Abstract Species of Corymbia are increasingly being tested for plantation establishment in South Africa. During disease surveys in KwaZulu-Natal plantations in 2020, a serious canker disease was found on Corymbia henryi. The cankers were seemingly caused by a fungus belonging to the family Cryphonectriaceae. The aims of the study were to identify the fungus causing the disease, to test its pathogenicity and to determine the genetic diversity of a population of isolates from infected trees. Phylogenetic analyses using sequence data for the internal transcribed spacer, β-tubulin 1 and β-tubulin 2 gene regions showed that the causal agent of the cankers was Chrysoporthe austroafricana. This is the first record of C. austroafricana causing cankers on a Corymbia species. A pathogenicity trial demonstrated that the fungus could infect C. henryi and cause cankers. Fifteen isolates of C. austroafricana were subjected to population genetic analyses using microsatellite markers. Eleven multilocus genotypes (MLGs) were detected. Mating-type distribution was approximately equal (MAT1-1:MAT1-2 = 8:7). The results indicate that a wide diversity of genotypes of C. austroafricana have undergone a host shift to infect C. henryi. This is a similar situation to that observed for Eucalyptus in South Africa and suggests that research efforts will be required to reduce the risk of Cryphonectria canker limiting afforestation using Corymbia species in the future.

Abstract Root systems form a significant part of tree biomass and function. Yet, roots are hidden from our eyes, making it difficult to track the belowground processes. By contrast, our capacity to detect aboveground changes in trees has been continuously improving using optical methods. Here, we tested two fundamental questions: (1) To what extent can we detect aboveground responses to mechanical damage of the root system? (2) To what extent are roots redundant? We applied three different non-destructive remote sensing means: (1) optical means to derive leaf greenness, (2) infrared means to detect the changes in leaf surface temperature and (3) spectral means to derive five vegetation indices (i.e. the photochemical reflectance index (PRI), the chlorophyll photosynthesis index (CIRed-edge), the anthocyanin reflectance index 1, the structure insensitive pigment index and the normalized difference water index (NDWI)). We recorded the above metrics for hours and days and up to a month following induced root damage in three key Mediterranean tree species: Aleppo pine (Pinus halepensis Mill.), Palestine oak (Quercus calliprinos Webb.) and Carob (Ceratonia siliqua L.). To induce root damage, we removed 25, 50 and 75 percent of the root system in each species and compared it with control saplings. Tree aboveground (canopy) responses to root damage increased over time and with damage level. Leaf warming (up to 3°C) and decreased PRI were the most significant and rapid responses, with temperature differences being visible as early as 2 days following root damage. NDWI and greenness were the least sensitive, with responses detectable only at 75 percent root damage and as late as 14 or 30 days following root damage. Responses varied vastly among species, with carob being the most sensitive and pine being the least. Changes in leaf temperature and PRI indicated that leaf transpiration and photosynthesis were impaired by root damage. Although trees build roots in excess, mechanical damage will eventually decrease transpiration and photosynthesis across tree species.

Abstract Forests are one of the most important components of the global carbon cycle. Consequently, forest protection as a nature-based climate solution has garnered increasing interest. Protected areas instated to safeguard biodiversity provide an opportunity to maximize carbon storage in situ, with important co-benefits between conservation and climate change mitigation. However, changing climate and disturbance regimes put this carbon storage function at risk. Here we investigated carbon sequestration and storage in a protected landscape in the German Alps (Berchtesgaden National Park) throughout the 21st century. We simulated the impacts of climate change as well as increasing wind and bark beetle disturbances on cumulative Net Ecosystem Production using a process-based forest landscape model. Considering a wide range of potential changes in wind frequency and speed under a variety of climate change scenarios, we addressed the question under which future conditions the landscape will turn from a carbon sink to a carbon source. While the landscape was a net carbon sink at the end of the simulation in 76 per cent of the simulation runs, increasing disturbances and climate change greatly reduced its carbon sink capacity. Under RCP2.6, the landscape remained a robust carbon sink even under elevated disturbance (probability of turning from sink to source between 0 per cent and 25 per cent). In contrast, carbon release was likely under RCP8.5 even with little change in the disturbance regime (probability: 30 per cent to 95 per cent). Productive areas in lower elevations that currently have the highest carbon density on the landscape were contributing most strongly to a reduction of the carbon sink strength. Our study reveals that the effect of protected areas acting as nature-based climate solutions might be overestimated if the risks from changing climate and disturbance regimes are neglected. We therefore call for a more explicit consideration of future forest dynamics in the discussion of the potential role of forests in climate change mitigation.

