Ram P. Sharma

Site index prediction from site and climate variables for Norway spruce and Scots pine in Norway

Abstract Site index prediction models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) were developed using Norwegian National Forest Inventory data. A number of multiple linear regression models with different combinations of site and climate variables were developed in order to facilitate their application to a range of situations where the accessibility of various explanatory data differs. The best models used year of stand origin, temperature sum, vegetation type groups, soil depth, aspect, slope and latitude to predict site index. These models explained a large part of the total variation ( = 0.86 and 0.72 for spruce and pine, respectively) and had little residual variation (RMSE = 2.04 and 1.95 m for spruce and pine, respectively). Alternative models using only year of stand origin, temperature sum and vegetation type groups, or soil depth in addition, had slightly lower but still useful predictive power. All the developed models exhibited a strong non-linear effect of the year of stand origin on site indices, which varied when temperature sum was included. The increase in site indices along with increasing year of stand origin was significantly faster after about 1940 for both species. Similar time trends were observed for mean temperature and precipitation sums for the periods of stand growth, but only exhibited a faster increase after about 1960. Even though increased temperature and precipitation after 1990 seem to contribute to increased site indices, increased nitrogen availability and atmospheric CO2 levels may also be important factors.

Modeling height-diameter relationships for Norway spruce, Scots pine, and downy birch using Norwegian national forest inventory data

We developed nonlinear mixed effects height-diameter models for three major tree species: Norway spruce (Picea abies [L.] Karst.); Scots pine (Pinus sylvestris L.); and downy birch (Betula pubescens [Ehrh.]) in Norway. We used data from four Norwegian national forest inventory (NFI) cycles (7th–10th NFI cycle) as model fitting data and data from the 6th NFI cycle as validation data. Among several bi-parametric functions tested as base functions in a preliminary analysis, the Näslund function showed the smallest residual variations, and therefore it was extended by incorporating stand variables as covariates that act as modifiers of the original parameters of the Näslund function. Sample plot-level random effects were also included in order to account for inter-plot variations within the populations. Unlike a basic mixed effects model, the extended mixed model described larger parts of variations in the height-diameter relationships and predicted heights without significant bias for validation data from the sample plots, where all measured heights of the focused species (species used for species-specific model) were used to predict random effects. For species independent models, when measured heights of other than focused species were used to predict random effects, a significant height prediction bias occurred. This bias could be reduced for certain diameter ranges by applying an extended ordinary least square model. We recommend using extended mixed effects models to estimate the missing heights on NFI sample plots and other sample plots, where measured tree heights of the focused species are available for prediction of random effects. When measured heights are not available, the extended ordinary least square model can be used.

Modelling individual tree basal area growth of Blue pine (Pinus wallichiana) for Mustang district in Nepal

Individual tree growth models are important decision-making tools for forest management. We developed individual tree basal area growth models with Blue pine (Pinus wallichiana) data from Lete and Kunjo areas of Mustang district in Nepal. The sample trees were identified from all applicable ages, sizes, site qualities, and stand conditions and were cut. Diameters and ages were measured on the cut surface of stump (at 30 cm above ground). With the application of the auto-regressive error-structured modelling approach, we fitted Bertalanffy function to the data from 94 stumps by using basal area growth per year as dependent variable and stump age or stump diameter as independent variable. The age-independent individual tree basal area growth model showed better fits (R 2 adj = 0.8324) than its age-dependent counterpart (R 2 adj = 0.8174). Because of having better fits and being easier for application, the age-independent model is recommended for predicting basal area growth per year at an individual tree level for Blue pine across Lete and Kunjo areas of Mustang district.

When, where and whom: assessing wildlife attacks on people in Chitwan National Park, Nepal

Wildlife attacks on people in and around protected areas have become one of the main challenges for wildlife management authorities. We assessed all correlates of wildlife attacks during 2003–2013 in the vicinity of Chitwan National Park, Nepal. We used data from various sources (discussion with stakeholders, field observations, questionnaire surveys). Wildlife attacks were significantly correlated to factors such as site, season and time, activity, gender and awareness. Moreover, 89% of recorded attacks occurred outside the Park. The number of attacks fluctuated widely and patterns of attacks were significantly uneven across seasons and months. Of the 87% of attacks that occurred during the day, 87% occurred in the morning. Most victims were male and c. 45% of attacks occurred when people were collecting forest resources or working on croplands. Attacks were carried out predominantly by rhinoceros Rhinoceros unicornis (38%), tigers Panthera tigris (21%), sloth bears Melursus ursinus (18%), elephants Elephas maximus (9%) and wild boar Sus scrofa (8%). The people attacked lived close to the Park, depended on farming for their livelihoods, and had little knowledge of animal behaviour. Attacks can be mitigated through proper management of habitats inside the Park and raising awareness of wildlife behaviour among local people. We recommend establishing a participatory emergency rescue team to deal with problematic animals in high-risk areas.

