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Background With the objective of increasing knowledge on biomass and carbon stocks, and thus improving the accuracy of published estimates, the present study explored wood density and carbon concentration of coarse woody debris (diameter ≥10) by decay class in a Seasonal Semi-deciduous Forest (SSF) area in the Atlantic Rain Forest and in a Cerrado sensu-stricto (CSS) area (Brazilian savanna), in Brazil. Two strata were identified in each area and ten sampling units were systematic located in each stratum. Data were collected according to the line intersect sampling method. Each tallied element, the diameter, length, and perpendicular width were recorded at the transect intersection point. Each element was classified into a decay class, and the species was identified when possible. Sample discs were cut from each element, from which cylindrical samples were extracted and oven-dried to determine density. These cylinders were milled and analyzed using a LECO-C632 to determine carbon concentration as percentage of mass. Results In both areas, wood density decreased as the decay class increased. For SSF the mean carbon concentration of all analyzed samples was 49.8% with a standard deviation of 3.3, with a range of 27.9–57.0% across 506 observations. For CSS the general mean was 49.6% with a standard deviation of 2.6, with a range of 31.2–54.5% over 182 observations. Carbon concentration barely change between decay classes. Carbon stock was estimated at 3.3 and 0.7 MgC/ha for the SSF and the CSS, respectively. Similar results were obtained when using a 50% conversion constant. Conclusions The present study concludes that wood density decreases as the woody debris becomes more decomposed, a pattern found in many previous studies. The carbon concentration, however, barely changes between decay classes, and that result is consistent with most of the literature reviewed. Our carbon concentrations are very close to the 50% used most commonly as a conversion factor. We strongly recommend that future studies of CWD evaluate wood density and carbon concentration by decay class to address the uncertainty still found in the literature.

Binary variables have two possible outcomes: occurrence or non-occurrence of an event (usually with 1 and 0 values, respectively). Binary data are common in ecology, including studies of presence/absence, alive/dead, and change/no-change. Logistic regression analysis has been widely used to model binary response variables. Unbalanced data (i.e., an extremely larger proportion of zeros than ones) are often found across a variety of ecological datasets. Sometimes the data are balanced (i.e., same amount of zeros and ones) before fitting the model, however, the statistical implications of balancing (or not) the data remain unclear. We assessed the statistical effects of balancing data when fitting a logistic regression model by studying both its statistical properties of the estimated parameters and its predictive capabilities. We used a base forest-mortality model as reference, and by using stochastic simulations representing different configurations of 0/1 data in a sample (unbalanced data scenarios), we fitted the logistic regression model by maximum likelihood. For each scenario we computed the bias and variance of the estimated parameters and several prediction indexes. We found that the variability of the estimated parameters is affected, with the balanced-data scenario having the lowest variability, thus, affecting the statistical inference as well. Furthermore, the prediction capabilities of the model are altered by balancing the data, with the balanced-data scenario having the better sensitivity/specificity ratio. Balancing, or not, the data to be used for fitting a logistic regression models may affect the conclusion that can arise from the fitted model and its subsequent applications.

Estimates of global carbon stocks in standing forests are subject to uncertainty because of regional and tree species differences that are usually ignored in global allometric equations. The absence of appropriate site-specific and individual tree allometric equations has led to broad use of pan moist tropical equations, which use has raised questions on the accuracy of the resulting predictions of standing biomass. Here we develop site-specific individual tree allometric equations for estimating biomass of Gilbertiodendron dewevrei, a canopy tree that dominates extensive areas of forest in the Congo basin region. We applied both antithetic and randomized branch sampling to sample for total aboveground biomass components. We evaluated a series of regression models (linear and non-linear) for predicting total aboveground biomass as a function of commonly measured variables including diameter, basal area and total height of 43 sample trees for Ituri and Yoko forests in the Democratic Republic of Congo. We found the best model for total aboveground biomass to be a linear, 3-knot cubic spline model that used only basal area as a predictor with the lowest AIC and BIC of 602 and 610 respectively. The incorporation of height in biomass equations did not significantly improve model performance, while models with diameter alone or in combination with height perform poorly. Our results show that models using only basal area are sufficient to accurately estimate biomass for Gilbertiodendron dewevrei and carbon stock, which is an important outcome given that height measurements are usually difficult to acquire. We also demonstrate that general models used can significantly overestimate amounts of carbon for Gilbertiodendron dewevrei as compared to the site and species-specific regression model that we have developed.

