Quadratic Mean Diameter Research Articles

Structure and Spatial Distribution of the Abundance of <i>Spondias mombin</i> L. Populations in the Subequatorial Zone of Benin (West Africa)

Conservation and sustainable use strategies for reliable management plans of valuable species are lacking due to the gap in basic scientific information on their structure and status. The objectives of this work were to study the structural characteristics and the spatial distribution of the abundance of <i>Spondias mombin</i> L. populations in the subequatorial zone of Benin. The data collection was based on the tessellation of study zone with ArcGIS analysis tools. The grid cell used was a square of 100 m side (1ha of surface). The occurrence of species in each grid was obtained using GBIF database. One hundred and seventy-five (175) trees of <i>S. mombin</i> were sampled through the four Phytodistricts of study zone. The main dendrometric variables measured from these individuals were diameter at breast height (1.30 m above ground level) and height. The parameters such as density, mean quadratic diameter, basal area and mean Lorey height were calculated. To study the spatial abundance distribution of <i>S. mombin,</i> we used Inverse Distance Weighting (IDW) interpolation method to realize abundance map from the occurrences. The results of structure showed a density of 3 trees/ha. The basal area was 1500 cm²/ha and the mean quadratic diameter was 25.10 cm. The mean of Lorey height obtained was 12 m<i>.</i> The diameter structure showed a predominance of bell-shape distribution with a relative abundance of young individuals. The low numbers of individuals in some diameter classes, the erratic distribution and the presence of old population of <i>S. mombin</i> indicate the strong anthropogenic pressure on the species. The abundance map showed that the species was present in all the phytodistricts and covers 93% of the study area’s municipalities. The conservation strategies, including both the species and its habitat, need to be developed to allow sustainable use of <i>S. mombin</i>.

Canopy and surface fuel estimations using RPAS and ground-based point clouds

Abstract Forest management activities intended to reduce wildfire risk rely on accurate characterizations of the amount and arrangement of canopy and surface fuels. Metrics that describe these fuels are typically estimated with various systems that transform plot-level field data into metrics that can be used within fire behaviour models. Remote sensing data have long been used to estimate these metrics across large spatial scales, but more advanced, high-density point clouds have the potential to estimate these metrics with higher accuracy. This study collected LiDAR and digital aerial photogrammetric (DAP) point clouds from a remotely piloted aerial system (RPAS), as well as mobile laser scanning (MLS) point clouds from a mobile ground-based system, and compared their ability to estimate fuel metrics. This involved the extraction of predictor variables from each point cloud, of which small subsets were used to estimate various fuel metrics. These included six overstory canopy metrics (stand height, canopy cover, tree density, canopy fuel load, canopy bulk density and canopy base height), three diameter at breast height (DBH)–related metrics (stand density index, basal area and quadratic mean diameter) and three surface fuel metrics (total woody debris (TWD), coarse woody debris (CWD) and fine woody debris (FWD)). Overall, canopy metrics were estimated most accurately by the RPAS LiDAR models, although none of the point clouds were able to accurately estimate DBH-related metrics. For the other six canopy metrics, RPAS LiDAR models had an average R2 value of 0.70; DAP – 0.63 and MLS – 0.63. CWD (&amp;gt;7 cm) and TWD loads were estimated most accurately by the MLS models (average R2 values – 0.70), followed by the RPAS LiDAR – 0.38 and DAP – 0.13. None of these models were able to accurately estimate FWD loads (≤7 cm in diameter), with the three types of point clouds having a maximum R2 value of 0.08. Overall, this research shows the relative ability of three types of high-density point clouds to estimate metrics relevant for fire behaviour modeling.

