Potential Distribution Maps Research Articles

Predictions of species distributions based only on models estimating future climate change are not reliable

Changes in climate and land use are the most often mentioned factors responsible for the current decline in species diversity. To reduce the effect of these factors, we need reliable predictions of future species distributions. This is usually done by utilizing species distribution models (SDMs) based on expected climate. Here we explore the accuracy of such projections: we use orchid (Orchidaceae) recordings and environmental (mainly climatic) data from the years 1901–1950 in SDMs to predict maps of potential species distributions in 1980–2014. This should enable us to compare the predictions of species distributions in 1980–2014, based on records of species distribution in the years 1901–1950, with real data in the 1980–2014 period. We found that the predictions of the SDMs often differ from reality in this experiment. The results clearly indicate that SDM predictions of future species distributions as a reaction to climate change must be treated with caution.

Climate change scenarios predict reduction in suitable habitats and range shifts for Ophiocordyceps sinensis (Berk.) in Hindu Kush Himalaya

The Hindu Kush Himalayan region has seen a faster pace of anthropogenic climate warming than the global average during the last 50 years. Since 1980s this region is experiencing intense climatic events notably elevation-dependent warming. Given its unique evolutionary background, rich variety of species, and significant endemism, it is crucial to comprehend the effects of climate change on species distributions in this area. Of particular interest are the fungi, which have been the subject of much fewer studies on how they will respond to climate change, despite the fact that they may have a significant role in the functioning and stability of ecosystems. We therefore selected Ophiocordyceps sinensis an alpine fungus species for predicting the effects of climate change on its distribution in Hindu Kush Himalaya. Regarded as one of the most expensive natural resources used in oriental medicine, Ophiocordyceps and its surrounding habitats are under threat from various ecological and anthropogenic factors. We used Species Distribution Modeling software Maxent 3.3.4 and a set of uncorrelated climatic (temperature and precipitation) and topographical (elevation, slope and aspect) variables at a spatial resolution of 2.5 arc minutes to model the suitable habitats. To predict the future distribution of O. sinensis we used future climate data from BCCCSM2- HR global circulation model for three emission scenarios of the shared socioeconomic pathways (SSPs) (SSP126, SSP245 and SSP585). Maxent model predicted current and future habitats with high accuracy. Current potential distribution map of O. sinensis shows that high suitability areas occur in India, China, Nepal and Bhutan. Prediction maps under all three scenarios showed a large reduction in suitable habitats as compared to current climatic conditions. Analysis of range change reveals that species exhibits both range expansion and range contraction under climate change scenarios. Range contraction is noticeably more than range expansion causing overall reduction in the suitable habitats occupied by O. sinensis. ‘Centroid Range Shift’ analysis revealed potential suitable habitats will shift to South-West direction under all future scenarios with almost overlapping centroids.

Role of climate change on Oriental spruce (Picea orientalis L.): Modeling and mapping

Global climate change is a process with dramatic consequences for ecosystems, and changes that may occur in the potential distribution of plant communities especially draw attention. This study aimed to reveal the potential distribution modeling and mapping of the Oriental spruce (Picea orientalis L.), distributed in a limited area, using current and future (year 2100) climate scenarios in Turkey. The maximum entropy method for potential distribution and Chelsa V2.1 technical specification IPSL-CM6A-LR scenarios (SSP126-SSP370-SSP585) were preferred to reveal the effect of climate change. Results for the current were in the “excellent” category with training and test data AUC 0.981 and 0.977, respectively. The variables contributing to the model were the precipitation amount of the driest month, mean diurnal air temperature range, annual precipitation amount, and mean annual air temperature. Variables contributing to the current model were analysed using the SSP126, SSP370, and SSP585 scenarios of the year 2100. It was assessed that the potential distribution for 2100 decreases according to SSP126, was fragmented according to SSP370, and decreased according to the SSP585 scenario. As a result, the authors determined that the high potential distribution is reduced 61% when the current mapping of Oriental spruce is compared with the SSP585 mapping.

