کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8845149 1617109 2018 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Modelling species habitat suitability from presence-only data using kernel density estimation
ترجمه فارسی عنوان
مدل سازی مناسب بودن زیستگاه گونه ها از داده های موجود تنها با استفاده از تخمینی تراکم هسته
کلمات کلیدی
مدل سازی و نقشه برداری مناسب محل زندگی، داده های موجود، در دسترس بودن منابع، برآورد تراکم هسته، نظارت بر محیط زیست،
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک بوم شناسی، تکامل، رفتار و سامانه شناسی
چکیده انگلیسی
We present a novel approach for modelling and mapping habitat suitability from species presence-only data that is useful for ecosystem and species monitoring. The approach models the relationship between species habitat suitability and environment conditions using probability distributions of species presence over environmental factors. Resource availability is an important issue for modelling habitat suitability from presence-only data, but it is in lack of consideration in many existing methods. Our approach accounts for resource availability by computing habitat suitability based on the ratio of species presence probability over environmental factors to background probability of environmental factors in the study area. A case study of modelling and mapping habitat suitability of the white-tailed deer (Odocoileus virginianus) using presence locations recorded in aerial surveys at Voyageurs National Park, Minnesota, USA was conducted to demonstrate the approach. Performance of the approach was evaluated through randomly splitting the presence locations into training data to build the model and test data to evaluate prediction accuracy of the model (repeated 100 times). Results show that the approach fit training data well (average training area under the curve AUC = 0.792, standard deviation SD = 0.029) and achieved better-than-random prediction accuracy (average test AUC = 0.664, SD = 0.025) that is comparable to the state-of-the-art MAXENT method (average training AUC = 0.784, SD = 0.021; average test AUC = 0.673, SD = 0.027). In addition, the suitability-environment responses modelled using our approach are more amenable to ecological interpretation compared to MAXENT. Compared to modelling habitat suitability purely based on species presence probability distribution (average training AUC = 0.743, SD = 0.030; average test AUC = 0.645, SD = 0.023), incorporating background distribution to account for resource availability effectively improved model performance. The proposed approach offers a flexible framework for modelling and mapping species habitat suitability from species presence-only data. The modelled species-environment responses and mapped species habitat suitability can be very useful for ecological monitoring at ecosystem or species level.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Indicators - Volume 93, October 2018, Pages 387-396
نویسندگان
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