Identification of suitable habitat for Taxus wallichiana and Abies pindrow in moist temperate forest using maxent modelling technique

Yousaf, A.; Hadi, Rana; Khan, Nasreen; Ibrahim, Farzana; Moin, Hina; Rahim, Sharmeen; Hussain, Majid

doi:10.1016/j.sjbs.2022.103459

Cited by 19 publications

(15 citation statements)

References 32 publications

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“…The distribution data The aggregated AUC values for the two experimental models were 0.802 and 0.861, respectively, satisfying the requirements for a "highly accurate" model assessment. However, these values were relatively lower compared to the typical outcomes obtained in conventional assessments of the species' potential distribution utilizing the MaxEnt model [43][44][45][46][47][48]. Consequently, the following paragraphs will examine the variables contributing to this inconsistency and evaluate the dependability of the research model's output findings from different perspectives.…”

Section: Analysis On the Cause Of Relatively Low Auc Value Of Two Sub...mentioning

confidence: 94%

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

Liang,

Tang,

Qin

2024

Agronomy

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Morinda officinalis, a traditional medicinal plant in southern China, has a well-established cultivation history in Zhaoqing and Yunfu City of the Guangdong Province, China, contributing significantly to the local economy. Inadequate cultivation practices of Morinda officinalis may heighten the risk of landslide occurrences due to its specific growth and harvesting characteristics. This issue presents a challenge to the sustainable advancement of agriculture and forestry in the area, underscoring the necessity for scholarly intervention to address and devise effective remedies. This research utilized the MaxEnt model to assess landslide susceptibility and habitat suitability for Morinda officinalis, aiming to delineate appropriate cultivation zones amidst changing climatic conditions. The findings indicate that the model demonstrated a high level of accuracy, achieving combined AUC values of 0.802 for landslide susceptibility and 0.861 for habitat suitability evaluations, meeting the criteria for classification as “highly accurate”. Regions such as the Yun’an District, Luoding City, and the Xinxing District in Yunfu City were identified as having a low landslide risk and being highly conducive to Morinda officinalis cultivation under current climate conditions. Future projections indicate an anticipated expansion of the species’ distribution area between 2021 and 2040 under different climate scenarios, with subsequent variations. Spatial analysis unveiled a notable trend in the research area, indicating greater suitability for cultivation in the southern region compared to the northern region. This suggests that Yunfu City holds promise for facilitating the cultivation of Morinda officinalis more effectively than Zhaoqing City. This research offers a practical approach for promoting the sustainable growth of the regional Morinda officinalis industry, while also serving as a valuable resource for other economic crops encountering comparable developmental obstacles.

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Section: Analysis On the Cause Of Relatively Low Auc Value Of Two Sub...mentioning

confidence: 94%

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

Liang,

Tang,

Qin

2024

Agronomy

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“…However, in order to calculate accuracy in the validation stage, we employed species that weren't used in the training stage 90 . What is evident is that in recent years, ROC-AUC has been widely employed to assess habitat suitability maps 91 .…”

Section: Choosing the Optimal Algorithmmentioning

confidence: 99%

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Dastres

Bijani

Naderi

et al. 2023

Preprint

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Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.

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“…The latter option was deemed appropriate for our case study due to several reasons. Generally, the predictive performance of Maxent has continuously been proven to be comparable to the most effective techniques [29], and it has been successfully employed to model the biogeographic distributions of species in various regions of the world [34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Spatial Modeling Softwarementioning

confidence: 99%

Spatial Distribution and Genetic Diversity of Turbot (Scophthalmus maximus, Linnaeus, 1758) in Bulgarian Black Sea Waters Relative to Fishing Pressure and Their Abiotic Environment

Zlateva,

Ivanova,

Dzhembekova

et al. 2023

JMSE

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The present study examined the genetic diversity and spatial distribution of turbot (Scophthalmus maximus), an economically important species on the Bulgarian Black Sea coast. Maximum entropy (MaxEnt) modeling software Version 3.4.4. was utilized to develop a habitat suitability model for S. maximus in the Bulgarian Black Sea region. Data collected via demersal and pelagic surveys and genetic sampling from 2017 to 2021 were utilized to link species occurrence localities with selected abiotic factors. Our findings showed that the species’ habitat preferences are strongly influenced by temperature and dissolved oxygen, and projections based on simplified climatic scenarios indicated potential distribution shifts and a substantial reduction in reproduction habitats in the northern region. The assessment of genetic diversity was based on mtDNA COIII sequencing; MtDNA revealed a low level of polymorphism in all analyzed populations. The extensive fishing pressure may have increased the likelihood of genetic and population bottlenecks and a consequent decline in genetic diversity in the Shabla, Nesebar, and Tsarevo populations. The Tajima’s D values for the latter indicated that turbot underwent a bottleneck followed by rapid population expansion. Our findings are essential for the conservation and effective management of S. maximus stocks in the region.

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Identification of suitable habitat for Taxus wallichiana and Abies pindrow in moist temperate forest using maxent modelling technique

Cited by 19 publications

References 32 publications

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

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

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques

Spatial Distribution and Genetic Diversity of Turbot (Scophthalmus maximus, Linnaeus, 1758) in Bulgarian Black Sea Waters Relative to Fishing Pressure and Their Abiotic Environment

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