Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods. The models were developed and fit to data collected by the Forest Inventory and Analysis program of the US Forest Service in Washington, Oregon, and California. For predicting cavity tree and snag abundance per stand, all three NB regression models performed better in terms of mean square prediction error than the NN imputation methods. The most similar neighbor imputation, however, outperformed the NB regression models in predicting overall cavity tree and snag abundance

Estimating Current Forest Attributes from Paneled Inventory Data Using Plot-Level Imputation: A Study from the Pacific Northwest

Information on current forest condition is essential to assess and characterize resources and to support resource management and policy decisions. The 1998 Farm Bill mandates the US Forest Service to conduct annual inventories to provide annual updates of each state's forest. In annual inventories, the sample size of I year (panel) is only a portion of the full sample and therefore the precision of the estimations for any given year is low. To achieve higher precision, the Forest Inventory and Analysis program uses a moving average (MA), which combines the data of multiple panels, as default estimator. The MA can result in biased estimates of current conditions and alternative methods are sought. Alternatives to MA have not yet been explored in the Pacific Northwest. Data from Oregon and Washington national forests were used to examine a weighted moving average (WMA) and three imputation approaches: most similar neighbor, gradient nearest neighbor, and randomForest (RF). Using the most recent measurements of the variables of interest as ancillary variables, RF provided almost unbiased estimates that were comparable to those of the MA and WMA estimators in terms of root mean square error.Keywords: nearest neighbor imputation, weighted moving average, missing panels, moving averag

Estimating Riparian Understory Vegetation Cover with Beta Regression and Copula Models

Understory vegetation communities are critical components of forest ecosystems. As a result, the importance of modeling understory vegetation characteristics in forested landscapes has become more apparent. Abundance measures such as shrub cover are bounded between 0 and 1, exhibit heteroscedastic error variance, and are often subject to spatial dependence. These distributional features tend to be ignored when shrub cover data are analyzed. The beta distribution has been used successfully to describe the frequency distribution of vegetation cover. Beta regression models ignoring spatial dependence (BR) and accounting for spatial dependence (BRdep) were used to estimate percent shrub cover as a function of topographic conditions and overstory vegetation structure in riparian zones in western Oregon. The BR models showed poor explanatory power (pseudo-R² ≤ 0.34) but outperformed ordinary least-squares (OLS) and generalized least-squares (GLS) regression models with logit-transformed response in terms of mean square prediction error and absolute bias. We introduce a copula (COP) model that is based on the beta distribution and accounts for spatial dependence. A simulation study was designed to illustrate the effects of incorrectly assuming normality, equal variance, and spatial independence. It showed that BR, BRdep, and COP models provide unbiased parameter estimates, whereas OLS and GLS models result in slightly biased estimates for two of the three parameters. On the basis of the simulation study, 93–97% of the GLS, BRdep, and COP confidence intervals covered the true parameters, whereas OLS and BR only resulted in 84–88% coverage, which demonstrated the superiority of GLS, BRdep, and COP over OLS and BR models in providing standard errors for the parameter estimates in the presence of spatial dependence. FOR. SCI. 57(3):212–221.Keywords: beta regression, Gaussian copula, spatial copula, shrub coverKeywords: beta regression, Gaussian copula, spatial copula, shrub cove

Geostatistical modeling of riparian forest microclimate and its implications for sampling

Les modèles de prédiction du microclimat pour différentes conditions de station dans les zones riveraines boisées des cours d’eau de tête de bassin sont peu développés et les procédures d’échantillonnage pour caractériser les gradients sous-jacents du microclimat riverain sont rares. Nous avons utilisé des données de microclimat riverain collectées le long de huit cours d’eau de tête de bassin dans la chaîne côtière de l’Oregon pour comparer le krigeage ordinaire (KO), le krigeage universel (KU) et le krigeage avec dérive externe (KDE) pour la prédiction localisée de la température moyenne maximale de l’air (Tair). Plusieurs caractéristiques topographiques et de la structure de la forêt ont été considérées comme paramètres spécifiques à la station. L’élévation au-dessus du cours d’eau et la distance du cours d’eau étaient les covariables les plus importantes dans les modèles de KDE qui donnaient de meilleurs résultats que le KO et le KU en termes d’écart-type. La répartition des échantillons a été optimisée sur la base de la variance de krigeage et des moyennes pondérées du critère de la plus courte distance à l’aide d’un algorithme de recuit simulé. La répartition optimisée des échantillons donnait de meilleurs résultats que la répartition systématique en termes de variance moyenne de krigeage, surtout lorsque le nombre d’échantillons était faible. Ces résultats suggèrent des méthodes pour augmenter l’efficacité du suivi du microclimat dans les zones riveraines.Predictive models of microclimate under various site conditions in forested headwater stream – riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (T air). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas

