy toward climate change.

Predicting the Potential Distribution Areas of the Asian Redtail Catfi sh (Hemibagrus wyckioides Fang and Chaux, 1949) in the Chao Phraya River, Thailand, using Maximum Entropy Modelling

Hemibagrus wyckioides is recognized as an economically important freshwater fi sh in Thailand. However, there are no available comprehensive data regarding the geographic distribution and habitat suitability of H. wyckioides in Thailand. In addition, its natural population has tended to continuously decrease. This study aimed to predict the suitable distribution areas of H. wyckioides in the Chao Phraya River, Thailand, using the maximum entropy (MaxEnt) algorithm based on climatic factors, including 11 temperature and 8 precipitation indices from the WorldClim database. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). The jackknife test was used to assess the contribution of each variable to the model. The results revealed that the MaxEnt model performed acceptably well, with an AUC value of 0.939, representing excellent accuracy in predicting the appropriate distribution areas of H. wyckioides in the Chao Phraya River. The potential distribution areas of H. wyckioides were observed mostly along the Chao Phraya River in Sing Buri and Ang Thong Provinces. According to the results of the jackknife test, 11 climatic variables impacted the distribution of H. wyckioides. The annual mean temperature (BIO1), precipitation of driest month (BIO14), and annual temperature range (BIO7) were the most important infl uencing variables, with impact values of 53.5%, 15.9%, and 11.9%, respectively. Therefore, understanding the potential distribution areas can assist researchers in monitoring, management, and conservation activities or with the development of new natural protected areas of H. wyckioides in high-quality areas in the Chao Phraya River.

Ranking of the Territory of Sochi by the Risk of Infection with HFRS Using the Method of Maximum Entropy

Relevance. Hemorrhagic fever with renal syndrome (HFRS), due to the severity of the course and high mortality, poses a serious threat to the health of the population of both the city of Sochi and its guests. Therefore, in order to implement more effective and less economically costly anti-epidemic and preventive measures, it is very important to constantly monitor the activity of the natural focus of HFRS, as well as to have a clear idea of the territories most dangerous for the risk of infection with this infection.Aims. Assessment of the epidemiological significance of the territory of the city of Sochi for the risk of infection with HFRS based on the maximum entropy method using a geographical information system.Materials & Methods. Based on the application of the maximum entropy algorithm implemented in the MaxEnt program, as well as the ArcGIS 10 program.8. Ranking of the territory of the city of Sochi according to the risk of infection with HFRS was performed. The paper uses: data on positive epizootological findings (a total of 131) for 2016-2021, which were obtained from the Sochi branch of the Federal Medical Institution «Center of Hygiene and Epidemiology in the Krasnodar Territory», the Sochi branch of the Federal Medical Institution «Black Sea Plague Station» of Rospotrebnadzor, as well as the Stavropol Plague Control Research Institute of the Rospotrebnadzor; materials on environmental conditions from the Biolclim data bank, vegetation index for 9 months (https://land.copernicus.eu/global/products/NDVI ). Preliminary preparation of the information was carried out using the ArcGIS 10 program.8. As a tool for building a training model, the MaxEnt program version 3.4.4 was used (https://biodiversityinformatics.amnh.org/open_source/maxent /).Results and discussion. The practical implementation of the tasks was to obtain maps of the epidemiological significance of the territory for the risk of infection with HFRS by superimposing the points of occurrence of the species (reservoir and carrier of HFRS) on maps of abiotic environmental factors affecting its spread. The implementation of this work consisted of the sequential implementation of four main stages: the first – the collection, generalization and transformation of bioclimatic and epizootic-epidemiological data; the second – the selection of the most significant data for the construction of the model; the third – the ranking of the territory of the city. Sochi on the risk of the spread of HFRS using GIS; the fourth is the analysis of the data obtained. In the course of the work, a model was obtained that allows dividing the study area according to the degree of risk of infection with HFRS with a high degree of reliability and significant prognostic value.Conclusions. The use of the model makes it possible to obtain new, more detailed data from a spatial point of view on the boundaries of potentially dangerous sites in the region in terms of GLPS. In particular, this applies to those territories where positive epizootological findings and cases of infection with HFRS have not been previously noted.

