Patterns of tree establishment and vegetation composition in relation to climate and topography of a subalpine meadow landscape, Jefferson Park, Oregon, USA

The forest alpine tundra ecotone (FTE, also known as alpine treeline or subalpine parkland), is a conspicuous feature of mountain landscapes throughout the world. Climate change-driven increases in temperature are believed to result in FTE movement and tree invasion of subalpine meadows, which have been documented throughout the Northern Hemisphere across a wide range of geographic locations, climatic regimes, forest types, land use histories, and disturbance regimes. Climate-driven FTE movement may have numerous ecological effects such as: positive temperature feedbacks, increased net primary productivity and carbon storage, and declines of plant populations and species. The magnitude of these ecological effects is highly uncertain, but will be largely determined by the rates and spatial extent of FTE movement and meadow invasion. FTE movement and meadow invasion are often considered at global or regional spatial scales in relation to climate, yet they are fundamentally driven by tree regeneration processes that are influenced by a variety of climatic and biophysical factors at micro site, landscape, and regional scales. Much of the research on the FTE has not taken a landscape approach incorporating multi-scale processes. For example, species distribution models used to project climate change effects on future species distributions and plant biodiversity in mountainous landscapes rely on species distribution data that is often sparse and incomplete across FTE landscapes. This dissertation attempts to overcome many of the limitations in FTE research by taking a landscape approach to develop a greater understanding of past spatiotemporal patterns of tree invasion, current spatial patterns of vegetation composition and structure, and potential future patterns of climate-driven tree invasion in the FTE. The setting for this research is Jefferson Park, a 260 ha subalpine parkland landscape in the Oregon High Cascades, USA. This study uses field plots, remotely sensed imagery, airborne Light Detection and Ranging (LiDAR), and simulation modeling to: 1) predictively map current fine-scale species distributions, vegetation structure, and tree ages; 2) reconstruct patterns of tree invasion over the last fifty years in subalpine meadows in relation to climatic conditions, landforms, microtopography, and seed dispersal limitations; and 3) develop a statistical model that projects future patterns of tree invasion into subalpine meadows under different climate scenarios in Jefferson Park. In chapter two, I generated fine-scale spatially-explicit predictions of current vegetation composition, structure, and tree ages in the Jefferson Park study area. Objectives of this chapter were threefold: 1) to characterize spatial patterns of tree ages, vegetation composition, and vegetation structure in a FTE landscape in the Oregon Cascades using predictive mapping; 2) determine how vegetation composition and structure were associated with gradients of environmental factors derived from multispectral satellite imagery and Light Detection and Ranging (LiDAR) data; and 3) determine if predictive mapping characterizations of tree age, vegetation composition, and vegetation structure were improved by the inclusion of LiDAR data. Predictive mapping of vegetation attributes was accomplished using gradient analysis with nearest neighbor imputation; integrating field plots, multispectral SPOT 5 satellite imagery, and LiDAR data. Vegetation composition was best described by SPOT 5 imagery and LiDAR-derived topography, while vegetation structure was best described by LiDAR-derived vegetation heights. Predictions of species occurrence were most accurate for tree species, moderate for shrub species and vegetation groups, and highly variable for graminoid species. Tree age, which was the most accurately predicted vegetation structure variable, indicates the study area was largely un-forested in 1600, gradually invaded by trees from 1600 to the 1920's, and rapidly invaded from the 1920's to 1980. Predictive mapping of vegetation structure variables such as basal area and stand density were subject to large amounts of error, possibly resulting from scale incompatibilities between vegetation patterns and plot size, and/or heterogeneous FTE landscapes where forest structure does not develop along consistent trajectories with stand age. This study suggests integrating multispectral satellite imagery, LiDAR data, and field plots can accurately predict fine-scale spatial