Modeli rasta prirodnih sastojina kalabrijskog bora u središnjem mediteranskom području Turske

Stand growth models are needed for a variety of forestry practices, primarily management plans and silvicultural studies. The goal of this study was to create stand-level models for natural, pure even-aged stands of Calabrian pine in the central Mediterranean region of Turkey. The study area consists of pure and natural Calabrian pine stands located within the boundaries of the Antalya and Mersin Regional Forestry Directorates in the central Mediterranean region of Turkey. Data was collected from 486 temporary plots scattered throughout the region. Two trees (the dominant tree and the tree representing the quadratic mean diameter of the stand) were measured in each plot, yielding 972 trees. The data showed that the age varied from 6 to 135 years, the site index (SI) from 8.5 to 33.5 m, and the density from 0.3 to 12.4. The density-dependent yield tables were generated using regression equations based on stand age, SI, and stand density with individual, two-factor, and three-factor interaction effects. For SI classes I, II, and III, the optimal rotation period that would result in the highest yields for pure Calabrian pine stands is 60, 65, and 75 years, respectively. The stand growth models developed (i.e., density-dependent yield tables) agreed with the fundamental growth principles and data provided in the literature.

Diameter Growth of Silver Fir (Abies alba Mill.), Scots Pine (Pinus sylvestris L.), and Black Pine (Pinus nigra Arnold) in Central European Forests: Findings from Slovenia

The main objectives of the study were to (1) determine the response of the diameter growth of silver fir, Scots pine, and black pine in Central European seminatural forests to tree, stand, and environmental factors and (2) test for differences in their growth rate on different soils. Based on 26,291 permanent sampling plots (500 m² each), we developed a linear mixed-effects model of the diameter increment for each of these tree species. The models explained 32%–47% of the total diameter increment variability. The models differ in the set of predictors. All models suggested a non-linear effect of tree diameter on diameter increment. Nine predictors were common to all three models (stand basal area, quadratic mean diameter, basal area of overtopping trees, the proportion of beech in the stand volume, inclination, elevation, mean annual temperature, mean diurnal range, and soil unit), and six predictors were specific for one or two models (tree diameter, logarithm of tree diameter, proportion of other broadleaves, site productivity, rockiness, eastness index). Tree diameter was the most important variable for fir growth, while climatic variables explained most of the variability in pine diameter growth. The soil unit contributed from 5.3% to 7.5% to the explained diameter increment variability. Although the developed models are only locally accurate and cannot be used outside the study area without validation, the model predictions can be compared to those in other stand growth simulators and other geographical regions.

Assessing the potential of mobile laser scanning for stand-level forest inventories in near-natural forests

Abstract Recent advances in LiDAR sensors and robotic technologies have raised the question of whether handheld mobile laser scanning (HMLS) systems can allow for the performing of forest inventories (FIs) without the use of conventional ground measurement (CGM) techniques. However, the reliability of such an approach for forest planning applications, particularly in non-uniform forests under mountainous conditions, remains underexplored. This study aims to address these issues by assessing the accuracy of HMLS-derived data based on the calculation of basic forest attributes such as the number of trees, dominant height and basal area. To this end, near-natural forests of a national park (NE Türkiye) were surveyed using the HMLS and CGM techniques for a management plan renewal project. Taking CGM results as reference, we compared each forest attribute pair based on two datasets collected from 39 sample plots at the forest (landscape) scale. Diameter distributions and the influence of stand characteristics on HMLS data accuracy were also analyzed at the plot scale. The statistical results showed no significant difference between the two datasets for any investigated forest attributes (P &amp;gt; 0.05). The most and the least accurately calculated attributes were quadratic mean diameter (root mean square error (RMSE) = 1.3 cm, 4.5 per cent) and stand volume (RMSE = 93.7 m3 ha−1, 16.4 per cent), respectively. The stand volume bias was minimal at the forest scale (15.65 m3 ha−1, 3.11 per cent), but the relative bias increased to 72.1 per cent in a mixed forest plot with many small and multiple-stemmed trees. On the other hand, a strong negative relationship was detected between stand maturation and estimation errors. The accuracy of HMLS data considerably improved with increased mean diameter, basal area and stand volume values. Eventually, we conclude that many forest attributes can be quantified using HMLS at an accuracy level required by forest planning and management-related decision making. However, there is still a need for CGM in FIs to capture qualitative attributes, such as species mix and stem quality.

