کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2024718 | 1542615 | 2014 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Artificial neural network modeling of microbial community structures in the Atlantic Forest of Brazil
ترجمه فارسی عنوان
مدل سازی شبکه عصبی مصنوعی از ساختارهای جامعه میکروبی در جنگل اقیانوس اطلس برزیل
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
دانش خاک شناسی
چکیده انگلیسی
Microbial communities vary across the landscape in forest soils, but prediction of their biomass and composition is a difficult challenge due to the large numbers of variables that influence their community structures. Here we examine the use of artificial neural network (ANN) models for extraction of patterns among soil chemical variables and microbial community structures in forest soils from three regions of the Atlantic Forest of Brazil. At each location, variations in soil chemical properties and FAME profiles of microbial community structures were mapped at 20Â ÃÂ 20Â m intervals within 10Â ha parcels. Geostatistical analyses showed that spatial variability in soil physical and chemical variables could be mapped at scale distances of 20Â m, but that FAME profiles representing the microbial communities were highly variable and had no spatial dependence at the same scale in most cases. RDA analysis showed that FAME signatures representing different microbial groups were positively associated with soil pH, OM, P and base cations concentrations, whereas microbial biomass was negatively associated with the same environmental factors. In contrast, ANN models revealed clear relationships between microbial community structures at each parcel location, and generated verifiable predictions of variations in FAME profiles in relation to soil pH, texture, and the relative abundances of base cations. The results suggest that ANN modeling provides a useful approach for describing the relationships between microbial community structures and soil properties in tropical forest soils that were not able to be captured using geostatistical and RDA analyses.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Soil Biology and Biochemistry - Volume 69, February 2014, Pages 101-109
Journal: Soil Biology and Biochemistry - Volume 69, February 2014, Pages 101-109
نویسندگان
Eder C. Santos, Eduardo Dutra Armas, David Crowley, Marcio Rodrigues Lambais,