Background: Relationship between height (H) and diameter (D) highly depends on site conditions and stand structures. In this regard, this study aimed to build a new site-specific model based on the Chapman-Richards function, for the Crimean pine stands in the Black Sea Backward Region of Türkiye. Methods: The model was developed using the mixed-effects framework and its accuracy and performance were assessed using a validation data set. The model was then compared to two highly predictive models derived from the Chapman-Richards function through fit statistics, curvature and bias measures of nonlinearity, and biological principles. Results: The new model resulted in the most favourable fit statistics and nonlinearity measures, and also exhibited a suitable H-D curve that was compatible with the ecological conditions of the study area. While the alternative models in the current literature showed good fit statistics, they resulted in biased parameter estimates and produced inappropriate H-D curves. The proposed model, however, demonstrated a satisfactory accuracy when validated using the validation data set. Conclusions: (1) It is essential to assess H-D models by considering both fit statistics and curvature and bias measures of nonlinearity; and (2) it is crucial to examine whether H-D models are appropriate for the specific ecological conditions of a particular region.

Background: Biological populations were studied to understand their ecology and to evaluate the relationships between living beings that comprise them. Mathematical functions used in probabilistic models should present multifunctionality, sensitivity, and flexibility to appropriately describe a natural phenomenon. The objective of this study was to develop a new probabilistic distribution with five parameters to maximize its flexibility and ensure a better goodness of fit when compared to other important distributions, such as Beta, Burr, Silva and Pareto. Methods: New distribution estimators were derived using the mathematical expectation of central and dispersion moments. Estimated values of the parameters were obtained using an optimization process developed by Abel Soares Siqueira, research software engineer at the Netherlands eScience Center in Amsterdam. Data for the application of the developed distribution method were collected at different sites in Brazil, where asymmetry and kurtosis were detected. Results: The Pellico-Behling Probability Distribution (5P) was applied to fit the datasets for Cariniana legalis, Acacia mearnsii, and Eucalyptus saligna. For the average mortality of 124 species, it was used with (4P). The distribution fitted to sampled datasets was compared with the fitted Beta and Burr (4P) distributions, except for Silva’s polynomial distribution that was fitted to the heights of the species Eucalyptus saligna and the Pareto distribution to mortality of 124 tropical species from a fragment of a semideciduous seasonal forest, to evaluate and verify its potential and robustness. Conclusions: The new distribution with five parameters is flexible and produced better goodness of fit than those obtained from the other distributions used for comparative purposes.

Background: Geospatial technologies have emerged as powerful tools for optimising forest management, improving operational precision, and supporting data-driven decision-making. This study aims to understand the technologies adopted by the New Zealand plantation forest industry and identify any barriers to the uptake of geospatial tools. This is the third such study, following comparable surveys in 2013 and 2018. Methods: An online survey was sent to 29 organisations in New Zealand’s forestry sector. Topics included organisation demographics, data acquisition, positioning technology, remote sensing technologies, software, and Artificial Intelligence (AI). Specifically, the survey focused on five remote sensing technologies: aerial photography, aerial videography, multispectral imagery, hyperspectral imagery, and LiDAR. Each section contained questions relating to the acquisition and application of the remote sensing technology and the software used for data processing. Questions were included to ascertain barriers to adoption. To identify changes in technology usage and uptake, results were compared to the 2013 and 2018 studies. Results: Twenty-seven of the 29 queried organisations responded, resulting in a 93% response rate. Responding organisations managed 1,283,000 hectares (74% of New Zealand’s plantation forest estate), with estate sizes ranging from about 7,000 to 200,000 hectares. Data acquisition from online portals included aerial imagery (100%), property ownership data (96%), and elevation data (89%), primarily from the Land Information New Zealand (LINZ) Data Service. Global Navigation Satellite Systems (GNSS) technology was universally employed. All respondents acquired aerial photography. In addition, 67% acquired multispectral imagery, 4% acquired hyperspectral imagery, and 93% acquired LiDAR data. The AI topic was surveyed for the first time and the technology was used by 30% of respondents when working with geospatial data. The main barrier to using remotely sensed data was the lack of perceived benefits, while the primary barrier to AI adoption was a lack of staff knowledge and training. Except for hyperspectral imagery, all remote sensing technologies saw increased uptake since 2013. LiDAR experienced the largest growth, with uptake increasing from 17% in 2013 to 93% in 2023. ArcGIS remains the primary tool for geospatial analysis, used by 96% of respondents. Notably, the use of open-source software such as QGIS increased by 31% over the past decade. Conclusions: This study demonstrated an overall increase in the usage of geospatial technology in the forestry sector. To promote further uptake, it is important not only to increase exposure to available tools and provide training, particularly on emerging technologies such as AI, but also to demonstrate the practical and economic value these technologies can offer.