Additive modeling systems to simultaneously predict aboveground biomass and carbon for Litsea glutinosa of agroforestry model in tropical highlands

Aim of study: To develop and cross-validate simultaneous modeling systems for estimating components and total tree aboveground biomass and carbon of Litsea glutinosa in an agroforestry model with cassava.&#x0D; Area of study: In the Central Highlands of Vietnam, the agroforestry model widely planted on fallow land of ethnic minorities is a mixture of 65% L. glutinosa in combination with 35% cassava (Manihot esculenta).&#x0D; Material and methods: Twenty-two 300-m2 circular sample plots were located, representing the range of tree age, plantation density, and a 6-7 year rotation cycle. In each sample plot, one selected tree with a diameter at breast height equal to the plot quadratic mean diameter was destructively sampled. The relationships among tree aboveground biomass and carbon (AGB/AGC) and their components with dendrometric variables diameter, height, age, and crown area were examined using factor analysis. To fit systems of equations for AGB/AGC and their components, we compared two methods: weighted nonlinear least-squares (WNLS) and weighted nonlinear seemingly unrelated regression (WNSUR).&#x0D; Main results: The results of the leave-one-out cross-validation showed that the simultaneous WNSUR approach to modeling systems of four tree components, total biomass, and carbon provided better results than independent WNLS models.&#x0D; Research highlights: The simultaneous WNSUR modeling system provided improved and reliable estimates of tree components, total biomass, and carbon for L. glutinosa in an agroforestry model with cassava compared to independently fitted WNLS models.

Effects of Biotic and Abiotic Factors on Biomass Conversion and Expansion Factors of Natural White Birch Forest (Betula platyphylla Suk.) in Northeast China

Biomass conversion and expansion factors (BCEFs) are widely utilized in national and regional biomass estimates and greenhouse gas reporting, as they can be used to directly transform the stocking volume into biomass. In this study, the power function was used as the basic model form with biotic variables, and abiotic variables were considered to improve the fitting results. Then, the random effects parameters were also introduced into the models to describe the variation of BCEFs among different forest management units. Random sampling strategies were applied to calibrate the random effects. The results showed that the stocking volume exhibited a negative proportional relationship in the stem BCEF (BCEFst), the root BCEF (BCEFro) and the total tree BCEF (BCEFto) models, and the quadratic mean diameter exhibited a positive proportional relationship in the branch BCEF (BCEFbr) and the foliage BCEF (BCEFfol) models. In addition, the fitting effect of generalized models with abiotic predictors was superior to that of the basic models. Considering the effects of abiotic variables on the BCEFs of each component, the results showed that BCEFst and BCEFto decreased as the mean annual precipitation increased; BCEFbr increased as the annual temperature increased; BCEFfol gradually decreased as the elevation increased; and BCEFro first increased with increasing mean annual temperature and then declined. In conclusion, abiotic factors explained the variation in BCEFs for the biomass components of the natural white birch forest. Although the fitting effect of generalized models with abiotic predictors was superior to that of the basic models, the mixed-effects model was preferable for modeling the BCEFs of each component. In addition, the prediction precision of the mixed-effects models enhanced gradually with increasing sample size, and the selection of eight plots for calibration and prediction based on the mixed-effects model was the best sampling strategy in this study of a natural white birch forest.

A quantile regression approach to model stand survival in Chinese fir plantations

The development of stand survival models can provide an important basis for the sustainable management of forest resources. In a new approach developed in this study, parameters of four survival quantile regression models were predicted from a quantile associated with a current stand density. The curves from these quantile regression models were then used to project future stand density for that stand. A three-fold cross-validation revealed that the quantile regression approach outperformed the least squares method based on three evaluation statistics, especially for longer projection lengths. These results were consistent for all four survival models evaluated. The best survival model is Clutter–Jones model, without constraints, but its ln( N)–ln( Dq) trajectories ( N = stand density and Dq = quadratic mean diameter) from the quantile regression showed the linear self-thinning trend.