Vegetation diversity and habitat suitability modeling of the invasive plant Bellucia pentamera in conservation forests of West Sumatra, Indonesia

Abstract. Solfiyeni S, Fadhlan A, Aziz A, Syahputra G, Azzahra A, Mildawati M. 2024. Vegetation diversity and habitat suitability modeling of the invasive plant Bellucia pentamera in Conservation Forests of West Sumatra, Indonesia. Biodiversitas 25: 781-791. The invasive plant Bellucia pentamera has spread to several regions in Indonesia, including the province of West Sumatra. Mapping the distribution of this invasive plant needs to be done to facilitate its management. offers a comprehensive approach to delineate the potential ecological range of a species and assess habitat suitability. This study aimed to evaluate the impact of B. pentamera invasion on vegetation diversity, conduct potential distribution mapping, and assess B. pentamera habitat suitability in West Sumatra conservation forests. The study utilized observational methods to determine coordinate points for B. pentamera presence and conducted vegetation analysis. The results from the vegetation analysis revealed disparities in composition and structure across three study areas, with diversity indices ranging from 2.60 to 3.84. The similarity index between study areas was relatively low, below 50%. The MaxEnt analysis delineated suitable locations for B. pentamera using the ROC curve (AUC) model, yielding values of 0.947 for training data and 0.923 for testing data. MaxEnt modeling identified suitable habitat for B. pentamera concentrated at Lima Puluh Kota, Tanah Datar, Padang, Solok and South Solok districts. This study's findings indicate a broad distribution of this species, with over 5% of the habitat deemed suitable for B. pentamera.

(Non)Resonance Bonds in Molecular Dynamics Simulations: A Case Study concerning C60 Fullerenes.

In the case of certain chemical compounds, especially organic ones, electrons can be delocalized between different atoms within the molecule. These resulting bonds, known as resonance bonds, pose a challenge not only in theoretical descriptions of the studied system but also present difficulties in simulating such systems using molecular dynamics methods. In computer simulations of such systems, it is often common practice to use fractional bonds as an averaged value across equivalent structures, known as a resonance hybrid. This paper presents the results of the analysis of five forms of C60 fullerene polymorphs: one with all bonds being resonance, three with all bonds being integer (singles and doubles in different configurations), one with the majority of bonds being integer (singles and doubles), and ten bonds (within two opposite pentagons) valued at one and a half. The analysis involved the Shannon entropy value for bond length distributions and the eigenfrequency of intrinsic vibrations (first vibrational mode), reflecting the stiffness of the entire structure. The maps of the electrostatic potential distribution around the investigated structures are presented and the dipole moment was estimated. Introducing asymmetry in bond redistribution by incorporating mixed bonds (integer and partial), in contrast to variants with equivalent bonds, resulted in a significant change in the examined observables.

Mapping queen snapper (Etelis oculatus) suitable habitat in Puerto Rico using ensemble species distribution modeling.

Queen snapper (Etelis oculatus) is of interest from an ecological and management perspective as it is the second most landed finfish species (by total pounds) as determined by Puerto Rico commercial landings (2010-2019). As fishing activities progressively expand into deeper waters, it is critical to gather data on deep-sea fish populations to identify essential fish habitats (EFH). In the U.S. Caribbean, the critically data-deficient nature of this species has made this challenging. We investigated the use of ensemble species distribution modeling (ESDM) to predict queen snapper distribution along the coast of Puerto Rico. Using occurrence data and terrain attributes derived from bathymetric datasets at different resolutions, we developed species distribution models unique to each sampling region (west, northeast, and southeast Puerto Rico) using seven different algorithms. Then, we developed ESDM models to analyze fish distribution using the highest-performing algorithms for each region. Model performance was evaluated for each ensemble model, with all depicting 'excellent' predictive capability (AUC > 0.8). Additionally, all ensemble models depicted 'substantial agreement' (Kappa > 0.7). We then used the models in combination with existing knowledge of the species' range to produce binary maps of potential queen snapper distributions. Variable importance differed across spatial resolutions of 30 m (west region) and 8 m (northeast and southeast region); however, bathymetry was consistently one of the best predictors of queen snapper suitable habitat. Positive detections showed strong regional patterns localized around large bathymetric features, such as seamounts and ridges. Despite the data-deficient condition of queen snapper population dynamics, these models will help facilitate the analysis of their spatial distribution and habitat preferences at different spatial scales. Our results therefore provide a first step in designing long-term monitoring programs targeting queen snapper, and determining EFH and the general distribution of this species in Puerto Rico.

