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مقایسه تکنیک های رگرسیونی و یادگیری ماشینی در تعیین گستره جغرافیایی اسپرس کوهی (Onobrychis cornuta L.) تحت تأثیر ویژگی های محیطی و تغییر اقلیم با استفاده از مدل IPSL-CM6A-LR | ||
| محیط شناسی | ||
| مقاله 7، دوره 49، شماره 1، خرداد 1402، صفحه 107-120 اصل مقاله (1012.14 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jes.2023.354111.1008382 | ||
| نویسندگان | ||
| زینب جعفریان* 1؛ محدثه امیری2 | ||
| 1گروه مرتعداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران | ||
| 2-گروه مرتعداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران -گروه علوم کشاورزی، دانشگاه فنی و حرفه | ||
| چکیده | ||
| پیش بینی تأثیر تغییر اقلیم بر اکوسیستم های بومی یکی از اهداف دیرینه اکولوژیست هاست و امری ضروری جهت حفاظت و مدیریت آنهاست. مدل های پراکنش گونه ای (SDM) پرکاربردترین ابزار برای پیش بینی اثرات تغییر اقلیم بر محدوده جغرافیایی گیاهان هستند. در این مطالعه، تکنیک های رگرسیونی (GLM و MARS) و یادگیری ماشینی (ANN و RF) همراه با متغیرهای محیطی برای پیش بینی پراکنش Onobrychis cornuta L. به کار رفتند. پاسخ گونه به اقلیم آینده (2070-2050) تحت سناریوهای خوش بینانه (SSP1-2.6)، بدبینانه (SSP3-7.0) و خیلی بدبینانه (SSP5-8.5) مدل اقلیمی IPSL-CM6A-LR از مدل های CMIP6 بررسی شد. طبق نتایج، مدل اجماعی و سپس MARS دقیق ترین پیش بینی را داشتند. مدل ANN با اختلاف معنی دار با سایر مدل ها (0.05>p) کمترین صحت پیش بینی را داشت. آنالیز حساسیت، ارتفاع (%24.64)، حداکثر دمای گرمترین ماه (%20.31)، تغییرات فصلی دما (%16.57) و میانگین دامنه دمای روزانه (%16) را مؤثرترین متغیرها بر پراکنش گونه معرفی کرد. طبق مدل اجماعی، رویشگاه مناسب گونه، 27 درصد از منطقه را به خود اختصاص داده است، اما تحت اقلیم آینده، پراکنش آن کاهش خواهد یافت. سناریوی SSP5-8.5 بیشترین تأثیر را بر جابجایی محدوده پراکنش گونه خواهد داشت. نقشه های پیش بینی حاصل اطلاعات ارزشمندی را برای راهکارهای حفاظتی شامل شناسایی مکان های مناسب جهت معرفی مجدد و کشت آن در چارچوب طرح های مدیریت مراتع فراهم می سازند. | ||
| کلیدواژه‌ها | ||
| آشیان اکولوژیک؛ مطلوبیت رویشگاه؛ تغییر اقلیم؛ سناریوهای SSP؛ مدل‌های CMIP6 | ||
| عنوان مقاله [English] | ||
| Comparison of Regression and Machine Learning techniques in Determination of Geographical Range of Onobrychis cornuta L. under Environmental Characteristics and Climate Change using the IPSL-CM6A-LR Model | ||
| نویسندگان [English] | ||
| Zeinab Jafarian1؛ mohaddeseh amiri2 | ||
| 1Department of Range Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 2-Department of range management, Faculty of natural resources, Sari Agricultural Science and Natural Resources University, Sari, Iran -Department of Agricultural Science, Technical and Vocational University, Tehran, Iran | ||
| چکیده [English] | ||
| Predicting the effect of climate change on native ecosystems is one of the longstanding goals of ecologists and is essential for their conservation and management. Species distribution models (SDMs) are the most widely used tools to predict the effects of climate change on the geographical range of plants. In this study, two regression techniques (GLM and MARS) and two machine learning techniques (ANN and RF), along with environmental factors were used to predict the distribution of Onobrychis cornuta L. The species response to future climate (2050-2070) was investigated under optimistic (SSP1-2.6), pessimistic (SSP3-7.0) and very pessimistic (SSP5-8.5) emission scenarios of the IPSL-CM6A-LR climatic model from CMIP6 models. Based on results, the ensemble model and then MARS presented the most accurate prediction. ANN had the lowest prediction accuracy with a significant difference from other models (p<0.05). The sensitivity analysis revealed altitude (24.64%), maximum temperature of the warmest month (20.31%), temperature seasonality (16.57%) and diurnal range of mean temperature (16%) as the most effective variables on the distribution. According to the ensemble model, the suitable habitat occupies about 27% of the area, but its distribution will be decrease under the future climate. The SSP5-8.5 scenario will have the greatest impact on the displacement of the species distribution range. The resulting prediction maps provide valuable information for conservation strategies, including identifying suitable places for its reintroduction and cultivation in the framework of rangeland management plans. | ||
| کلیدواژه‌ها [English] | ||
| ecological niche, habitat suitability, climate change, SSP scenarios, CMIP6 models | ||
| مراجع | ||
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