Post-stratified model-assisted (MA) and hybrid (HY) estimators are used with repeated airborne laser scanning (ALS) strip sampling and national forest inventory field data for stratum-wise and overall estimation of aboveground biomass (AGB) stock and change. The study area covered the southern portion of the Hedmark County in Norway. Both MA and HY estimation substantially reduced the uncertainty in AGB change when compared with estimation using the field survey only. Relative efficiencies (relative variance) of 4.15 (MA) and 3.36 (HY) for overall estimates were found. The results suggest the MA estimator for single-time estimation and the HY as more appropriate for change estimation by cover class. With the HY estimator, a nested post-stratification scheme is demonstrated, combining cover classes with change classes, which enables detailed reporting for change according to cause within each cover class, and has the potential to improve the estimation precision. Finally, parametric bootstrapping is demonstrated as an empirical alternative to estimate the model-error component in the HY estimator. The model error estimated with parametric bootstrapping converged to the analytically determined value of the HY estimator within 1000 bootstrap samples.

Patterns of structural change associated with monodominant tropical forest complexes have remained enigmatic for decades. Here, we extend previous efforts in presenting a longitudinal, local-scale analysis of forest dynamics in central Africa. Using four 10-ha census plots measured across three time periods (959,312 stems ≥1 cm DBH), we analyzed changes in a number of biometrical attributes for four distinct forest types capturing the developmental gradient from mixed species forest to Gilbertiodendron dewevrei-dominated forest. We modeled above-ground biomass (AGB), basal area (BA), and stem density across all species, and diameter at breast height (DBH), recruitment, and mortality for Gilbertiodendron dewevrei. We hypothesized that trends in these attributes are consistent with a slow spread of Gilbertiodendron dewevrei into adjacent mixed species forest. We identified statistically significant increases in AGB and BA across sites and positive, though nonsignificant, increases in AGB and BA for most forest types. DBH and relative recruitment increased significantly for Gilbertiodendron dewevrei stems, while relative mortality did not. When looking from mixed species to transitional to monodominant forest types, we found a statistically significant pattern of developmental aggradation and net expansion of monodominant forest. We do not attribute this to atmospheric forcing but to a combination of (a) landscape-scale recovery or response to widespread disturbance (primarily historical fires), (b) Gilbertiodendron dewevrei’s ectomycorrhizal association, and (c) Gilbertiodendron dewevrei’s exceptional stress tolerance traits.

Individual tree heights are needed in many situations, including estimation of tree volume, dominant height, and simulation of tree growth. However, height measurements are tedious compared to tree diameter measurements, and therefore height–diameter (H–D) models are commonly used for prediction of tree height. Previous studies have fitted H–D models using approaches that include plot-specific predictors in the models and those that do not include them. In both these approaches, aggregation of the observations to sample plots has usually been taken into account through random effects, but this has not always been done. In this paper, we discuss four alternative model formulations and report an extensive comparison of 16 nonlinear functions in this context using a total of 28 datasets. The datasets represent a wide range of tree species, regions, and ecological zones, consisting of about 126 000 measured trees from 3717 sample plots. Specific R-functions for model fitting and prediction were developed to enable such an extensive model fitting and comparison. Suggestions on model selection, model fitting procedures, and prediction are given and interpretation of the predictions from different models are discussed. No uniformly best function, model formulation, or model fitting procedure was found. However, a 2-parameter Näslund and Curtis function provided satisfactory fit in most datasets for the plot-specific H–D relationship. Model fitting and height imputation procedures developed for this study are provided in an R-package for later use.

Although differences in canopy openness, herbivory and their interaction may promote species coexistence, how these factors affect pioneer tree species and potentially limit growth, and survival has been poorly studied, particularly in tropical South Asia. We monitored the effect of canopy openness and herbivore damage on seedling survival and growth of 960 individuals of six pioneer tree species: Dillenia triquetra, Macaranga indica, Macaranga peltata, Schumacheria castaneifolia, Trema orientalis, and Wendlandia bicuspidata. Seedlings were placed in four gap-understory positions—center, outer gap edge, inner forest edge, and understory—in four large, natural gaps within the Sinharaja World Heritage Reserve, Sri Lanka. Canopy openness positively affected survival probability beyond the 550-d experiment, while herbivory decreased survival and was highest in understory conditions. The relative order of species survival stayed fairly consistent between gap-understory positions and followed their known shade tolerance rankings. When averaged across all experimental conditions, T. orientalis had the lowest survival probability estimate beyond the 550-d experiment (0.05), but the greatest capacity for growth where it successfully established, while the species with highest averaged survival probability (0.79), D. triquetra, showed the lowest growth. One species, W. bicuspidata, responded positively to herbivory by re-sprouting. Coexistence of D. triquetra, T. orientalis, and W. bicuspidata can be explained by a trade-off among species in survival, growth, and response to herbivory. In addition to variation in canopy light environment, herbivory may be important in determining pioneer species distribution through fine-scale niche partitioning and should be carefully considered in reforestation efforts.