characterizations of species distributions and tree invasion in the FTE. This study also indicates that sample design can influence spatial patterns of model uncertainty, which needs to be considered if predictive mapping of vegetation and sensitive ecosystems is a component of inventory and monitoring programs. In chapter three, I focused on quantifying spatiotemporal patterns of subalpine parkland tree invasion in Jefferson Park over the past five decades in relation multi-scale climatic and biophysical controls. LiDAR data provided previously unavailable fine-scale spatial characterizations of microtopography and vegetation structure. I utilized LiDAR, georeferenced field plots, and tree establishment reconstructions to quantify spatiotemporal patterns of tree invasion in relation to late season snow persistence, landform types, fine-scale topographic variability, distances from potential seed sources, and climate variation within 130 ha of the subalpine parkland landscape of Jefferson Park. Tree occurrence (i.e. tree presence in 2 m plots and grid cells) occurred in 7.75% of study area meadows in 1950 and increased to 34.7% in 2007. Landform types and finer-scale patterns of topography and vegetation structure influenced summer snow depth, which influenced temporal and spatial patterns of tree establishment. Tree invasion rates were higher on debris flow landforms, which had lower summer snow depth than glacial landforms, suggesting potentially rapid treeline responses to disturbance events. Tree invasion rates were strongly associated with reduced annual snow fall on glacial landforms, but not on debris flows. Tree establishment was spatially constrained to micro sites with high topographic positions and close proximity to overstory canopy, site conditions associated with low summer snow depth. Seed source limitations placed an additional species-specific spatial constraint on where trees invaded meadows. Climate and topography had an interactive effect, with trees establishing on higher topographic positions during both high snow/low temperature and low snow/high temperature periods, but had greater than expected establishment on lower topographic positions during low snow/high temperature periods. Within the context of larger landform types, topography and proximity to overstory trees constrained where trees established in the meadows, even during climate periods with higher temperatures and lower snowfall. Results of this study suggest large scale climate-driven models of vegetation change may overestimate treeline movement and meadow invasion, because they do not account for biophysical controls limiting tree establishment at multiple spatial scales. In chapter four, I used field data and analyses from chapter 3 to parameterize a spatially and temporally explicit statistical model of fine-scale tree invasion within 130 ha of the Jefferson Park study area. The model incorporated both the climatic and biophysical controls found in chapter 3 to influence tree invasion. The model was used in two ways: (1) to spatially project patterns of tree invasion from 1950 to 2007 in response to historical climate; and (2) to project future tree invasion of the study area from 2007 to 2064 under six different annual snowfall scenarios. Modeling addressed the following questions: (1) Can fine-scale (2 m pixel size) patterns of historical tree invasion be accurately predicted? (2) How sensitive is future tree invasion (and therefore meadow persistence) to different future snowfall scenarios? (3) Are non-climatic factors such as landforms and biotic interactions associated with different spatial patterns of tree invasion? From 1950 to 2007, simulated historical meadow area declined from 82% to 65% of the study area. Model outputs of historical area, spatial distributions, and spatial clustering of tree invasion generally agreed with independent validation, and suggest biotic interactions due to young tree establishment facilitation are important on glacial landforms but not debris flows. Simulations of future scenarios indicated meadow declined to 36 to 43% of the study area by 2064. Projected meadow area declined with reduced annual snow fall, but not under prolonged high and low snow fall periods. Meadows persisted under all future scenarios in 2064. This model suggests subalpine meadows may significantly decline under climate warming, but will still persist in 2064. Micro sites and recruitment limitation may be equally or more important factors than climate change in influencing subalpine landscape change, suggesting local high-elevation persistence of subalpine meadows under future climate warming