Model Penduga Biomassa Hutan Mangrove Menggunakan Citra Satelit Sentinel -2A di Kabupaten Rembang Jawa Tengah

Mangrove forest is one of the forests that has the highest carbon storage in the tropics and is very high compared to the average carbon storage in various other forest types in the world, which contains around 1.023 Mg of carbon per hectare. This study aims to obtain a model for estimating the biomass potential of mangrove forests in Rembang Regency by utilizing Sentinel-2A Satellite imagery and making a map of the distribution of mangrove forest biomass potential through the best equation model. Sampling was done by purposive systematic sampling method. The correlation test results obtained a value of 0.640 which states that there is a strong relationship between biomass and NDVI. Biomass estimation modeling uses equation models namely Linear, Quadratic, Power, and Exponential. Model selection is done by considering several parameters such as R², RMSE, Bias, χ²-hit, SA, and SR. The selected model for estimating mangrove forest biomass potential is the quadratic model ???? = 17.4 -110.2 ???????????????? + 220 ????????????????² with an R² value of 0.474, RMSE value of 51.55, Chi-square test result of 3.46 and has the highest score value. The potential biomass distribution map generated from the selected model consists of 3 classes, namely classes < 3.7 (tons/ha), 3.7-10.5 (tons/ha), 10.5-35.7 (tons/ha). Mangrove forests in Rembang Regency have dominant biomass potential in the 10.5-35.7 (tons/ha) class.

Modelling the wind potential energy for metallurgical sector in Albania

The metallurgical industry, in the context of the global energy crisis and the new European green deal, needs urgent investments on energy and resource efficiency. The metallurgical sector, which includes the production of different metals is an energy-intensive industry that requires large amounts of energy for various processes such as smelting, refining, and casting. One of the largest consumptions of energy in Albania comes from the metallurgical sector during the production of iron, steel, chromium and aluminum which corresponds respectively to three private companies called “Kurum International Ltd”, “AlbChorme Ltd” and “Everest Ltd”. During the last three years, these companies have temporary interrupted the production process due to the higher electricity price that come from imports. Based on it, our research work presents the energy efficiency analysis in the Albanian metallurgical sector by focusing on the implementation of wind energy in the above mentioned private metallurgical companies, because adding new generation capacity from Renewable Energy Sources in a context of industrial energy communities, will contribute to improve the security of supply for this industry. The Wind Balkan Atlas, New European Wind Atlas, and Wind Atlas Analysis and Application Program (WAsP) has been used to select the appropriate areas and to develop the wind potential distribution maps, as well as to select the most suitable type of wind turbine based on capacity factors. Two areas were selected close to the metallurgical sectors in the regions of “Vajkal” in Bulqizë and “Selitë e Malit” in Tirana. It has been installed the power of 9 MW for each wind farm, with a capacity factor of 40% and 36.6% respectively, and with a total annual energy production of about 60 GWh/year, these wind farms will cover about 26% of the total annual consumption of companies. Clean Energy Management Software (RETScreen Expert) was used for the detailed economic analysis and environmental impact of proposed wind farms. The economic sensitivity analysis of the proposed wind farms showed that even for the highest installation cost value of 1350 €/kW, for discount rates 5, 7, and 11%, the LCoE values are within the statistically established range for wind farms in Europe.