Accurate estimation of forest biomass and carbon stocks at regional to national scales is a key requirement in determining terrestrial carbon sources and sinks on United States (US) forest lands. To that end, comprehensive assessment and testing of alternative volume and biomass models were conducted for individual tree models employed in the component ratio method (CRM) currently used in the US' National Greenhouse Gas Inventory. The CRM applies species-specific stem volume equations along with specific gravity conversions and component expansion factors to ensure consistency between predicted stem volumes and weights, and additivity of predicted live tree component weights to match aboveground biomass (AGB). Data from over 76 600 stem volumes and 6600 AGB observations were compiled from individual studies conducted in the past 115 years – what we refer to as legacy data – to perform the assessment. Scenarios formulated to incrementally replace constituent equations in the CRM with models fitted to legacy data were tested using cross-validation methods, and estimates of AGB were scaled using forest inventory data to compare across 33 states in the eastern US. Modifications all indicated that the CRM in its present formulation underestimates AGB in eastern forests, with the range of underestimation ranging from 6.2 to 17 per cent. Cross-validation results indicated the greatest reductions in estimation bias and root-mean squared error could be achieved by scenarios that replaced stem volume, sapling AGB, and component ratio equations in the CRM. A change in the definitions used in apportioning biomass to aboveground components was also shown to increase prediction accuracy. Adopting modifications tested here would increase AGB estimates for the eastern US by 15 per cent, accounting for 1.5 Pg of C currently unaccounted for in live tree aboveground forest C stock assessments. Expansion of the legacy data set currently underway should be useful for further testing, such as whether similar gains in accuracy can be achieved in estimates of regional or national-scale C sequestration rates.

Inferences for forest-related spatial problems can be enhanced using remote sensing-based maps constructed with nearest neighbours techniques. The non-parametric k-nearest neighbours (k-NN) technique calculates predictions as linear combinations of observations for sample units that are nearest in a space of auxiliary variables to population units for which predictions are desired. Implementations of k-NN require four choices: a distance or similarity metric, the specific auxiliary variables to be used with the metric, the number of nearest neighbours, and a scheme for weighting the nearest neighbours. The study objective was to compare optimized k-NN configurations with respect to confidence intervals for airborne laser scanning-assisted estimates of mean volume or biomass per unit area for study areas in Norway, Italy, and the USA. Novel features of the study include a new neighbour weighting scheme, a statistically rigorous method for selecting feature variables, simultaneous optimization with respect to all four k-NN implementation choices and comparisons based on confidence intervals for population means. The primary conclusions were that optimization greatly increased the precision of estimates and that the results of optimization were similar for the k-NN configurations considered. Together, these two conclusions suggest that optimization itself is more important than the particular k-NN configuration that is optimized.

The knowledge and modelling of upper crown exposed to light (UCL) provides relevant informations for forest management of native species, since UCL within the forest is the main photosynthetic area of trees. This study aimed to model the UCL of Cedrela fissilis (Vell.) open-grown trees, using techniques and software for digital design and morphometric analysis. The study included a sample of 53 open-grown trees located in different site conditions. All trees were photographed in two perspectives and analysed with Computer Aided Design (CAD) software for measurement of the dendrometric variables. The analysed data showed trees with diameter at breast height (dbh) between 30–40 and 40–50 cm and UCL mean values of 129.07 and 219.64 m2, respectively. The UCL was positively correlated with crown diameter (cd, r = 0.97, P < 0.0001), an index of dbh and tree height (d2h, r = 0.90, P < 0.0001) and with basal area increment (Ig). The proposed methodology was efficient in obtaining morphological data of isolated trees and to analyse data related to the canopy.

