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Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. using MaxEnt model in the Eastern Ghats, India

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Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. using MaxEnt model in the Eastern Ghats, India

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... Finally, the average predicted occurrence probability of Larus saundersi of the 10 replicates was reclassified into 4 classes according to arbitrarily defined probability classes. We are aware of the other studies on threshold-based classifications [51][52][53]. These thresholds are justified depending on the goal of the study. ...
... These thresholds are justified depending on the goal of the study. In this study, the suitability classes were determined by the occurrence probability thresholds according to [51], with most suitable (0.6-1), suitable (0.4-0.6), less suitable (0.1-0.4), and unsuitable (0-0.1) to generate a habitat suitability map for Larus saundersi. ...
... The most suitable and suitable habitats were mainly distributed in the southwestern part of the study area, while the less suitable habitats were on both sides of the Liaohe River (Figure 4). The area and percentage for each class are shown in [51][52][53]. These thresholds are justified depending on the goal of the study. ...
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Identifying waterfowl habitat suitability under changing environments, especially land-use change, is crucial to make waterfowl habitat conservation planning. We took Wetland Nature Reserve of Liaohe Estuary, the largest breeding area of Saunders’s Gulls (Larus saundersi) in the world, as our study area, generated land-use-type maps through interpretation of satellite images from four different years (1988, 2000, 2009, 2017), and predicted the potential breeding habitat of Saunders’s Gulls by MaxEnt model based on the land-use map, along with other environmental variables (NDVI, distance to roads and artificial facilities, distance to rivers and water bodies, DEM and distance to shoreline) for the four years, respectively. The models were evaluated using the area under the curve (AUC). We analyzed the changes of the breeding habitat from 1988 to 2017 and utilized RDA to explore the relationships among the changes of suitable habitat of Larus saundersi and the dynamics of land uses. Our results showed that the most suitable habitat decreased by 1286.46 ha during 1988-2009 and increased by 363.51 ha from 2009 to 2017. The suitable habitat decreased by 582.48 ha from 1988 to 2009 and then increased to 1848.96 ha in 2017, while the unsuitable habitat increased by 2793.87 ha during 1988–2009 and then decreased by 178.83 ha from 2009 to 2017. We also found that land use, distance to the coastline, distance to artificial facilities, distance to rivers, distance to roads, and NDVI had certain degrees of impact on the Larus saundersi distribution. The contribution of land use ranged from 16.4% to 40.3%, distance to coastline from 34.7% to 48.0%, distance to artificial facilities from 5.9% to 11.1%, distance to rivers from 5.5% to 11.0%, distance to roads from 3.9% to 12.5%, and NDVI from 0.3% to 6.3%. The change in suitable habitat of Larus saundersi has a positive relationship with the change of seepweed marsh. Human-induced changes in seepweed marsh and coastline position are the main factors influencing the potential breeding habitat of Saunders’s Gulls. We suggest strict conservation of seepweed marsh and implementation of habitat management practices to better protect Saunders’ Gull’s breeding habitat.
... Of 19 bio-climatic variables, five extremely correlated variables, having a negligible effect on the model, were removed to reduce the masking effect and produce a model with better predictability [42]. The test was run by Pearson's correlation coefficient (r) using ENM Tool (version 1.3), and a cross-correlation 'r' value of more than 0.80 was taken as a cut of threshold [25,42] (Additional file 1: Table S3). ...
... Of 19 bio-climatic variables, five extremely correlated variables, having a negligible effect on the model, were removed to reduce the masking effect and produce a model with better predictability [42]. The test was run by Pearson's correlation coefficient (r) using ENM Tool (version 1.3), and a cross-correlation 'r' value of more than 0.80 was taken as a cut of threshold [25,42] (Additional file 1: Table S3). Finally, the remaining 14 bio-climatic variables with a higher permutation significance and percent contribution were used for modelling. ...
Article
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Background Kyasanur forest disease (KFD), known as monkey fever, was for the first time reported in 1957 from the Shivamogga district of Karnataka. But since 2011, it has been spreading to the neighbouring state of Kerala, Goa, Maharashtra, and Tamil Nadu. The disease is transmitted to humans, monkeys and by the infected bite of ticks Haemaphysalis spinigera. It is known that deforestation and ecological changes are the main reasons for KFD emergence, but the bio-climatic understanding and emerging pathways remain unknown. Methods The present study aims to understand the bio-climatic determinants of distribution of tick vector of KFD in southern India using the Maximum Entropy (MaxEnt) model. The analysis was done using 34 locations of Haemaphysalis spinigera occurrence and nineteen bio-climatic variables from WorldClim. Climatic variables contribution was assessed using the Jackknife test and mean AUC 0.859, indicating the model performs with very high accuracy. Results Most influential variables affecting the spatial distribution of Haemaphysalis spinigera were the average temperature of the warmest quarter (bio10, contributed 32.5%), average diurnal temperature range (bio2, contributed 21%), precipitation of wettest period (bio13, contributed 17.6%), and annual precipitation (bio12, contributed 11.1%). The highest probability of Haemaphysalis spinigera presence was found when the mean warmest quarter temperature ranged between 25.4 and 30 °C. The risk of availability of the tick increased noticeably when the mean diurnal temperature ranged between 8 and 10 °C. The tick also preferred habitat having an annual mean temperature (bio1) between 23 and 26.2 °C, mean temperature of the driest quarter (bio9) between 20 and 28 °C, and mean temperature of the wettest quarter (bio8) between 22.5 and 25 °C. Conclusions The results have established the relationship between bioclimatic variables and KFD tick distribution and mapped the potential areas for KFD in adjacent areas wherein surveillance for the disease is warranted for early preparedness before the occurrence of outbreaks etc. The modelling approach helps link bio-climatic variables with the present and predicted distribution of Haemaphysalis spinigera tick.
... ENMs have been used in the past to assess the potential impacts of climate change on species range shifts risk (Remya et al., 2015;Ashraf et al., 2017). A crop suitability assessment based on ENM showed that larger area losses occur in the tropical regions of Africa, and southern and eastern Asia for crops such as rice, sweet potato, and yam (Beck, 2013). ...
... The bioclimatic predictors represent an annual average, seasonal, and intra-seasonal as well as limiting environmental factors (O'Donnell and Ignizio, 2012). These predictors are related to plant physiological processes and have been widely used in species distribution modeling (Remya et al., 2015;Hijmans and Graham, 2006) and in cropland suitability mapping including rice (Läderach et al., 2013;Beck, 2013;Liu et al., 2015). Most of the 19 bioclimatic predictors are highly correlated, which may represent a major source of error in the correlative models (Braunisch et al., 2013). ...
Article
CONTEXT Although rice production has increased significantly in the last decade in West Africa, the region is far from being rice self-sufficient. Inland valleys (IVs) with their relatively higher water content and soil fertility compared to the surrounding uplands are the main rice-growing agroecosystem. They are being promoted by governments and development agencies as future food baskets of the region. However, West Africa's crop production is estimated to be negatively affected by climate change due to the strong dependence of its agriculture on rainfall. OBJECTIVE The main objective of the study is to apply a set of machine learning models to quantify the extent of climate change impact on land suitability for rice using the presence of rice-only data in IVs along with bioclimatic indicators. METHODS We used a spatially explicit modeling approach based on correlative Ecological Niche Modeling. We deployed 4 algorithms (Boosted Regression Trees, Generalized Linear Model, Maximum Entropy, and Random Forest) for 4-time periods (the 2030s, 2050s, 2070s, and 2080s) of the 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8) from an ensemble set of 32 spatially downscaled and bias-corrected Global Circulation Models climate data. RESULTS AND CONCLUSIONS The overall trend showed a decrease in suitable areas compared to the baseline as a function of changes in temperature and precipitation by the order of 22–33% area loss under the lowest reduction scenarios and more than 50% in extreme cases. Isothermality or how large the day to night temperatures oscillate relative to the annual oscillations has a large impact on area losses while precipitation increase accounts for most of the areas with no change in suitability. Strong adaptation measures along with technological advancement and adoption will be needed to cope with the adverse effects of climate change on inland valley rice areas in the sub-region. SIGNIFICANCE The demand for rice in West Africa is huge. For the rice self-sufficiency agenda of the region, “where” and “how much” land resources are available is key and requires long-term, informed planning. Farmers can only adapt when they switch to improved breeds, providing that they are suited for the new conditions. Our results stress the need for land use planning that considers potential climate change impacts to define the best areas and growing systems to produce rice under multiple future climate change uncertainties.
... ENMs have been used in the past to assess the potential impacts of climate change on species range shifts risk (Remya et al., 2015;Ashraf et al., 2017). A crop suitability assessment based on ENM showed that larger area losses occur in the tropical regions of Africa, and southern and eastern Asia for crops such as rice, sweet potato, and yam (Beck, 2013). ...
... The bioclimatic predictors represent an annual average, seasonal, and intra-seasonal as well as limiting environmental factors (O'Donnell & Ignizio, 2012). These predictors are related to plant physiological processes and have been widely used in species distribution modeling (Remya et al., 2015;Hijmans & Graham, 2006) and in cropland suitability mapping including rice (Läderach et al., 2013;Beck, 2013;Liu et al., 2015). Most of the 19 bioclimatic predictors are highly correlated, which may represent a major source of error in the correlative models (Braunisch et al., 2013). ...