Parks are developing nature play areas to improve children's health and “connect” them with nature. However, these play areas are often located in protected natural areas where managers must balance recreation with associated environmental impacts. In this exploratory study, we sought to describe these impacts. We also investigated which ages, gender, and play group sizes most frequently caused impact and where impacts most frequently occur. We measured the lineal and aerial extent and severity of impacts at three play areas in the eastern United States. Methods included soil and vegetation loss calculations, qualitative searches and tree and shrub damage classifications. Additionally, we observed 12 h of play at five play areas. Results showed that measurable negative impacts were caused during 33% of the time children play. On average, 76% of groundcover vegetation was lost at recreation sites and 100% was lost at informal trails. In addition, approximately half of all trees and shrubs at sites were damaged. Meanwhile, soil exposure was 25% greater on sites and trails than at controls. Boys and small group sizes more frequently caused impact, and informal recreation sites were most commonly used for play. No statistically significant correlations were found between age or location and impact frequency. Managers interested in developing nature play areas should be aware of, but not deterred by these impacts. The societal benefits of unstructured play in nature may outweigh the environmental costs. Recommended management strategies include selecting impact-resistant sites, improving site resistance, promoting low impact practices, and managing adaptively.

To estimate the aboveground biomass (AGB) for large areas, two-stage sampling designs using airborne laser scanning (ALS) as a strip sampling tool in combination with subsampling of field plots hav...

∙ Key message The study presents novel model-based estimators for growing stock volume and its uncertainty estimation, combining a sparse sample of field plots, a sample of laser data, and wall-to-wall Landsat data. On the basis of our detailed simulation, we show that when the uncertainty of estimating mean growing stock volume on the basis of an intermediate ALS model is not accounted for, the estimated variance of the estimator can be biased by as much as a factor of three or more, depending on the sample size at the various stages of the design. ∙ Context This study concerns model-based inference for estimating growing stock volume in large-area forest inventories, combining wall-to-wall Landsat data, a sample of laser data, and a sparse subsample of field data. ∙ Aims We develop and evaluate novel estimators and variance estimators for the population mean volume, taking into account the uncertainty in two model steps. ∙ Methods Estimators and variance estimators were derived for two main methodological approaches and evaluated through Monte Carlo simulation. The first approach is known as two-stage least squares regression, where Landsat data were used to predict laser predictor variables, thus emulating the use of wall-to-wall laser data. In the second approach laser data were used to predict field-recorded volumes, which were subsequently used as response variables in modeling the relationship between Landsat and field data. Results ∙ The estimators and variance estimators are shown to be at least approximately unbiased. Under certain assumptions the two methods provide identical results with regard to estimators and similar results with regard to estimated variances. ∙ Conclusion We show that ignoring the uncertainty due to one of the models leads to substantial underestimation of the variance, when two models are involved in the estimation procedure.

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes designbased and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, modelbased, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters. Keywords: Design-based inference, Model-assisted estimation, Model-based inference, Hybrid inference, National forest inventory, Remote sensing, Sampling

Pioneer tree species of tropical rain forests in the seedling and sapling size classes are generally associated with disturbances to the forest canopy. Pioneers have usually been lumped together within the same broad guild. Many studies have demonstrated differentiation of late successional shade-tolerant species within a forest but only a few studies have shown pioneers to be complex in differentiation. Most assume pioneers to be generalist and similar in growth and establishment. It is therefore not well understood, how sympatric pioneer species differ across life history stages in relation to disturbance characteristics such as canopy openness, type, intensity and historical condition of disturbance. Here we studied seedlings, saplings and larger trees of eight pioneer species common in and around the Sinharaja World Heritage Reserve in Sri Lanka to characterize species differences in relation to canopy openness, type and intensity of disturbance. We analyzed canopy hemispherical photographs and conditions of disturbance and assessed the probability of species’ occurrence and the relative importance of disturbance characteristics by fitting progressively more complex multinomial logistical regression models. Canopy openness demonstrated the greatest association with seedling and sapling distribution. Trema orientalis, was found in the highest light conditions, while all other species showed a decrease in the probability of occurrence with increasing canopy openness. For larger trees, the historical condition of disturbance was shown to be a more important factor than the light characteristics. Irrespective of size classes, T. orientalis and Macaranga peltata were restricted to high light environments and large disturbances whereas Macaranga indica, Dillenia triquetra, Schumacheria castaneifolia, and Wendlandia bicuspidata were found in relatively lower light environments and smaller disturbances. Alstonia macrophylla and Melastoma malabathricum were able to exist across wide ranging light conditions. In seedlings and saplings, disturbance type was more influential than both the historical condition of disturbance and the intensity of the disturbance, with some evidence that species do occur differentially in certain disturbance types. In summary this study provides evidence that pioneers of all life history stages differentiate in relation to amount of light and disturbance characteristics and that such differentiations are important mechanisms for the coexistence and diversity of early seral tropical tree species. Findings from our study suggests that pioneers used for reforestation and site amelioration need to be selected carefully to match degree of exposure, history and type of disturbance of the site that is to be reforested.

