Nontimber forest products (NTFPs) play an important role in the lives of people who rely on forests. An absence of data on the size of harvests, their location, and the economic value of NTFPs prevents effective management and full utilization by all stakeholder groups. We set out to measure one important NTFP sector -- the medicinal plant trade in the diverse deciduous forests of the eastern United States, by surveying licensed buyers of ginseng (Panax quinquefolius) in 15 states about purchasing of other untracked species. To combat potential coverage and non-response bias we created a place-based model that predicted the probability of purchasing non-ginseng medicinals based on buyer location and used this to build more robust estimates. This viable method for estimating NTFP output is a replicable system that can be applied in other regions and for other products. We reviewed the literature and hypothesized biophysical and socioeconomic factors that might contribute to the prevalence of non-ginseng purchasing, and tested them on the respondents using multinomial logistic regression. The significant variables were used in two-step cluster analysis to categorize respondents and non-respondents in high or low production areas. Volume was assigned to non-respondents based on respondent behavior within each cluster. Both were then summed to estimate total output. The results depict trade volume and prices paid to harvesters for 11 medicinal NTFP species. There was significant variation between products. Two species, black cohosh (Actaea racemosa) and goldenseal (Hydrastis canadensis), accounted for 72 percent of trade volume and 77 percent of the value paid to harvesters. The total first-order value for all species estimated was 4.3 million $USD. The discrepancy between point-of-sale and retail value implies room for increasing value for all stakeholders at the base of the supply chain. Harvests for most species were concentrated in the central Appalachian coalfields. We also sought to understand what motivated or deterred participation by conducting qualitative interviews with buyers and other stakeholders. Buyers were interested in knowing the size and value of the trade, but had concerns about losing access to the resource, which was rooted in past experience with land managers and policy-makers, and conflicting discourse between stakeholders about the state of the trade and of wild populations. Many institutional deliverables are not well matched with the realities or priorities of the traditional trade. We describe potential avenues for collaboration and reciprocity, including providing market research and certifying or providing technical support for sustainably wild harvested material in addition to ongoing support for cultivation. / PHD / Nontimber forest products (NTFPs) are sources of sustenance and livelihood for people around the world. This broad category includes parts of plants such as barks, roots, and fruits, and fungi harvested for food, medicine, decoration, for use in crafts and cultural and spiritual ceremonies. They are harvested for personal use, and sold into local and global supply chains. Commercially harvested NTFPs have a dual nature. They have the potential for providing income without having the kind of large-scale disturbance caused by logging or other more impactful extractive industry. At the same time, most forests are not managed for NTFP production, and the ecological impacts of most NTFP activity are difficult to assess. Habitat loss and harvesting pressure has led to the monitoring and regulation in the trade of one iconic medicinal NTFP American ginseng (Panax quinquefolius) For the majority of NTFPs, the scale, value and distribution of the trade is unknown, presenting a barrier to effective management and institutional investment in the trade. We sought to better understand one important NTFP supply chain, the trade in medicinal plants occurring in eastern deciduous forests using a voluntary survey program. To accomplish this, we surveyed and interviewed registered ginseng buyers in 15 states about the other products they purchase. This dissertation is divided into three parts with three different objectives. The first is to describe the trade in medicinal NTFPs from eastern forests. This includes what species are being harvested, how harvests are distributed throughout the study area, the value of surveyed species to producers, and market structure close to the point of sale. We found that the majority of the trade was taking place in central Appalachia. The majority of the trade in terms of value and total output was concentrated in two species, goldenseal (Hydrastis canadensis) and black cohosh (Actaea racemosa). The second chapter seeks to create a replicable method for projecting total volume for the most commonly harvested species, including predicting the buying of the majority of respondents who did not participated. We created a model that predicted the likelihood of a respondent purchasing non-ginseng based on characteristics of their location associated with the trade. The third chapter uses interviews with buyers and other participants to explore how to improve participation in NTFP studies and make the results more useful for stakeholders.