Host–parasite distributions under changing climate: Tsuga heterophylla and Arceuthobium tsugense in Alaska

Dwarf mistletoes (Arceuthobium species) influence many processes within forested ecosystems, but few studies have examined their distribution in relation to climate. An analysis of 1549 forested plots within a 14.5 million ha region of southeast Alaska provided strong indications that climate currently limits hemlock dwarf mistletoe (Arceuthobium tsugense (Rosendahl) G.N. Jones) to a subset of the range of its primary tree host, western hemlock (Tsuga heterophylla (Raf.) Sarg.), with infection varying from a high of 20% of trees at sea level to only 5% by 200 m elevation. Three types of modeling approaches (logistic, most similar neighbors, and random forests) were tested for the ability to simultaneously predict abundance and distribution of host and pathogen as a function of climate variables. Current distribution was explained well by logistic models using growing degree-days, indirect and direct solar radiation, rainfall, snowfall, slope, and minimum temperatures, although accuracy for predicting A. tsugense presence at a particular location was only 38%. For future climate scenarios (A1B, A2, and B1), projected increases for A. tsugense habitat over a century ranged from a low of 374% to a high of 757%, with differences between modeling approaches contributing more to uncertainty than differences between climate scenarios

The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases

Almost universally, forest inventory and monitoring databases are incomplete, ranging from missing data for only a few records and a few variables, common for small land areas, to missing data for many observations and many variables, common for large land areas. For a wide variety of applications, nearest neighbor (NN) imputation methods have been developed to fill in observations of variables that are missing on some records (Y-variables), using related variables that are available for all records (X-variables). This review attempts to summarize the advantages and weaknesses of NN imputation methods and to give an overview of the NN approaches that have most commonly been used. It also discusses some of the challenges of NN imputation methods. The inclusion of NN imputation methods into standard software packages and the use of consistent notation may improve further development of NN imputation methods. Using X-variables from different data sources provides promising results, but raises the issue of spatial and temporal registration errors. Quantitative measures of the contribution of individual X-variables to the accuracy of imputing the Y-variables are needed. In addition, further research is warranted to verify statistical properties, modify methods to improve statistical properties, and provide variance estimators.Keywords: registration error, forest measurements, consistent notation, input data for forest planning, nearest neighbor imputation, sources of X-variablesKeywords: registration error, forest measurements, consistent notation, input data for forest planning, nearest neighbor imputation, sources of X-variable

The changing culture of silviculture

Changing climates are altering the structural and functional components of forest ecosystems at an unprecedented rate. Simultaneously, we are seeing a diversification of public expectations on the broader sustainable use of forest resources beyond timber production. As a result, the science and art of silviculture needs to adapt to these changing realities. In this piece, we argue that silviculturists are gradually shifting from the application of empirically derived silvicultural scenarios to new sets of approaches, methods and practices, a process that calls for broadening our conception of silviculture as a scientific discipline. We propose a holistic view of silviculture revolving around three key themes: observe, anticipate and adapt. In observe, we present how recent advances in remote sensing now enable silviculturists to observe forest structural, compositional and functional attributes in near-real-time, which in turn facilitates the deployment of efficient, targeted silvicultural measures in practice that are adapted to rapidly changing constraints. In anticipate, we highlight the importance of developing state-of-the-art models designed to take into account the effects of changing environmental conditions on forest growth and dynamics. In adapt, we discuss the need to provide spatially explicit guidance for the implementation of adaptive silvicultural actions that are efficient, cost-effective and socially acceptable. We conclude by presenting key steps towards the development of new tools and practical knowledge that will ensure meeting societal demands in rapidly changing environmental conditions. We classify these actions into three main categories: reexamining existing silvicultural trials to identify key stand attributes associated with the resistance and resilience of forests to multiple stressors, developing technological workflows and infrastructures to allow for continuous forest inventory updating frameworks, and implementing bold, innovative silvicultural trials in consultation with the relevant communities where a range of adaptive silvicultural strategies are tested. In this holistic perspective, silviculture can be defined as the science of observing forest condition and anticipating its development to apply tending and regeneration treatments adapted to a multiplicity of desired outcomes in rapidly changing realities