Modeling above-ground biomass for three tropical tree species at their juvenile stage

Accurate prediction of biomass for juveniles (sapling and seedling) of any stand is important to estimate total biomass or carbon stock in the stand. In this study allometric biomass models were developed for prediction of above-ground biomass for three major tropical tree species (Shorea robusta, Terminalia tomentosa, and Acacia catechu) at their juvenile stage. Biomass data for this study were acquired from 120 destructively sampled juvenile individuals (40 for each species) of these species in the lowland of western Nepal. Among several mathematical models tested, an exponential model with diameter and total height as explanatory variables showed the best fits to the data (i.e. smallest root mean square error (RMSE) and Akaike information criterion (AIC), and largest R2adj). Also the same model form with diameter, height and wood density as explanatory variables fitted the data equally well. All other models with diameter alone or its combination with other variables showed relatively poorer fits. The first two best models of the forms and explained >92% above-ground biomass proportion, resulting in a small random variation of residuals around zero (RMSE = 62 g). Thus, for more accuracy, one of these two models was recommended to predict above-ground biomass of juveniles of three species. Since the models developed in this study are explicitly site-specific, their application should be restricted to site, size and stand conditions similar to the basis of this study. Further works for validation and verification of the presented models with new data from a wider range of site, size and stand conditions of Shorea robusta, Terminalia tomentosa, and Acacia catechu are recommended.

Modelling individual tree height to crown base of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.)

Height to crown base (HCB) of a tree is an important variable often included as a predictor in various forest models that serve as the fundamental tools for decision-making in forestry. We developed spatially explicit and spatially inexplicit mixed-effects HCB models using measurements from a total 19,404 trees of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.) on the permanent sample plots that are located across the Czech Republic. Variables describing site quality, stand density or competition, and species mixing effects were included into the HCB model with use of dominant height (HDOM), basal area of trees larger in diameters than a subject tree (BAL- spatially inexplicit measure) or Hegyi’s competition index (HCI—spatially explicit measure), and basal area proportion of a species of interest (BAPOR), respectively. The parameters describing sample plot-level random effects were included into the HCB model by applying the mixed-effects modelling approach. Among several functional forms evaluated, the logistic function was found most suited to our data. The HCB model for Norway spruce was tested against the data originated from different inventory designs, but model for European beech was tested using partitioned dataset (a part of the main dataset). The variance heteroscedasticity in the residuals was substantially reduced through inclusion of a power variance function into the HCB model. The results showed that spatially explicit model described significantly a larger part of the HCB variations [R2adj = 0.86 (spruce), 0.85 (beech)] than its spatially inexplicit counterpart [R2adj = 0.84 (spruce), 0.83 (beech)]. The HCB increased with increasing competitive interactions described by tree-centered competition measure: BAL or HCI, and species mixing effects described by BAPOR. A test of the mixed-effects HCB model with the random effects estimated using at least four trees per sample plot in the validation data confirmed that the model was precise enough for the prediction of HCB for a range of site quality, tree size, stand density, and stand structure. We therefore recommend measuring of HCB on four randomly selected trees of a species of interest on each sample plot for localizing the mixed-effects model and predicting HCB of the remaining trees on the plot. Growth simulations can be made from the data that lack the values for either crown ratio or HCB using the HCB models.

Injury Severity of Wildlife Attacks on Humans in the Vicinity of Chitwan National Park, Nepal

