Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan‐Amazonia

et al. 2021

Preprint

Habitat suitability models are useful tools for a variety of wildlife management objectives. Distributions of wildlife species can be predicted for geographical areas that have not been extensively surveyed. The basis of these models’ work is to minimize the relationship between species distribution and biotic and abiotic environments. For some species, there is information about presence and absence that allows the use of a variety of standard statistical methods, however, the absence data is not available for most species. Nowadays, the methods that need presence-only data are expanded. One of these methods is the Maximum Entropy (MaxEnt) modeling. The purpose of this study is to model the habitat of Urial (Ovis orientalis arkal) in the Samelghan plain in the North East of Iran with the MaxEnt method. This algorithm uses the Jackknife plot and percent contribution values to determine the significance of the variables. The results showed that variables such as southern aspects, Juniperus-Acer, Artemisia-Perennial plants, slope 0-5%, and asphalt road were the most important factors affecting the species’ habitat selection. The area under curve (AUC) Receiver Operating Characteristic (ROC) showed an excellent model performance. Suitable habitat was classified based on the threshold value (0.0513) and the ROC, which based on the results 28% of the area was a suitable habitat for Urial.

Section: Methodsmentioning

confidence: 99%

Habitat Suitability modeling of Urial (Ovis orientalis arkal) in the Samelghan plain by using Maximum Entropy Method

et al. 2021

Preprint

“…For this reason, a modeling technique that does not require absence data was used to identify the environmental factors that explain the distribution of Urial in the Samelghan area. The MaxEnt is a correlative model based on the principle of maximum entropy to predict or infer species occurrence using presence-only data and environmental variables [7,[30][31][32][33][34][35]. This algorithm is one of the methods that, despite the small number [30] of presence points have high predictability, and has been widely used by researchers due to time savings and reduced study costs [36,37].…”

Section: Occurrence and Environmental Datamentioning

confidence: 99%

Habitat Suitability Modeling for Wildlife Management Objectives by Using Maximum Entropy Method

et al. 2021

J. Hum. Earth Future

Habitat suitability models are useful tools for a variety of wildlife management objectives. Distributions of wildlife species can be predicted for geographical areas that have not been extensively surveyed. The basis of these models' work is to minimize the relationship between species distribution and biotic and abiotic environments. For some species, there is information about presence and absence that allows the use of a variety of standard statistical methods. However, absence data is not available for most species. Nowadays, the methods that need presence-only data have been expanded. One of these methods is the Maximum Entropy (MaxEnt) model. The purpose of this study is to model the habitat of Urial (Ovis orientalis arkal) in the Samelghan plain in the North East of Iran with the MaxEnt method. This algorithm uses the Jackknife plot and percent contribution values to determine the significance of the variables. The results showed that variables such as southern aspects, Juniperus-Acer, Artemisia-Perennial plants, slope 0-5%, and asphalt road were the most important factors affecting the species’ habitat selection. The area under the curve (AUC) Receiver Operating Characteristic (ROC) showed excellent model performance. Suitable habitat was classified based on the threshold value (0.0513) and the ROC, which, based on the results, 28% of the area was a suitable habitat for Urial. Doi: 10.28991/HEF-2021-02-04-05 Full Text: PDF

“…MaxEnt uses entropy to generalize specific observations of presence-only data and does not require or even incorporate points where the species is absent within the theoretical framework (Kamyo & Asanok, 2020). Maxent model is a useful technique to predict the potentially suitable habitat (Evcin et al ., 2019; Qin et al ., 2020), geographical species distribution (Phillips et al ., 2006; Jiménez-Valverde, 2012; Merow et al ., 2014; Xu et al ., 2015; Mi et al ., 2016; Fronczak et al ., 2017; Wan et al ., 2019; Wang et al ., 2019; Kamyo & Asanok, 2020) on the basis of the most significant environmental conditions (Phillips et al ., 2006; Moreno et al ., 2011; Tourne et al ., 2019). MaxEnt model on simulating the suitable geographical distribution of species, has more advantages than other models, including a good performance with incomplete datasets, short model running time, easy operation, small sample size requirements, and high simulation precision (Hernandez et al ., 2006; Phillips et al ., 2006; Pearson et al ., 2007; Ortega-Huerta and Peterson, 2008; Li et al ., 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The presence-only data allow for easy public and private involvement in biological monitoring and are the dominant source of species occurrence data (Elith et al, 2011;. Among the algorithms available, one of the most widely used methods of developing SDMs is the Maximum entropy (MaxEnt) method (Phillips et al, 2017;Tourne et al, 2019). MaxEnt uses entropy to generalize specific observations of presence-only data and does not require or even incorporate points where the species is absent within the theoretical framework (Kamyo & Asanok, 2020).…”

Section: Introductionmentioning

confidence: 99%

Goitered Gazelle’s (Gazella Subgutturosa) Habitat Desirability Modeling by Using Maximum Entropy (Maxent) Method

et al. 2022

Preprint

The models predicting the spatial distribution of species can simulate the suitability of species habitats on different spatial scales, based on species records and site characteristics to gain insight into ecological or evolutionary drivers or to help predict habitat suitability across large scales. Species distribution models (SDMs) based on presence-absence or presence-only data use widely in biogeography to characterize the ecological niche of species and to predict the geographical distribution of their habitat. Although presence-absence data is generally of higher quality, it is also less common than presence-only data because it requires more rigorous planning to visit a set of pre-determined sites. Among the algorithms available, one of the most widely used methods of developing SDMs is the Maximum Entropy (MaxEnt) method. The MaxEnt uses entropy to generalize specific observations of presence-only data and does not require or even incorporate points where the species is absent within the theoretical framework. The purpose of this study is to predict the suitable habitat for Goitered gazelle (Gazella subgutturosa) in the Samelghan plain in northeastern Iran. The results showed that the variables of the Mediterranean climate classes, slope 0-5% class and semi-dense pastures with type Acantholimon-Astragalus are more important than other environmental variables used in modeling. The area under curve (AUC), Receiver Operating Characteristic (ROC), and the classification threshold shows model performance. Based on the ROC (AUC=0.99) results in this study, it was found that Maxent's performance was very good. Desirability habitat was classified based on the threshold value (0.0277) and the ROC, which approx 11% of the area, predicted suitable habitat for Goitered gazelle.