Determination of model maps for the potential distribution of Anatolian black pine (Pinus nigra Arnold.) in natural forest areas in the Central Black Sea region

Özdemir ŞENTÜRK 1*, Serkan GÜLSOY 2, Mehmet Güvenç NEGİZ 3, Fatih KARAKAYA 2 ORCID: https://orcid.org/0000-0003-2513-6070; https://orcid.org/0000-0003-2011-8324; https://orcid.org/0000-0002-6338-3307; https://orcid.org/0000-0002-6534-6382 1 Gölhisar Vocational School, Departmant of Forestry, Burdur Mehmet Akif Ersoy University, Gölhisar, Burdur, Turkey 2 Faculty of Forestry, Department of Forest Engineering, Isparta University of Applied Sciences, Isparta, Turkey 3 Sütçüler Prof. Dr. Hasan GÜRBÜZ Vocational School, Department of Forest Engineering, Isparta University of Applied Sciences, Sütçüler, Isparta, Turkey *Corresponding author: e-mail: osenturk@mehmetakif.edu.tr Abstract The purpose of this study was to determine potential distribution modeling and mapping of Anatolian black pine in the Vezirköprü district. Presence / absence data of the species was collected from 586 sample areas. Environmental variables (elevation, slope, bedrock, radiation index etc.) were obtained from the digital maps. In addition, climatic variables were downloaded from WorldClim database. Generalized Additive Model and Classification and Regression Tree were used for potential distribution modeling of the species. The validation value of Generalized Additive Model was 0.84 while the cross-validation test value was found to be 0.82. Also, the ROC values of the tree model were found to be 0.804 for the training data set and 0.750 for the testing data set. According to the Classification and Regression Tree method, the locations above 650 m and without meta-sandstone are suitable for the potential distribution of the species. Under the condition that there is meta-sandstone, the areas where the temperature is between 7.6-11.0 °C and the slope degree is more than 23% coincide with the potential distribution of the species. Also, Generalized Additive Model showed that places where gabbro, ophiolitic melange, serpentine, and mixed material were seen as main bedrock type, sloping sites where average elevation was from 600 to 1150 m and temperature is between approximately 8.5-11.3 °C were the most suitable conditions for the potential distribution of the species in the district. In both models, it was determined that especially rock formations, climatic variable, and altitude are effective in the potential distribution of the species. On the other hand, although there are some differences in the variables in the models, the potential distribution maps formed by these variables overlapped quite. As a result, the information obtained in the study is important for the forestry practices such as afforestation, regeneration and the monitoring the species under future climate change conditions. Keywords: generalized additive model; classification and regression tree; species distribution model; Vezirköprü

Evaluation of dynamics of charge accumulation and dissipation processes in Ge15Se85 thin film under electron beam irradiation by mapping surface potential distribution

The interaction of Ge15Se85 amorphous chalcogenide films with an electron beam has been studied. Three ranges of electron irradiation doses, in which a surface relief of various shapes is formed, are found. The presence and persistence of the surface potential for a long time after film irradiation have been established using the Kelvin probe force microscopy (KPFM). It is shown that the formation of surface relief is result of the charge accumulation in the interaction region. Based on the mapping of surface potential distribution in irradiated regions using KPFM we propose models of electric field distribution in the irradiated spots and suggest mechanism of electron-induced mass transfer. It has been suggested that the formation of a high surface relief structures is due to the electron-induced plastic effect. It is shown that the appearance of asymptotically sharp Taylor cones in chalcogenide films is due to charge collapse in the interaction region when the volume charge density and internal electric field strength exceed their critical values.