Factors influencing productivity of pine-dominated stands in South Korea

Stand productivity research has mainly focused on increasing yield and has recently begun to examine changes in carbon storage. The Korean government is interested in finding ways to increase forest carbon capture to meet carbon neutrality requirements because approximately 63% of the land is covered by forests. In addition, 69% of these forests are older than 30 years old and their productivity and aboveground carbon storage rates are expected to decline. The purpose of this study was to examine the effect of quadratic mean diameter (QMD), stand basal area, site index, slope, climate (MAT and MAP), stand age, stand structural diversity, and stand composition on the productivity of aging Korean red pine-dominated stands. Based on the effects of these factors, we explored how to manage pine forests with the focus of increasing their productivity. Random forest regression was used for the analysis, and periodic basal area increment (PBAI) was used as the dependent variable of stand productivity. Our results show that the most influential factor on stand productivity was QMD. PBAI dramatically decreased from approximately 0.8 to 0.53 m2/ha·year as QMD increased up to 18 cm. Since diameter (QMD) increment is closely associated with changes in tree competition; increasing tree competition with increasing QMD and stand basal area may lead to decreases in PBAI owing to decreases in growth rate due to space and resource limitations and increases in mortality. PBAI decreased when basal area increased from 22 to 51.5 m2/ha. PBAI increased for site index values between 8 and 12.5 m and decreased for stand age values up to approximately 31 years. For climate factors, PBAI generally increased with increasing MAP and slightly increased as MAT increased up to approximately 11.2 °C and then decreased at higher MAT. PBAI initially increased with increasing slope values, decreased with values lower than 15°, and remained stable at slope values in the range of 16–34°. Stand structural diversity, which ranged from 1.32 to 1.62, exhibited a similar negative influence on PBAI associated with increasing stand density. With regard to pine composition, pine stands with a large proportion of pine basal area, showed higher productivity due to the simple stand structure resulting in better growth of shade intolerant pine. This study found that stand density increases with the development of pine stands and that density increases had negative influences on stand productivity. Collectively, our results suggest that stand density management is essential for increasing stand productivity and carbon sequestration in the Korean red pine-dominated stands of South Korea.

Drought then wildfire reveals a compound disturbance in a resprouting forest.

The frequency and intensity of forest disturbances, such as drought and fire, are increasing globally, with an increased likelihood of multiple disturbance events occurring in short succession. Disturbances layered over one another may influence the likelihood or intensity of subsequent events (a linked disturbance) or impact response and recovery trajectories (a compound disturbance), with substantial implications for ecological spatiotemporal vulnerability. This study evaluates evidence for disturbance interactions of drought followed by wildfire in a resprouting eucalypt-dominated forest (the Northern Jarrah Forest) in southwestern Australia. Sites were stratified by drought (high, low), from previous modeling and ground validation, and fire severity (high, moderate, unburnt), via remote sensing using the relative difference normalized burn ratio (RdNBR). Evidence of a linked disturbance was assessed via fine fuel consumption and fire severity. Compound disturbance effects were quantified at stand scale (canopy height, quadratic mean diameter, stem density) and stem scale (mortality). There was no evidence of prior drought influencing fine fuel consumption or fire severity and, hence, no evidence of a linked disturbance. However, compound disturbance effects were evident; stands previously affected by drought experienced smaller shifts in canopy height, quadratic mean diameter, and stem density than stands without prior drought impact. At the stem scale, size and fire severity were the strongest determinants of stem survival. Proportional resprouting height was greater in high drought sites than in low drought sites (p < 0.01), meaning, structurally, the low drought stands decreased in height more than the high drought stands. Thus, a legacy of the drought was evident after the wildfire. Although these resprouting eucalypt forests have been regarded as particularly resilient, this study illustrates how multiple disturbances can overwhelm the larger tree component and promote an abundance of smaller stems. We suggest that this is early evidence of a structural destabilization of these forests under a more fire-prone, hotter, and drier future climate.