Spatio-Seasonal Habitat Suitability Model of Anisakis spp. in Chub Mackerel (Scomber Japonicus) as a Scientific-Based Approach to Provide Safety Assessment Policy of Fisheries Product in Indonesia

The infestation of Anisakis spp. in Chub mackerel (Scomber japonicus) may have significant health and socioeconomic implications. Understanding the spatio-seasonal occurrence of Anisakis spp. in its host, S. japonicus, is crucial ecologically and for effective management. This research aimed to develop a predictive map (spatial-seasonal patterns) for Anisakis’s infestation in S. japonicus using the maximum entropy (MaxEnt) algorithm. Anisakis spp. and S. japonicus occurrences were obtained through a comprehensive Bibliographic analysis of the Scopus database (2017-2022) and the Ocean Biodiversity Information System (OBIS) database to collect the necessary data. Environmental predictors were sourced from the Global Marine Environment Data. The resulting model demonstrated a reliable performance, as indicated by an Area Under Curve (AUC) value on the Receiver Operating Characteristic (ROC) chart exceeding 0.8. The findings of this study revealed that the infestation of Anisakis spp. in S. japonicus is projected to be more prevalent during the fourth quarter of each year. Furthermore, the environmental factors influencing the infestation were identified as diffuse attenuation, water depth, and distance from the coast. These research outcomes can be a foundational reference for developing an effective control system for inspecting fresh or frozen fish within the quarantine department. By utilizing the spatial-seasonal patterns and environmental predictors identified in this study, authorities can implement targeted measures to prevent and mitigate the infestation of Anisakis spp. in S. japonicus, safeguarding public health and maintaining the quality of fish products.

Identifying potential habitats of Himalayan Red Panda Ailurus fulgens (Cuvier, 1825) (Mammalia: Carnivora: Ailuridae) in Neora Valley National Park, West Bengal, India

The Himalayan Red Panda Ailurus fulgens (Cuvier, 1825) is a globally Endangered species whose population is reported to be declining in the wild. It is a priority species for the Neora Valley National Park (NVNP) since it is the flagship species of this ecosystem. Moreover, this landscape functions as an important connecting link of the Himalayan Red Panda habitat between the state of West Bengal and Sikkim. The spatial habitat of the Himalayan Red Panda in this National Park is little known. Our study attempts to identify the spatial distribution of potential habitats for the Himalayan Red Panda using the maximum entropy algorithm (MaxEnt 3.4.1). The model predicted a 55 km2 of potential habitat with the current climate scenario. With climate change, predicted potential habitats are likely to experience significant loss and upward shift to a relatively higher elevation. Hence, the management of the NVNP should identify the potential habitats and accomplish realistic goals to help conserve the Red Pandas.­­

Environmental and geographic low suitability overlapping of geoduck clams in the Pacific Northeast predicted by Ecological Niche Modeling

The known distribution limits of geoduck clams (genus Panopea) remain geographically separated globally. However, in the northeast Pacific, P. generosa is distributed from Alaska to northern Mexico, but approaches, and likely overlaps, the distribution of P. globosa, which is mainly distributed in the Gulf of California. There is incipient evidence that both populations coexist in the central part of the Baja California Peninsula on the Pacific coast. However, knowledge of the biogeographic distribution and environmental conditions for both species is still developing. To address the hypothesis of coexistence in the same geographic space, we developed distribution area models using ecological niche modeling through the maximum entropy algorithm. The algorithm used presence-only records and environmental layers (predictive variables) such as mean surface temperature, mean salinity, mean depth, temperature range, and primary productivity. The results indicate that both species overlap in a narrow portion of the environmental space (Grinnellian ecological niche), which is reflected in a restricted geographic distribution with conditions of low abiotic suitability. Our results contribute to directing new research in the areas of ecology, biogeography, paleontology, and geoduck clam fisheries management.

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.