Reforestation in the tropics takes place across a wide variety of edaphic and climatic conditions. Reforestation trials have demonstrated that edaphic conditions may have a strong effect on species growth and survival. However it is unclear how the relative importance of soil conditions influences species survival and growth under varying amounts of rainfall and lengths of dry season. Two-year growth and mortality of 49 tree species were evaluated in four sites across Panama, representing a soil fertility–rainfall matrix. Despite strong contrasts in environmental conditions, 65% of individual species did not show consistent differences in growth between high- and low-fertility sites or between wet and dry sites. However, early growth and survival were more strongly affected by soil fertility than by rainfall patterns for the second-largest group; 30% of the species grew significantly better in both high-fertility sites than in both low-fertility sites, compared to 6% in both wet sites vs. both dry sites. In the two high-fertility sites, growth of 47% and 69% of the species was better than their across-site means. On the other hand, 55% and 73% of the species grew significantly slower than their across-site averages in the two low-fertility sites. Survival did not appear to be associated to either soil fertility or rainfall. In each site, only a few species had a significantly higher or lower within-site survival than across-site survival. Diversifying the choice of tree species increases the options for reforestation strategies that match species characteristics to local site conditions and to the objectives and management possibilities of landholders. Testing the performance of potential species under different site conditions in screening trials is paramount, both to inform selection from among the vast diversity of tree species in the tropics that show good growth and survival under different local site conditions and to filter out unsuitable species and avoid early failure of the reforestation effort.

Inasmuch as LiDAR is becoming an increasingly prominent tool for forest inventory, it is timely to develop a framework to understand the statistical properties of LiDAR-based estimates. A model-assisted approach to estimation and inference when using LiDAR as a tool to inventory aboveground forest biomass is presented. An empirical example is also presented, yet the article’s focus is largely methodological. The sampling plan in the example is viewed as a two-stage design, with slightly different primary sampling units between the profiling and scanning laser surveys. A regression estimator is presented that uses biomass data from the Norwegian National Forest Inventory as the response variable and laser-derived variables as covariates. A major thrust of this article is the presentation of the variance of the estimators of total biomass and biomass per hectare as well as variance estimators.

In forest inventories, regression models are often applied to predict quantities such as biomass at the level of sampling units. In this paper, we propose a model-based inference framework for combining sampling and model errors in the variance estimation. It was applied to airborne laser (LiDAR) data sets from Hedmark County, Norway, where the model error proportion of the total variance was found to be large for both scanning (airborne laser scanning) and profiling LiDAR when biomass was estimated. With profiling LiDAR, the model error variance component for the entire county was as large as 71% whereas for airborne laser scanning, it was 43% of the total variance. Partly, this reflects the better accuracy of the pixel-based regression models estimated from scanner data as compared with the models estimated from profiler data. The framework proposed in our study can be applied in all types of sample surveys where model-based predictions are made at the level of individual sampling units. Especially, it should be useful in cases where model-assisted inference cannot be applied due to the lack of a probability sample from the target population or due to problems of correctly matching observations of auxiliary and target variables.

Systematic sampling is easy, efficient, and widely used, though it is not generally recognized that a systematic sample may be drawn from the population of interest with or without restrictions on randomization. The restrictions or the lack of them determine which estimators are unbiased, when using the sampling design as the basis for inference. We de­ scribe the selection of a systematic sample, with and without restriction, from populations of discrete elements and from linear and areal continuums (continuous populations). We also provide unbiased estimators for both restricted and unre­ stricted selection. When the population size is known at the outset, systematic sampling with unrestricted selection is most likely the best choice. Restricted selection affords estimation of attribute totals for a population when the population size - for example, the area of an areal continuum - is unknown. Ratio estimation, however, is most likely a more pre­ cise option when the selection is restricted and the population size becomes known at the end of the sampling. There is no difference between restricted and unrestricted selection if the sampling interval or grid tessellates the frame in such a way that all samples contain an equal number of measurements. Moreover, all the estimators are unbiased and identical in this situation. Resume: L'echantillonnage systematique est simple, efficace et largement utilise, bien qu'Il ne soit pas communement ac­ cepte qu'on puisse prelever un echantillon systematique dans la population visee avec ou sans contraintes sur le caractere aleatoire de l'echantillonnage. La presence ou l'absence de contraintes determine quels estimateurs sont non biaises lorsqu'on utilise le plan dechantillonnage comme base pour faire de I'inference statistique. Nous decrivons la selection d'un echantillonnage systematique avec et sans contraintes a partir de populations delements discrets et a partir de contin­ uums lineaire et areal (populations continues). Nous fournissons egalement des estimateurs non biaises it la fois pour une selection avec ou sans contraintes, Lorsque la taille de la population est connue au depart, l'echantillonnage systematique par selection sans contraintes est tres probablement le meilleur choix. La selection avec contraintes permet d'estimer les totaux des attributs pour une population lorsque la taille de la population est inconnue (par exemple l'aire d'un continuum areal). Cependant, I'estimation par ratio est fort probablement une option plus precise lorsque Ie choix est soumis a des contraintes et que la taille de la population devient connue a la fin de I'echantillonnage. II n'y a pas de difference entre la selection avec contraintes et la selection sans contraintes si l'intervalle d'echanrillonnage et la grille du damier sont tels que tous les echantillons contiennent un nombre egal de mesures. En outre, tous les estirnateurs sont identiques et sans biais dans cette situation. (Traduit par la Redaction)

