Aims: Plant responses to disturbances and environmental variation can manifest in communities as compositional nestedness (i.e., one community is a subset of another) and/or turnover (two communities represent different compositional gradient spaces). Yet, different sampling designs can artificially give an illusion of such compositional differences among two datasets, making it problematic to harmonize them in multi-species analysis. We test the prediction that sampling differences which increase beta- diversity components (nestedness and turnover) among two vegetation datasets will decrease their exchangeability. Location: Boreal forests of Tanana River region, interior Alaska, USA. Methods: We develop novel methods for comparing compositional variation among two datasets in nonmetric multidimensional scaling (NMDS) ordination. Resampled NMDS establishes internal sampling variability for each dataset independently, and reciprocal NMDS determines external exchangeability when the two are mutually exchanged. We first compare simulated data with specified beta-diversity differences, then evaluate two forest inventories based on local vs regional sampling designs in Alaska’s boreal forests. Results: As simulated species turnover and nestedness increased, internal sampling variability remained essentially constant, but external exchangeability progressively declined. Species turnover (not nestedness) had the larger negative effect on exchangeability. Among the boreal forest inventories, internal sampling variability was relatively similar, and exchangeability was weakly moderate, but the regional inventory exhibited much better fit to broad-scale environment. Species turnover (not nestedness) contributed the majority of beta-diversity differences among the two forest inventories, suggesting that strong environmental gradients were unequally represented. Conclusions: Species turnover alters multivariate outcomes more drastically than species nestedness. Therefore, combining two vegetation datasets may be inadvisable when turnover prevails. Instead, a multi-scale perspective, with separate but complementary forest inventory analyses, can portray local and regional variation at appropriate scales. Our method is tractable for assessing exchangeability of potentially inconsistent sampling designs, like those that are common in synthesis studies and long-term ecological monitoring.
Background: Volume and taper equations are essential for obtaining estimates of total and merchantable stem volume. Taper functions provide advantages to merchantable volume equations because they estimate diameter inside or outside bark at specific heights on the stem, enabling the estimation of total and merchantable stem volume, volume of individual logs, and a height at a given diameter.
Methods: Using data collected from 1218 trees (1093 Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) and 125 western hemlock (Tsuga heterophylla)), we evaluated the performance of one simple polynomial function and four variable-exponent taper functions in predicting upper stem diameter. Sample trees were collected from different parts of the states of Oregon, Washington, and California. We compared inside-bark volume estimates obtained from the selected taper equation with estimates obtained from a simple logarithmic volume equation for the data obtained in this study and the equations used by the Forest Inventory and Analysis program in the Pacific Northwest (FIA-PNW) in the state of California and western half of the states of Oregon and Washington.
Results: Variable exponent taper equations were generally better than the simple polynomial taper equations. The FIA-PNW volume equations performed fairly well but volume equations with fewer parameters fitted in this study provided comparable results. The RMSE obtained from taper-based volume estimates were also comparable with the RMSE of the FIA-PNW volume equations for Douglas-fir and western hemlock trees respectively.
Conclusions: The taper equations fitted in this study provide added benefit to the users over the FIA-PNW volume equations by enabling the users to predict diameter at any height, height to a given diameter, and merchantable volume in addition to cubic volume including top and stump (CVTS) of Douglas-fir and western hemlock trees in the Pacific Northwest. The findings of this study also give more confidence to the users of FIA-PNW volume equations.
In an Area Based Approach (ABA) to forest inventories using Airborne Laser Scanning (ALS) data, the sample plot size may vary or the cell size may differ from the plot size. Although this resolution mismatch may cause bias and increase in prediction error, it has not been thoroughly studied. The aim of this study was to clarify the meaning of resolution dependence in ABA, and to further identify its causal factors and quantify their effects. In general, a number of factors contribute to resolution dependence in ABA forest inventories, including the varying point density of the ALS data, the type of response variable, how the predictor variables are computed, and the properties of the prediction model. For quantification, we used field plots with mapped tree locations, which enabled the generation of different sized sample plots inside a larger plot. Plot level above ground biomass (AGB) was the response variable employed in all the models. The error rate seemed to increase when the prediction plots were larger than the fitting plots, and vice versa. The maximum BIAS was 1.50% and the maximum change of RMSE compared to its value in native resolution was 0.97% when there was a 4-fold difference in resolution. This indicates that the resolution effect is small in most real-world use cases, however, resolution effect should be carefully considered in ALS-assisted large area inventories that target unbiased estimates of forest parameters.