Predicting the potential worldwide distribution of Aedes aegypti under climate change scenarios

Background: Climate change is one of the most important factors associated with medically important insect pests such as mosquitoes (Diptera: Culicidae). Diseases spread by mosquitoes are increasing due to changes in global temperature and weather patterns that are altering vector host ranges allowing spread into new regions. Zika, dengue fever, chikungunya and yellow fever are arboviral infections that are spread by Aedes aegypti (Culicidae). The objective of the current research is to study the potential geographic distribution habitats of Ae. aegypti in the world under current and future climate conditions. Methods: Data of Ae. aegypti was obtained from the global biodiversity information facility and used 19 bioclimatic layers (bio01-bio19) and elevation from the WorldClim database. The scenarios used are the Beijing climate center climate system model (BCC-CSM2-MR) and the institute Pierre-Simon Laplace, coupled model intercomparison project (IPSL-CM6A-LR) with two shared socio-economic pathways (SSPs) for each of the general circulation model (GCMs): SSP126 and SSP585. Results: The results revealed that altitude, temperature, seasonality (standard deviation *100) (bio4), and annual precipitation (bio12) were the most important environmental variables that affect the distribution of Ae. aegypti. Conclusions: The models showed that Africa and South America maintained very high and excellent habitat suitability for Ae. Aegypti under the current potential distribution map.

Insight into (Z)-ethyl-2-(2-((E)-2,4-dinitro benzylidene amino)-4-oxo-3-phenylthiazolidin-5-ylidene) acetate in-silico anti-SARS-CoV-2 performance: Synthesis, structural-spectral characterizations and DFT computations

This study reports on the synthesis of a 4-thiazolidinone derivative (EN2Th), namely, (Z)-Ethyl-2-(2-((E)-2,4-dinitro benzylidene amino)-4-oxo-3-phenylthiazolidin-5-ylidene) acetate. Its crystal structure has been determined by single crystal X-ray diffraction and the spectral characterizations were carried out by means of 1H and 13C NMR, and FT-IR techniques. The molecule of EN2Th crystallizes in the monoclinic P21/c symmetry and its crystal structure consists of a herringbone packing along [100]. The density functional theory (DFT) at the B3LYP/6-311G (d,p) level has been employed for structure optimization, the results confirm a good consistency between the theoretical and the experimental geometrical data. Hirshfeld surface and 2D fingerprints indicate C−H···O (29.7%) and H···H (24.5%) as the most relevant intermolecular interactions in the crystal packing of EN2Th. According to the reduced density gradient (RDG) analysis, EN2Th exhibits vdW to weak repulsive regions with low electron densities. The theoretical electronic circular dichroism (ECD) spectrum displays an exciton coupled transitions associated with the Z-configuration of the thiazolidin-4-one moiety reflecting the steric effect of the ester group. Charge transfer inside the 4-thiazolidinone derivative EN2Th was considered via the frontier molecular orbitals (FMOs) and natural bond orbital (NBO) investigations along with molecular electrostatic potential (MEP) distribution maps. The HOMO and LUMO energies allowed the estimation of the chemical reactivity descriptors. In accordance with the NBO results, the predicted hyperpolarizabilities indicate a strong donor to acceptor π-electrons delocalization in EN2Th. The anti-viral performance of EN2Th was evaluated in silico against SARS-CoV-2 main protease (MPro) via molecular docking simulations. Finally, ADMET (absorption, distribution, metabolism, excretion and toxicity) online software tools were utilized to uncover the title compound's potential cytotoxicity.

The potential global distribution of an emerging forest pathogen, Lecanosticta acicola, under a changing climate

Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.

Identification of Erosivity Potential on Tropical Zone Using Spatial Method

As a geological process that wears away soil, rock, or dissolved material, erosion may result in the loss of soil layers that are rich in nutrients for plant growth and reducing soil ability to absorb and store water. High rainfall causes a high level of erosivity which can lead to the increase of erosion intensity. Therefore, in predicting erosion, rain condition needs to be considered the most, especially for the tropics. This study was conducted in Samboja District, East Kalimantan Province, Indonesia. The purpose of this study is to create zoning potential for erosion based on topographic correlation with land cover and support practices factors spatially in a map form. The uniform distribution of soil types allows the integration of the erodibility factor together with the erosivity factor. Several erosion equations suitable for tropical areas were used to estimate the erosion, using the BMKG rainfall data for the last 16 years (2006-2020). The results of the study are maps of erosion potential distribution at minimum, maximum, and average conditions. The change in the distribution of potential erosion from the minimum to the average occurred only at very limited points, and it was almost negligible. A significant change was found under condition of maximum erosion potential. The striking change in the zone of potential erosion under condition of the average to the maximum potential for erosion occurred in the zone from Class III to Class IV, and Class IV to Class V.