Article
Full-text available
CONTEXT Although rice production has increased significantly in the last decade in West Africa, the region is far from being rice self-sufficient. Inland valleys (IVs) with their relatively higher water content and soil fertility compared to the surrounding uplands are the main rice-growing agroecosystem. They are being promoted by governments and development agencies as future food baskets of the region. However, West Africa's crop production is estimated to be negatively affected by climate change due to the strong dependence of its agriculture on rainfall. OBJECTIVE The main objective of the study is to apply a set of machine learning models to quantify the extent of climate change impact on land suitability for rice using the presence of rice-only data in IVs along with bioclimatic indicators. METHODS We used a spatially explicit modeling approach based on correlative Ecological Niche Modeling. We deployed 4 algorithms (Boosted Regression Trees, Generalized Linear Model, Maximum Entropy, and Random Forest) for 4-time periods (the 2030s, 2050s, 2070s, and 2080s) of the 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8) from an ensemble set of 32 spatially downscaled and bias-corrected Global Circulation Models climate data. RESULTS AND CONCLUSIONS The overall trend showed a decrease in suitable areas compared to the baseline as a function of changes in temperature and precipitation by the order of 22–33% area loss under the lowest reduction scenarios and more than 50% in extreme cases. Isothermality or how large the day to night temperatures oscillate relative to the annual oscillations has a large impact on area losses while precipitation increase accounts for most of the areas with no change in suitability. Strong adaptation measures along with technological advancement and adoption will be needed to cope with the adverse effects of climate change on inland valley rice areas in the sub-region. SIGNIFICANCE The demand for rice in West Africa is huge. For the rice self-sufficiency agenda of the region, “where” and “how much” land resources are available is key and requires long-term, informed planning. Farmers can only adapt when they switch to improved breeds, providing that they are suited for the new conditions. Our results stress the need for land use planning that considers potential climate change impacts to define the best areas and growing systems to produce rice under multiple future climate change uncertainties.
... ENMs have been used in the past to assess the potential impacts of climate change on species range shifts risk (Remya et al., 2015;Ashraf et al., 2017). A crop suitability assessment based on ENM showed that larger area losses occur in the tropical regions of Africa, and southern and eastern Asia for crops such as rice, sweet potato, and yam (Beck, 2013). ...
... The bioclimatic predictors represent an annual average, seasonal, and intra-seasonal as well as limiting environmental factors (O'Donnell and Ignizio, 2012). These predictors are related to plant physiological processes and have been widely used in species distribution modeling (Remya et al., 2015;Hijmans and Graham, 2006) and in cropland suitability mapping including rice (Läderach et al., 2013;Beck, 2013;Liu et al., 2015). Most of the 19 bioclimatic predictors are highly correlated, which may represent a major source of error in the correlative models (Braunisch et al., 2013). ...
Article
Full-text available
CONTEXT: Although rice production has increased significantly in the last decade in West Africa, the region is far from being rice self-sufficient. Inland valleys (IVs) with their relatively higher water content and soil fertility compared to the surrounding uplands are the main rice-growing agroecosystem. They are being promoted by governments and development agencies as future food baskets of the region. However, West Africa’s crop production is estimated to be negatively affected by climate change due to the strong dependence of its agriculture on rainfall. OBJECTIVE: The main objective of the study is to apply a set of machine learning models to quantify the extent of climate change impact on land suitability for rice using the presence of rice-only data in IVs along with bioclimatic indicators. METHODS: We used a spatially explicit modeling approach based on correlative Ecological Niche Modeling. We deployed 4 algorithms (Boosted Regression Trees, Generalized Linear Model, Maximum Entropy, and Random Forest) for 4-time periods (the 2030s, 2050s, 2070s, and 2080s) of the 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8) from an ensemble set of 32 spatially downscaled and bias-corrected Global Circulation Models climate data. RESULTS AND CONCLUSIONS: The overall trend showed a decrease in suitable areas compared to the baseline as a function of changes in temperature and precipitation by the order of 22–33% area loss under the lowest reduction scenarios and more than 50% in extreme cases. Isothermality or how large the day to night temperatures oscillate relative to the annual oscillations has a large impact on area losses while precipitation increase accounts for most of the areas with no change in suitability. Strong adaptation measures along with technological advancement and adoption will be needed to cope with the adverse effects of climate change on inland valley rice areas in the sub-region. SIGNIFICANCE: The demand for rice in West Africa is huge. For the rice self-sufficiency agenda of the region, “where” and “how much” land resources are available is key and requires long-term, informed planning. Farmers can only adapt when they switch to improved breeds, providing that they are suited for the new conditions. Our results stress the need for land use planning that considers potential climate change impacts to define the best areas and growing systems to produce rice under multiple future climate change uncertainties.
... Of 19 bio-climatic variables, five extremely correlated variables, having a negligible effect on the model, were removed to reduce the masking effect and produce a model with better predictability [42]. The test was run by Pearson's correlation coefficient (r) using ENM Tool (version 1.3), and a cross-correlation 'r' value of more than 0.80 was taken as a cut of threshold [25,42] (Additional file 1: Table S3). ...
... Of 19 bio-climatic variables, five extremely correlated variables, having a negligible effect on the model, were removed to reduce the masking effect and produce a model with better predictability [42]. The test was run by Pearson's correlation coefficient (r) using ENM Tool (version 1.3), and a cross-correlation 'r' value of more than 0.80 was taken as a cut of threshold [25,42] (Additional file 1: Table S3). Finally, the remaining 14 bio-climatic variables with a higher permutation significance and percent contribution were used for modelling. ...
Article
Full-text available
Background Kyasanur forest disease (KFD), known as monkey fever, was for the first time reported in 1957 from the Shivamogga district of Karnataka. But since 2011, it has been spreading to the neighbouring state of Kerala, Goa, Maharashtra, and Tamil Nadu. The disease is transmitted to humans, monkeys and by the infected bite of ticks Haemaphysalis spinigera. It is known that deforestation and ecological changes are the main reasons for KFD emergence, but the bio-climatic understanding and emerging pathways remain unknown. Methods The present study aims to understand the bio-climatic determinants of distribution of tick vector of KFD in southern India using the Maximum Entropy (MaxEnt) model. The analysis was done using 34 locations of Haemaphysalis spinigera occurrence and nineteen bio-climatic variables from WorldClim. Climatic variables contribution was assessed using the Jackknife test and mean AUC 0.859, indicating the model performs with very high accuracy. Results Most influential variables affecting the spatial distribution of Haemaphysalis spinigera were the average temperature of the warmest quarter (bio10, contributed 32.5%), average diurnal temperature range (bio2, contributed 21%), precipitation of wettest period (bio13, contributed 17.6%), and annual precipitation (bio12, contributed 11.1%). The highest probability of Haemaphysalis spinigera presence was found when the mean warmest quarter temperature ranged between 25.4 and 30 °C. The risk of availability of the tick increased noticeably when the mean diurnal temperature ranged between 8 and 10 °C. The tick also preferred habitat having an annual mean temperature (bio1) between 23 and 26.2 °C, mean temperature of the driest quarter (bio9) between 20 and 28 °C, and mean temperature of the wettest quarter (bio8) between 22.5 and 25 °C. Conclusions The results have established the relationship between bioclimatic variables and KFD tick distribution and mapped the potential areas for KFD in adjacent areas wherein surveillance for the disease is warranted for early preparedness before the occurrence of outbreaks etc. The modelling approach helps link bio-climatic variables with the present and predicted distribution of Haemaphysalis spinigera tick.
... Maximum entropy (Maxent) models based on the principle of maximum entropy (Phillips and Dudík, 2008;Phillips et al., 2009), are one of the most popular tools among different habitat suitability models widely used in biogeography and conservation biology (O'Dwyer et al., 2017;Phillips et al., 2006), due their multiple advantages, such as use of presence-only data, simple and precise mathematical formulation, good performance, and good efficiency to handle complex interactions between response and predictor variables. (Moreno et al., 2011;Remya et al., 2015;Zhang et al., 2018). To investigate the habitat selection of the Siberian Cranes, a Maxent model for WSP was constructed. ...
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Stopover habitats are crucial refuelling and resting sites for migratory birds to ensure their complete migration and successful reproduction and survival. The Siberian Crane (Grus leucogeranus) has been recognized as critically endangered according to ‘Red List’ by the International Union for Conservation of Nature (IUCN); however their stopover habitats are threatened by various causes and thus, are depleting. To identify the range and environmental characteristics of the stopover habitats selected by the Siberian Cranes during their migration in Northeast China, important factors influencing these habitats and habitat suitability distribution were studied by using the maximum entropy model. Subsequently, climate conditions and wetland types were the most important factors, based on which the Siberian cranes selected the stopover habitats. The stopover habitats selected by the Siberian Cranes were primarily located in areas with mean annual total precipitation less than 400 mm, mean annual temperature between 4 °C and 7 °C, and seasonal brackish and alkaline marshes. Areas within and near the Momoge National Nature Reserve on the West Songnen Plain were vital resting sites for the Siberian Crane. The spatial distribution of habitat suitability evidently varied, and 20% areas of the reserve, which demonstrated a high degree of habitat suitability, were observed outside the reserve boundaries, thus, indicating gaps in conservation of the Siberian Cranes habitats in Northeast China. The results of this study highlight the need for implementing effective measures to conserve the Siberian Cranes habitat to maintain sustainable ecosystems.
... The multicollinearity between 19 bio-climatic predictors was determined using Pearson correlation coefficient (r) with help of ENM Tools software v1.3.1.,cross-correlation value set at (r) � 0.90 in order to remove strongly correlated predictors [44][45][46][47]. (Table 1). ...