New records and potential distribution of Bufo luchunnicus (Yang & Rao, 2008) in Vietnam

Abstract The Luchun Stream Toad – Bufo luchunnicus (Yang and Rao, 2008) is a Critically Endangered species with a restricted distribution in southern China and northwestern Vietnam. Habitat loss and disturbance is the main threat to this species, but a lack of understanding of its natural history has hindered urgent conservation efforts to safeguard the species and its habitats. As a result of our field surveys, we herein report two new population records of B. luchunnicus in Vietnam and provide novel data on its natural history. In addition, species distribution models (MaxEnt – Maximum Entropy Algorithm) using current climatic data are applied to predict the potential distribution of B. luchunnicus.

Carrying capacity estimation and habitat suitability of Sumatran elephant in Datuk Gedang Wildlife Corridor, Bukit Tigapuluh Landscape, Jambi, Indonesia

Abstract. Fikri N, Rahman DA, Santoso N. 2023. Carrying capacity estimation and habitat suitability of Sumatran elephant in Datuk Gedang Wildlife Corridor, Bukit Tigapuluh Landscape, Jambi, Indonesia. Biodiversitas 24: 5548-5557. Rapid land use changes, including deforestation and land conversion into human areas, lead to the fragmentation of Sumatran elephant (Elephas maximus sumatranus) habitat. This study aimed to assess food capacity and determine habitat suitability for Sumatra elephants in Datuk Gedang Wildlife Corridor Bukit Tigapuluh Landscape. The research was conducted from January to May 2023. Data on potential, productivity, and carrying food capacity were collected with a direct method in the field with vegetation analysis. Carrying capacity estimation is based on data on productivity, land cover area, proper use, and Sumatran elephant food needs in a day. The habitat suitability model used the present coordinate point data from the GPS collar period of January-December 2022 with eight environment variables. The data were then analyzed for the build Sumatera Elephant habitat suitability model using the Maximum Entropy (MaxEnt) algorithm. We recorded 95 species of the potential Sumatran elephant food. The total feed productivity is 1,032,740.722 kg/ha /day and the carrying capacity is 258 elephants. There were three models of habitat suitability categories: high 4,238.66 ha (7%), medium 8,522.34 ha (14%), and low 11,130.36 ha (18%). The studied landscape can adequately accommodate the current population of Sumatran elephants depending on landscape governance (policy and spatial planning) outside conservation areas.

The Gulf toadfish goes south: assessing the potential habitat suitability of Opsanus beta in response to climate change

Climate change can directly influence invasive species range, and may reflect in greater habitat suitability, consequently increasing losses in native biodiversity. Habitat suitability modelling can help identify priority areas for conservation and rapid response for invasive taxa. This study aimed to model the habitat suitability of Opsanus beta, an invasive fish species, in South America under current and future climate change scenarios. Three modelling techniques, Generalized Linear Model (GLM), Maximum Entropy Algorithm (Maxent), and Random Forest (RF), were applied, and their results were compared and then put together in an Ensemble model to visualize habitat suitability. We found that the current habitat suitability of O. beta in South America is relatively low, with its occurrences mostly related to coastal port areas, suggesting that the species was introduced from ballast water. The increase in mean temperature at mean depth and temperature range were the most relevant variables influencing the species habitat suitability. The gradual increase in habitat suitability for the 2100 climate change scenarios, particularly in more severe climate change scenarios, such as the RCP 8.5, was also observed. The study highlights the critical need to use habitat suitability models to mitigate the impact of future climate change scenarios on invasive species. The findings call for preventive measures to be taken in time to prepare areas that may become the target of dispersal and establishment of O. beta.

Species range shifts of notorious invasive fish species in China under global changes: Insights and implications for management

