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Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and nonlinear machine learning methods, such as random forest (RF), boosted regression tree (BRT), cubist (Cubist) and multivariate adaptive regression splines (MARS), provides a new way for predicting individual tree growth. However, the application of these approaches to individual tree growth modelling is still limited and short of a comparison of their performance. The objectives of this study were to compare and evaluate the performance of the RF, BRT, Cubist and MARS models for modelling the individual tree diameter growth based on tree size, competition, site condition and climate factors for larch–spruce–fir mixed forests in northeast China. Totally, 16,619 observations from long-term sample plots were used. Based on tenfold cross-validation, we found that the RF, BRT and Cubist models had a distinct advantage over the MARS model in predicting individual tree diameter growth. The Cubist model ranked the highest in terms of model performance (RMSEcv [0.1351 cm], MAEcv [0.0972 cm] and R2cv [0.5734]), followed by BRT and RF models, whereas the MARS ranked the lowest (RMSEcv [0.1462 cm], MAEcv [0.1086 cm] and R2cv [0.4993]). Relative importance of predictors determined from the RF and BRT models demonstrated that the competition and tree size were the main drivers to diameter growth, and climate had limited capacity in explaining the variation in tree diameter growth at local scale. In general, the RF, BRT and Cubist models are effective and powerful modelling methods for predicting the individual tree diameter growth.

& Key message Natural regeneration patterns of conifer species were studied. Seedling regeneration follows patterns responding to stand structure and site condition factors along shade and drought tolerance gradients. Our findings can assist in adaptive forest management for maintaining sustainable regeneration and plant biodiversity. & Context Seedling regeneration can vary with stand factors of overstory trees and understory non-tree vegetation and site conditions. & Aims Natural seedling regeneration patterns of coniferous species were investigated using Forest Inventory and Analysis (FIA) data of 10 common species across the Inland Northwest, USA. & Methods Zero-inflated negative binomial models were developed to understand the responses of natural regeneration to stand factors and site conditions. & Results Seedling occurrence varies along shade and drought tolerance gradients responding to stand structure and site conditions. Two moderate shade-tolerant species of different drought tolerance contributed as a transition. Strong response patterns were revealed for seedling density, in which seedling density was improved with the presence of conspecific trees while limited by competition, especially from the understory vegetation layer. & Conclusion Overstory structure and understory vegetation could improve or hinder natural regeneration of coniferous tree species given different shade tolerance and site conditions. Our findings can be effectively implemented in adaptive forest management for maintaining sustainable regeneration of specific conifers in broad temperate mixed forests.

© iForest -Biogeosciences and Forestry IntroductionUnderstanding how climate change affects forest site productivity is of great importance for adaptive forest management under climate change conditions. Forest site productivity, as a quantitative estimate of the potential for producing plant biomass at a site, is a significant indicator of forest quality, as well as an important variable of forest growth models in forest management (Skovsgaard & Vanclay 2008). However, site productivity presents great spatial and temporal variability (Skovsgaard & Vanclay 2013). Climate change directly or indirectly affects forest productivity due to rising atmospheric CO2 concentration and temperatures, changes in the amount and timing of precipitation and the interactions of forest ecosystems (Kirilenko & Sedjo 2007, Medlyn et al. 2011. Experiments, observations and models indicate that the forest productivity changes with climate change, but the direction and magnitude of the effects are still uncertain (Medlyn et al. 2011). Using dominant height as a measure of site productivity, Bontemps et al. (2009) reported an increase of more than 50% in common beech at the end of the 20 th century in north-eastern France, whereas Badeau et al. (1995) found an increase of 27%. Charru et al. (2010) found an increase of 27.8% in stand basal area increment between 1977 and 1987, and a decrease of approximately 5% between 1987 and 2004. Both positive and negative estimated impacts on forest growth have been reported depending on the climate scenarios employed (Lutz et al. 2013).One of the most widely used indicators of forest site productivity in forest management is the site index (SI), which is the mean height of the dominant trees growing on a site at a reference age (Carmean 1975, Skovsgaard & Vanclay 2008. The site index is assumed to be constant over time, and thus included as a driving variable in forest growth and yield models. In reality, the site index often varies greatly due to changes in the genetic make-up of stands, climatic conditions and management practices (Monserud & Rehfeldt 1990, Valentine 1997, Weiskittel et al. 2011). Empirical models have been developed to estimate the site index from climate, soil and vegetation information, thus allowing the possibility of predicting changes in the site index, forest growth and forest yield under climate change (McKenney & Pedlar 2003, Nigh et al. 2004, Monserud et al. 2006, 2008, Albert & Schmidt 2009, Aertsen et al. 2011, Nothdurft et al. 2012. The climate variables that influence the site index vary with species and sites, as do the magnitude of these variables (Messaoud & Chen 2011). Nigh et al. (2004 developed climate-sensitive site index models of three species (spruce, lodgepole pine and Douglas fir) in the interior of British Columbia, Canada. They found that the site index for all species increased as the temperature rose, and that site index increased with precipitation for the two latter species. Monserud et al. (2006) found that the strongest linear predictors of site...

Transit-oriented development (TOD) is advocated by urban planners as a tool to promote transportation and land use integration, and it has especially been applied in new town developments in Chinese cities. Since 2000, urban metro system construction in Beijing has accelerated and many new towns in suburban areas have emerged due to rail transit connections with the central city. This paper is a case study of Yizhuang new town, which is located in the southeast suburban area of Beijing. It explores whether light rail is successful in promoting transportation and land use integration. First, by comparing the land use within a 1 km radius of three rail stations, findings suggest that the TOD model in terms of land use may not be implemented. Second, by tracing land use changes over time, we found that commercial functions are critical to support TOD development. Third, the mismatch of urban functions between low-skilled labor employment opportunities and high-skilled residential neighborhoods emphasizes the failure of planned reverse commuting patterns. In conclusion, our findings suggest that without a proper land use policy, TOD based on light rail only may not be a successful model in the case of Yizhuang new town. The policy implication is that the planning code of land use should be more flexible and adaptable, in order to promote the integration of transportation and land use in a synergetic way.