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تعیین آشیان اکولوژیک گونه باریجه (Ferula gummosa) با استفاده از مدل‌های ماشین بردار در منطقه حفاظت شده قرخود | ||
| نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
| دوره 76، شماره 3، آبان 1402، صفحه 305-319 اصل مقاله (1.69 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jrwm.2024.335991.1633 | ||
| نویسندگان | ||
| حمیدرضا کشتکار* 1؛ حسن یگانه2؛ امید کاوسی1 | ||
| 1گروه احیای مناطق خشک و کوهستانی، دانشکده منابع طبیعی، دانشگاه تهران، کرج، ایران | ||
| 2گروه مدیریت مرتع، دانشگاه علوم کشاورزی و منابع طبیعی گرگان، گرگان، ایران | ||
| چکیده | ||
| گیاه باریجه (Ferula gummosa)، از گونه‌های کمیاب و ارزشمند در مراتع ایران است که به دلیل ارزش بالای اقتصادی، مورد بهره‌برداری ذینفعان محلی قرار می‌گیرد. در این مطالعه به بررسی و مقایسه عملکرد شش مدل پیش‌بینی کننده (شبکه عصب مصنوعی، جنگل تصادفی، مدل خطی تعمیم یافته، مدل تقویت شده تعمیم یافته، مدل پاکت دامنه سطحی، و روش تجزیه و تحلیل درخت طبقه‌بندی) پرداخته شد. همچنین جهت ارزیابی تأثیر برهمکنش متغیرهای توپوگرافی با سایر متغیرها، دو مجموعه متغیر محیطی جهت واسنجی مدل‌ها کمی‌سازی شده و مورد استفاده قرار گرفت. مجموعه متغیر اول حاوی یازده عامل، مشتمل بر متغیرهای توپوگرافیک، اقلیمی، ادافیکی و سنجش از دوری است و مجموعه متغیر دوم حاوی شش عامل، مشتمل بر متغیرهای اقلیمی، ادافیکی و سنجش از دوری می‌باشد. عملکرد مدل با استفاده از شاخص (TSS)، (ROC) و (Accuracy) ارزیابی شد. بر اساس شاخص‌های ارزیابی، مدل تقویت شده تعمیم یافته بهتر از سایر روش‌های یادگیری ماشینی توانست آشیان اکولوژیک گیاه باریجه را پیش‌بینی کند. همچنین نتایج نشان داد که حذف متغیرهای توپوگرافی، دقت مدل‌ها را بر اساس شاخص TSS، بین 11 تا 25 درصد کاهش می‌دهد. ارزیابی اهمیت نسبی متغیرهای پیش‌بینی کننده نشان داد که متغیر درجه شیب، شاخص نرمال‌شده تفاوت پوشش گیاهی، شاخص رطوبت سطحی، و گروه‌های خاک بیشترین تأثیر را در تعیین زیستگاه گونة باریجه دارند. بر اساس نتایج حاصل شده از مدل برگزیده، حدود 45 درصد از سطح منطقه حفاظت شده قرخود از نظر مطلوبیت زیستگاه باریجه، در وضعیت عالی قرار دارد. لذا این منطقه پتانسیل بسیار زیادی برای کاشت و توسعه این گونه‌ی ارزشمند داشته و می‌تواند مدنظر مدیران بخش محیط زیست و منابع طبیعی جهت اولویت‌بندی اقدامات اصلاحی و حفاظتی قرار گیرد. | ||
| کلیدواژه‌ها | ||
| باریجه؛ مطلوبیت زیستگاه؛ متغیر توصیفی؛ متریک ارزیابی؛ خراسان شمالی | ||
| عنوان مقاله [English] | ||
| Ecological Habitat Modeling of Ferula gummosa in Ghorkhoud Protected Area Using Machine Learning Algorithms | ||
| نویسندگان [English] | ||
| Hamidreza Keshtkar1؛ Hassan Yeganeh2؛ Omid Kavoosi1 | ||
| 1Dept. of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, University of Tehran, Karaj, Iran | ||
| 2Dept. of Rangland Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran | ||
| چکیده [English] | ||
| Ferula gummosa is one of the rare and valuable species in Iran's rangelands, which is exploited by local stakeholders due to its high economic value. Protecting this species can help maintain the biodiversity and stability of mountainous areas. This study was conducted to compare the performance of six predictive models: Artificial Neural Networks, Random Forest, Classification Tree Analysis, Surface Range Envelope, Generalized Boosting Machines, and Generalized Linear Models. To evaluate the interactions between topographic factors and other variables, two environmental datasets were quantified and used for model calibration. The first dataset includes eleven factors covering topographic, climatic, edaphic, and remote sensing variables. Meanwhile, the second dataset contains six factors, focusing on climatic, edaphic, and remote-sensing variables. Model accuracy was evaluated using the True Skill Statistic (TSS), the area under the curve of the Receiver Operating Characteristics (ROC), and the Accuracy Index. The evaluation indices indicate that the Generalized Boosting Machine (GBM) model predicted the ecological niche of F. gummosa more accurately than the other methods. Additionally, the results showed that removing topographical variables reduced the model accuracy by 11 to 25%. The slope, NDVI, wetness, and soil groups were found to be the most important factors in mapping potentially suitable habitats for the target plant. According to the results obtained from the GBM model, approximately 45% of the Ghorkhoud area is in excellent condition. This knowledge can aid in the selection of predictors for practical Species Distribution Model (SDM) applications and provide information on which modeling techniques are most useful for a group of species. | ||
| کلیدواژه‌ها [English] | ||
| Ferula gummosa, Habitat suitability, explanatory variable, evaluation metric, North Khorasan | ||
| مراجع | ||
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