A Combination of Biotic and Abiotic Factors and Diversity Determine Productivity in Natural Deciduous Forests

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.

Projection Matrix Models: A Suitable Approach for Predicting Sustainable Growth in Uneven-Aged and Mixed Hyrcanian Forests

“…Therefore, the findings of our study suggest that, compared to other methods, the projection matrix model estimates allowable cut lower and closer to the net increment, and thus can prevent the possible consequences of harvest over increment of the single-selection method. Forest cutting is used as a tool for forest cultivation (biological production) and wood harvesting (mechanical production) and leads the forest toward desirable quantitative and qualitative production [50]. To use this tool properly, it is necessary to accurately estimate the allowable cut [51].…”

Effect of bedrock, tree size and time on growth and climate sensitivity of Norway spruce in the High Tatras

“…Intrinsic factors include genetic traits (Wang et al 2022), species characteristics (Pretzsch 2021) and physiological processes (Avanzi et al 2019) determining the tree's growth potential and ability to capture and allocate resources e ciently. Extrinsic factors encompass large-scale environmental factors such as climate and landscape characteristics and small scale factors such as soil nutrients and water availability for growth, development, and competition (Robertson 1992;Cienciala et al 2016;Chakraborty et al 2017;Bayat et al 2021). Additionally, disturbances such as re, wind, or insect outbreaks can have both negative and positive impacts on tree growth (Splechtna et al 2005;Szewczyk et al 2011).…”

Ecological and Socio-Economic Factors on the Rate of Participation and Sustainable Forest Management

“…Previous research have shown that ecological and socio-economic factors have a significant effect on forest sustainability (Ouyang et al, 2021;Khan and Hou, 2021;Zahraee et al, 2020). Forests are dependent on various ecological factors, such as climate, soil, water availability, topography, and biotic factors like plant and animal species (Bayat et al, 2021). These factors play a crucial role in determining the health and productivity of forests and their ability to sustainably provide various environmental, economic, and social benefits (Raihan et al, 2023).…”