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

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه 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.

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