Alternative expressions for stand diameter in complex forests

Quadratic mean diameter is the most frequently reported descriptor of the diameter distribution of forests. As such, it is often used as an indicator of forest stand structure, developmental stage, and ecological and economic potential. However, quadratic mean diameter can be heavily influenced by the presence or absence of large numbers of small stems in lower canopy strata, and it is also sensitive to left-truncation of the diameter distribution, making its interpretation across inventories with different protocols challenging. Here, we examine three alternative expressions of stand diameter: the arithmetic and quadratic mean diameter of the thickest 100 trees per hectare, and the basal area-weighted mean diameter. Using data from the United States Forest Inventory and Analysis program for New York and New England, these alternative expressions showed closer correlation with multiple stand structural variables than did quadratic mean diameter, including merchantable cubic and board foot volume per hectare, aboveground live tree carbon per hectare, and total number of live and dead standing trees greater than 40 ​cm diameter at breast height per hectare (previously proposed as an index of old-growth structure). Arithmetic and quadratic mean diameter of the thickest 100 trees per hectare showed nearly identical performance, and the strongest correlations across the board. We develop closed-form expressions for these variables when the diameter distribution is a Weibull, and illustrate their behavior relative to quadratic mean diameter for that situation. While the reasons for prevalence of quadratic mean diameter as an indicator remain valid, we suggest that these alternative measures should be more widely reported and analyzed to give a more informative depiction of stand structure and development in complex forests.

Adverse radiation effect and freedom from progression following repeat stereotactic radiosurgery for brain metastases.

The authors previously evaluated risk and time course of adverse radiation effects (AREs) following stereotactic radiosurgery (SRS) for brain metastases, excluding lesions treated after prior SRS. In the present analysis they focus specifically on single-fraction salvage SRS to brain metastases previously treated with SRS or hypofractionated SRS (HFSRS), evaluating freedom from progression (FFP) and the risk and time course of AREs. Brain metastases treated from September 1998 to May 2019 with single-fraction SRS after prior SRS or HFSRS were analyzed. Serial follow-up magnetic resonance imaging (MRI) and surgical pathology reports were reviewed to score local treatment failure and AREs. The Kaplan-Meier method was used to estimate FFP and risk of ARE measured from the date of repeat SRS with censoring at the last brain MRI. A total of 229 retreated brain metastases in 124 patients were evaluable. The most common primary cancers were breast, lung, and melanoma. The median interval from prior SRS/HFSRS to repeat SRS was 15.4 months, the median prescription dose was 18 Gy, and the median duration of follow-up imaging was 14.5 months. At 1 year after repeat SRS, FFP was 80% and the risk of symptomatic ARE was 11%. The 1-year risk of imaging changes, including asymptomatic RE and symptomatic ARE, was 30%. Among lesions that demonstrated RE, the median time to onset was 6.7 months (IQR 4.7-9.9 months) and the median time to peak imaging changes was 10.1 months (IQR 5.6-13.6 months). Lesion size by quadratic mean diameter (QMD) showed similar results for QMDs ranging from 0.75 to 2.0 cm (1-year FFP 82%, 1-year risk of symptomatic ARE 11%). For QMD < 0.75 cm, the 1-year FFP was 86% and the 1-year risk of symptomatic ARE was only 2%. Outcomes were worse for QMDs 2.01-3.0 cm (1-year FFP 65%, 1-year risk of symptomatic ARE 24%). The risk of symptomatic ARE was not increased with tyrosine kinase inhibitors or immunotherapy before or after repeat SRS. RE on imaging was common after repeat SRS (30% at 1 year), but the risk of a symptomatic ARE was much less (11% at 1 year). The results of repeat single-fraction SRS were good for brain metastases ≤ 2 cm. The authors recommend an interval ≥ 6 months from prior SRS and a prescription dose ≥ 18 Gy. Alternatives such as HFSRS, laser interstitial thermal therapy, or resection with adjuvant radiation should be considered for recurrent brain metastases > 2 cm.