Background: Estimation of forest biomass has become critical as afforestation has been proposed to sequester carbon from the atmosphere in order to mitigate climate change. New Zealand Dryland Forestry Innovation (NZDFI), in collaboration with the University of Canterbury’s School of Forestry and the Marlborough Research Centre, has initiated a research and development programme to gather seed, breed, propagate, identify site limitations, model growth, investigate silviculture, and develop wood products from a suite of eucalypts that grow durable heartwood. The aim is to supply naturally durable wood for uses that formerly required either imports of durable wood or copper-chrome-arsenate treated pine. Methods: As part of a project examining land-use and greenhouse gas budget case studies in Marlborough, New Zealand, we collected and summarised data describing above-ground biomass (AGB) of Eucalyptus bosistoana F.Meull., and Eucalyptus globoidea Blakely trees across a wide range of combinations of height (h) and diameter at breast height (dbh). One hundred and eleven trees were felled, separated into stems, branches and foliage, and the components were weighed in the field. Subsamples of these tree parts were collected and weighed in the field after separating bark from stem discs. The subsamples were dried in an oven at 105°C, and then weighed. Ratios of dry to wet weights for samples were applied to total green weights from the field in order to calculate AGBs of tree components. Systems of non-linear equations were simultaneously fitted to the data to ensure additivity; that sums of estimates of tree part AGBs versus dhb, h and slenderness (h/dbh) equalled estimates from a model of total tree AGB versus the same independent variables. The study also included the development of a plot-level estimation model of above-ground CO2-e/ha for E. globoidea and its incorporation in an on-line growth and yield simulator. Moreover, a comparison of two pathways to estimating AGB by aerial LiDAR was made: One including estimates of dbh and h from LiDAR and applying the tree-level equations developed in this study, and one going directly from LiDAR metrics to estimates of AGB. Results: A system of models created for both species with a dummy variable denoting species yielded the least biased residuals, with 22 coefficients estimated in one simultaneous fit. Standard errors varied with plant part and with the size of the prediction, requiring transformations prior to fitting. R2 values also varied with part, but were typically between 0.96 and 0.98. An exception was foliage and seeds which were influenced by one tree with an unusually high loading of seeds. The standard error for plot level estimates of CO2-e was 1.9 tonnes CO2-e /ha and residuals were relatively unbiased. Directly predicting individual tree AGB from LiDAR metrics yielded less biased estimates than predicting dbh and h and then using those estimates to predict AGB. Conclusions: A system of related, additive equations with a dummy variable denoting species represented the above-ground biomass of Eucalyptus globoidea and Eucalyptus bosistoana with precision adequate for prediction of biomass for fuel and carbon storage to mitigate climate change. Direct predictions of biomass from LiDAR metrics were less biased than predictions of biomass from tree height and diameter at breast height that were in turn predicted from LiDAR metrics.

Background: Thermal modification of nondurable Eucalyptus nitens timber was reported to result in excessive checking and only marginally improved durability when heat treating in steam or atmospheric environments. This study investigated if oil heat-treatment of E. nitens above 210°C was able to overcome previously reported difficulties. Methods: Eucalyptus nitens clears were oil heat-treated to 210°C, 220°C and 230°C and assessed for density, stiffness, strength, colour and decay resistance. Results: Oil heat-treated E. nitens samples showed mass loss matching the highest durability class when tested against the brown-rot Rhodonia placenta (Fr.) Niemelä, K.H.Larss. & Schigel and the white-rot Trametes versicolor (L.) Lloyd, matching Durability Class-2 rated Eucalyptus muelleriana A.W.Howitt heartwood. Oil heat-treated E. nitens samples outperformed Pinus radiata treated with chromated copper arsenate (CCA) to Hazard Class (H3) grade when tested for the brown-rot R. placenta. While oil heat-treatment reduced mean stiffness (MoE) and strength (MoR), the resulting material exceeded characteristic SG8 grade values. No checking was observed in the oil heat-treated E. nitens boards. Letting samples cool outside the oil bath limited uptake of oil to less than 4 mm in depth. The planed product became darker the higher the oil heat-treatment temperature. Conclusions: Oil heat-treatment above 210°C has the potential to refine E. nitens timber, avoiding excessive degrade and providing decay resistance.