Article
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Climate change has been discussed as to exert shifts in geographical range of plants, animals or insect species by increasing, reducing or shifting its appropriate climatic habitat. Globally, Pakistan has been ranked at 5 th position on the list of countries most vulnerable to climate change in 2020. Climate change has resulted in the losses of biodiversity and alteration in ecosystem as a result of depletion of natural habitats of species in Pakistan as well as in the world. Ants have been regarded as indicators of environmental change and ecosystem processes. Brachyponera nigrita (Emery, 1895) was reported for the first time from Pakistan (Pothwar region). Objective of our studies was to model geographic distribution of newly recorded ant species, B . nigrita based on two representative concentration pathways (RCP) (RCP4.5 and RCP8.5) for 2050s using maximum entropy model (Maxent) in Pakistan. In modeling procedure, 21occurrence records and 8 variables namely Bio4 (Temperature seasonality), Bio8 (Mean temperature of wettest quarter), Bio10 (Mean temperature of warmest quarter), Bio12 (Annual precipitation), Bio13 (Precipitation of wettest month), Bio15 (Precipitation seasonality), Bio17 (Precipitation of driest quarter) and Bio18 (Precipitation of warmest quarter) were used to determine the current and future distributions. Performance of the model was evaluated using AUC (area under curves) values, partial ROC, omission rates (E = 5%) and AICc (Model complexity).The results showed the average AUC value of the model was 0.930, which indicated that the accuracy of the model was excellent. The jackknife test also showed that Bio4, Bio18, Bio17 and Bio15 contributed 98% for the prediction of potential distribution of the species as compared to all other variables. Maxent results indicated that distribution area of B . nigrita under future predicted bioclimatics 2050 (RCP 4.5 and RCP8.5) would be increased in various localities of Pakistan as compared to its current distribution. In Pothwar region, moderately suitable and highly suitable areas of this species would increase by 505.932321km ² and 572.118421km ² as compared to current distribution under 2050 (RCP 4.5), while under 2050 (RCP 8.5), there would be an increase of 6427.2576km ² and 3765.140493km ² respectively in moderately suitable and highly suitable areas of B . nigrita . This species was associated with termites, collembolans and larval stages of different insects. White eggs, creamy white pupae and many workers of this species were observed in a variety of habitats. Unknown nesting ecology, species identification characters supported with micrographs has been given which will help researchers for further ecological studies.
... Estos resultados fueron consistentes con estudios similares aplicando el modelo Maxent para predecir la distribución potencial de maracuyá (Bezerra et al, 2019;Giannini et al., 2013;Elias et al., 2017;Ocampo et al., 2013) y especies forestales (Wei at al., 2018;Yuan et al., 2015;Remya et al., 2015;Yi et al., 2016;Sharma et al., 2018). Para el cultivo de cholupa, los resultados fueron razonables 0.7< AUC <0.8). ...
Article
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Contextualización: El cambio climático y las actividades antrópicas sobre los recursos naturales se constituyen como los principales causantes de la pérdida de biodiversidad y la redistribución de las especies. Vacío de conocimiento: Sin embargo, los efectos a nivel de comunidades y ecosistemas, así como los impactos en cultivos agrícolas en escala regional, son poco estudiados. Los modelos de distribución de especies se han convertido, por lo tanto, en valiosas herramientas para la predicción de áreas potencialmente aptas para especies cultivables, su gestión y planificación. Propósito: Este estudio consistió en la predicción de potenciales áreas cultivables de maracuyá (Passiflora edulis var. flavicarpa Degener), granadilla (Passiflora ligularis Juss), y cholupa (Passiflora maliformis L.) en una región tropical, a través del modelo MaxEnt, en escenarios de cambio climático. Metodología: Se utilizaron como datos de entrada (para el modelo MaxEnt) registros de presencia de las especies analizadas, obtenidos a partir de sus coordenadas geográficas. En total, fueron usados 141 registros de presencia de maracuyá, 256 registros de granadilla y 40 registros de cholupa, así como 12 variables bioclimáticas para las proyecciones actuales y futuras en los periodos 2050 y 2070, considerando así dos escenarios RCPs (Representative Concentration Pathways) del Coupled Model Intercomparison Project (CMIP5) (RCP 4.5 y RCP 8.5). Resultados y conclusiones: Los resultados revelan que las potenciales áreas cultivables para las especies analizadas podrían pronosticarse a través de MaxEnt utilizando registros de presencia en campo y variables bioclimáticas. Así mismo, las simulaciones indicaron que las áreas de ocurrencia potencial para las especies analizadas podrían disminuir en el futuro dependiendo de los escenarios climáticos (RCP 4.5 y RCP 8.5) para los periodos 2050 y 2070. Para los cultivos de maracuyá, granadilla y cholupa, las mayores reducciones en las áreas de ocurrencia potencial corresponden al 23 %, 25 % y 31 % respectivamente, y se presentarían en el período 2070 en un escenario pesimista (RCP 8.5). Este es el primer estudio que pronostica las potenciales áreas cultivables de pasifloras utilizando el modelo Maxent y escenarios de cambio climático en escala regional en una región tropical. El abordaje propuesto puede proveer importantes herramientas para la gestión y aprovechamiento sostenible de las especies estudiadas.
... The results showed that the BRT, FDA, GLM, MARS, MDA, RF, and SVM used in this study can accurately predict to certain degrees habitat suitability for endangered plants. When we compared the results of these models in this study with other studies that used MaxEnt, it was found that in some studies, the accuracy of MaxEnt was higher than those of the models in this study (Li et al., 2020a(Li et al., , 2020bRemya et al., 2015;Xu et al., 2019), whereas in other studies, the accuracy of MaxEnt was lower than those of the models in this study (Syfert et al., 2013;Wei et al., 2018;Zhang et al., 2018). Rahimian Boogar et al. (2019) demonstrated that both SVM and MaxEnt models predicted the habitat suitability of juniperus spp. ...
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Habitat suitability modeling and mapping are important aspects of long-term strategies for sustaining plant ecosystems. In this study, seven state-of-the-art machine learning models including boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), multivariate adaptive regression splines (MARS), mixture dis-criminant analysis (MDA), random forest (RF), and support vector machine (SVM) were applied to model habitat suitability for Ferula gummosa medicinal plant in the Firozkuh County of Tehran. Different factors that affect the habitat of this plant were prepared for modeling, including slope angle, silt percentage, sand percentage, aspect, annual mean rainfall, clay percentage, topographic wetness index, elevation, distance from rivers, drainage density, annual mean temperature, plan curvature, profile curvature, land use, litho-logical units, and organic carbon. After running the models in R software, their evaluation using various measures (area under the curve, accuracy, precision, F-measure, fallout, true skill statistics, and corrected classify instances) indicated that the RF model was the best one for assessing Ferula gummosa habitat suitability. The SVM, MARS, MDA, GLM, FDA, and BRT models also displayed acceptable performances. The results of our study contribute to the understanding of the stability of the medicinal plant Ferula gummosa and to help avoid its extinction in the future.
... Overall, under the contemporary climate, the predicted potential distribution of P. menziesii using the MaxEnt is generally congruent with the established range map of P. menziesii within the continental United States (Little, 1971). Recently, MaxEnt bioclimatic models have been widely used to investigate the suitable climate space of other plants and plant pathogens, with high predictive performance (e.g., Kumar and Stohlgren, 2009;Khanum et al., 2013;Remya et al., 2015;Stewart et al., 2018Stewart et al., , 2020Tang et al., 2021). ...
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Climate change and associated disturbances are expected to exacerbate forest root diseases because of altered distributions of existing and emerging forest pathogens and predisposition of trees due to climatic maladaptation and other disturbances. Predictions of suitable climate space (potential geographic distribution) for forest pathogens and host trees under contemporary and future climate scenarios will guide the selection of appropriate management practices by forest managers to minimize adverse impacts of forest disease within forest ecosystems. A native pathogen (Armillaria solidipes) that causes Armillaria root disease of conifers in North America is used to demonstrate bioclimatic models (maps) that predict suitable climate space for both pathogen and a primary host (Pseudotsuga menziesii, Douglas-fir) under contemporary and future climate scenarios. Armillaria root disease caused by A. solidipes is a primary cause of lost productivity and reduced carbon sequestration in coniferous forests of North America, and its impact is expected to increase under climate change due to tree maladaptation. Contemporary prediction models of suitable climate space were produced using Maximum Entropy algorithms that integrate climatic data with 382 georeferenced occurrence locations for DNA sequence-confirmed A. solidipes. A similar approach was used for visually identified P. menziesii from 11,826 georeferenced locations to predict its climatic requirements. From the contemporary models, data were extrapolated through future climate scenarios to forecast changes in geographic areas where native A. solidipes and P. menziesii will be climatically adapted. Armillaria root disease is expected to increase in geographic areas where predictions suggest A. solidipes is well adapted and P. menziesii is maladapted within its current range. By predicting areas at risk for Armillaria root disease, forest managers can deploy suitable strategies to reduce damage from the disease.
... Sequences, annealing temperature (TºC), total number of fragments amplified (NT) and total number of polymorphic fragments (NP) R = Purine (A or G) and Y = Pirimidine (C or T). and future climate configurations (2050) (Hijmans et al. 2005) with a spatial resolution of 1 km 2 . The climatic variables were selected using Pearson's correlation coefficient (r) using the ENMTools 1.3 software (Warren et al. 2010), and variables with correlation values below ± 0.85 were selected (Remya et al. 2015). ...
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Hancornia speciosa is one target species for genetic diversity ethnobotanical and medicinal applications within the Brazilian Northeast Atlantic Forest biodiversity hotspot. However, information on the genetic variability of populations associated with modeling the potential distribution in the state of Sergipe has not yet been performed. Therefore, the objective of this study was to predict the potential occurrence of H. speciosa in areas in which fruits are intensively used in an extractive practice. The maximum entropy method was used to detect the distribution patterns of H. speciosa in variable environments within the referred hotspot. The diversity of four natural populations, situated in areas of extractivism, was determined by ISSR molecular markers. The species appears to occur more densely in the coastal regions of the state of Sergipe. However, data prediction occurrence indicates that the areas of natural presence have been reduced due to anthropic actions.
... ArcGIS was used to convert the ASC file output by MaxEnt into raster format file. According to IPCC's explanation of the probability (P) of species' presence and combined with previous research results, the suitability grades were divided into four categories and displayed in different colors on the map, which were the following: highly suitable area (P ≥ 0.66, red), moderately suitable area (0.33 ≤ P < 0.66, orange), lowly suitable area (0.05 ≤ P < 0.33, yellow), and unsuitable area (P < 0.05, white) (Remya et al. 2015;Zou et al. 2015;Wang et al. 2018). ...