Most of the studies conducted about wildlife attacks on humans so far have disproportionately focused on fatal attacks, but further exploration is needed to understand other injury severities (minor, serious, death). This paper focuses on assessment of the extent of injury caused by wildlife attacks on humans in the vicinity of the Chitwan National Park (CNP) of Nepal for a period between 2003 and 2013. In the vicinity of this park, people suffer from the attacks by various wild animals such as rhino (Rhinoceros unicornis), tiger (Panthera tigris), sloth bear (Melursus ursinus), elephant (Elephas maximus), and wild boar (Sus scrofa). We used data collected from group discussion (n=33), key stakeholder interview (n=36), field observation, and household questionnaire survey (n=329). Our results showed that wildlife attacks were significantly correlated to site environment, season, victims’ gender, age, awareness, and activities. The injury severity significantly correlated to attacking animal species (p<0.0001). Fatal cases occurred on 1-people in-3, and rest suffered with minor to severe injuries. On an average, 30 attacks occurred annually. Most fatalities were caused by elephant attacks (68%) followed by tiger (57%), rhino (29%), bear (4%), and by wild boar attacks (4%). Most fatalities (84%) occurred at incident sites, where some victims had to loss their lives due to delay in rescue. The victims were found facing substantially harsh physical, psychological, and economical problems. Patterns of the attacks were significantly uneven across months (p<0.001). Uneducated persons, fishermen, and collectors of forest resources received more fatal attacks than others. We suggest for creation of awareness among local people about species-specific behaviour of attacking animals. The medical trauma centre should be established in the vicinity of the CNP and existing local medical centres should be upgraded for immediate treatment of the victims.

Parameter estimation of nonlinear mixed-effects models using first-order conditional linearization and the EM algorithm

Nonlinear mixed-effects (NLME) models are flexible enough to handle repeated-measures data from various disciplines. In this article, we propose both maximum-likelihood and restricted maximum-likelihood estimations of NLME models using first-order conditional expansion (FOCE) and the expectation–maximization (EM) algorithm. The FOCE-EM algorithm implemented in the ForStat procedure SNLME is compared with the Lindstrom and Bates (LB) algorithm implemented in both the SAS macro NLINMIX and the S-Plus/R function nlme in terms of computational efficiency and statistical properties. Two realworld data sets an orange tree data set and a Chinese fir (Cunninghamia lanceolata) data set, and a simulated data set were used for evaluation. FOCE-EM converged for all mixed models derived from the base model in the two realworld cases, while LB did not, especially for the models in which random effects are simultaneously considered in several parameters to account for between-subject variation. However, both algorithms had identical estimated parameters and fit statistics for the converged models. We therefore recommend using FOCE-EM in NLME models, particularly when convergence is a concern in model selection.

Modeling individual tree height growth of Norway spruce and Scots pine from national forest inventory data in Norway

ABSTRACT We developed individual tree height growth models for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.) in Norway based on national forest inventory data. Potential height growth is based on existing dominant height growth models and reduced due to competition by functions developed in this study. Three spatially explicit and two spatially non-explicit competition indices were tested. Distance effects and diameter ratio effects were estimated from the data simultaneously with parameters of the potential modifier functions. Large height measurement errors in the national forest inventory data caused large residual variation of the models. However, the effects of competition on height growth were significant and plausible. The potential modifier functions show that height growth of dominant trees is largely unaffected by competition. Only at higher levels of competition, height growth is reduced as a consequence of competition. However, Scots pine also reduced height growth at very low levels of competition. Distance effects in the spatially explicit competition indices indicated that the closest neighbors are most important for height growth. However, for Scots pine also competitors at larger distance affected height growth. The five competition indices tested in this study explained similar proportions of the variation in relative height growth. Given that unbiased predictions can only be expected for the same plot size, we recommend a spatially explicit index, which describes the distance function with a negative exponential, for use in growth simulators.

Allometric Bark Biomass Model for Daphne bholua in the Mid-Hills of Nepal

Bark of Daphne bholua is an important non-timber forest product and makes a substantial contribution to the Nepalese economy. A precise estimate of the amount of D. bholua bark in mountain forests is possible using a biomass model. We developed an allometric bark biomass model for naturally grown D. bholua in Baglung District in the mid-hills of Nepal. The model was based on data from 101 destructively sampled D. bholua on 20 sample plots representing different growth stages (regeneration, established, and matured), site qualities, and stand densities, and we used diameter and height–diameter ratio as predictors. Among 9 functions evaluated, a simple power function showed the best fit to the data. This model described most of the variations in bark biomass with no substantial trends in the residuals. Leave-one-out cross-validation also confirmed the high precision of this model, because it described most of the variations in bark biomass with no substantial trends in the prediction errors. The model can be applied for a precise prediction of bark biomass for individuals of D. bholua with diameters and height–diameter ratios similar to those used in this study. It is site-specific, and its application should therefore be limited to sites with growth stage, site quality, stand density, and species distribution similar to those that formed the basis of this study. Further validation and verification of this model, with a larger dataset collected from sites with a wider range of these characteristics, is recommended.