Calculated Outstanding Energy-Storage Media by Aluminum-Decorated Carbon Nitride (g-C3N4): Elucidating the Synergistic Effects of Electronic Structure Tuning and Localized Electron Redistribution

Hydrogen, as an important clean energy source, is difficult to store and transport, which hinders its applications in real practice. Developing robust yet affordable storage media remains to be a challenge for scientists. In this study, Ab Initio Molecular Dynamics (AIMD) simulations were employed to evaluate the performance of aluminum (Al) decorated carbon nitride (g-C3N4, heptazine structure) in hydrogen storage; and a benchmarking study with Mg-doped g-C3N4 was also performed to provide theoretical insights for future study. We found that each 2 × 2 supercell can accommodate four Al atoms, and that partial charge from single Al sites can be transferred to adjacent nitrogen atoms of g-C3N4. These isolated Al sites tend to be electronically positive charged, serving as active sites for H2 adsorption, predominately by triggering enhanced electrostatic interactions. The H2 molecules are adsorbed by both Al and N atoms, and are easily polarized, giving rise to electrostatic interactions between the gas molecules and the surface. Effective adsorption sites were determined by electronic potential distribution maps of the optimized configurations. Each 2 × 2 supercell can adsorb up to 36 H2 molecules, and the corresponding adsorption energies are within the range of −0.10 to −0.26 eV. The H2 storage capacity of the Al-decorated g-C3N4 is 7.86 wt%, which surpasses the goal of 5.5 wt%, set by the US department of energy. This proposed Al-decorated g-C3N4 material is therefore predicted to be efficient for hydrogen storage. This work may offer some fundamental understandings from the aspect of electronic sharing paradigm of the origin of the excellent hydrogen storage performance by metal decorated 2D materials, acting as an demonstration for guiding single metal atom site-based materials’ designing and synthesis.

Species Distribution Modelling under Climate Change Scenarios for Maritime Pine (Pinus pinaster Aiton) in Portugal

To date, a variety of species potential distribution mapping approaches have been used, and the agreement in maps produced with different methodological approaches should be assessed. The aims of this study were: (1) to model Maritime pine potential distributions for the present and for the future under two climate change scenarios using the machine learning Maximum Entropy algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental data set and climate change scenarios; and (3) to perform an agreement analysis for the species distribution maps produced with both methodological approaches. The species distribution maps produced by each of the methodological approaches under study were reclassified into presence–absence binary maps of species to perform the agreement analysis. The results showed that the MaxEnt-predicted map for the present matched well the species’ current distribution, but the species ecological envelope map, also for the present, was closer to the species’ empiric potential distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in species ecological envelopes maps were higher and with greater future losses than the latter. The 76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance for the species occupancy in the present, decreasing around 30%–35% in the future under the climate change scenarios. Both methodologies proved to be complementary to set species’ best suitability areas, which are key as support decision tools for planning afforestation and forest management to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and productivity.

Climatic envelopes of the genus Lacerta Linnaeus, 1758 in Türkiye: an application of ecological niche modeling.

Six species belonging to the genus Lacerta live in Türkiye. In this study, both present and future potential distribution maps were created based on occurrence data and climatic variables for these six species. Two scenarios for future projections (shared socioeconomic pathways, SSPs,: 245 and 585) and two timeframes (2041-2060 and 2081-2100) were used. The present and future potential distributions of these species were compared. As a result, it was predicted that the distribution ranges in the six species will expand in the future, and this expansion has revealed new environments.