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Fritillariae Cirrhosae Bulbus (FCB) is a famous traditional Chinese medicine, mainly used for relieving cough and resolving phlegm. According to Chinese Pharmacopoeia (2020), the medicine comes from dried bulbs of five species and one variety in Fritillaria. Due to climate change and human disturbance, the wild resources have become critically endangered in recent years. Following three climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) under 2050s and 2070s, geographic information technology (GIS) and maximum entropy model (MaxEnt) were used to simulate the ecological suitability of FCB, a third-grade rare and endangered medicinal plant species. The results showed that the key environmental variables affecting the distribution of FCB were altitude, human activity intensity, and mean temperature of coldest quarter. Under current climate situation, the highly suitable areas were mainly located in the east of Qinghai Tibet Plateau, including Western Sichuan, southeastern Tibet, southern Gansu, Northwestern Yunnan, and Eastern Qinghai, with a total area of 31.47×104 km2, the area within the nature reserve was 7.13×104 km2, indicating that there was a large protection gap. Under the future climate change scenarios, the areas of the highly and poorly suitable areas of FCB showed a decreasing trend, while the areas of the moderately and total suitable areas showed an increasing trend. The geometric center of the total suitable area of the medicine will move to the northwest. The results could provide a strategic guidance for protection,development, and utilization of FCB though its prediction of potential distribution based on the key variables of climate change.
... The purpose of this study was to further understand the species distribution characteristics and ecological adaptability of small deciduous shrubs, and to provide theoretical reference for the protection and utilization of the wild resources of H. macrophylla, as well as future large-scale introduction and cultivation (Qin et al., 2020 (Coban et al., 2020) are used for future climate data. The RCP scenarios consist of four pathways, including RCP2.6, RCP4.5, RCP6.0, and RCP8.5 , Remya et al., 2015. RCP4.5 and RCP6.0 are both moderate greenhouse gas emission scenarios, and RCP4.5 has a higher priority than RCP6.0 . ...
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Climate change has a significant impact on the growth and distribution of vegetation worldwide. Hydrangea macrophylla is widely distributed and considered a model species for studying the distribution and responses of shrub plants under climate change. These results can inform decision-making regarding shrub plant protection, management, and introduction of germplasm resources, and are of great importance for formulating ecological countermeasures to climate change in the future. We used the maximum entropy model to predict the change, scope expansion/reduction, centroid movement, and dominant climate factors that restrict the growth and distribution of H. macrophylla in China under current and future climate change scenarios. It was found that both precipitation and temperature affect the distribution of suitable habitat for H. macrophylla. Akaike information criterion (AICc) was used to select the feature combination (FC) and the regularization multiplier (RM). After the establishment of the optimal model (FC = QP, RM = 0.5), the complexity and over-fitting degree of the model were low (delta AICc = 0, omission rate = 0.026, difference between training and testing area under the curve values = 0.0009), indicating that it had high accuracy in predicting the potential geographical distribution of H. macrophylla (area under the curve = 0.979). Overall, from the current period to future, the potential suitable habitat of this species in China expanded to the north. The greenhouse effect caused by an increase in CO2 emissions would not only increase the area of high-suitability habitat in Central China, but also expand the area of total suitable habitat in the north. Under the maximum greenhouse gas emission scenario (RCP8.5), the migration distance of the centroid was the longest (e.g., By 2070s, the centroids of total and highly suitable areas have shifted 186.15 km and 89.84 km, respectively).
... As the temperature and precipitation continue to rise in the QTP, woody plants are likely to migrate into the interior of the plateau and gradually replace herbs, thereby posing a significant challenge to the effectiveness of current protected areas (Lovejoy, 2006;Gao et al., 2016;Attorre et al., 2018). Species distribution models that combine species occurrence records with environmental variables have been widely used for the prediction of potential species distribution range shifts in many regions (Remya et al., 2015;Li et al., 2021). Among various plant categories, the threatened species are more sensitive to climate change due to their limited geographic ranges and low population size (Huang, 2011;Berry et al., 2013;Garcia et al., 2013;Fortini and Dye, 2017). ...
... As the temperature and precipitation continue to rise in the QTP, woody plants are likely to migrate into the interior of the plateau and gradually replace herbs, thereby posing a significant challenge to the effectiveness of current protected areas (Lovejoy, 2006;Gao et al., 2016;Attorre et al., 2018). Species distribution models that combine species occurrence records with environmental variables have been widely used for the prediction of potential species distribution range shifts in many regions (Remya et al., 2015;Li et al., 2021). Among various plant categories, the threatened species are more sensitive to climate change due to their limited geographic ranges and low population size (Huang, 2011;Berry et al., 2013;Garcia et al., 2013;Fortini and Dye, 2017). ...
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The Qinghai-Tibetan Plateau (QTP) harbors abundant and diverse plant life owing to its high habitat heterogeneity. However, the distribution pattern of biodiversity hotspots and their conservation status remain unclear. Based on 148,283 high-resolution occurrence coordinates of 13,450 seed plants, we identified hotspots by integrating data from species richness, species complementarity and spatial phylogenetics. Nine hotspot areas were identified that contained 89% of species but covered only 7% of the total land area of the QTP. Four of nine hotspots were identified firstly, including west Nyainqentanglha Mountains, the middle reaches of Lancang and Jinsha Rivers, the upper reaches of Yellow River and Qilian Mountains. Analysis of conservation efficiency indicated national nature reserves (NNRs) covered 55% of the hotspots, whereas NNRs and provincial nature reserves (PNRs) together protected 73% of the hotspots. Conservation efforts , such as establishing new protected areas and upgrading the level of existing nature reserves, should be strengthened in the conservation gaps. Targeted conservation should be carried out for species endemic to QTP due to their narrow distribution range and low conservation effectiveness. Niche modeling for 336 threatened plants indicated there were apparent range shifts of suitable habitat areas from the eastern edge to the center of the plateau under future climate scenarios, and conservation priority should be focused on the southern QTP for where have stable habitats.
... Many studies have used various species distribution techniques to understand the species' ecological niche distribution and predict the potential habitat suitability. Among the species distribution models, MaxEnt model seems to be the preferred one for SDM studies [37,[50][51][52][53]. The ease of use and simple steps necessary to run MaxEnt appear to have enticed many researchers to use it as a black box, despite mounting evidence that using MaxEnt with default parameter values (i.e., auto-features) does not always result in the optimal model [54,55]. ...
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Gelam tree or Melaleuca cajuputi (M. cajuputi) is an important species for the local economy as well as coastal ecosystem protection in Terengganu, Malaysia. This study aimed at producing a current habitat suitability map and predicting future potential habitat distribution for M. cajuputi in Terengganu based on Species distribution modeling (SDM) using the Maximum Entropy principle. Our modeling results show that for the current potential distribution of M. cajuputi species, only 10.82% (1346.5 km2) of Terengganu area is suitable habitat, which 0.96% of the areas are under high, 2.44% moderate and 7.42% poor habitat suitability. The model prediction for future projection shows that the habitat suitability for M. cajuputi would decrease significantly in the year 2050 under RCP 4.5 where the largest contraction from suitable to unsuitable habitat area is about 442.1 km2 and under RCP 2.6 is the highest expansion from unsuitable to suitable habitat area (267.5 km2). From the future habitat suitability projection, we found that the habitat suitability in Marang would degrade significantly under all climate scenarios, while in Setiu the habitat suitability for M. cajuputi remains stable throughout the climate change scenarios. The modeling prediction shows a significant influence on the soil properties, temperature, and precipitation during monsoon months. These results could benefit conservationist and policymakers for decision making. The present model could also give a perception into potential habitat suitability of M. cajuputi in the future and to improve our understanding of the species’ response under the changing climate.
... Figure 4 shows the habitat suitability distributions of H. japonica in China, according to the combination technology of Maxent and ArcGIS software. The habitat suitability results were expressed as probability with a range of 0-1 [51]. Using the reclassification tool of ArcMap 10.2, the probability results obtained were divided into four levels, of which 0-0.2 was considered unsuitable, 0.2-0.4 was considered generally suitable, 0.4-0.6 was considered moderately suitable and 0.6-1 was considered as highly suitable [52]. ...
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Hylomecon japonica is considered a natural medicinal plant with anti-inflammatory, anticancer and antibacterial activity. The assessment of climate change impact on its habitat suitability is important for the wild cultivation and standardized planting of H. japonica. In this study, the maximum entropy model (Maxent) and geographic information system (ArcGIS) were applied to predict the current and future distribution of H. japonica species, and the contributions of variables were evaluated by using the jackknife test. The area under the receiver operating characteristic curve (AUC) value confirmed the accuracy of the model prediction based on 102 occurrence records. The predicted potential distributions of H. japonica were mainly concentrated in Jilin, Liaoning, Shaanxi, Chongqing, Henan, Heilongjiang and other provinces (adaptability index > 0.6). The jackknife experiment showed that the precipitation of driest month (40.5%), mean annual temperature (12.4%), the precipitation of wettest quarter (11.6%) and the subclass of soil (9.7%) were the most important factors affecting the potential distribution of H. japonica. In the future, only under the shared socioeconomic Pathway 245 (SSP 245) scenario model in 2061–2080, the suitable habitat area for H. japonica is expected to show a significant upward trend. The area under other scenarios may not increase or decrease significantly.
... accessed on 15 March 2020) [42] for the future climate data for the 2050s (average for 2041-2060) and 2070s (average for 2061-2080). Variables representing the two future scenarios ((representative concentration pathway RCP 4.5 (intermediate scenario) and RCP 8.5 (very high emission scenario)) were an ensemble of 7 GCM Models (BCC-CSM1-1, GFDL-CM3, HadGEM2-ES, MIROC5, MIROC-CHEM, MIROC-ESM, NorESM1-M), because of their good predictive ability of climate for India [43,44]. Predictors were obtained at two-and-a-half-minute spatial resolution (approximately 5 km 2 per pixel), which is an adequate resolution for ecological niche models based only on climate variables [29]. ...