Acid deposition (AD) results largely from the combustion of fossil fuels, and has been found to negatively impact forest ecosystems. AD may acidify soils through base cation leaching or Al mobilization, may cause accumulation of nitrates and sulfates in soils, and in some cases has been related to forest decline. The Monongahela National Forest (MNF) lies downwind from many sources of AD pollution, and average deposition pH is around 4.4. Therefore, managers are concerned about the possible deleterious effects of AD on the forest ecosystem. During the 2006 Forest Plan revision, evaluation of site sensitivity to acidification was specifically stated as a step in the Forest's adaptive management process. To meet this management objective, forest practitioners must understand the effects AD has on the forest, prescribe appropriate practices, and be able to monitor for future changes. To address the needs of MNF managers we used Forest Inventory and Analysis (FIA) sites to evaluate forest growth patterns on the Forest and determined the relationship between growth and key indicators of soil acidity. Furthermore, we used those relationships to create a map of site resistance to acidification across the MNF. To further develop a monitoring scheme we assessed two soil sampling protocols and two soil analysis methods for their suitability for monitoring AD-related changes in soil chemistry. Additionally, we evaluated the utility of dendrochronological and foliar sampling as AD-specific monitoring methods. Across all FIA sites on the MNF periodic mean annual volume increment (PMAVI) ranged from -9.5 m³ha⁻¹yr¹ to 11.8 m³ha⁻¹yr¹, suggesting lower-than-expected growth on two-thirds of the sites. Growth was compared to soil indicators of acidity on 30 FIA sites. In the surface horizon, effective base saturation (+), Ca concentration (+), base saturation (+), K concentration (+), Fe concentration (-), Ca/Al molar ratio (+), and Mg/Al molar ratio (+), were correlated with PMAVI (p ≤ 0.1). In the subsurface horizon pH_(w) (+), effective base saturation (+), Al concentration (-), and K concentration (-) were correlated with PMAVI. Site resistance to acidification was mapped based on site parent material, aspect, elevation, soil depth, and soil texture. There was a significant (p ≤ 0.1) positive correlation between PMAVI and a resistance index developed using five soil and site factors. Resistance was also compared with key soil indicators of AD-induced decline on 28 sites across the forest, and pH, effective base saturation, and Al content were found to be the best indicators related to resistance index. Resistance index was used to create a map of the MNF, of which 14% was highly resistant (RI ≥ 0.7), 57% was moderately resistant (0.7 > RI > 0.45) and 29% was slightly resistant (RI ≤ 0.45). The first of our monitoring program evaluations compared soil sampling and analysis methods on 30 FIA plots. Analyses of variance showed that soil pH, effective base saturation, Ca/Al molar ratio, and sum of bases varied significantly with sampling protocol. We also compared lab analyses methods and found that if sampling by horizon, a linear relationship can be used to estimate Ca/Al_SrCl₂ ratio using NH₄Cl extractions. The second monitoring approach evaluated the utility of a northern red oak (Quercus rubra L.) dendrochronology on two FIA plots. This analysis suggests that pollution on the MNF caused a decrease in growth rate during the 50-year period from 1940 to 1990. There were no differences among ring width increment and basal area increment between the two sites. From 1900 to 2007 the two sites showed 58.5% similarity in growth trends, but these could not be attributed to a dissimilar influence of AD. The third monitoring approach evaluated the relationship between foliar and soil chemical indicators. Across FIA plots, nutrient concentrations varied by tree species. The first year results from a potted-seedling study suggest that soil acidity influences growth, and foliar concentrations are related to growth rates. This evaluation of the effects of AD on the MNF can be used to develop adaptive management plans and a monitoring program that will meet the AD-related objectives of the 2006 Forest Management plan. / Master of Science

Most estimates of wood supply do not reflect the true availability of wood resources. The availability of wood resources ultimately depends on collective wood harvesting decisions across the landscape. Both social and biophysical constraints impact harvesting decisions and thus the availability of wood resources. While most constraints do not completely inhibit harvesting, they may significantly reduce the probability of harvest. Realistic assessments of woody availability and distribution are needed for effective forest management and planning. This study focuses on predicting the probability of harvest at forested FIA plot locations in Virginia. Classification and regression trees, conditional inferences trees, random forest, balanced random forest, conditional random forest, and logistic regression models were built to predict harvest as a function of social and biophysical availability constraints. All of the models were evaluated and compared to identify important variables constraining harvest, predict future harvests, and estimate the available wood supply. Variables related to population and resource quality seem to be the best predictors of future harvest. The balanced random forest and logistic regressions models are recommended for predicting future harvests. The balanced random forest model is the best predictor, while the logistic regression model can be most easily shared and replicated. Both models were applied to predict harvest at recently measured FIA plots. Based on the probability of harvest, we estimate that between 2012 and 2017, 10 – 21 percent of total wood volume on timberland will be available for harvesting. / Master of Science

Lewis‘ woodpeckers (Melanerpes lewis) are generally associated with open ponderosa pine (Pinus ponderosa), open riparian, and burned pine habitats in the West; however, this species has recently been found to nest in aspen (Populus tremuloides) stands in Utah. This study describes the habitat characteristics of Lewis‘ woodpecker nest sites in aspen and investigates how well aspen stand characteristics predict Lewis‘ woodpecker occupancy. I surveyed for Lewis‘ woodpeckers at previously occupied nesting locations in aspen and took habitat measurements at nest sites. In addition, nest-centered Forest Inventory and Analysis (FIA)-type plots provided stand-level habitat characteristics. I used logistic regression to determine which stand-level habitat variables were associated with nest locations; significant variables were then used to select FIA plots in Utah that contained predicted suitable nesting habitat. Criteria used to select FIA plots were aspen type stands, percent canopy cover less than 46%, and average tree diameter at breast height greater than 27.9 cm (11 inches). I then conducted occupancy surveys at FIA plots predicted to contain “suitable” and “non-suitable” Lewis’ woodpecker habitat to field validate the predictive model. No predicted non-suitable plots (n=26) were occupied and only one predicted suitable plot (n=49) was occupied. My results indicated that Lewis’ woodpeckers are rare throughout Utah in aspen stands even though there seems to be abundant nesting habitat available. My results also indicated that variables measured by FIA do not, in isolation, provide sufficient capability to predict Lewis’ woodpecker nesting habitat or actual use, and that more data are needed to accurately predict Lewis’ woodpecker nesting habitat, such as distance to, age, and severity of fires.