Background: Red needle cast (RNC), caused by Phytophthora pluvialis Reeser, W.L. Sutton & E.M. Hansen, is a significant foliar disease impacting Pinus radiata D.Don in New Zealand. First detected in 2005, the disease has now been observed in all regions of the country. In the most severe cases, defoliation of entire tree crowns can occur at a landscape scale. While some evidence of growth loss and productivity reduction has been reported, quantitative estimates of the effect of RNC on productivity are needed to inform disease management and mitigation decisions. This study aims to assess both short- and long-term losses in radial growth due to RNC. Methods: We used tree cores to quantify yearly basal area increments at two plantations: a 32-year-old stand in Wharerata Forest, with documented history of outbreaks both severe and cyclic in nature, and a 26-year-old stand in Kinleith Forest, where 8 years of continuous disease severity monitoring has been conducted at the tree level. A Bayesian multilevel modelling framework was used to predict growth losses due to RNC at both sites, accounting for yearly weather and outbreak severity. Results: We predicted a 31% to 51.5% radial growth loss in the year following an RNC outbreak, with reduced growth detectable for 3 to 4 years after disease, amounting to up to 30.6% growth loss over the course of a single event. Recurring disease events every three to four years can lead to a 20% reduction in total radial area growth over the period encompassing the presence of the disease, with no evidence that each additional RNC event aggravates growth loss. Conclusions:

Background: The ecological science associated with transitioning exotic forest to native dominance (hereafter transitional forestry, transition forests) is currently limited, yet this form of forest has expanded rapidly. In part, this is related to forest-based carbon credit schemes which have driven large-scale afforestation in exotic trees. In other circumstances (e.g., for soil conservation or values-based reasons such as biodiversity conservation), forest owners wish to transition their exotic forest to native forest without harvest. Knowledge is required to inform management of realistic expectations for regeneration and succession both spatially and temporally. Methods: Mature (>20-year-old) Pinus radiata plantations were surveyed along three elevation-climate gradients in the Waikato region of New Zealand to explore the composition and structure (including native carbon stocks) of plantation understories and whether these parameters vary spatially over several decades. Mammalian browsing was also recorded. Results: Native woody stem densities were variable. Factors indicated as driving variability were stand age, elevation, topographic shelter, soil hydrology, solar radiation, and air temperature. On average, understories comprised five native woody species in the seedling tier, a single native species in the sapling tier, and a single native species in the tree tier. The most common species were the sub-canopy tree species Melicytus ramiflorus, Geniostoma ligustrifolium, and Aristotelia serrata. Tall old-growth species, such as Beilschmiedia tawa, Podocarpus totara, Pectinopitys ferruginea, and Prumnopitys taxifolia, occurred in only particular circumstances and on average at densities too low to form a meaningful part of a future forest canopy. Average species richness was low, although some diversity hotspots occurred. Carbon stocks in native trees and tree ferns in the understories were on average 1.55±0.38 tCha–1. Heavy browsing by mammalian herbivores was recorded at 60% of plots. Conclusions: These data indicate typical understorey conditions in mature P. radiata plantations for this area of New Zealand in the absence of management to promote a native transition. These data also highlight the importance of browser control, enrichment planting of tall old-growth species, and canopy manipulations to accelerate regeneration and succession in non-harvest P. radiata plantations. The data suggest transitional forestry should only be attempted at scales that can be reasonably managed, and there is a need for caution against large-scale establishment of P. radiata for transitional forestry as at large scales, achieving adequate levels of management are uncertain.

Background: Red needle cast (RNC) is a foliar disease of radiata pine (Pinus radiata D.Don) in New Zealand caused by Phytophthora pluvialis Reeser, W.L.Sutton & E.M.Hansen and, to a lesser extent, Phytophthora kernoviae Brasier, Beales & S.A.Kirk. Incidence and severity of RNC vary substantially between years. To investigate the impact of seasonal weather variables on this variation, RNC was assessed annually for ten years at radiata pine transects. Methods: Fifty-three transects were established in 2015 in the Central North Island and Gisborne Region (east coast North Island) of New Zealand, with twenty-three monitored until 2024 (surviving harvest). The relationship between seasonal weather variables and RNC severity was analysed using two non-parametric statistical approaches: (1) correlation analyses (Spearman correlations, rs, where positive values indicate an increase in RNC severity with an increase in the explanatory variable); and (2) binary recursive partitioning (with models trained on 85% of observations and tested on the remaining 15%). Results: Disease expressed more consistently, and severity was generally greater, at Gisborne sites. Disease severity peaked in 2017 and 2023 in both regions. Autumn (March-May) variables tended to be prevalent amongst predictors of RNC severity. Autumn soil moisture index (calculated from cumulative rainfall and evapotranspiration) was the most strongly correlated variable for the Gisborne dataset (rs = 0.70) and, along with vapour pressure, were the key partitioning variables in the recursive partitioning model. The strongest correlating variable for the Central North Island dataset was autumn potential evapotranspiration (rs = -0.46) while the most important variable and first data partition was autumn vapour pressure. Model evaluation metrics indicated good performance: R2 values were 0.63 and 0.68 for the Gisborne and Central North Island test datasets respectively, and mean absolute errors were 18.1 % and 7.8 % for the respective datasets. Conclusions: The importance of autumn more than summer weather variables in determining disease expression differs from the findings of previous studies and indicates that conditions during periods of exponential epidemic growth may be as, or more, important than initial inoculum level in determining RNC severity. Proactive control activities may require long-term weather forecasting or frequent monitoring during this season.