Ground plots, airborne profiling and space lidar (light detection and ranging) measurements of canopy height and crown closure, space radar topographic data, a Landsat cover type map, and a vegetation zone map were used in a model-assisted, two-phase sampling design to estimate the aboveground biomass and carbon resources of Quebec. It was determined that a simple random sampling estimator, with covariance terms added, could be used to quantify the variability of regional Geoscience Laser Altimeter System (GLAS) biomass estimates where interorbit distances are, on average, ≥15 km apart. Prediction error increased standard errors, on average, 24.4%, 4.6%, and 2.8% at the cover type, vegetation zone, and provincial levels, respectively. Inclusion of the covariance term in the calculation of grouped cover type variances increased the vegetation zone standard errors up to 3.7 times and the provincial standard errors 15.6 times. In the southern commercial forests of Quebec, GLAS underestimated ground-based biomass values by 7.3% (stratified linear model) and 10.2% (nonstratified linear model). Quebec forests support 2.57 ± 0.33 gigatonnes of carbon (nonstratified linear model). Approximately 25% of that carbon was found to be located in two southern vegetation zones (northern hardwood and mixedwood), another 25% in two northern vegetation zones (taiga and treed tundra), and the remaining 50% in the boreal zone.

An exotic insect, the hemlock woolly adelgid, Adelges tsugae Annand (Hem., Adelgidae), is spreading through eastern North America, killing hemlock trees [Tsuga canadensis (L.) Carriere], and thereby impoverishing ecosystems. Adelges tsugae, like many alien invasive insects, is difficult to monitor or sample in the forest. Monitoring of A. tsugae has been hampered by lack of information about its distribution within tree crowns. In order to assist future monitoring and biocontrol of A. tsugae, this study investigates the crown distribution of A. tsugae by sampling from the entire height of mature hemlock trees in a forest with an established infestation. In addition to A. tsugae, sampling includes scale insects, which are another group of important pests on hemlock trees. This study demonstrates the utility of a randomized branch sampling (RBS) plan for monitoring both invasive insects as well as native insects that are difficult to sample. Results from the RBS show that in trees with high populations of A. tsugae, branches from the lower crown have slightly higher densities of A. tsugae than upper crown branches. In trees with low A. tsugae populations, the upper crown may have higher A. tsugae densities than the lower crown. North pointing branches also have higher densities of A. tsugae than branches pointing in other cardinal directions. Future sampling efforts for A. tsugae can take advantage of higher densities in certain portions of the crown to increase accuracy.

The hemlock woolly adelgid (HWA) (Adelges tsugae) has spread through the range of eastern hemlock (Tsuga canadensis) leaving dead hemlock trees in its wake. We combined county and previously unavailable township level infestation records with geographic variables including plant hardiness zone, elevation, forest cover type, urbanization, precipitation, temperature, and hemlock range in a geographic information system to build models linking HWA spread to the structure and characteristics of the environment. We explored the connections between site variables and spread rate using quantile regression, Tobit regression, a parametric model for heterogeneous error variance, and classification and regression tree modeling. The results show that since 1990 HWA has spread at a rate closer to 12.5 km/yr rather than the 20–30 km/yr previously estimated, however spread rates vary significantly with environmental variables. County-based data show a faster rate of range expansion in the south, 15.6 km/yr, and very slow spread in the northern part of the current range of HWA. The rate of spread based on township records for Pennsylvania and north is 8.13 km/yr. Our study suggests that HWA spreads more slowly in colder areas, with a mean minimum temperature of − 26.1°C (plant hardiness zone 5B) delineating areas of slower range expansion. HWA also spreads more slowly during cold years, those with lower than average mean January temperatures. Our analysis demonstrates the value of quantile regression, Tobit regression, and classification and regression tree models for understanding the spread of invasive, exotic insects.