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Climate change and climate variability are projected to alter the geographic suitability of lands for crop cultivation. Early awareness of the future climate of the current cultivation areas for a perennial tree crop like coconut is needed for its adaptation and sustainable cultivation in vulnerable areas. We analyzed coconut’s vulnerability to climate change in India, based on climate projections for the 2050s and the 2070s under two Representative Concentration Pathways (RCPs): 4.5 and 8.5. Based on the current cultivation regions and climate change predictions from seven ensembles of Global Circulation Models, we predict changes in relative climatic suitability for coconut cultivation using the MaxEnt model. Bioclimatic variables Bio 4 (temperature seasonality, 34.4%) and Bio 7 (temperature annual range, 28.7%) together contribute 63.1%, which along with Bio 15 (precipitation seasonality, 8.6%) determined 71.7% of the climate suitability for coconuts in India. The model projected that some current coconut cultivation producing areas will become unsuitable (plains of South interior Karnataka and Tamil Nadu) requiring crop change, while other areas will require adaptations in genotypic or agronomic management (east coast and the south interior plains), and yet in others, the climatic suitability for growing coconut will increase (west coast). The findings suggest the need for adaptation strategies so as to ensure sustainable cultivation of coconut at least in presently cultivated areas.
... ArcGIS was used to convert the ASC file output by MaxEnt into raster format file. According to IPCC's explanation of the probability (P) of species' presence and combined with previous research results, the suitability grades were divided into four categories and indicated by different colors, i.e., highly suitable habitat (P ≥ 0.66, red), moderately suitable habitat (0.33 ≤ P < 0.66, orange), poorly suitable habitat (0.05 ≤ P < 0.33, cyan), and unsuitable habitat (P < 0.05, green) (Remya et al. 2015;Zou et al. 2015). ...
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Blumea balsamifera is a famous Chinese Minority Medicine, which has a long history in Miao, Li, Zhuang, and other minority areas. In recent years, due to the influence of natural and human factors, the distribution area of B. balsamifera resources has a decreasing trend. Therefore, it is very important to analyze the suitability of B. balsamifera in China. Following three climate change scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) under 2050s and 2070s, geographic information technology (GIS) and maximum entropy model (MaxEnt) were used to simulate the ecological suitability of B. balsamifera. The contents of L-borneol and total flavonoids of B. balsamifera in different populations were determined by gas chromatography (GC) and ultraviolet spectrophotometry (UV). The results showed that the key environmental variables affecting the distribution of B. balsamifera were mean temperature of coldest quarter (6.18–26.57 ℃), precipitation of driest quarter (22.46–169.7 mm), annual precipitation (518.36–1845.29 mm), and temperature seasonality (291.31–878.87). Under current climate situation, the highly suitable habitat was mainly located western Guangxi, southern Yunnan, most of Hainan, southwestern Guizhou, southwestern Guangdong, southeastern Fujian, and western Taiwan, with a total area of 24.1 × 10⁴ km². The areas of the moderately and poorly suitable habitats were 27.57 × 10⁴ km² and 42.43 × 10⁴ km², respectively. Under the future climate change scenarios, the areas of the highly, moderately, and poorly suitable habitats of B. balsamifera showed a significant increasing trend, the geometric center of the total suitable habitats of B. balsamifera would move to the northeast. In recent years, the planting area of B. balsamifera has been reduced on a large scale in Guizhou, and its ex situ protection is imperative. By comparison, the content of L-borneol, total flavonoids and fresh leaf yield had no significant difference between Guizhou and Hainan (P > 0.05), which indicated that Hainan is one of the best choice for ex situ protection of B. balsamifera.
... Various ecological niche models use bioclimatic and non-climatic variables to explicate current distributions of species and further predict future distributions (Li et al. 2018). The Maximum Entropy Model (MaxEnt) has an outstanding predictive power in simulation and evaluation of presence-only data, and creates distribution maps and variable response curves by testing the reserved part of the training data (Phillips et al. 2006;Yang et al. 2013;Remya et al. 2015;Zhang et al. 2019). ...
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The rise in global temperature is one of the main threats of extinction to many vulnerable species by the twenty-first century. The negative impacts of climate change on the northern highlands of Pakistan (NHP) could change the species composition. Range shifts and range reduction in the forested landscapes will dramatically affect the distribution of forest-dwelling species, including the Galliformes (ground birds). Three Galliformes (e.g., Lophophorus impejanus, Pucrasia macrolopha, and Tragopan melanocephalus) are indicator species of the environment and currently distributed in NHP. For this study, we used Maximum Entropy Model (MaxEnt) to simulate the current (average for 1960–1990) and future (in 2050 and 2070) distributions of the species using three General Circulation Models (GCMs) and two climate change scenarios, i.e., RCP4.5 (moderate carbon emission scenario) and RCP8.5 (peak carbon emission scenario). Our results indicated that (i) under all three climate scenarios, species distribution was predicted to both reduce and shift towards higher altitudes. (ii) Across the provinces in the NHP, the species were predicted to average lose around one-third (35%) in 2050 and one-half (47%) by 2070 of the current suitable habitat. (iii) The maximum area of climate refugia was projected between the altitudinal range of 2000 to 4000 m and predicted to shift towards higher altitudes primarily > 3000 m in the future. Our results help inform management plans and conservation strategies for mitigating the impacts of climate change on three indicator Galliforms species in the NHP.
... Fire is used primarily to maintaining understory grasses for livestock (FAO, 2006) and has increased in frequency in the region in the past two decades (Neeraja et al., 2021). Climate change is already increasing the frequency and intensity of drought in this region (Sharma & Mujumdar, 2017;Remya et al., 2015;Ramachandran et al., 2018). While our study focuses on a particular forest, we note that the focal species and the techniques used to harvest them are similar across the region. ...
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Understanding how anthropogenic activities, such as harvesting, influence plant populations is important to quantify sustainable practices that conserve species of socioeconomic importance. There is limited knowledge on how harvesting of branches and non‐timber forest products affect populations of trees in the dry tropics. We measure demographic vital rates of three dry tropical tree species in the presence and absence of harvesting and apply integral projection models to quantify population growth rates, which represent the mean fitness across the life cycle. Our results show that the three species vary in their demographic rates and life history. Harvesting significantly decreases the growth of two species. Current levels of harvesting only significantly decreased the population growth rate of one species that experienced both branch and main stem harvesting. Life table response experiments reveal that the negative effect of harvesting on the population growth rate of this species is primarily due to individuals being forced to re‐sprout from their base. Few individuals were observed recruiting from seed, and this might be due to the presence of other threats, such as fire, soil erosion, and grazing. Our results provide knowledge on the demography and the effects of harvesting on endemic tree species of the Eastern Ghats, a region for which few demographic studies are available. These results are relevant to conserving forest biodiversity for the benefits of people and can contribute to quantitative threat assessment for IUCN red listing. Branch harvesting has variable effects on vital rates and population growth rate of dry forest tree species. Harvesting significantly decreases the growth of two of the study species.
... For the future species distribution model, we used the bioclimatic variables for the year 2050 (the midpoint for the 2041-2060 period) based on two different climate projections, namely Geophysical Fluid Dynamics Laboratory climate model version 3 (GFDL-CM3) [63] developed by the National Oceanic and Atmospheric Administration and the Norwegian Earth System Model 1-medium resolution (NorESM1-M) [64,65] developed by the Norwegian Climate Center [66]. The 12 bioclimatic variables we used for modeling current distribution were also used for the future ENMs (Table 2 andTable 3). ...
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Bats perform critical ecosystem functions, including the pollination, seed dispersal, and regulation of invertebrate populations. Yet, bat populations are declining worldwide primarily due to habitat loss and other anthropogenic stressors. Thus, studies on bat ecology, particularly on environmental determinants of bat occupancy, are paramount to their conservation. High mobility, nocturnal behavior, and roosting site selection of bats make conventional surveys challenging. Moreover, little is known about geographic distribution, habitat suitability, and responses to climate change among tropical bat species. To bridge these research gaps, we applied ecological niche modeling to two Ceylonese bat species, Kerivoula malpasi and Kerivoula picta, to map their geographic distribution. Seasonal variations in temperature and precipitation were critical environmental predictors of bat distribution in general. Southwestern lowland forests contained the most optimal habitats for the relatively wide-ranging Kerivoula picta, while the central highlands provided the most suitable habitats for the narrow-ranging Kerivoula malpasi. No tangible changes in the highly suitable habitats were evident in response to projected climate change for either species. Yet, the optimal ranges of K. malpasi can become fragmented in the future, whereas the most optimal habitats for K. picta are likely to become spatially contiguous in the future. Habitat availability or fundamental niche alone is insufficient to reliably forecast species persistence, thus we caution against considering these two bat species as resilient to climate change. Our findings will enable the conservation authorities to initiate preemptive conservation strategies, such as the establishment of landscape-scale habitat connectivity and management of buffer zones around conservation lands. We also encourage conservation authorities to employ ecological niche models to map potential species distributions and to forecast range shifts due to climate change.
... Lastly, we used ArcGIS 10.4 (ESRI, Redlands, CA, USA) to compute the area and distribution of the PCAs. Habitat suitability results were expressed in probability, ranging from 0-1 [65]. Using the "Reclassify" tool of ArcMap 10.3, the obtained probability results were divided into two levels, of which 0-0.7 was inappropriate and 0.7-1 was appropriate and high potential [66,67]. ...
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Climate change has a profound impact on the conservation and management of the Picea species, and establishing more nature reserves would be an effective way to conserve wild species in general. Based on a novel computational method using ecological niche modeling to predict the potential geographical distribution of species and a spatial decision support system, the planning process could predict the future distribution of the Picea species and thus select appropriate nature reserves. In this research, we utilized systematic conservation planning to define priority conservation areas for the Picea species in China according to future climate predictions. We hypothesized that: (1) the distribution of the Picea species could be changed under predicted climate conditions in China; (2) the current national nature reserves had sufficient capacity to conserve Picea species under predicted climate conditions in China; and (3) there were still deficiencies in the planned conservation for the Picea species based on predicted climate predictions in China. The results of a spatial analysis showed that the predicted climate would have an impact on the area of distribution of the Picea species. Current nature reserves have a strong potential to conserve the Picea species. However, the conservation of the Picea species in the existing nature reserves was not adequate. There were still many Picea specimens outside the reserve that would be threatened. This research systematically improved the research on the Picea species, and it also scientifically identified the suitable growth and conserved areas of the Picea species in China to provide an empirical basis for the conservation and management of the Picea species.