Comparing statistical techniques to classify the structure of mountain forest stands using CHM-derived metrics in Trento province (Italy)

In some cases a canopy height model (CHM) is the only available source of forest height information. For these cases it is important to understand the predictive power of CHM data for forest attributes. In this study we examined the use of lidar-derived CHM metrics to predict forest structure classes according to the amount of basal area present in understory, midstory, and overstory trees. We evaluated two approaches to predict size-based forest classifications: in the first, we attempted supervised classification with both linear discriminant analysis (LDA) and random forest (RF); in the second, we predicted basal areas of lower, mid, and upper canopy trees from CHM-derived variables by k-nearest neighbour imputation (k-NN) and parametric regression, and then classified observations based on their predicted basal areas. We used leave-one-out cross-validation to evaluate our ability to predict forest structure classes from CHM data and in the case of prediction-based classification approach we look at the performances in predicting basal area. The strategies proved moderately successful with a best overall classification accuracy of 41% in the case of LDA. In general, we were most successful in predicting the basal areas of small and large trees (R² respectively of 71% and 69% in the case of k-NN imputation).Keywords: lidar, linear discriminant analysis, forest structure, parametric regression, random forests, k-nearest neighbour imputatio

Do insect outbreaks reduce the severity of subsequent forest fires?

Understanding the causes and consequences of rapid environmental change is an essential scientific frontier, particularly given the threat of climate-and land use-induced changes in disturbance regimes. In western North America, recent widespread insect outbreaks and wildfires have sparked acute concerns about potential insect-fire interactions. Although previous research shows that insect activity typically does not increase wildfire likelihood, key uncertainties remain regarding insect effects on wildfire severity (i.e., ecological impact). Recent assessments indicate that outbreak severity and burn severity are not strongly associated, but these studies have been limited to specific insect or fire events. Here, we present a regional census of large wildfire severity following outbreaks of two prevalent bark beetle and defoliator species, mountain pine beetle (Dendroctonus ponderosae) and western spruce budworm (Choristoneura freemani), across the US Pacific Northwest. Wefirst quantify insect effects on burn severity with spatial modeling at the fire event scale and then evaluate how these effects vary across the full population of insect-fire events (n = 81 spanning 1987-2011). In contrast to common assumptions of positive feedbacks, we find that insects generally reduce the severity of subsequent wildfires. Specific effects vary with insect type and timing, but both insects decrease the abundance of live vegetation susceptible to wildfire at multiple time lags. By dampening subsequent burn severity, native insects could buffer rather than exacerbate fire regime changes expected due to land use and climate change. In light of these findings, we recommend a precautionary approach when designing and implementing forest management policies intended to reduce wildfire hazard and increase resilience to global change.Keywords: fire ecology, remote sensing, regime change, forest health, defoliator, bark beetle, disturbance interaction

Complex mountain terrain and disturbance history drive variation in forest aboveground live carbon density in the western Oregon Cascades, USA

Forest carbon (C) density varies tremendously across space due to the inherent heterogeneity of forest ecosystems. Variation of forest C density is especially pronounced in mountainous terrain, where environmental gradients are compressed and vary at multiple spatial scales. Additionally, the influence of environmental gradients may vary with forest age and developmental stage, an important consideration as forest landscapes often have a diversity of stand ages from past management and other disturbance agents. Quantifying forest C density and its underlying environmental determinants in mountain terrain has remained challenging because many available data sources lack the spatial grain and ecological resolution needed at both stand and landscape scales. The objective of this study was to determine if environmental factors influencing aboveground live carbon (ALC) density differed between young versus old forests. We integrated aerial light detection and ranging (lidar) data with 702 field plots to map forest ALC density at a grain of 25 m across the H.J. Andrews Experimental Forest, a 6369 ha watershed in the Cascade Mountains of Oregon, USA. We used linear regressions, random forest ensemble learning (RF) and sequential autoregressive modeling (SAR) to reveal how mapped forest ALC density was related to climate, topography, soils, and past disturbance history (timber harvesting and wildfires). ALC increased with stand age in young managed forests, with much greater variation of ALC in relation to years since wildfire in old unmanaged forests. Timber harvesting was the most important driver of ALC across the entire watershed, despite occurring on only 23% of the landscape. More variation in forest ALC density was explained in models of young managed forests than in models of old unmanaged forests. Besides stand age, ALC density in young managed forests was driven by factors influencing site productivity, whereas variation in ALC density in old unmanaged forests was also affected by finer scale topographic conditions associated with sheltered sites. Past wildfires only had a small influence on current ALC density, which may be a result of long times since fire and/or prevalence of non-stand replacing fire. Our results indicate that forest ALC density depends on a suite of multi-scale environmental drivers mediated by complex mountain topography, and that these relationships are dependent on stand age. The high and context-dependent spatial variability of forest ALC density has implications for quantifying forest carbon stores, establishing upper bounds of potential carbon sequestration, and scaling field data to landscape and regional scales.Keywords: Landscape heterogeneity, Forest carbon, Lidar, Wildfire, Topography, Forest managemen