Due to global changes, e.g., climate change and trade globalization, China is facing an increasingly severe threat from invasive freshwater fish species, which have the potential to cause negative impacts across various aspects and pose significant challenges for their eradication once established. Therefore, prioritizing the understanding of invasive species’ potential ranges and their determinants is vital for developing more targeted management strategies. Moreover, it is equally essential to consider the transitory range dynamics of invasive species that reflect changes in habitat availability and accessibility. Here, we used species distribution models (the maximum entropy algorithm) to assess the potential distributions of six notorious invasive fish species (i.e., Coptodon zillii, Cyprinus carpio, Gambusia affinis, Hemiculter leucisculus, Oreochromis mossambicus, and Oreochromis niloticus) in current and future (i.e., the 2030s, 2050s, and 2070s) periods along with their determinants, under two Shared Socio-economic Pathways scenarios (SSP1-2.6 and SSP5-8.5; global climate model: MRI-ESM2-0). Our results showed that the habitat suitability for the six species substantially benefited from temperature conditions (i.e., annual mean temperature or maximum temperature of warmest month). Throughout the given time periods, dramatic range expansions would occur for C. zillii, G. affinis, O. mossambicus, and O. niloticus, ranging from 38.61% to 291.90%. In contrast, the range of C. carpio would change slightly and irregularly, while H. leucisculus would contract marginally, with losses ranging from 1.06% to 12.60%. By the 2070s, species richness of these species would be relatively high in South, Central, and East China and parts of Southwest China. Furthermore, transitory fluctuations in the species ranges for all six species were observed throughout the entire time period (the 2030s–2070s). Given the range shifts for each species during different time periods, as well as time costs and budgets, adaptation strategies should be developed and implemented in the areas where they are most needed in each time period.

Spatial Analysis of Mountain and Lowland Anoa Habitat Potential Using the Maximum Entropy and Random Forest Algorithm

The Anoa is a wild animal endemic to Sulawesi that looks like a small cow. Anoa are categorized as vulnerable to extinction on the IUCN red list. There are two species of Anoa, namely Lowland Anoa (Bubalus depressicornis) and Mountain Anoa (Bubalus quarlesi). In this study, a comparison of potential habitat models for Anoa species was conducted using Machine Learning algorithms with the Maximum Entropy (MaxEnt) and Random Forest (RF) methods. This modeling uses eight environmental variables. Where based on the results of Bubalus quarlesi potential habitat modeling, the RF 75:25 model is the best algorithm with the highest variable contribution, namely humidity of 82.444% and a potential area of 5% of Sulawesi Island, with an Area Under Curve (AUC) of 0.987. Meanwhile, the best Bubalus depressicornis habitat potential model is the RF 70:30 algorithm, with the highest variable contribution, namely population of 88.891% and potential area of 36% of Sulawesi Island, with AUC 0.967. This indicates that Anoa extinction is very sensitive to the presence of humidity and human population levels.

Analyzing spatial patterns and influencing factors of different illegal land use types within ecological spaces: A case study of a fast-growing city

The conflict between socio-economic development and ecological protection results in large areas of illegal land use within ecological spaces. Determining the spatial patterns and influencing mechanisms of illegal land use is important because it is harmful to the natural environment. Previous studies mainly considered macro-scale influencing factors, but rarely analyzed the influencing mechanisms behind different types of illegal land use (e.g., residential areas, industrial areas). Therefore, we assessed the spatial distribution of suspected illegal land use and then investigated the influencing factors of various illegal land use types by combining remote sensing and the maximum entropy algorithm. The accuracies of the models for almost all types of illegal land use were much higher than 0.8. The results indicated that 36.14% of the subdistricts exhibited illegal land use issues in terms of the first-level ecological red lines, and 56.63% of subdistricts had this problem within the second-level ecological red lines. Rural residential areas, residential areas, and industrial areas were the major types of illegal land use. In addition, population, elevation, and transportation had a great impact on the occurrence probability of illegal land use. Particularly, transportation-related factors, such as distance from roads, distance from trunk lines, and distance from highways, had a large impact on illegal residential and industrial areas. Proximity to public transportation facilities was associated with a greater risk of illegal commercial areas. Therefore, local governments should strengthen the management of high-risk areas and monitor them closely to prevent conflicts between urban development and ecological protection plans. They must pay sufficient attention to areas with higher population density, lower elevation, and closer proximity to public transportation facilities. Overall, these conclusions can facilitate the fine-scale protection of ecological spaces and provide a theoretical basis for sustainable ecological planning.