Modeling the potential distribution of floristic assemblages of high Andean wetlands dominated by Juncaceae and Cyperaceae in the Argentine Puna

Aims: The aim of this work was to model the distribution of suitable environmental conditions of vegas with specific floristic characteristics. Vegas are high Andean wetlands that represent the main sequestered carbon stocks, biodiversity hotspots, and water regulating systems in the region. In these wetlands, plant communities are the main biological factor that determines functional processes, and plant species assemblages are associated with different ecogeographic features. Study area: Argentine Central Andean Puna ecoregion. Methods: For two different floristic assemblages of vegas, we develop ecological niche models of n-dimensional minimum volume ellipsoids through NicheToolBox, then obtain potential distribution maps. One floristic assemblage was dominated by the cushion-structured plant Oxychloe andina (Juncaceae) and the other by plants of the Cyperaceae family. Results: Elevation and precipitation were the main environmental factors determining the distribution of the two floristic assemblages. Juncaceae dominated vegas tend to be located in high, humid, and cold places, while Cyperaceae vegas are found at a lower elevation, with less humidity, and higher temperatures. According to the dominant climatic gradient in the region, potential distribution maps show that vegas of Juncaceae are commonly found towards the Northeast of the Puna while Cyperaceae vegas are more frequent at lower elevations to the South of the region. Conclusions: This study represents the first approach to niche modeling based on plant communities in vegas of the Argentine Puna, providing knowledge on the environmental factors that limit their distribution. This information could serve as a planning tool in a region exposed to growing perturbations such as mining and climate change. Taxonomic reference: Zuloaga et al. (2019). Abbreviations: AUC = Area Under the ROC Curve; NDVI = Normalized Difference Vegetation Index; ROC = Receiver Operating Characteristic.

Geographical distribution and predict potential distribution of Angelica L. genus.

Climate change is very important for the distribution of plant resources, especially for crops. Angelica plants have a long history of use and significant crop resources in China, whose rhizomes are extensively usedin medicineandfood. In this paper, 1599 georeferenced herbarium records were analyzed using DIVA-GIS, and the ecological distribution and richness of the current and future distribution simulation were analyzed using the MaxEnt model. The results show that they are from 32 provinces in China. Among these provinces, Sichuan, Gansu, and Yunnan have the largest number of herbariums. According tothe MaxEnt model simulation, it is found that the precipitation in the warmest season, annual precipitation, and the driest monthly precipitation arethe foremost bioclimatic variables that control the distribution of eight selected Angelica plants (A. biserrata, A. gigas, A. laxifoliata, A. likiangensis, A. longicaudata, A. omeiensis, A. polymorpha, and A. valida). It can be seen from the potential distribution map that the suitable growth areas of A. polymorpha and A. valida have increased, while the suitable growth areas of the six species of Angelica have decreased in varying degrees, 18.24%, 20.01%, 9.91%, 53.16%, 10.06%, and 12.64% respectively. Therefore, it is necessary to protect the Angelica plants.

Occurrence Prediction of Western Conifer Seed Bug (Leptoglossus occidentalis: Coreidae) and Evaluation of the Effects of Climate Change on Its Distribution in South Korea Using Machine Learning Methods

The western conifer seed bug (WCSB; Leptoglossus occidentalis) causes huge ecological and economic problems as an alien invasive species in forests. In this study, a species distribution model (SDM) was developed to evaluate the potential occurrence of the WCSBs and the effects of climate on WCSB distribution in South Korea. Based on WCSB occurrence and environmental data, including geographical and meteorological variables, SDMs were developed with maximum entropy (MaxEnt) and random forest (RF) algorithms, which are machine learning methods, and they showed good performance in predicting WCSB occurrence. On the potential distribution map of WCSBs developed by the model ensemble with integrated MaxEnt and RF models, the WCSB occurrence areas were mostly located at low altitudes, near roads, and in urban areas. Additionally, environmental factors associated with anthropogenic activities, such as roads and night lights, strongly influenced the occurrence and dispersal of WCSBs. Metropolitan cities and their vicinities in South Korea showed a high probability of WCSB occurrence. Furthermore, the occurrence of WCSBs in South Korea is predicted to intensify in the future owing to climate change.