Dynamic growth model for oak stands in Moscow, Russia

The most objective information about the state of Moscow’s forests is provided by long-term observation data on permanent trial plots. Such data makes it possible to identify changes in forest stands under the influence of recreationists, environmental pollution, and climate change. Long-term observational data are particularly valuable in modeling forest stand growth and productivity. The goal of the study is to develop a dynamic model of the growth of oak stands in Moscow based on long-term observation data. The modelling data were obtained from 7 plots of the permanent sample plot network established by the Forest Experimental Station of the Russian State Agrarian University – Moscow Timiryazev Agricultural Academy. There are a total of 42 inventories and the number of inventories per plot range from 3 to 9. Inventories were carried out between 1927 and 2009. We used three initial state variables for prediction, as in many studies. Taking into account the peculiarities of the inventory of forest stands in Russia, the mean height, quadratic mean diameter and number of trees per hectare were used as initial variables. All obtained models meet the requirements for forest inventory in Russia in terms of error values of stand attributes. The model in this study provides a simple and reliable system for predicting the growth and yield of Moscow oak stands.

Assessing and mitigating systematic errors in forest attribute maps utilizing harvester and airborne laser scanning data

Cut-to-length harvesters collect useful information for modeling relationships between forest attributes and airborne laser scanning (ALS) data. However, harvesters operate in mature forests, which may introduce selection biases that can result in systematic errors in harvester data-based forest attribute maps. We fitted regression models (harvester models) for volume (V), height (HL), stem frequency (N), above-ground biomass, basal area, and quadratic mean diameter (QMD) using harvester and ALS data. Performances of the harvester models were evaluated using national forest inventory plots in an 8.7 Mha study area. We estimated biases of large-area synthetic estimators and compared efficiencies of model-assisted (MA) estimators with field data-based direct estimators. The harvester models performed better in productive than unproductive forests, but systematic errors occurred in both. The use of MA estimators resulted in efficiency gains that were largest for HL (relative efficiency, RE = 6.0) and smallest for QMD (RE = 1.5). The bias of the synthetic estimator was largest for N (39%) and smallest for V (1%). The latter was due to an overestimation of deciduous and an underestimation of spruce forests that by chance balanced. We conclude that a probability sample of reference observations may be required to ensure the unbiasedness of estimators utilizing harvester data.

Analyzing TLS Scan Distribution and Point Density for the Estimation of Forest Stand Structural Parameters

In recent decades, the feasibility of using terrestrial laser scanning (TLS) in forest inventories was investigated as a replacement for time-consuming traditional field measurements. However, the optimal acquisition of point clouds requires the definition of the minimum point density, as well as the sensor positions within the plot. This paper analyzes the effect of (i) the number and distribution of scans, and (ii) the point density on the estimation of seven forest parameters: above-ground biomass, basal area, canopy base height, dominant height, stocking density, quadratic mean diameter, and stand density index. For this purpose, 31 combinations of TLS scan positions, from a single scan in the center of the plot to nine scans, were analyzed in 28 circular plots in a Mediterranean forest. Afterwards, multiple linear regression models using height metrics extracted from the TLS point clouds were generated for each combination. In order to study the influence of terrain slope on the estimation of forest parameters, the analysis was performed by using all the plots and by creating two categories of plots according to their terrain slope (slight or steep). Results indicate that the use of multiple scans improves the estimation of forest parameters compared to using a single one, although using more than three to five scans does not necessarily improves the accuracy. Moreover, it is also shown that lower accuracies are obtained in plots with steep slope. In addition, it was observed that each forest parameter has a strategic distribution depending on the field of view of the TLS. Regarding the point density analysis, the use of 1% to 0.1% (≈136 points·m−2) of the initial point cloud density (≈37,240.86 points·m−2) generates an R2adj difference of less than 0.01. These findings are useful for planning more efficient forest inventories, reducing acquisition and processing time as well as costs.

