Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh, India using machine learning and statistical modeling

Sarma, Kamal; Roy, Saurov Jyoti; Kalita, Bikash; Baruah, Prantik Sharma; Bawri, Amal; Nath, Manash Jyoti; Baruah, Ujjal Deka; Sahariah, Dhrubajyoti; Saikia, Anup; Tanti, Bhaben

doi:10.1016/j.chnaes.2022.05.009

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…As a plant growth prerequisite, precipitation is the primary limiting factor for almost all species 37 , 38 . Variations in precipitation and consequent changes in temperature and humidity disrupt the balance of soil moisture and most physiological plant functions 36 , 39 . The driest month in China occurs in March, April, or May, accompanied by higher temperatures.…”

Section: Discussionmentioning

confidence: 99%

“…The average logical value combined with the actual distribution were used to classify distribution-level values and corresponding distribution ranges. The specific suitability is divided into five categories: fail (0–0.15), poor (0.15–0.3), fair (0.3–0.45), good (0.45–0.6), and excellent (>0.6) 35 , 36 . Different suitable levels indicate the differential potential distribution probability of the species in specific areas.…”

Section: Methodsmentioning

confidence: 99%

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Predicting suitable habitat for the endangered tree Ormosia microphylla in China

Wei,

Wang,

Xie

et al. 2024

Sci Rep

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Climate change has significantly influenced the growth and distribution of plant species, particularly those with a narrow ecological niche. Understanding climate change impacts on the distribution and spatial pattern of endangered species can improve conservation strategies. The MaxEnt model is widely applied to predict species distribution and environmental tolerance based on occurrence data. This study investigated the suitable habitats of the endangered Ormosia microphylla in China and evaluated the importance of bioclimatic factors in shaping its distribution. Occurrence data and environmental variables were gleaned to construct the MaxEnt model, and the resulting suitable habitat maps were evaluated for accuracy. The results showed that the MaxEnt model had an excellent simulation quality (AUC = 0.962). The major environmental factors predicting the current distribution of O. microphylla were the mean diurnal range (bio2) and precipitation of the driest month (bio14). The current core potential distribution areas were concentrated in Guangxi, Fujian, Guizhou, Guangdong, and Hunan provinces in south China, demonstrating significant differences in their distribution areas. Our findings contribute to developing effective conservation and management measures for O. microphylla, addressing the critical need for reliable prediction of unfavorable impacts on the potential suitable habitats of the endangered species.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predicting suitable habitat for the endangered tree Ormosia microphylla in China

Wei,

Wang,

Xie

et al. 2024

Sci Rep

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show abstract

“…A higher AUC value indicates a considerable deviation in the geographic distribution of the simulation object from a random distribution. It also means a stronger correlation between the simulation result and the environmental variables, i.e., the model has a better prediction accuracy [41]. The AUC statistic is classified into five performance categories: excellent (0.9-1.0), good (0.8-0.9), fair (0.7-0.8), poor (0.6-0.7), and fail (0.5-0.6) [42].…”

Section: Maxent Modeling Of Species Distributionmentioning

confidence: 99%

Predicting Suitable Habitats for China’s Endangered Plant Handeliodendron bodinieri (H. Lév.) Rehder

Wang,

Xie,

Wei

et al. 2023

Diversity

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Demarcating a plant species’ actual and potential biogeographical distribution is crucial for understanding the key environmental variables shaping its habitat conditions. We used MaxEnt and species distribution modeling to predict the likely range of China’s endangered species, Handeliodendron bodinieri (H. Lév.) Rehder, based on forty-four validated distribution records and eight selected environmental variables. Combined with percentage contribution and permutation importance, the jackknife statistical method was applied to test and evaluate pertinent factors restricting the potential distribution of H. bodinieri. The response curves of critical bioclimatic factors were employed to determine the potential species range. The generated MaxEnt model was confirmed to have excellent simulation accuracy. The current core potential distribution areas are concentrated in the Guangxi and Guizhou provinces of Southwest China, with a significant inter-regional difference. The precipitation of the warmest quarter (Bio18) and minimum temperature of the coldest month (Bio6) had the greatest impact on the distribution area of H. bodinieri. The findings could provide useful information and a reasonable reference for managers to enhance the protection of this declining species.

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“…Machine learning technology has recently been created, particularly for SDMs 108 . Numerous studies attest to the remarkable accuracy of algorithmically generated habitat suitability maps [109][110][111] . From our perspective, the main issue with the majority of these comparisons is that they only validate model performance (de ned as the match up among both predicted and observed species' distributions) against the needs under current conditions, despite the fact that most models are approximately accurate in trying to project distributions under present environmental conditions.…”

Section: The Perspective Of Hsms and Mltsmentioning

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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Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh, India using machine learning and statistical modeling

Cited by 11 publications

References 40 publications

Predicting suitable habitat for the endangered tree Ormosia microphylla in China

Predicting suitable habitat for the endangered tree Ormosia microphylla in China

Predicting Suitable Habitats for China’s Endangered Plant Handeliodendron bodinieri (H. Lév.) Rehder

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

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