Exploring the relative importance of biotic and abiotic factors that alter the self-thinning rule: Insights from individual-based modelling and machine-learning

Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.

Changes in mangrove community structures affecting sediment carbon content in Yingluo Bay of South China

This study aimed to assess how sediment organic carbon (SOC) dynamics were affected by mangrove community structures. Sediment attributes and community structures of the Avicennia marina, Aegiceras corniculatum, Rhizophora stylosa, and Bruguiera gymnorrhiza communities were analyzed, based upon field investigation in Yingluo Bay of South China. Aboveground biomass (AGB), belowground biomass (BGB) and tree height obviously increased with the self-thinning process, while basal area significantly decreased. The self-thinning exponent was 1.382 for AGB and 1.254 for BGB, conforming to the 4/3 self-thinning rule. However, self-thinning exponent for basal area was only 0.4866. SOC content non-linearly increased with the increase of AGB, BGB, and tree height. Mangrove-derived carbon increased through in situ organic material inputs with mangrove growth. The negative correlation between tree density and SOC content was not in line with the previous studies for planted mangroves. SOC dynamics of natural mangroves may be partially different from planted mangroves.

Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility

Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges); however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RF, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-FR-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926); therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas.

Modelling the Effect of Microsite Influences on the Growth and Survival of Juvenile Eucalyptus globoidea (Blakely) and Eucalyptus bosistoana (F. Muell) in New Zealand

The effect of microsite on juvenile forest plantation yield is rarely explored. This is because juvenile plantation growth is considered to be reasonably homogenous due to a lack of resource competition between trees prior to canopy closure. However, models of juvenile plantation height growth and survival that are sensitive to microsite variation could aid decisions relating to site preparation, plantation establishment and early silvicultural treatments. In this study, juvenile Eucalyptus bosistoana and E. globoidea height growth and survival proportion were modelled against topographic and environmental microsite characteristics as independent variables. The experiment included three different sites situated in a sub-humid region of New Zealand. A total of 540 plots were planted with 18,540 trees in regular rows and columns. Micro-topographical variables significantly influenced height growth and survival proportion of both E. bosistoana and E. globoidea, but species differed in their responses. More sheltered microsites yielded greater height growth and survival for both species. The height of both species was influenced by wind exposure, morphometric protection, and distance from the nearest ridge. E. bosistoana height was also influenced by topographic position and surface plan curvature. Survival was affected by surface profile curvature for both species, while E. globoidea survival was also impacted by surface plan curvature and distance from the top ridge. This study identified microsite factors influencing juvenile height and survival of two Eucalyptus species.

A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of Fagus orientalis in Northern Iran

This paper examines the possible ecological controls on the diameter increment of oriental beech (Fagus orientalis Lipsky) in a high altitude forest in northern Iran. The main objectives of the study are computer-generated abiotic surfaces and associated plot estimates of (i) growing-season-cumulated potential solar radiation, (ii) seasonal air temperature, (iii) topographic wetness index in representing soil water distribution, and (iv) wind velocity generated from the simulation of fluid-flow dynamics in complex terrain. Plot estimates of the tree growth are based on averaged plot measurements of diameter at breast height increment during a growing period of nine years (2003–2012). Biotic variables related to the tree diameter increment involve averaged 2003 tree diameter and basal area measured in individual forest plots. In the modelling data (144 plots), the assemblage of modelled and observed site variables explained 75% of the variance in plot-level diameter increment. In the validation data (32 plots), the degree of explained variance was 77%. Mean tree diameter at breast height showed the strongest correlation with diameter increment, explaining 32% of the variation between-plot, followed by the configuration of topography and re-distribution of surface water (19.5%) and plot basal area (16.9%). On average, localised estimates of solar radiation and wind velocity potentially contribute to about 20% of the control on plot-level mean increment in oriental beech of the area. The results of the genetic programming showed that controlling the stand basal area and tree size by thinning and/or selective harvesting can have a favourable impact on the future distribution of mean diameter in oriental beech.