Diameter distributions and tree-lists provide information about forest stocks disaggregated by size and species and are key for informing forest management. Diameter distributions and tree-lists are multivariate responses, which makes the evaluation of methods for their prediction reliant on the use of dissimilarity metrics to summarize differences between observations and predictions. We compared four strategies for selection of k nearest neighbors (k-NN) methods to predict diameter distributions and tree-lists using LiDAR and stand-level auxiliary data and analyzed the effect of the k-NN distance and number of neighbors in the performance of the predictions. Strategies differed by the dissimilarity metric used to search for optimal k-NN configurations and the presence or absence of post-stratification. We also analyzed how alternative k-NN configurations ranked when tree-lists were aggregated using different DBH classes and species groupings. For all dissimilarity metrics, k-NN configurations using random-forest distance and three or more neighbors provided the best results. Rankings of k-NN configurations based on different dissimilarity metrics were relatively insensitive to changes on the width of the DBH classes and the definition of the species groups. The selection of the k-NN methods was clearly dependent on the choice of the dissimilarity metric. Further research is needed to find suitable ways to define dissimilarity metrics that reflect how forest managers evaluate differences between predicted and observed tree-lists and diameter distributions.
The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. Each downloadable .zip file contains the database as well as reference info and documentation.
Forest densification, wildfires, and disease can reduce the growth and survival of hardwood trees that are important for biological and cultural diversity within the Pacific Northwest of USA. Large, full-crowned hardwoods that produce fruit and that form large cavities used by wildlife were sustained by frequent, low-severity fires prior to Euro-American colonization. Shifts in fire regimes and other threats could be causing declines in, large hardwood trees. To better understand whether and where such declines might be occurring, we evaluated recent trends in Forest Inventory and Analysis (FIA) data from 1991–2016 in California and southern Oregon. We included plots that lay within areas of frequent fire regimes during pre-colonial times and potential forest habitats for fisher, a rare mammal that depends on large live hardwoods. We analyzed changes in basal area for eight hardwood species, both overall and within size classes, over three time periods within ecoregions, and in public and private land ownerships. We found the basal area to generally be stable or increasing for these species. However, data for California black oak suggested a slight decline in basal area overall, and among both very large trees and understory trees; that decline was associated with fire mortality on national forest lands. In addition, mature trees with full crowns appeared to sharply decline across all species. Many trends were not statistically significant due to high variation, especially since more precise data from remeasured trees were only available for the two most recent time periods. Continued analysis of these indicators using remeasured trees will help to evaluate whether conservation efforts are sustaining large, full-crowned trees and their associated benefits.
This publication is part of a General Technical Report about the Forest Resources of the United States, 2017. It provides forest resource statistics from the U.S. affiliated jurisdictions of the insular Caribbean and Pacific, contributing to the 2020 Resources Planning Act (RPA) Assessment. Resource data about forest area and size trends, land tenure systems, tree species composition and richness, forest volume and carbon dynamics, timber and nontimber forest products, and forest health are discussed for Puerto Rico, the U.S. Virgin Islands, American Samoa, Commonwealth of the Northern Mariana Islands, Guam, the Republic of the Marshall Islands, Federated States of Micronesia, and the Republic of Palau. Tables are available in .pdf and Excel format online at https://www.fia.fs.fed.us/ program-features/rpa/index.php. Users may also query Forest Inventory and Analysis data using the online EVALIDator tool, selecting the radio button labeled "Use RPA definition of forestland" on the second page of the query tool, available online at https://apps.fs.usda.gov/Evalidator/evalidator.jsp.
A mapping-grade dual frequency GNSS receiver was tested under dense forest canopy to determine the effect of occupation time on horizontal accuracy. The U.S. Forest Service Forest Inventory and Analysis unit in the Pacific Northwest has been using 32 of these units to collect over 7,000 plot locations since 2013. In this study, one-hour GNSS static occupations were collected at 33 ground-surveyed positions with Trimble GeoXH6000 mapping-grade and Javad Triumph1 survey-grade receivers. Rover files were differentially post-processed and horizontal accuracy of each post-processed position was computed. Results indicated that 1.85 m accuracy (n = 990) could be achieved with the GeoXH6000 receiver with 15-minute occupations; however, maximum horizontal error was 7.01 m. Increasing occupation time to 20 minutes did not result in a significant improvement in accuracy. No correlation was found between the horizontal precision of a post-processed position reported by the postprocessing software and the field-measured horizontal occuracy of the positions.