... MaxEnt requires a proper distribution of occurrence points in the ecological space rather than the geographical space [8,9]. The advantages of MaxEnt are that it offers acceptable results even with a limited available sample size [10] and is also capable of projecting shifts in species distribution under various climate change scenarios [11,12,13] and thus extensively used for calculating potential species distributions of plants and animals for many purposes in biogeography, conservation biology and ecology [14,15]. The species occurrence sites are regarded as suitable habitat to meet species' ecological requirements which are taken as the reference data for the favourable environmental variables determining the occurrence of the species. ...
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Vultures are ecologically important primarily because of their scavenging role in cleaning carcasses of the environment. The Long-billed vulture Gyps indicus has suffered catastrophic declines in parts of its range and, thus, information about its global distribution and factors influencing its occurrence within this range are essential for its conservation. To this end, we estimated the spatial distribution of Long-billed vulture (LBV) and variables affecting this distribution. We used occurrence points (n = 10) from field survey conducted during 2016-2018 and past records and available literature, environmental variables related to these sites and Maximum Entropy (MaxEnt) software to predict the distribution of this species and its relationship to environmental variables. Out of ~82167.58 km2 study area, the LBV had a predicted range of 1856.79 Km2 i.e. 2.26 % of study area. The district with densest potential distribution was in East Siang, followed by Namsai, Lower Dibang Valley and a scanter potential distribution was around lower part of Papumpare, Changlang, and areas adjacent to the boundaries of neighboring state Assam. Elevation was related to the vulture’s most probable range: in particular higher temperature and low precipitation were important variables regardless of the season of observations examined. Conservation of identified habitats and mitigation of anthropogenic impacts are recommended for immediate and long-term conservation of the LBV in Arunachal Pradesh, India.
... According to the ecological principle called the niche theory along with its specific algorithm, the niche characteristics of the plant are indicated by spatial distribution and environmental variables, to forecast the appropriate area of the plant (Guisan and Zimmermann, 2000;Phillips et al., 2006). The development of the species distribution model provides effective methods for the research of environmental science (Du et al., 2017), the protection of biodiversity (Remya et al., 2015), and the defense of invasive species (Luizza et al., 2016) among other benefits. There is a variety of species distribution models based on different algorithms, such as CLIMEX (Sutherst and Maywald, 1985), GARP (Stockwell et al., 2006), ENFA (Hirzel et al., 2002), DOMAIN (Carpenter et al., 1993), andMaxEnt (Philips et al., 2006), among the existing models. ...
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Predicting the spatial distribution of species in relation to suitable areas under global climate change could provide some references for conservation and long-term management strategies for the species. In this study, the MaxEnt was optimized by adjusting the feature combination and regulation magnification parameters with the ENMeval data package. Based on 127 Cremastra appendiculata spatial distribution locations and 14 environmental factors, the potential distribution areas of C. appendiculata under the present and future climate conditions (2050s, 2070s) were simulated, and the dominant environmental factors influencing the spatial distribution of C. appendiculata were analyzed. The feature combination (FC) and the regularization multiplier (RM) were selected as per the Akaike information criterion (AIC). The model showed complexity and degree of over-fitting (delta AICc = 0, omission rate = 0.106, the difference in the curve values between the training and testing areas was 0.021) after establishing the optimal model (FC = LQH and RM = 2.5), and the results indicated that the optimal model performed well in simulating the potential spatial distribution of C. appendiculata (the area under the receiver operating characteristic curve = 0.933). The results showed that the suitable habitat of C. appendiculata currently in China is 187.60 × 104 km2, while the highly suitable habitat is 118.47 × 104 km2, the moderately suitable habitat is 53.25 × 10 4 km2, and the poorly suitable habitat is 15.88 × 104 km2. There is an increasing trend in the suitable habitat of C. appendiculata under six climate scenarios, including SSP1-2.6, SSP2-4.5, and SSP5-8.5 in the 2050s and the 2070s, and that habitat will extend to the northwest as a whole. The highly suitable habitat of C. appendiculata in nature reserves is 0.47 × 104 km2; consequently, there is a large gap in the protection of C. appendiculata. The distribution of C. appendiculata was influenced by the temperature, precipitation, and normalized vegetation index.
... The environmental variables, which included 19 bioclimatic variables, were downloaded with a spatial resolution of 5 km from the WorldClim Global environmental database. 1 In its AR5 assessment, the Intergovernmental Panel on Climate Change (IPCC) chose four RCPs to depict the future climate scenario, namely RCPs 2.6, 4.5, 6.0, and RCP8.5 (Remya et al., 2015). The future environmental variables generated by the MIROC-ESM-CHEM model for two climatic scenarios (RCPs 4.5 and 8.5) were taken from the Climatic Change, Agriculture and Food Security website. ...
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As a significant threat to agriculture, pests have caused a great disservice to crop production and food security. Understanding the mechanisms of pests' outbreaks and invasion is critical in giving sound suggestions on their control and prevention strategies. The African rhinoceros beetle, Oryctes monoceros (Olivier), as the most damaging pest of palms, banana, sugarcane, and pineapple, severely threatens their production due to its ability to kill both young and matured hosts. Analyzing the effect of climate change on major parameters of O. monoceros life history has been an important issue recently, given its sensitivity to thermal conditions. However, information on how climate change alters geographical distribution of O. moncoeros is poorly understood. By combining environmental variables and occurrence records, we were able to assess environmental risk factors for O. monoceros and create risk maps for the pest using the Boosted Regression Tree (BRT) model. Our results significance of environmental variables showed that the annual temperature variation (39.45%), seasonality of temperature (23.00%), the isothermality (18.76%), precipitation of the hottest quarter months (6.07%), average variation of day time temperature (3.27%), were relatively important environmental factors that affected the distribution O. monoceros. We also found that the projected potential distributions of the pest's habitats in all future global warming scenarios exceeded its present known distribution. The model predicts that habitat suitability for O. monoceros is predominantly concentrated along Africa's west and east coastlines, Asia's south coasts, South America's north and east coasts, and a few locations spread over North America's southern coasts and coastal regions. These outputs provide a solid theoretical foundation for O. monoceros risk evaluations and control.
... (https://earth.google.com). To describe the future climate scenarios, the Intergovernmental Panel on Climate Change (IPCC) proposed multiple representative concentration pathways (RCPs) (Remya et al., 2015), for such studies. The future environmental variables at 5 min spatial resolution generated from the MIROC-ESM-CHEM model, were downloaded from the Climatic Change, Agriculture, and Food Security website (www.ccafs-climate.org). ...
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Climate change is expected to have a significant influence on species range expansion, habitat shifts, and risk of biological invasion due to changes in survival rates, and rapid reproduction. This will tend to affect their geographical distribution and dispersal patterns, thereby threatening agriculture production and food security. Therefore, it is essential to understand the impact of climate change on the range shifts of an invasive species like the Asiatic rhinoceros beetle, Oryctes rhinoceros Linnaeus (Coleoptera: Dynastinae: Scarabaeidae), to inform policy formulation and preventive measures. To achieve this, we used environmental variables and occurrence records of O. rhinoceros to predict the current and future potential distribution of the pest under two representative concentration pathways (RCPs 4.5 and 8.5) for three time periods (2030, 2050, and 2080). We employed Boosted Regression Tree (BRT) and ArcGIS to create risk maps for the pest. The BRT model predicts an expansion of O. rhinoceros outside the current known distribution. The environmental variables which contributed the most to the geographical distribution of the pest were minimum temperature of coldest month (26.81%), followed by precipitation of wettest month (20.61%), temperature annual range (11.34%), mean diurnal range (11.33%), and elevation (4.49%). Under the different climate change scenarios, O. rhinoceros will continue to threaten the economically important host plants until 2080. As a result, there will be a need for effective strategies to prevent its spread. Our predictions are reliable and have the potential to estimate the global distribution of the pest, as well as provide suggestions for prompt of O. rhinoceros prevention and management.
... En este estudio, localizamos y cuantificamos las áreas con potenciales hábitats adecuados para C. pubescens y C. calisaya por ser especies que crecen para ser aprovechadas para la obtención de quinina (especialmente la última) tanto en América como en Africa y Asia (Hodge,1948 ;Swanevelder, 2001) y, en consecuencia, los resultados pueden ser comparados. El patrón de ordenamiento de los hábitats actuales y futuros de ambas especies ha sido estudiado usando modelos de distribución de especies (species distribution modelling -SDM), ya que se ha demostrado su utilidad para la evaluación de otras especies tropicales (Remya et al., 2015;Koch et al., 2017;Förderer et al., 2018) y hemos usado el algoritmo de máxima entropía MaxEnt por su pertinencia al tener datos de ocurrencias y no de ausencias de la especie (Barbet-Massin, M. et al., 2012;Guillera-Arroita et al., 2014). Así, hemos podido identificar y modelizar las áreas aptas para ambas Cinchonas ya sea para la producción de corteza o para su conservación y repoblamiento analizando los cambios en los potenciales hábitats a causa del cambio climático mediante un análisis comparativo entre el escenario actual y el escenario en 2070. ...
... To perform the spatial modeling of the species, initially, 28 variables were included (Table S1) and rescaled to a spatial resolution of 250 m. Likewise, in order to minimize the multiple multicollinearities of these variables, they were filtered using the Pearson correlation coefficient through the R 3.6 software (The R Foundation, Vienna, Austria) and r = ±0.8 was established as the cut-off value for the highly correlated variables [26][27][28]. Finally, the 14 variables (Table 1) were chosen for the final modeling: 7 bioclimatic variables were included, in addition to relative humidity from WorlClim [29] and 3 topographic variables derived from the digital elevation model (DEM), available on the United States Geological Survey (USGS) portal [30]. ...