The Image-based Change Estimation program (ICE) was developed by the US Forest Service Forest Inventory & Analysis (FIA) program and the Geospatial Technology Applications Center in response to the 2014 Farm Bill calling for more timely and accurate estimates of land cover and use change. ICE monitors change throughout the US on a state by state basis by assessing each FIA plot using high resolution imagery from two dates in time. In the western US, FIA measures 10% of the plots each year to report on status, trends, and sustainability of our Nation’s forests. However, this 10 year cycle misses disturbances because a temporal gap occurs from disturbance event to measurement. This study compares field- and image-based observations of land cover and use change to improve sampling procedures in Utah. Image-based data collected from 2011 and 2014 imagery and field-based plots measured between 2011 and 2016 are compared using three methods to compile the ICE data, termed hierarchical, majority, and point center, to determine a standardized system and better understand their relationships. Additionally, ICE change agents were compared with causes of tree mortality observed on FIA forest plots to assess how well ICE evaluates causes of change and the differences of change vs. mortality agents were explored by conducting a second review of the imagery to find trends in data discrepancies. This knowledge can help image interpreters better recognize and identify change.

Multiple studies have documented declines of forest breeding birds in the eastern United States, but the temporal and spatial scales of most studies limit inference regarding large scale bird-habitat trends. A potential solution to this challenge is integrating existing long-term datasets such as the U.S. Forest Service Forest Inventory and Analysis (FIA) program and U.S. Geological Survey Breeding Bird Survey (BBS) that span large geographic regions. The purposes of this study were to determine if FIA metrics can be related to BBS population indices at multiple spatial and temporal scales and to develop predictive models from these relationships that identify forest conditions favorable to forest songbirds. I accumulated annual route-level BBS data for 4 species guilds (canopy nesting, ground and shrub nesting, cavity nesting, early successional), each containing a minimum of five bird species, from 1966-2004. I developed 41 forest variables describing forest structure at the county level using FIA data from for the 2000 inventory cycle within 5 physiographic regions in 14 states (AL, GA, IL, IN, KY, MD, NC, NY, OH, PA, SC, TN, VA, and WV). I examine spatial relationships between the BBS and FIA data at 3 hierarchical scales: 1) individual BBS routes, 2) FIA units, and 3) and physiographic sections. At the BBS route scale, I buffered each BBS route with a 100m, 1km, and 10km buffer, intersected these buffers with the county boundaries, and developed a weighted average for each forest variable within each buffer, with the weight being a function of the percent of area each county had within a given buffer. I calculated 28 variables describing landscape structure from 1992 NLCD imagery using Fragstats within each buffer size. I developed predictive models relating spatial variations in bird occupancy and abundance to changes in forest and landscape structure using logistic regression and classification and regression trees (CART). Models were developed for each of the 3 buffer sizes, and I pooled the variables selected for the individual models and used them to develop multiscale models with the BBS route still serving as the sample unit. At the FIA unit and physiographic section scales I calculated average abundance/route for each bird species within each FIA unit and physiographic section and extrapolated the plot-level FIA variables to the FIA unit and physiographic section levels. Landscape variables were recalculated within each unit and section using NCLD imagery resampled to a 400 m pixel size. I used regression trees (FIA unit scale) and general linear models (GLM, physiographic section scale) to relate spatial variations in bird abundance to the forest and landscape variables. I examined temporal relationships between the BBS and FIA data between 1966 and 2000. I developed 13 forest variables from statistical summary reports for 4 FIA inventory cycles (1965, 1975, 1989, and 2000) within NY, PA, MD, and WV. I used linear interpolation to estimate annual values of each FIA variable between successive inventory cycles and GLMs to relate annual variations in bird abundance to the forest variables. At the BBS route scale, the CART models accounted for > 50% of the variation in bird presence-absence and abundance. The logistic regression models had sensitivity and specificity rates > 0.50. By incorporating the variables selected for the models developed within each buffer (100m, 1km, and 10km) around the BBS routes into a multiscale model, I was able to further improve the performance of many of the models and gain additional insight regarding the contribution of multiscale influences on bird-habitat relationships. The majority of the best CART models tended to be the multiscale models, and many of the multiscale logistic models had greater sensitivity and specificity than their single-scale counter parts. The relatively fine resolution and extensive coverage of the BBS, FIA, and NLCD datasets coupled with the overlapping