Modeling the Missing DBHs: Influence of Model Form on UAV DBH Characterization

The reliability of forest management decisions partly depends on the quality and extent of the data needed for the decision. However, the relatively high cost of traditional field sampling limits sampling intensity and data quality. One strategy to increase data quality and extent, while reducing the overall sample effort, is using remote sensing-based data from unmanned aerial vehicles (UAV). While these techniques reliably identify most tree locations and heights in open-canopied forests, their ability to characterize diameter at breast height (DBH) is limited to estimates of a fraction of trees within the area. This study used UAV-derived DBHs and explanatory variables to test five model forms in predicting the missing DBHs. The results showed that filtering UAV DBHs using regionally derived height to DBH allometries significantly improved model performance. The best predicting model was slightly biased, with a 5.6 cm mean error and a mean absolute error of 6.8 cm. When applied across the stand, the number of trees was underestimated by 26.7 (3.9%), while the basal area and quadratic mean diameter were overestimated by 3.3 m2 ha−1 (13.1%) and 1.8 cm (8.3%), respectively. This study proposes a pathway for remotely sensed DBHs to predict missing DBHs; however, challenges are highlighted in ensuring the model training dataset represents the population.

A climate-sensitive mixed-effects tree recruitment model for oaks (Quercus spp.) in Hunan Province, south-central China

Tree recruitment is an essential component and plays a fundamental role in forest management decision-making. However, the existing tree recruitment models for oak (Quercus spp.) species have been developed using traditional modeling functions, and have neglected the effects of climate, which do not apply to long-term projections of future forest composition under a climatic change scenario. In this study, we developed tree recruitment models based on the data of 548 permanent sample plots distributed in Hunan Province, south-central China. Five commonly used recruitment approaches (negative binomial model, zero-inflated Poisson, zero-inflated negative binomial model, Hurdle-Poisson model, and Hurdle-negative binomial model) were employed to analyze the data. Then the random effects of the regions were included to develop the nonlinear mixed-effects models and account for spatial and temporal correlations. Among the various candidate predictors tested including stand characteristics, site conditions, and climate, stand basal area (BA), quadratic mean diameter (QMD), basal area of the species (Bai), cosine of the slope combined with the natural logarithm of elevation (CIE), and mean annual temperature (MAT) had significant effects on oak recruitment. The zero-inflated negative binomial model was selected as the optimal model compared to the other four basic models. The performance of the models improved significantly when the random effects were added. These results and knowledge of adaptive management will be useful for developing practical forest management plans.

Stand-level structural characteristics dictate hurricane resistance and resilience more than silvicultural regime in longleaf pine woodlands

Hurricanes frequently impact coastal forests throughout the southeastern United States and are expected to increase in frequency and intensity in the future. Given this projection, interest has grown in adopting forest management practices that can reduce the risk of hurricane damage by increasing stand resistance and resilience. Empirical evidence suggests that uneven-aged stands are more resistant and resilient to hurricanes than even-aged stands, due to their heterogeneous stand structure and multiple age classes. Nevertheless, opportunities to compare the impact of hurricanes on stands managed under different silvicultural regimes at the same location are uncommon. Moreover, no research has detailed how different stocking regulation methods influence hurricane risk in uneven-aged stands. An opportunity to conduct a natural experiment presented itself in 2004 when Hurricane Ivan passed 24 km west of the Escambia Experimental Forest in southern Alabama, USA. At the time of the storm, the experimental forest contained long-term experiments examining the performance of longleaf pine (Pinus palustris) managed under different silvicultural systems. Here, we report on the resistance and resilience of three even-aged stands compared to six uneven-aged stands that were managed under BDq (two stands), Volume Control Guiding Diameter Limit (VC-GDL) (two stands), and Diameter Limit Cutting (DLC) (two stands) stocking control methods.Average stand resistance (proportion of basal area remaining in 2005) was not statistically different among even-aged (0.866)and uneven-aged stands (all variants pooled) (0.858) p > 0.05). Among uneven-aged variants, resistance was highest in the VC-GDL stands (0.876) and lowest in the DLC stands (0.840). Across all stands, resistance was positively related to stand density (p = 0.03) and negatively associated with increases in quadratic mean diameter (p = 0.03) and size class diversity (p = 0.01). Post-hurricane (2005) departure indices found left shifts (shift in density towards smaller size classes) in the diameter distribution of 33 % of even-aged stands and 83 % of pooled uneven-aged stands. Among uneven-aged variants, significant left shifts in diameter distribution were greatest in stands managed with VC-GDL and lowest with BDq. Average post-hurricane resilience (post hurricane basal area recovery) was statistically similar in the pooled uneven-aged stands (0.42) and even-aged stands (0.37). Yet, stands with greater tree size class diversity demonstrated increased resilience. Regardless of management regime, stands carrying a high density of smaller diameter trees are more resistant to hurricane damage than stands managed at a lower density with a higher proportion of larger diameter trees. Moreover, while not statistically significant, our findings suggest that uneven-aged stands possess a greater potential for rapid hurricane recovery than even-aged stands. Therefore, land managers seeking to foster hurricane resistance and resilience should consider modifying stand prescriptions to carry a higher proportion of stocking in smaller size classes.