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... Our model performance was high, with an AUC value of more than 0.9, suggesting that the model's predictions are reliable. Several studies have assessed MaxEnt model performance using AUC (Remya et al., 2015;Yang et al., 2013). ...
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... Compared to other SDM tools, a maximum entropy algorithm can develop a good model with small number of occurrences (Harapan et al. 2020). Because of this reason, many studies on threatened plants, which typically have small amounts of occurrence data, use MaxEnt to model species distributions (Adhikari et al. 2012;Yang et al. 2013;Padalia et al. 2014;Pradhan 2015;Remya et al. 2015;Yuan et al. 2015;Yi et al. 2016;Pranata et al. 2019;Ito et al. 2020;Anand et al. 2021;Du et al. 2021;Felix et al. 2021;Liu et al. 2021;Mahatara et al. 2021;Nguyen et al. 2021;Purohit and Rawat 2021;Su et al. 2021;Yang et al. 2021;Ye et al. 2021). With effective conservation planning focused on ensuring redundancy and resiliency for sustainable future populations (Redford et al. 2011), SDMs are a valuable tool for the conservation community (Mcshea 2014). ...
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... Habitat suitability modeling using Maxent A variety of SDMs have been proposed for predicting species habitat and occurrence locales across various geographic extents and are classified based on their computational algorithm or data requirements (Wang et al., 2020). Maxent is perhaps the most popular and widely applied SDM in the fields of ecology and conservation (Remya et al., 2015). This model is a type of machine learning and takes the principle of maximum entropy for estimating probability distribution. ...
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Hancornia speciosa is the target of research on genetic diversity, ethnobotanical and medicinal studies. However, information on the genetic variability of populations associated with modeling the potential distribution in the state of Sergipe has not yet been performed. The objective of this study was to predict the potential occurrence of H. speciosa in areas of high use of their fruits. The maximum entropy method was used to detect the distribution patterns of H . speciosa in variable environments. The diversity of four natural populations, situated in areas of extractivist, was determined by ISSR molecular markers. The species occurs more densely in the coastal regions of Sergipe. The prediction of occurrence indicates that the species reduces areas of occurrence, mainly due to anthropic actions. It is suggested that the species needs public policies aimed at its conservation and the priority populations for conservation.
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Miller's witch-alder (Fothergilla milleri, Hamamelidaceae) is a newly described globally imperiled shrub that is known from disjunct populations in coastal Alabama, the panhandle of Florida, and one location in Georgia. Little is currently known about the natural history or ecology of the species. We conducted inventories of three populations of F. milleri in Alabama and Florida. A total of 3060 ramets were found; 45% were in one subpopulation 0.5 ha in area and 79% were in one population within an area of 4.5 ha. Low ramet counts in few locations makes the species particularly vulnerable to extinction due to stochastic events. Also, all seed capsules found within the surveys were infected by an aphid, which is possibly preventing sexual reproduction within the populations. Microsite habitat data indicated F. milleri grows in a unique transitional habitat between upland conifer forest and wetlands. The sites where extant populations of F. milleri inhabit tend to have acidic and well-drained soils with a high sand content. Further, many populations were under thick midstory vegetation. Thus, fire or other forms of removal may be needed to release those subpopulations before they may become shaded out. Lastly, a habitat suitability model was developed to better facilitate conservation efforts. However, only 0.5% of the study area was within the highest level of suitability. Fothergilla milleri is facing multiple threats that could lead to its extirpation from the wild, and direct and intense conservation action may be necessary to ensure F. milleri remains on the landscape.
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Thesis
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Makine öğrenme tekniği kullanılarak türlerin güncel ve gelecek yayılış alanlarını modellemek günümüzde önemli çalışmalardan biri haline gelmiştir. Ülkemize ait ve Peyzaj Mimarlığı meslek disiplininin en önemli tasarım elemanı olan bitkisel materyalin iklim değişikliğinden nasıl etkileneceğinin analiz edilmesi, bu türlerin bitkilendirme çalışmalarında gelecek kullanımının planlanabilmesi için büyük önem taşımaktadır. Türlerin var olduğu alanları ifade eden noktasal veriler ve bu alanlara ait sayısal biyoiklim verileri kullanılarak oluşturulmuş katmanlar sayesinde farklı iklim senaryolarına göre türün günümüz ve gelecekteki potansiyel yayılış alanları MaxEnt programı ile belirlenebilmektedir. Bu kapsamda tez çalışması 2 ana bölümden oluşmaktadır. İlk bölümde Fabaceae (Baklagiller) familyası’ndan peyzaj tasarımı çalışmalarında en yaygın kullanılan 7 tür seçilerek bu türlere ait var verileri (presence data) ve Worldclim 2.1 versiyonu 2.5 dakika (yaklaşık 16 km2) konumsal çözünürlükteki 19 adet biyoiklimsel değişken kullanılarak türün günümüz koşullarındaki potansiyel yayılış alanı tahmin edilmiştir. İkinci bölümde ise türlerin yayılış alanlarının iklim değişiminden nasıl etkileneceğini belirlemek için ise 6. IPCC raporu temel alınarak oluşturulmuş ve eşleştirilmiş model karşılaştırma Projesi (CMIP6) modellerinden olan IPSL-CM6A-LR iklim değişim modeli kullanılarak türün SSP2 4.5 ve SSP5 8.5’e senaryolarına göre 2041-2060 ve 2081-2100 periyodlarına ait potansiyel yayılış alanı modellenmiş, ayrıca türlere ait üretilen günümüz ve gelecekteki yayılış alanları arasındaki alansal ve konumsal farklar değişim analizi ile ortaya konulmuştur. Günümüz ve gelecek yayılış alanlarının modellenmesinde MaxEnt 3.4.1 versiyonu kullanılmıştır. Fabaceae familyasına ait bazı türlerin günümüz potansiyel yayılış alanı ile gelecekte iklim değişikliğinden nasıl etkileneceği belirlendiği tez çalışmasında bütün türlerin yayılış alanlarında azalma olacağı, Adenocarpus complicatus (L.) GAY ve Ceratonia siliqua L. türlerinin ise SSP5 8.5 2090 yılı senaryosunda Türkiye koşullarında artık doğal olarak yetişemeyeceği tahmin edilmiştir.
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Alnus cremastogyne is a broad-leaved tree species with fast-growing and promising nitrogen-fixing capacity. Its plantation is under steady growing due to the commercial and restoration importance in China. However, little is known about the effects of projected climate change on its adaptability and future distribution. In the present study, we simulated the ecological suitability of A. cremastogyne under three climate change scenarios (CCS) (i.e., SSP1-2.6, SSP2-4.5 and SSP5-8.5) in 2050 s and 2070 s using geographic information technology (GIS) and maximum entropy model (MaxEnt). The results demonstrated that under current climate situation, the highly suitable areas were mainly located in central and eastern Sichuan, most of Hunan, central and northern Jiangxi, central and eastern Guizhou, most of Chongqing and southeast Hubei, with a total area of 52.1 × 10⁴ km². The areas of the moderately and poorly suitable areas were 65.52 × 10⁴ km² and 92.99 × 10⁴ km², respectively. Under the future CCS, both the highly suitable area and the total suitable area of A. cremastogyne showed increasing trends, 1.22-fold and 1.57-fold higher for highly suitable area by 2050, 1.27-fold and 1.41-fold by 2070, respectively. While the areas of the poorly suitable areas would decline. Moreover, a northwestward migration of the geometric center of the total suitable area was projected. Annual precipitation (5.9–1314.5 mm), the minimum temperature of coldest month (-0.2–16.8 ℃) and the mean temperature of coldest quarter (0.7–11.7 ℃) were the three most important environmental variables determining the distribution of A. cremastogyne. Our results highlight the plasticity of A. cremastogyne to climate change and also the feasibility of intercropping with this species to improve soil nitrogen availability.
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Blumea balsamifera is a famous Chinese Minority Medicine, which has a long history in Miao, Li, Zhuang and other minority areas. In recent years, due to the influence of natural and human factors, the distribution area of B. balsamifera resources has a decreasing trend. Therefore, it is very important to analyze the suitability of B. balsamifera in China. Following three climate change scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) under 2050s and 2070s, geographic information technology (GIS) and maximum entropy model (MaxEnt) were used to simulate the ecological suitability of B. balsamifera . The contents of L-borneol and total flavonoids of B. balsamifera in different populations were determined by gas chromatography (GC) and ultraviolet spectrophotometry (UV). The results showed that the key environmental variables affecting the distribution of B. balsamifera were mean temperature of coldest quarter (6.18-26.57 ℃), precipitation of driest quarter (22.46-169.7 mm), annual precipitation (518.36-1845.29 mm) and temperature seasonality (291.31-878.87). Under current climate situation, the highly suitable habitat was mainly located western Guangxi, southern Yunnan, most of Hainan, southwestern Guizhou, southwestern Guangdong, southeastern Fujian and western Taiwan, with a total area of 24.1×10 ⁴ km ² . The areas of the moderately and poorly suitable habitats were 27.57×10 ⁴ km ² and 42.43×10 ⁴ km ² , respectively. Under the future climate change scenarios, the areas of the highly, moderately, and poorly suitable habitats of B. balsamifera showed a significant increasing trend, the geometric center of the total suitable habitats of B. balsamifera would move to the northeast. In recent years, the planting area of B. balsamifera has been reduced on a large scale in Guizhou, and its ex situ protection is imperative. By comparison, the content of L-borneol, total flavonoids and fresh leaf yield had no significant difference between Guizhou and Hainan (P > 0.05), which indicated that Hainan one of the best choice for ex-situ protection of B. balsamifera .