Numerous theories have been developed and tested to explain the high botanical diversity in tropical forests, ranging from nonequilibrium theories emphasizing the importance of chance to equilibrium theories depicting highly specialized species occupying narrow ecological niches. Niche-based theories have most often evaluated species adaptation to different light environments, but some studies have evaluated the importance of edaphic attributes in controlling species distributions. We evaluated the role of edaphic factors in controlling the distribution of African mahogany in the genus Entandrophragma on a 100-ha plot in the Dzanga-Sangha Dense Forest Reserve, Central African Republic. This study went beyond simple characterization of edaphic conditions in topographic or other classes to test for specific associations with chemical and physical soil parameters known to be important to plant growth. Trees ≥30 cm dbh of the four species of Entandrophragma evaluated were nonrandomly distributed in the forest. Torus translation tests indicated that none of the species exhibited any topographic preferences. However, three of the four species had significant associations with at least two soil chemical attributes. Randomization tests evaluating links between soil chemical and physical properties and topographic position underscored the complexity of the relationship and suggest that inferring edaphic attributes from broadly and simply defined habitat classes may significantly underestimate the importance of soil heterogeneity in contributing to species coexistence.

Mangroves provide extensive ecosystem services that support local livelihoods and international environmental goals, including coastal protection, biodiversity conservation and the sequestration of carbon (C). While voluntary C market projects seeking to preserve and enhance forest C stocks offer a potential means of generating finance for mangrove conservation, their implementation faces barriers due to the high costs of quantifying C stocks through field inventories. To streamline C quantification in mangrove conservation projects, we develop predictive models for (i) biomass-based C stocks, and (ii) soil-based C stocks for the mangroves of the Asia-Pacific. We compile datasets of mangrove biomass C (197 observations from 48 sites) and soil organic C (99 observations from 27 sites) to parameterize the predictive models, and use linear mixed effect models to model the expected C as a function of stand attributes. The most parsimonious biomass model predicts total biomass C stocks as a function of both basal area and the interaction between latitude and basal area, whereas the most parsimonious soil C model predicts soil C stocks as a function of the logarithmic transformations of both latitude and basal area. Random effects are specified by site for both models, which are found to explain a substantial proportion of variance within the estimation datasets and indicate significant heterogeneity across-sites within the region. The root mean square error (RMSE) of the biomass C model is approximated at 24.6 Mg/ha (18.4% of mean biomass C in the dataset), whereas the RMSE of the soil C model is estimated at 4.9 mg C/cm3 (14.1% of mean soil C). The results point to a need for standardization of forest metrics to facilitate meta-analyses, as well as provide important considerations for refining ecosystem C stock models in mangroves.

Several line- and point-based sampling methods can be employed to estimate the aggregate dimensions of trees standing on a forested tract or pieces of coarse woody debris lying on the forest floor. Line methods include line intersect sampling, horizontal line sampling, and transect relascope sampling; point methods include variable- and fixed-radius plot sampling, and point relascope sampling. We demonstrate that the line methods can be interpreted as applications of importance sampling and that point methods can be interpreted as two-stage applications of importance sampling and crude Monte Carlo. Interestingly, each of the line methods also can be implemented as a point method. Operationally, the two stages of a point method effectively reduce to a single stage. Estimators of target parameters are derived from the Monte Carlo approach for all six methods. Two new methods of estimating cross-sectional areas of slanted or tilted log-shaped objects are suggested for use in line intersect sampling. Boundary problems also are discussed.

Numerical switches are employed to change the values of two exponents in a variable-form model of bole taper. The taper of a bole is modeled, with respect to height from the ground, by three connected geometric segments (i.e., a basal, middle, and top segment). The numerical switches provide for either graded or abrupt transition from the basal to the middle segment and from the middle to the top segment. The height of the base of the crown was assumed to be the point of transition between the middle and top segments. Crown height can be either measured or estimated with a simple submodel that emerged from the taper model. Compared with estimated crown height, the use of measured crown height improved the precision of the taper model when fitted for Liquidambar styraciflua L., but not Pinus elliottii Engelm., or Pinus ponderosa Dougl. ex P. and C. Laws. The taper model also was fitted for Liriodendron tulipifera L., but crown height was not available. Correlation among residuals within individual boles was accounted for in the fitting procedure. The taper model proved to be reasonably precise for all four species, and the parameters are easy to interpret.

Four populations were repeatedly sampled with the aim of examining interval estimation of the cumulative amount, T, of some characteristic that has a positively skewed distribution, a feature of many biological populations. With samples of various sizes, the empirical sampling distribution of the simple expansion estimator was remarkably symmetric and approximately normal, as was that of the customary ratio-of-means estimator. While the nominal coverage rate of (1 − α)100% intervals based on the Student’s t distribution was nearly achieved in all cases, there was a substantially greater tendency for such intervals to fail from below rather than above. This behavior is attributed to the strong positive correlation between the estimator of the population total and the corresponding estimator of its variance when sampling from a finite, and perforce nonnormal, population.