multiscale approach of these analyses allowed me to incorporate levels of variation in both habitat and bird occurrence and abundance into my models that likely represented a more comprehensive range of ecological variability in the bird-habitat relationships relative to studies conducted at smaller scales and/or using data at coarser resolutions. At the FIA unit and physiographic section scales, the regression trees accounted for an average of 54.1% of the variability in bird abundance among FIA units, and the GLMs accounted for an average of 66.3% of the variability among physiographic sections. However, increasing the observational and analytical scale to the FIA unit and physiographic section decreased the measurement resolution of the bird abundance and landscape variables. This limits the applicability and interpretive strength of the models developed at these scales, but they may serve as indices to those habitat components exerting the greatest influences on bird abundance at these broader scales. The GLMs relating average annual bird abundance to annual estimates of forest variables developed using statistical report data from the 1965, 1975, 1989, and 2000 FIA inventories explained an average of 62.0% of the variability in annual bird abundance estimates. However, these relationships were a function of both the general habitat characteristics and the trends in bird abundance specific to the 4-state region (MD, NY, PA, and WV) used for these analyses and may not be applicable to other states or regions. The small suite of variables available from the FIA statistical reports and multicollinearity among all forest variables further limited the applicability of these models. As with those developed at the FIA unit and physiographic sections scales, these models may serve as general indices to the habitat components exerting the greatest influences on bird abundance trends through time at regional scales. These results demonstrate that forest variables developed from the FIA, in conjunction with landscape variables, can explain variations in occupancy and abundance estimated from BBS data for forest bird species with a variety of habitat requirements across spatial and temporal scales. / Ph. D.

<p>Climate change has dramatically altered the ecological landscape of the eastern U.S., leading to shifts in phenological events and redistribution of tree species. However, shifts in phenology and species distributions have implications for the productivity of different populations and <a></a>the communities these species are a part of. Here, I utilized two studies to quantify the effects of climate change on forests of the eastern U.S. First, I used phenology observations at a common garden of 28 populations of northern red oak (<i>Quercus rubra</i>) across seven years to assess shifts in phenology in response to warming, identify population differences in sensitivity to warming, and correlate sensitivity to the productivity of the populations. Second, I utilized data from the USDA Forest Service’s Forest Inventory and Analysis Program to identify forest communities of the eastern U.S., assess shifts in their species compositions and spatial distributions, and determine which climate-related variables are most associated with changes at the community level. In the first study, I found that populations were shifting their spring phenology in response to warming, with the greatest sensitivity in populations from warmer, wetter climates. However, these populations with higher sensitivity did not have the highest productivity; rather, populations closer to the common garden with intermediate levels of sensitivity had the highest productivity. In the second study, I found that there were 12 regional forest communities of the eastern U.S., which varied in the amount their species composition shifted over the last three decades. Additionally, all 12 communities shifted their spatial distributions, but their shifts were not correlated with the distance and direction that climate change predicted them to shift. Finally, areas with the highest changes across all 12 communities were associated with warmer, wetter, lower temperature-variable climates generally in the southeastern U.S. Taken together, these studies provide insight into the ways in which forests are responding to climate change and have implications for the management and sustainability of forests in a continuously changing global environment.</p>

<p dir="ltr">Non-native biological invasions are one of the leading concerns for global biodiversity. The establishment of invasive species reduces local biodiversity, shifts species composition, changes successional trajectories, and alters ecosystem functions. This thesis examines two aspects of invasive plants: (1) the distribution and the most important climatic and anthropogenic drivers of invasive trees across the eastern United States, and (2) an evaluation of invasive plant removal and herbaceous recovery from a government cost-share program that provides financial support for invasive plant management by private landowners.