FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3210820376> ?p ?o ?g. }

• W3210820376 endingPage "1450" @default.
• W3210820376 startingPage "1450" @default.
• W3210820376 abstract "The relative importance of different biotic and abiotic variables for estimating forest productivity remains unclear for many forest ecosystems around the world, and it is hypothesized that forest productivity could also be estimated by local biodiversity factors. Using a large dataset from 258 forest monitoring permanent sample plots distributed across uneven-aged and mixed forests in northern Iran, we tested the relationship between tree species diversity and forest productivity and examined whether several factors (solar radiation, topographic wetness index, wind velocity, seasonal air temperature, basal area, tree density, basal area in largest trees) had an effect on productivity. In our study, productivity was defined as the mean annual increment of the stem volume of a forest stand in m3 ha−1 year−1. Plot estimates of tree volume growth were based on averaged plot measurements of volume increment over a 9-year growing period. We investigated relationships between productivity and tree species diversity using parametric models and two artificial neural network models, namely the multilayer perceptron (MLP) and radial basis function networks. The artificial neural network (ANN) of the MLP type had good ability in prediction and estimation of productivity in our forests. With respect to species richness, Model 4, which had 10 inputs, 6 hidden layers and 1 output, had the highest R2 (0.94) and the lowest RMSE (0.75) and was selected as the best species richness predictor model. With respect to forest productivity, MLP Model 2 with 10 inputs, 12 hidden layers and 1 output had R2 and RMSE of 0.34 and 0.42, respectively, representing the best model. Both of these used a logistic function. According to a sensitivity analysis, diversity had significant and positive effects on productivity in species-rich broadleaved forests (approximately 31%), and the effects of biotic and abiotic factors were also important (29% and 40%, respectively). The artificial neural network based on the MLP was found to be superior for modeling productivity–diversity relationships." @default.
• W3210820376 created "2021-11-08" @default.
• W3210820376 creator A5000652481 @default.
• W3210820376 creator A5002615132 @default.
• W3210820376 creator A5009081110 @default.
• W3210820376 creator A5045879211 @default.
• W3210820376 creator A5069455101 @default.
• W3210820376 date "2021-10-25" @default.
• W3210820376 modified "2023-09-26" @default.
• W3210820376 title "A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests" @default.
• W3210820376 cites W1509240185 @default.
• W3210820376 cites W1594032928 @default.
• W3210820376 cites W1964396812 @default.
• W3210820376 cites W1964561454 @default.
• W3210820376 cites W1965716462 @default.
• W3210820376 cites W1979892499 @default.
• W3210820376 cites W2000421448 @default.
• W3210820376 cites W2009892094 @default.
• W3210820376 cites W2017504888 @default.
• W3210820376 cites W2037687375 @default.
• W3210820376 cites W2068177234 @default.
• W3210820376 cites W2069419149 @default.
• W3210820376 cites W2090733691 @default.
• W3210820376 cites W2090933635 @default.
• W3210820376 cites W2102091761 @default.
• W3210820376 cites W2103317434 @default.
• W3210820376 cites W2108298562 @default.
• W3210820376 cites W2119188591 @default.
• W3210820376 cites W2123522025 @default.
• W3210820376 cites W2126880424 @default.
• W3210820376 cites W2129458498 @default.
• W3210820376 cites W2145445252 @default.
• W3210820376 cites W2153978805 @default.
• W3210820376 cites W2158030393 @default.
• W3210820376 cites W2158231312 @default.
• W3210820376 cites W2168592174 @default.
• W3210820376 cites W2169286869 @default.
• W3210820376 cites W2273925209 @default.
• W3210820376 cites W2322658902 @default.
• W3210820376 cites W2519343095 @default.
• W3210820376 cites W2565695934 @default.
• W3210820376 cites W2754577616 @default.
• W3210820376 cites W2773578505 @default.
• W3210820376 cites W2786392816 @default.
• W3210820376 cites W2796345070 @default.
• W3210820376 cites W2895252122 @default.
• W3210820376 cites W2898022513 @default.
• W3210820376 cites W2914447396 @default.
• W3210820376 cites W2917992737 @default.
• W3210820376 cites W2937182499 @default.
• W3210820376 cites W2938654583 @default.
• W3210820376 cites W2939123584 @default.
• W3210820376 cites W2950028502 @default.
• W3210820376 cites W2957155639 @default.
• W3210820376 cites W2965758891 @default.
• W3210820376 cites W2968090514 @default.
• W3210820376 cites W2979898807 @default.
• W3210820376 cites W3006249579 @default.
• W3210820376 cites W3008384792 @default.
• W3210820376 cites W3011918200 @default.
• W3210820376 cites W3015768310 @default.
• W3210820376 cites W3095067955 @default.
• W3210820376 cites W3116500961 @default.
• W3210820376 cites W3119209318 @default.
• W3210820376 cites W3120383306 @default.
• W3210820376 cites W3129307326 @default.
• W3210820376 cites W3150501222 @default.
• W3210820376 cites W3153012659 @default.
• W3210820376 cites W3160991018 @default.
• W3210820376 cites W3174945380 @default.
• W3210820376 doi "https://doi.org/10.3390/f12111450" @default.
• W3210820376 hasPublicationYear "2021" @default.
• W3210820376 type Work @default.
• W3210820376 sameAs 3210820376 @default.
• W3210820376 citedByCount "10" @default.
• W3210820376 countsByYear W32108203762022 @default.
• W3210820376 countsByYear W32108203762023 @default.
• W3210820376 crossrefType "journal-article" @default.
• W3210820376 hasAuthorship W3210820376A5000652481 @default.
• W3210820376 hasAuthorship W3210820376A5002615132 @default.
• W3210820376 hasAuthorship W3210820376A5009081110 @default.
• W3210820376 hasAuthorship W3210820376A5045879211 @default.
• W3210820376 hasAuthorship W3210820376A5069455101 @default.
• W3210820376 hasBestOaLocation W32108203761 @default.
• W3210820376 hasConcept C110872660 @default.
• W3210820376 hasConcept C130217890 @default.
• W3210820376 hasConcept C132215390 @default.
• W3210820376 hasConcept C139719470 @default.
• W3210820376 hasConcept C147103442 @default.
• W3210820376 hasConcept C162324750 @default.
• W3210820376 hasConcept C18903297 @default.
• W3210820376 hasConcept C204983608 @default.
• W3210820376 hasConcept C28631016 @default.
• W3210820376 hasConcept C33283694 @default.
• W3210820376 hasConcept C39432304 @default.
• W3210820376 hasConcept C53565203 @default.
• W3210820376 hasConcept C54286561 @default.
• W3210820376 hasConcept C73935091 @default.

• next