کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6445363 | 1640781 | 2016 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Testing quantitative pollen dispersal models in animal-pollinated vegetation mosaics: An example from temperate Tasmania, Australia
ترجمه فارسی عنوان
تست مدل های پراکندگی گرده کمی در موزاییک های پوشش گیاهی حیوانی: نمونه ای از تاسمانیای معتدل استرالیا
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موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
زمین شناسی
چکیده انگلیسی
Reconstructing past vegetation abundance and land-cover changes through time has important implications in land management and climate modelling. To date palaeovegetation reconstructions in Australia have been limited to qualitative or semi-quantitative inferences from pollen data. Testing pollen dispersal models constitutes a crucial step in developing quantitative past vegetation and land cover reconstructions. Thus far, the application of quantitative pollen dispersal models has been restricted to regions dominated by wind-pollinated plants (e.g. Europe) and their performance in a landscape dominated by animal-pollinated plant taxa is still unexplored. Here we test, for the first time in Australia, two well-known pollen dispersal models to assess their performance in the wind- and animal-pollinated vegetation mosaics of western Tasmania. We focus on a mix of wind- (6 taxa) and animal- (7 taxa) pollinated species that comprise the most common pollen types and key representatives of the dominant vegetation formations. Pollen Productivity Estimates and Relevant Source Area of Pollen obtained using Lagrangian Stochastic turbulent simulations appear to be more realistic when compared to the results from the widely used Gaussian Plume Model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Quaternary Science Reviews - Volume 154, 15 December 2016, Pages 214-225
Journal: Quaternary Science Reviews - Volume 154, 15 December 2016, Pages 214-225
نویسندگان
M. Mariani, S.E. Connor, M. Theuerkauf, P. Kuneš, M.-S. Fletcher,