Model-based ideas in finite-population sampling have received renewed discussion in recent years.Their relationship to the classical ideas in sampling theorydo not appear to be universally well understood by samplers in applied disciplines such as forestry, and ecology more broadly.The two inferential paradigms are constrasted, andexplanations are supplemented with examples of discrete aswell as continuously distributed populations. The treatment of spatial structureis examined, also.

Pipestem, un modele d'allocation du carbone et de croissance a l'echelle du peuplement, est formule selon une suite d'equations differentielles dont les parametres tiennent compte des taux annuels (i) de production photosynthetique, (ii) de production de matiere seche, de respiration de construction, et de respiration de base du feuillage, des racines fines et des cellules vivantes du bois, (iii) de renouvellement du feuillage et des racines fines et (iv) de perte de tissus ligneux causee par l'elagage naturel de la cime et l'eclaircie naturelle du peuplement. L'allocation du carbone est effectuee en accord avec la theorie tubulaire du transport de la seve. Le modele decrit les changements temporels dans le bilan du carbone, la production, la perte et la quantite de matiere seche sur pied, et la densite des tiges. Il calcule aussi des variables syntheses du peuplement telles que la surface terriere, la hauteur moyenne et la longueur moyenne de la cime. Pipestem est couple a un modele a etat constant de flux du carbone de facon a mesurer les effets de la temperature et d'autres variables environnementales dont les fluctuations a court terme se traduisent en modifications de croissance. Des analyses faites avec ces modeles couples en utilisant des parametres derives de Pinus taeda L. suggerent que l'influence des variables environnementales sur les taux de production et de perte de matiere seche augmente avec l'âge du peuplement et avec l'augmentation du taux de consommation des produits de la photosynthese pour la respiration de base.

The total foliar area or mass of a tree is difficult to measure, as is its bark or cambial area, and various other components of aboveground biomass. A variety of sampling methods is proposed and estimators of these characteristics are presented. Based on probability precepts, all estimators are unbiased. An unbiased estimator of variance for each estimator also is presented. The basis in probability rather than a fitted regression equation provides some important safeguards, and is a useful alternative when fitted re- gression functions are unavailable for a particular species and physiographic condition.

A linear mixed-effects model that accounts for the covariances among repeated measurements and for random plot effects is developed. A continuous-time autocorrelation error structure permits the model to be applied to irregularly spaced, unbalanced data. A strategy for fitting this model and diagnostic tools for assessing its goodness of fit are presented and demonstrated. The model is fitted to two permanent-plot data bases and shows marked improvement compared with models that do not account for the error structure.

A probabilistic sampling approach for design-unbiased estimation of area-related quantitative characteristics of spatially dispersed population units is proposed. The developed field protocol includes a fixed number of 3 units per sampling location and is based on partial triangulations over their natural neighbors to derive the individual inclusion probabilities. The performance of the proposed design is tested in comparison to fixed area sample plots in a simulation with two forest stands. Evaluation is based on a general approach for areal sampling in which all characteristics of the resulting population of possible samples is derived analytically by means of a complete tessellation of the areal sampling frame. The example simulation shows promising results. Expected errors under this design are comparable to sample plots including a much greater number of trees per plot. peerReviewed

The method of weighted least squares can be used to achieve homogeneity of variance with linear regression that has a heterogeneous error structure. A weight function commonly used when constructing regression equations to predict tree volume is [Formula: see text], where k1 ≈ 1.0–2.1. This paper examines the weight function [Formula: see text] for modelling the error structure in two loblolly pine (Pinustaeda L.) data sets and one white oak (Quercusalba L.) data set. The weight function [Formula: see text] is recommended for all three data sets, for which the k1 values ranged from 1.80 to 2.07.

Traditionally, forest crown position is classified into one of four categories: dominant, codominant, intermediate, and suppressed. The crown definitions have two primary components: a tree's stature relative to the stand's canopy level and the amount and type of light received by its crown. While this classification is meant primarily for even-aged, single level canopy stands, it is applied widely to uneven-aged stands and to those with multilevel canopies. The objective of this study was to examine the repeatability of estimating crown position in a variety of stands in the southern Appalachian spruce–fir forest. We found that crown position was difficult to similarly reclassify on the second visit in uneven-aged stands. Distinguishing a dominant from a codominant crown resulted in the lowest remeasurement proportion of agreement. We propose that the canopy position definitions be clarified and suggest an alternate system of crown classification.

Qualitative response models constitute a class of regression models used to predict one of a discrete number of mutually exclusive outcomes. These models differ from continuous regression models in that the response variable takes only discrete values. In forestry applications, the use of such models has been largely confined to mortality studies where the dependent variable is dichotomous. However, it is common in forestry to deal with variables that are either naturally discrete or continuous but recorded discretely. Consequently, there is a need for models that are appropriate for polychotomous dependent variables. Two models that appear to be suitable for forestry applications are presented, namely the ordered and unordered multinomial models, with emphasis on their theoretical justification, statistical inference, and model selection criteria. Using permanent plot data from loblolly pine (Pinustaeda L.) plantations on cutover, site-prepared areas throughout the southern United States, these models were fitted to assess the merchantability of loblolly pine trees. The results demonstrate the potential of qualitative response models for meaningful implementation in a variety of forestry applications and, also, for suggested topics for future research work.