Potential impacts of climate change on wild relatives of lentil (Lens spp.): An ecological niche model approach

Ecological niche models play a pivotal role in assessing the impacts of climatic changes on species, and in the development of conservation strategies. This study aimed to evaluate the potential impact of different climatic factors using ecological niche modeling. These models were constructed using occurrence data and bioclimatic variables with the MaxEnt (Maximum Entropy Algorithm) modeling algorithm. The analysis covers three timeframes: the current and two future periods (2060 to 2080 and 2080 to 2100). Our findings indicate that the suitable habitats of wild lentils will be impacted to varying degrees under all future climate scenarios, with the most significant impact projected in the worst-case scenario, which predicts a temperature increase of 5°C by 2100. It is recommended to consider habitat reduction or shift in four taxa while developing conservation strategies. Comparing habitat suitability across timeframes, some species such as L. nigricans (M. Bieb.) Godron exhibited no drastic changes. On the other hand, the models predict slight reductions, shifts, or fragmentations in the potential habitats of L. culinaris subsp. odemensis (Ladiz.) M.E. Ferguson & al., L. lammotei Czefr., and L. ervoides (Brign.) Grande, in future periods. Particularly, a significant reduction and shift in the projected potential habitats of L. culinaris subsp. orientalis (Boiss.) Ponert, accepted as the progenitor of the lentil, means that conservation measures should be taken. In conclusion, this study emphasizes the significance of the protection of habitats for lentil wild relatives in specific regions, even when species are not currently under threat. It is recommended to develop ex situ and in situ conservation strategies that consider potential impacts of climate changes, especially in the centers of genetic diversity.

Incorporating intraspecific variation into species distribution models improves climate change analyses of a widespread West African tree species (Pterocarpus erinaceus Poir, Fabaceae)

Climate change is predicted to affect species distribution worldwide. Most of the methods used to evaluate such impact so far assume that species respond to the environmental gradients in a uniform way along their distribution range. Because populations occupying different niches may differ in their response to climate change due to local adaptation, accounting for intraspecific variation in species distribution models (SDMs) may yield more reliable predictions for widely distributed species. Pterocarpus erinaceus Poir is a highly valued but endangered tree species, which occurs in the Sudanian (SZ), Sudano-Guinean (SGZ) and Guinean (GZ) ecological zones of Benin. Here, we used two (whole-species and intraspecific-level) SDM approaches to evaluate how local adaptation, quantified through niche differentiation, influences the potential impact of climate change on the distribution of P. erinaceus in Benin. The maximum entropy (MaxEnt) algorithm was employed to simulate the current and future distributions of the species under various Shared Socioeconomic Pathways (SSPs) climate scenarios. The results showed three distinct populations of the species according to the ecological zones of Benin. The intraspecific populations displayed no niche overlap and thus were considered as locally adapted. Mean diurnal range was the main variable that determined the current distribution of the SZ population (percent contribution of 45.9%) while the distribution of the SGZ and GZ populations were determined by isothermality (percent contribution of 58.7% and 76.2%, respectively). While the whole-species SDMs showed that climate change would lead to significant reductions in the species suitable habitats in SZ under SSP2–4.5, SSP1–2.6, and SSP5–8.5, the SDMs based on intraspecific populations indicated a high decrease in habitat suitability in the GZ and an upward shift of the SGZ towards the SZ under the future climate scenarios. Our results suggest that incorporating intraspecific variation into SDMs improves predictions of the impact of climate change and helps to identify appropriate population-based conservation strategies.

Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm

Abstract Displacement monitoring method of reservoir dam is a key research topic at present. In order to better display the overall efficiency of horizontal displacement and vertical displacement monitoring, a numerical simulation analysis method of ecological monitoring of small reservoir dam based on the maximum entropy algorithm is proposed. The virtual value is calculated by the maximum entropy algorithm, and the probability distribution function of random variables is obtained. The comprehensive prediction model of ecological monitoring results is constructed by the probability distribution function, and the daily monitoring values of ecological history of small reservoir dams are obtained. The maximum entropy probability density function is used to calculate the initial moment of small reservoir displacement samples, calculate the abnormal probability of the dam, get the maximum entropy probability density, realize the unbiased distribution of simulation values, and complete the dam deformation monitoring of small reservoirs. The simulation experiment is verified by numerical simulation. The results show that this method can effectively monitor the horizontal and vertical displacement of the dam; monitor the water-level hydrograph of pressure pipes at each measuring point; and obtain the changes of ecological runoff, temperature difference, and sediment discharge around the dam of small reservoirs in real time, which provides data guarantee for improving the ecological added value of small reservoirs.