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Climate change and human activities have caused the degeneration of the natural habitats of medicinal plants. Mentha pulegium L. is one of the most common medicinal plants in Tunisia that features high economic and ecological values. Predicting species' suitable habitats, through modeling, has evolved as a useful tool for the assessment of resource conservation to protect medicinal plants. Herein, we used MaxEnt model to predict current and future distributions of M. pulegium under two representative concentration pathways (RCP2.6 and RCP8.5) for the years 2050 and 2070. MaxEnt modeling was in the “Excellent” category since all the AUCs were above 0.9. Results showed that high and moderate suitable habitats for the current distribution of M. pulegium encompassed ca. 9929 km² and 16,423 km², respectively. These areas are mainly located in North Tunisia. Precipitation of the coldest quarter (Bio19) was identified as the most critical factor shaping M. pulegium distribution. Compared to the current distribution, the highly and moderately suitable areas for M. pulegium under the two RCPs (RCP2.6 and RCP8.5) would decrease in the 2050s and 2070s. The model projected a shift of the suitable area from Northeastward to Center-eastward. These results may provide a useful tool for developing adaptive management strategies to enhance M. pulegium protection and sustainable utilization in the context of global climate change.
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Mountain stewartia (Stewartia ovata) is a rare shrub or small tree endemic to the higher elevation regions of Georgia, Tennessee, and Alabama with isolated populations occurring in Kentucky, North Carolina, South Carolina, Virginia, and Mississippi. The species is often misidentified or overlooked by land managers and conservationists. As a result, mountain stewartia's habitat and distribution descriptions are limited for restoration and conservation use. Modeling a species' habitat suitability has become a critical first step in conserving rare and imperiled plant species. These models allow conservationists to locate previously undocumented populations and prioritize populations and habitats for conservation. This study presents a habitat suitability model for mountain stewartia across its known natural range based on maximum entropy (Maxent) modeling with nine environmental predictor variables and 60 occurrences from herbarium records (n=22), research-grade iNaturalist observations (n=25), and other author identified locations (n=3). The resulting habitat suitability map was classified into bins for spatial analysis. A total of 376,030 ha (0.44% of the study area) was designated within the top tier bin with the highest suitable habitat. Further, 133,344 ha (0.16% of the study area) of the top bin was found on publicly owned lands, indicating approximately 35.56% of the highest habitat suitability occurs within public lands. The presented model could allow plant conservationists to prioritize areas for conservation, reintroduction, and may lead to the discovery of previously undocumented populations.
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(1) Background. Conifers are the main plantation species in southern China, including Masson Pine (MP), Chinese fir (CF) and Chinese thuja (CT). Clarifying the suitable site conditions for these conifers is helpful for large-area afforestation, so as to manage forests to provide a higher level of ecosystem services. To achieve the research goals, we take the conifers in Hubei Province of southern China as a case study. (2) Methods. The situations of conifers, as well as environmental conditions of 448 sampling plots, were then investigated. The suitable growth environment of conifers in the studied area was determined by the maximum entropy algorithm, and the suitability spatial distribution of coniferous forests at the provincial level was also analyzed. (3) Results. The effect of the conifers suitability prediction model reached an accurate level, where AUC values of MP, CF and CT training set were 0.828, 0.856 and 0.970, respectively. Among multiple environmental factors, such as geography and climate, altitude is the most important factor affecting conifer growth. The contribution of altitude to the growth suitability of MP, CF and CT was 38.1%, 36.2% and 36.1%, respectively. Suitable areas of MP, CF and CT were 97,400 ha, 74,300 ha and 39,900 ha, accounting for 52.45%, 39.97% and 21.46% of the studied area, respectively. We concluded that the suitable site conditions of conifer plantations were 2800-5600 oC annual accumulated temperature, 40-1680 m a.s.l., and < 40° slopes. (4) Conclusions. The study suggests that accurate spatial suitability evaluation should be carried out to provide sufficient support for the large-area afforestation in southern China. However, due to our data and study area limitations, further studies are needed to explore the above findings for a full set of plantation species in an extensive area of southern China.
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Global temperatures are predicted to rise from between 1.4 to 5.8°C by 21st century, which could result in a 20 to 30% extinction of species. The negative impacts of climate change on the northern highlands of Pakistan (NHP) could change the species composition. Range shifts and range reduction in the forested landscapes will dramatically affect the distribution of forest dwelling species, including the Galliformes (ground birds). Three Galliformes (e.g., Lophophorus impejanus , Pucrasia macrolopha and Tragopan melanocephalus ) are indicator species of the environment and currently distributed in NHP. For this study, we used Maximum Entropy Model (MaxEnt) to simulate the current and future (in 2050 and 2070) distributions of the species using three General Circulation Models (GCMs) and two climate change scenarios, i.e., RCP4.5 (moderate carbon emission scenario) and RCP8.5 (peak carbon emission scenario). Our results indicated that (i) all the three species would be negatively affected by the climate change in 2050 and in 2070. (ii) Under all three climate scenarios, species distribution was predicted to both reduce and shift towards higher altitudes. (iii) Across the provinces in the NHP, the species were predicted to lose over one quarter in 2050 and one-third by 2070 of the current suitable habitat. (iv) The maximum area of climate refugia was projected between the altitudinal range of 2000 m to 4000 m and predicted to shift towards higher altitudes primarily >3000 m in the future. The proposed implications such as establishment and upgradation of the protected areas, ban on hunting, timber mafia and temporary settlements of the local people in the forested landscapes should be under special consideration to mitigate the impact of climate change.
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Dendrobium is a valuable traditional Chinese herb that contains active ingredients such as polysaccharides and alkaloids that have anti-aging, antioxidant, and immunomodulating effects. The appropriate distribution range of Dendrobium should be predicted from the perspective of ecological niche theory in order to preserve and utilize medicinal plant resources. In this study, Dendrobium nobile, Dendrobium officinale, and Dendrobium moniliforme were selected to predict the potential suitable distributions and ecological niche shifts. A comparison of 19 environmental variables of the three Dendrobium species revealed three climatic factors that differed significantly when the species were compared two at a time. The principal component analysis was carried out in order to screen seven climatic factors for ecological niche shift analysis. All three Dendrobium species were found to have a very similar ecological niche, but with a relatively small range of variability regarding certain climatic factors. Finally, the current and future suitable areas for these three Dendrobium species in China were predicted using the MaxEnt model and ArcGIS using the two representative concentration pathways (RCP 2.6 and 8.5). Overall, the analysis of the climatic factors’ comparisons, niche shift, and current and future suitable areas of these three Dendrobium species provides a basis for medicinal plant resource conservation and utilization, and our methods could be applied to the study of other similar valuable medicinal plants.
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The ever-increasing human population, globalisation, and desire to keep pets have resulted in the translocation of many species into non-native environments. As a result, some of the non-native reptile species have been introduced to South Africa through the pet trade. However, little is known about the extent of trade in reptiles via online and physical pet stores in South Africa and their potential climatically suitable areas. We assessed the physical pet store and online trade of reptiles in South Africa. We found 69 physical pet stores and 18 online advertising websites selling 1,912 individuals of 66 species and 859 individuals of 50 reptile species, respectively. In total, we found 88 unique species representing 18 families from both sources, of which 86.4% were non-native species and 32 species were CITES-listed. Snakes were the most dominant (76.1%) traded group. Ball python Python regius (n = 601), corn snake Pantherophis guttatus (n = 553) and central bearded dragon Pogona vitticeps (n = 419) were the most-traded reptiles. Prices ranged from ZAR100.00 to ZAR6,000.00, with sharp-nosed viper Deinagkistrodon acutus acutus being the most expensive species. For present distributions, the red-eared slider Trachemys scripta elegans, P. guttatus, and Western diamondback rattlesnake Crotalus atrox had the largest predicted climatic suitability. The future predictions for the latter two species were predicted to increase while red-eared slider suitability shifted. Most popular species were available in large volumes, sold at relatively low prices and had high climatic suitability, representing a high potential invasion risk. We, therefore, propose that the existing pet trade regulations should be revised to include a more restricted trade on the trade of endangered, non-CITES listed and potential invasive pet species.
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Abstract.—Modeling approaches that relate known occurrences of species to landscape features to discover ecological properties and predict geographic occurrences have seen extensive recent application in ecology, systematics, and conservation. A key component in this process is estimation or characterization of species’ distributions in ecological space, which can then be useful in understanding their potential distributions in geographic space. Hence, this process is often termed ecological niche modeling or (less boldly) species distribution modeling. Applications of this approach vary widely in their aims, products, and requirements; this variety is reviewed herein, examples are provided, and differences in data needs and possible interpretations are discussed.
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A wide range of modelling algorithms is used by ecologists, conservation practitioners, and others to predict species ranges from point locality data. Unfortunately, the amount of data available is limited for many taxa and regions, making it essential to quantify the sensitivity of these algorithms to sample size. This is the first study to address this need by rigorously evaluating a broad suite of algorithms with independent presence-absence data from multiple species and regions. We evaluated predictions from 12 algorithms for 46 species (from six different regions of the world) at three sample sizes (100, 30, and 10 records). We used data from natural history collections to run the models, and evaluated the quality of model predictions with area under the receiver operating characteristic curve (AUC). With decreasing sample size, model accuracy decreased and variability increased across species and between models. Novel modelling methods that incorporate both interactions between predictor variables and complex response shapes (i.e. GBM, MARS-INT, BRUTO) performed better than most methods at large sample sizes but not at the smallest sample sizes. Other algorithms were much less sensitive to sample size, including an algorithm based on maximum entropy (MAXENT) that had among the best predictive power across all sample sizes. Relative to other algorithms, a distance metric algorithm (DOMAIN) and a genetic algorithm (OM-GARP) had intermediate performance at the largest sample size and among the best performance at the lowest sample size. No algorithm predicted consistently well with small sample size (n < 30) and this should encourage highly conservative use of predictions based on small sample size and restrict their use to exploratory modelling.