Tree mortality is a fundamental process governing forest dynamics, but understanding tree mortality patterns is challenging because large, long-term datasets are required. Describing size-specific mortality patterns can be especially difficult, due to few trees in larger size classes. We used permanent plot data from Nothofagus solandri var. cliffortioides (mountain beech) forest on the eastern slopes of the Southern Alps, New Zealand, where the fates of trees on 250 plots of 0.04 ha were followed, to examine: (1) patterns of size-specific mortality over three consecutive periods spanning 30 years, each characterised by different disturbance, and (2) the strength and direction of neighbourhood crowding effects on sizespecific mortality rates. We found that the size-specific mortality function was U-shaped over the 30-year period as well as within two shorter periods characterised by small-scale pinhole beetle and windthrow disturbance. During a third period, characterised by earthquake disturbance, tree mortality was less size dependent. Small trees (,20 cm in diameter) were more likely to die, in all three periods, if surrounded by a high basal area of larger neighbours, suggesting that sizeasymmetric competition for light was a major cause of mortality. In contrast, large trees ($20 cm in diameter) were more likely to die in the first period if they had few neighbours, indicating that positive crowding effects were sometimes important for survival of large trees. Overall our results suggest that temporal variability in size-specific mortality patterns, and positive interactions between large trees, may sometimes need to be incorporated into models of forest dynamics.
This research aims to explore how business models of SMEs revolve in the face of a crisis to be resilient. The business model canvas was used as a tool to analyse business models of SMEs in Greater Christchurch. The purpose was to evaluate the changes SMEs brought in their business models after hit by a series of earthquake in 2010 and 2011. The idea was to conduct interviews of business owners and analyse using grounded theory methods. Because this method is iterative, a tentative theoretical framework was proposed, half way through the data collection. It was realised that owner specific characteristics were more prominent in the data than the elements business model. Although, SMEs in this study experienced several operational changes in their business models such as change of location and modification of payment terms. However, the suggested framework highlights how owner specific attributes influence the survival of a small business. Small businesses and their owners are extremely interrelated that the business models personify the owner specific characteristics. In other words, the adaptation of the business model reflects the extent to which the owner possess these attributes. These attributes are (a) Mindsets – the attitude and optimism of business owner; (b) Adaptive coping – the ability of business owner to take corrective actions; and (c) Social capital – the network of a business owner, including family, friends, neighbours and business partners.
The rapid classification of building damage states or placards after an earthquake is vital for enabling an efficient emergency response and informed decision-making for rehabilitation and recovery purposes. Traditional methods rely heavily on inspector-led on-site surveys, which are often time-consuming, resource-intensive, and susceptible to human error. This study introduces a machine learning-supported surrogate model designed to streamline the assessment of building damage, focusing on the automated assignment of damage placards within the context of New Zealand's post-earthquake evaluation frameworks. The study evaluates two key safety evaluation protocols—Rapid Building Assessment (RBA) and Detailed Damage Evaluation (DDE)—and integrates corresponding databases derived from the 2010–2011 Canterbury Earthquake Sequence (CES) in Christchurch. Six ML classifiers—Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and Gradient Bagging (GBag)—were rigorously tested across both databases. The results indicate that the RF-based surrogate model outperforms the other classifiers across both RBA and DDE protocols. Two distinct sets of critical predictors have been further identified for each protocol, allowing for the rapid retrieval of essential data for future on-site surveys, while retaining the RF model's predictive accuracy. The developed surrogate model provides a pragmatic tool for practising engineers to rapidly assign placards to damaged structures and for policymakers and building owners to make informed recovery decisions for earthquake-affected buildings.