The computed estimate of basal area was the focus of this investigation into the accuracy of tree diameter measurements. Diameter at breast height was measured on 101 trees, the breast height disc was removed from the stem, and each disc outline was then digitized to determine its actual area. Actual areas were compared with the basal areas computed with the field measurements of diameter by expressing the deviations as a proportion of the actual areas. These relative errors averaged 3.1% when basal area was computed with a taped diameter at breast height, and they averaged −2.5% when the mean of two calipered, perpendicular diameter at breast height measurements were used. Noncircularity accounted for about 2.6% of the bias, on average, in basal areas from taped diameters. Root mean square error of relative basal area errors ranged from 7.3 to 7.9% among these methods. The difference between basal areas computed with a taped diameter at breast height and with calipered diameter at breast height was computed and expressed as a proportion of the actual basal area. On average, a difference of 5% was observed. Interval estimates for these differences are presented.

International audience; We investigated the relationship between cover type and root biomass distribution and allocation to different root size classes in a naturally regenerated, dry, Rocky Mountain Douglas-fir (Pseudotsuga menziesii var. glauca) forest in the southern interior of British Columbia, Canada. The site was selectively harvested 32 years previously; residual stems were 30 cm and 130-170 years old at breast height at the time of study. A total of nine pits (each measuring $1.0\ {\rm m}\times 1.0$ m) were excavated to a depth of 1.0 m under three different cover types: mature timber, grassy (Calamagrostis rubescens) openings (canopy gaps), and regeneration clumps. Total (all diameters) live root biomass ranged from 4.7 kg/m$^2$ under the mature timber to 1.9 kg/m$^2$ under both regeneration clumps and grassy openings. Thin root (0.1 cm < $\phi\leq 0.5$ cm) biomass was similar across all three cover types (0.8 kg/m$^2$). We suggest that the similarity of thin root biomass across the three cover types is indicative of strong root competition at this resource-poor site: there appears to be no below-ground "root gap" corresponding to the canopy opening above the pinegrass-dominated patches.; Distribution de la biomasse racinaire sous trois types de couvert dans une forêt irrégulière de Pseudotsuga menziesii du Canada occidental. Nous avons étudié la relation entre le type de couvert et la répartition de la biomasse racinaire entre différentes classes de dimension des racines, dans une forêt sèche régénérée naturellement de Douglas des Montagnes Rocheuses (Pseudotsuga menziesii var. glauca) située dans la zone intérieure méridionale de la Colombie Britannique au Canada. Cette station avait subi une coupe à la dimension 32 ans auparavant. Les tiges restantes, âgées de 130 à 170 ans, avaient, au moment de l'étude, un diamètre à hauteur d'homme de 30 cm. On a creusé neuf fosses mesurant chacune $1,0 \times 1,0 \times 1,0$ m sous trois types de couvert : peuplement adulte, clairières à graminées (Calamagrostis rubescens) et bouquets de régénération. La biomasse racinaire totale (tous diamètres) va de 4,7 kg/m$^2$ sous peuplement adulte à 1,9 kg/m$^2$ sous les bouquets de régénération et sous clairières à graminées. La biomasse des racines fines (0,1 cm < $\phi\leq $ 0,5 cm) était du même ordre sous les trois types de couvert (0,8 kg/m$^2$). Nous suggérons que cette similitude des valeurs de biomasse de racines fines entre les trois types de couvert est l'indice de l'existence d'une forte concurrence entre racines, dans cette station pauvre en ressources. Les ouvertures dans le couvert forestier se traduisent par des clairières à Calamagrostis qui ne sont pas des zones sans racines.

A sampling methodology to provide an unbiased estimate of bole volume is introduced, with specific regard to its incorporation as an alternative to detailed dendrometry in a 3P sampling framework. In a demonstration of the technique to estimate merchantable volume in a 91-tree population, the average relative sampling error was 7.4%. A two-tool implementation of 3P sampling is proposed and discussed.

An explanation of the theory and utility of the jackknife procedure is provided. The latter is exemplified by a sampling simulation using ratio-of-means estimation from three forestry populations. A universal reduction in the bias of the ratio estimator was achieved, but of greater importance is the considerably more accurate estimates of its variance that are possible by jackknifing. Another use of the jackknife is provided by the derivation of an estimator of the theoretically complex variance of quadratic mean stem diameter from prism sampling data.

A composite estimator of the coefficient vector in a linear regression model is derived by weighting inversely proportional to variance. The resulting estimator is identical to the mixed estimator and to the single-update Kalman filter.