Difference in Regeneration Conditions in Pinus ponderosa Dominated Forests in Northern California, USA, over an 83 Year Period

Forest inventories based on field surveys can provide quantitative measures of regeneration such as density and stocking proportion. Understanding regeneration dynamics is a key element that supports silvicultural decision-making processes in sustainable forest management. The objectives of this study were to: (1) describe historical regeneration in ponderosa pine dominated forests by species and height class, (2) find associations of regeneration with overstory, soil, and topography variables, (3) describe contemporary regeneration across various management treatments, and (4) compare differences in regeneration between historical and contemporary forests. The study area, a ponderosa pine (Pinus ponderosae Dougl. ex P. and C. Law) dominated forest, is located within the Blacks Mountain Experimental Forest (BMEF) in northeastern California, United States, which was designated as an experimental forest in 1934. We used 1935 and 2018 field surveyed regeneration data containing information about three species—ponderosa pine, incense-cedar (Calocedrus decurrens (Torr.) Florin) and white fir (Abies concolor (Grod. and Glend)—and four height classes: class 1: 0–0.31 m, class 2: 0.31–0.91 m, class 3: 0.91–1.83 m, and class 4: >1.83 m and <8.9 cm diameter at breast height. We used stocking as proxy for regeneration density in this study. We found that historically, stocking in the BMEF was dominated by shade-intolerant ponderosa pine in height classes 2 and 3. Two variables—overstory basal area per hectare (m² ha⁻¹ ) and available water capacity at 150 cm, which is the amount of water that is available for plants up to a depth of 150 cm from the soil surface—were significantly associated with stocking, and a beta regression model fit was found to have a pseudo-R² of 0.49. We identified significant differences in contemporary stocking among six management scenarios using a Kruskal–Wallis non-parametric one-way ANOVA. Control compartments had the highest stocking followed by burned compartments. In contemporary forest stands, recent treatments involving a combination of burning and thinning resulted in high stocking in height classes 2 and 3. Overall, the stocking in historical BMEF stands was higher than in contemporary stands and was dominated by ponderosa pine.Science, Faculty ofNon UBCResources, Environment and Sustainability (IRES), Institute forReviewedFacult

Imputing Mean Annual Change to Estimate Current Forest Attributes

When a temporal trend in forest conditions is present, standard estimates from paneled forest inventories can be biased. Thus methods that use more recent remote sensing data to improve estimates are desired. Paneled inventory data from national forests in Oregon and Washington, U.S.A., were used to explore three nearest neighbor imputation methods to estimate mean annual change of four forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha). The random Forest imputation method outperformed the other imputation approaches in terms of root mean square error. The imputed mean annual change was used to project all panels to a common point in time by multiplying the mean annual change with the length of the growth period between measurements and adding the change estimate to the previously observed measurements of the four forest attributes. The resulting estimates of the mean of the forest attributes at the current point in time outperformed the estimates obtained from the national standard estimator.Keywords: national forest inventories, forest inventory and analysis, forest monitoring, Pacific Northwest, paneled inventory data, nearest neighbor imputatio

Surface fuel loads following a coastal-transitional fire of unprecedented severity: Boulder Creek fire case study

British Columbia experienced three years with notably large and severe wildfires since 2015. Multiple stand-replacing wildfires occurred in coastal-transitional forests, where large fires are typically rare and thus information on post-fire carbon is lacking. Because of their carbon storage potential, coastal-transitional forests are important in the global carbon cycle. We examined differences in surface fuel carbon among fire severity classes in 2016, one year after the Boulder Creek fire, which burned 6 735 ha of coastal-transitional forests in 2015. Using remotely-sensed indices (dNBR) we partitioned the fire area into unburned (control), low-, moderate-, and high-severity classes. Field plots were randomly located in each class. At each plot, surface fuel carbon was quantified by typeâ coarse, small, and fine woody material, duff, and litterâ and carbon mass by fuel type was compared among severity classes. Total surface fuel carbon did not differ significantly between burned and unburned plots, however there was significantly less duff and litter carbon in burned plots. Remotely-sensed severity classes did not properly capture wildfire impacts on surface fuels, especially at lower severities. Pre-fire stand characteristics are also important drivers of surface fuel loads. This case-study provides baseline data for examining post-fire fuel carbon dynamics in coastal-transitional British Columbia.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author