Background: Robust species-specific height-diameter (H-D) equations are necessary for the estimation and prediction of tree volume, yield, biomass or carbon stocks. In addition, information about height growth characteristics allows for the analysis of stand growth dynamics. But there is a general lack of species-specific growth models for most New Zealand native tree species considered for plantation and wood production. Therefore, the aim of this study was to develop a species- and site-specific H-D model for planted lowland tōtara (Podocarpus totara G. Benn. ex D. Don). Methods: The models were developed using data from 719 individually measured trees aged 11 to 110 years from eight different sites in the North Island of New Zealand. Two different modelling approaches, traditional non-linear and linear mixed effect, were used. The process included selecting, testing, conditioning, and extending a total of 18 different equations by incorporating site-specific tree variables. Results: The most precise model predicting the H-D relationship was reported by linear mixed-effect models that include diameter at breast height (DBH at 1.4 m, cm) and age (years). The final model had a low root mean square error (RMSE, 0.21, m), mean absolute error (MAE, 0.16, m) and high R2 (0.94), which slightly increased during validation. Conclusions: The study demonstrated a robust process and reported the most plausible and parsimonious model to predict P. totara’s H-D relationship, which serves as the basis for species-specific growth dynamics. The reported models provide for the first time the opportunity to predict the H-D relationship of planted P. totara in New Zealand. This fills a long existing knowledge gap and provides forest growers and managers important decision-making information.

Background: Handroanthus impetiginosus (lavender trumpet tree) is valued for its construction, medicinal, and ecological uses. However, its slow initial growth and weak root system hinder seedling production. Rhizobacteria from the genera Bacillus and Azospirillum enhance plant growth and resilience. This study evaluated their effects on H. impetiginosus seedling development. Methods: The experiment was conducted in a greenhouse at the Experimental Nursery of Ornamental and Forestry Plants - College of Agricultural and Veterinary Sciences (UNESP/FCAV), Jaboticabal, Brazil, during the summer 2022/23. A completely randomised design included five treatments: Bacillus subtilis, B. megaterium, B. amyloliquefaciens, Azospirillum brasilense, and a control (no inoculation). Each treatment had four replicates of 20 plants. Seeds were sown in 280 cm³ tubes with a commercial substrate, composed of peat, vermiculite, roasted rice husk, calcined dolomitic limestone, NPK 14-16-18, and micronutrients. The rhizobacteria were inoculated at 30 and 60 days after sowing. Growth parameters (shoot height, stem diameter, root length, and biomass) were assessed at 107 days. Photosynthetic performance and microbiological colonisation were also evaluated. Data were analysed using ANOVA, Tukey’s test and Pearson correlation. Results: Azospirillum brasilense significantly enhanced growth, with the highest averages for shoot height (13.9 cm), stem diameter (1.72 mm), shoot dry mass (0.172 g), and total dry mass (0.686 g). It also improved seedling quality indices, including the Dickson Quality Index and shoot height-to-stem diameter ratio. Photosynthetic efficiency increased when inoculated with Azospirillum brasilense, as indicated by greater leaf area and chlorophyll content. Colony Forming Units (CFU) analysis showed higher bacterial colonisation in the substrate, roots, and aerial parts of A. brasilense-treated plants, with strong correlations between colonisation and plant growth. Conclusions: Azospirillum brasilense was the most effective rhizobacterium promoting H. impetiginosus seedling growth and quality. Its use could enhance reforestation and nursery production efficiency, accelerating seedling establishment. These findings highlight the potential of rhizobacteria to improve seedling vigour and adaptation in early growth stages.