Natural Regeneration Dynamics of Longleaf Pine Under Frequent, Low-Intensity Prescribed Fires in Southern Alabama, USA

Abstract Frequent (≤3 y) prescribed burn has been conducted in the Escambia Experimental Forest in southern Alabama, USA, since 2001.The change in the density of new germinants (≤1 y) and older seedlings and saplings (&amp;gt;1 y) of longleaf pine (Pinus palustris Mill.) from stands of varying conditions was quantified using the locally estimated scatterplot smoothing and spatial statistical methods. A relatively longer fire interval or time since last fire (TSLF) &amp;gt; 600 days favored the recruitment of young seedlings. However, a short fire return interval or TSLF≤ 600 days promoted the recruitment of older seedlings and saplings. Large basal area (BA) and site index (SI) tended to diminish natural regeneration, whereas large quadratic mean diameter (QMD) and age were facilitative. The interactive effect of these factors indicated that a ratio of TSLF/BA of 60 to 120 and QMD/SI of 1.8 to 2.2 would maximize the recruitment of young seedlings. The recruitment of older seedlings and saplings increased linearly with QMD/SI but had no relationship with TSLF/BA. Recruitment was largely absent when QMD/SI was &amp;lt;2.0. As a result, we recommend that QMD/SI be no greater than 2.0 and TSLF/BA range from 60 to 120 to regenerate longleaf pine naturally.

Mortality and Growth in a Sessile Oak [Quercus petraea (Matt.) Liebl.]-Dominated Young Stand Managed through Silvicultural Operations of Different Types and Intensities

A small-scale R&D project, including a block with four plots (P1-4) of 200 m2, was established in 2001 in a 15-year-old sessile oak-dominated stand, regenerated naturally through the application of group shelterwood cutting. In each plot, “potential” final crop trees were selected, based on vigour-quality-distribution criteria, and painted. Silvicultural interventions (cleaning-respacing and thinning), of different types and intensities were performed in P1-3 (P4 was kept as control) as well as P5 (500 m2), established in 2009, in 2001, 2004, and 2009. The mortality intensity between 2001 and 2019 was the highest in P4 and the lowest in P1, with the minimum stand density. Sessile oak showed the highest mortality, followed by Hungarian oak and Turkey oak. The fastest diameter growers were the “potential” final crop trees, their quadratic mean diameter (QMD) reaching values close to 20 cm at 35 (30-40) years in the plots with the lowest stand density. In all plots, trees have reached heights corresponding to the QMD of ca. 15 m, which are typical to a sessile oak stand of high productivity (production class II). The best solution for managing sessile oak young and medium-aged stands seems to be a “dynamic”, crop tree silviculture, with the most valuable individuals selected as “potential” final crop trees at the end of thicket stage. These trees should be favoured by subsequent heavy intensity thinning from above, in order to produce timber with as uniform as possible radial increments of 2-3 mm, as requested by veneer and high-quality saw log buyers.