To account for ecological objectives in forest management planning, potential habitats need to be mapped, characterized and evaluated for utility in alternative management practices. Airborne laser scanning (ALS) is increasingly used to derive predictive maps of habitat quality. Unlike ecologically driven approaches that require spatially and temporally co-located training data of the specific species, we tested whether indicative information on the habitat potential could be obtained by means of an unsupervised classification of ALS data. Based on a literature review, altogether five ALS features quantifying vegetation height, cover and diversity were expected to capture the essential variation in the habitat requirements of western capercaillie (Tetrao urogallus L.) and hazel grouse (Tetrastes bonasia L.), which are the most important game birds occurring in the studied area. The features were extracted from sparse density, leaf-off ALS data at a resolution of 256 m2 and partitioned using an unsupervised k-means algorithm. By analysing the persistence of the cluster ensemble formed by the partitioning, altogether 158 plots in 16 structural classes were assigned for field measurements to determine which real-world forest phenomena affected the clustering. The clustering was found to stratify the area mainly in terms of size-related attributes such as timber volume and basal area. The understorey, shrub and herb layers had less correspondence with the clustering, indicating that an unsupervised classification is not directly suitable for habitat mapping. The result was improved using empirical threshold values for the ALS features determined according to the plots labelled as the most potential habitats in the field measurements. This semi-supervised classification of the data indicated 4 per cent of the total forest area as suitable for the specific species, which appears a reasonable estimate of the core area of the habitats considered. Overall, the partitioning formed aggregated, stand-like spatial patterns, even though the neighbourhoods of the individual 256 m2 cells were not considered at all. The result could be further refined by spatial optimization to produce indicative maps for forest management planning with ALS as the sole data source.

We demonstrate the efficacy of using close-range photogrammetry from a consumer grade camera as a tool in generating high-resolution, three-dimensional coloured point clouds for detailed analysis or monitoring of wheel ruts. Ground-based timber harvesting results in vehicle traffic on 12‐70 per cent of the site, depending on the system used, with a variable probability of causing detrimental soil disturbance depending on climatic, hydrological and soil conditions at the time of harvest. Applying the technique described in this article can reduce the workload associated with the conventional manual measurement of wheel ruts, while providing a greatly enhanced source of information that can be used in analysing both physical and biological impact, or stored in a repository for later operation management or monitoring. Approaches for deriving and quantifying properties such as rut depths and soil displacement volumes are also presented. In evaluating the potential for widespread adoption of the method among forest orenvironmental managers,the studyalso presentstheworkflowandprovidesacomparisonoftheeaseofuseandqualityoftheresultsobtainedfromonecommercialandtwo open source image processing software packages. Results from a case study showed no significant difference between packages on point cloud quality in terms of model distortion. Comparison of photogrammetric profiles against profiles measured manually resulted in root mean square errors of between 2.07 and 3.84 cm for five selected road profiles. Maximal wheel rut depth for three different models were 1.15, 0.99 and 1.01 m, and estimated rut volumes were 9.84, 9.10 and 9.09 m 3 , respectively, for 22.5 m long sections.

The structural attributes of an old-growth forest remnant of &lt;it&gt;Pinus nigra&lt;/it&gt; subsp. &lt;it&gt;salzmannii&lt;/it&gt; located in southern Spain and those from nearby managed forests were compared. Data on environmental and structural variables were collected in 74 forest inventory plots. Significant differences were tested by means of the Mann–Whitney &lt;it&gt;U&lt;/it&gt;-test. A partial redundancy analysis was conducted to assess the relative contribution of both environmental and management variables in explaining differences in the current structure of old-growth and managed stands. Additionally, 25 trees of different size (range 25.4–98 cm) and age (67–803 years) were cored and dendrochronological methods and bootstrap linear regression were used to study the effects of size and age on tree growth. The old-growth stand exhibited a higher density of large trees (diameter at breast height &gt;50 cm) and greater variability in tree size than managed stands, but lower tree density and, consequently, similar values of stand basal area and volume. Environmental variables accounted for 5.7 per cent of the total variance observed in structural attributes, while the contrasting silvicultural condition (old-growth &lt;it&gt;vs&lt;/it&gt; managed) accounted for 10.2 per cent. Growth, measured in units of stand basal area, increased continuously with tree size.

Funding This study was internally funded by the University of Aberdeen. Acknowledgements The authors wish to thank Mr Charles Taylor and Mr Mark Brazendale of Tay Forest District, Forestry Commission Scotland for their support in carrying out this study.