</p><p><br></p><p dir="ltr">Our first study focused on identifying the distribution of invasive trees, and the factors associated with their distribution. This is essential to predicting spread and planning subsequent management. Using USDA Forest Inventory Analysis (FIA) data and random forest modeling, we examined the distribution, and variables associated with the distribution, of invasive tree species. Invasive trees were found in 10,511 out of 299,387 FIA plots. Invasive species basal area and density (trees per ha; TPH) were highest within the central and southern Appalachian Mountains, Michigan, the Northeast, and the southern Coastal Plain of the United States. A random forest model of invasive species basal area (R²= 0.47, RMSE = 0.47) and density (R²=0.46, RMSE=0.50) vs. environmental variables found that both invasive basal area and density were most strongly associated with human footprint, followed by various climatic variables. An equivalent model of native tree basal (R²=0.53, RMSE=9.25) and TPH (R²=0.47, RMSE=8.64) found that native tree basal area and density were most strongly associated with aridity followed by various climatic variables. As human footprint increased, invasive tree basal area and density increased. These results suggest that the distribution of invasive trees is reliant on human alterations to forests.</p><p><br></p><p dir="ltr">Our second study focused on Environmental Quality Incentives Program (EQIP), a federal cost-share program that has provided $25 billion of financial assistance to farmers and non-industrial private forest owners. Few studies have examined whether this program facilitates the recovery of the herbaceous layer while decreasing the dominance of invasive plant species. We surveyed the herbaceous layer of EQIP-treated and untreated (reference) forests across three physiographic regions of Indiana. Using non-metric multidimensional scaling (NMDS) ordination and linear mixed effects models, we evaluated the species composition, richness, diversity, evenness, floristic quality index, and herbaceous-layer cover of EQIP and reference sites. We also used linear mixed models to evaluate how EQIP site treatment affected the diversity of native plant species. Sites treated with EQIP contracts typically had significantly higher native species richness, Shannon’s diversity, and floristic quality than reference sites. There were significant separations in species composition between EQIP treated and reference forests state-wide and in the southern non-glaciated region of Indiana, although composition overlapped between EQIP and reference forests. Our study suggests that EQIP-funded treatments promote increased species richness and diversity. However, the persistent overlap in species composition we observed may signify biotic homogenization due to a long-shared history of anthropogenic disturbances between EQIP and reference sites. Therefore, active restoration of the herbaceous layer might be needed to allow a full recovery after invasive removal.</p>

<p dir="ltr">Global forest ecosystems provide essential ecosystem services that contribute to water and climate regulation, food production, recreation, and raw materials. They also serve as crucial habitats for numerous terrestrial species of amphibians, birds, and mammals worldwide. However, recent decades have witnessed unprecedented changes in forest ecosystems due to climate change, shifts in species distribution patterns, increased planted forest areas, and various disturbances such as forest fires, insect infestations, and urbanization. These changes can have far-reaching impacts on ecological networks, human well-being, and the well-being of global forest ecosystems. To address these challenges, I present four studies to quantify forest dynamics through mapping, classification, and projection, using artificial intelligence tools in combination with a vast amount of training data. (I) I present a spatially continuous map of planted forest distribution across East Asia, produced by integrating multiple sources of planted and natural forest data. I found that China contributed 87% of the total planted forest areas in East Asia, most of which are located in the lowland tropical/subtropical regions and Sichuan Basin. I also estimated the dominant genus in each planted forest location. (II) I used continent-wide forest inventory data to compare the range shifts of forest types and their constituent tree species in North America in the past 50 years. I found that forest types shifted more than three times as fast as the average of their constituent tree species. This marked difference was attributable to a predominant positive covariance between tree species ranges and the change of species relative abundance. (III) Based on individual-level field surveys of trees and breeding birds across North America, I characterized New World wood-warbler (<i>Parulidae</i>) species richness and its potential drivers. I identified forest type as the most powerful predictor of New World wood-warbler species richness, which adds valuable evidence to the ongoing physiognomy versus composition debate among ornithologists. (IV) In the appendix, I utilized continent-wide forest inventory data from North America and South America and the combination of supervised and unsupervised machine learning algorithms to produce the first data-driven map of forest types in the Americas. I revealed the distribution of forest types, which are useful for cost-effective forest and biodiversity management and planning. Taken together, these studies provide insight into the dynamics of forest ecosystems at a large geographic scale and have implications for effective decision-making in conservation, management, and global restoration programs in the midst of ongoing global change.</p>