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the central Oregon Cascades, USA. Mapped explanatory data included tasseled-cap indices and disturbance history metrics (year, magnitude, and duration of disturbance) from LTS imagery, lidar-derived vegetation metrics, climate, topography, and soil parent material. Vegetation data from USDA Forest Service forest inventory plots was summarized at two plot sizes (plot and subplot) and geographically located with two levels of accuracy (standard and improved). Maps of vegetation composition and structure were developed with the Gradient Nearest Neighbor (GNN) method of NN imputation using different combinations of explanatory variables, plot spatial resolution, and plot positional accuracy. Lidar vegetation indices greatly improved predictions of live tree structure, moderately improved predictions of snag density and down wood volume, but did not consistently improve species predictions. LTS disturbance metrics improved predictions of forest structure, but not to the degree of lidar indices, while also improving predictions of many species. Absence of disturbance attribution (i.e. disturbance type such as fire or timber harvest) in LTS disturbance metrics may have limited our ability to predict forest structure. Absence of corrected lidar intensity values may also have lowered accuracy of snag and species predictions. However, LTS disturbance attribution and lidar corrected intensity values may not be able to overcome fundamental limitations of remote sensing for predicting snags and down wood that are obscured by the forest canopy. Improved GPS plot locations had little influence on map accuracy, and we suggest under what conditions improved GPS plot locations may or may not improve the accuracy of predictive maps that link remote sensing with forest inventory plots. Subplot NN imputation maps had much lower accuracy compared to maps generated using response variables from larger whole plots. No single map had optimal results for every mapped variable, suggesting map users and developers need to prioritize what forest vegetation attributes are most important for any given map application.Keywords: Lidar, Landsat time series, Disturbance, Nearest-neighbor imputation, Forest composition and structur

Does wildfire likelihood increase following insect outbreaks in conifer forests?

Although there is acute concern that insect-caused tree mortality increases the likelihood or severity of subsequent wildfire, previous studies have been mixed, with findings typically based on stand-scale simulations or individual events. This study investigates landscape- and regional-scale wildfire likelihood following outbreaks of the two most prevalent native insect pests in the US Pacific Northwest (PNW): mountain pine beetle (MPB; Dendroctonus ponderosae) and western spruce budworm (WSB; Choristoneura freemani). We leverage seamless census data across numerous insect and fire events to (1) summarize the interannual dynamics of insects (1970–2012) and wildfires (1984–2012) across forested ecoregions of the PNW; (2) identify potential linked disturbance interactions with an empirical wildfire likelihood index; (3) quantify this insect-fire likelihood across different insect agents, time lags, ecoregions, and fire sizes. All three disturbance agents have occurred primarily in the drier, interior conifer forests east of the Cascade Range. In general, WSB extent exceeds MPB extent, which in turn exceeds wildfire extent, and each disturbance typically affects less than 2% annually of a given ecoregion. In recent decades across the PNW, wildfire likelihood does not consistently increase or decrease following insect outbreaks. There is evidence, however, of linked interactions that vary across insect agent (MPB, WSB), space (ecoregion), and time (interval since insect onset). Specifically, in most cases following MPB activity, fire likelihood is neither higher nor lower than in non-MPB-affected forests. In contrast, fire likelihood is lower following WSB activity across multiple ecoregions and time lags. In addition, insect-fire likelihood is not consistently associated with interannual fire extent, suggesting that other factors (e.g., climate) control the disproportionately large fire years accounting for regional fire dynamics. Thus, although both bark beetles and defoliators alter fuels and associated fire potential, the windows of opportunity for increased or decreased fire likelihood are too narrow—or the phenomena themselves too rare—for a consistent signal to emerge across PNW conifer forests. These findings suggest that strategic plans should recognize (1) the relative rarity of insect-fire interactions and (2) the potential ecosystem restoration benefits of native insect outbreaks, when they do occur

Does wildfire likelihood increase following insect outbreaks in conifer forests?

Although there is acute concern that insect‐caused tree mortality increases the likelihood or severity of subsequent wildfire, previous studies have been mixed, with findings typically based on stand‐scale simulations or individual events. This study investigates landscape‐ and regional‐scale wildfire likelihood following outbreaks of the two most prevalent native insect pests in the US Pacific Northwest (PNW): mountain pine beetle (MPB; Dendroctonus ponderosae) and western spruce budworm (WSB; Choristoneura freemani). We leverage seamless census data across numerous insect and fire events to (1) summarize the interannual dynamics of insects (1970–2012) and wildfires (1984–2012) across forested ecoregions of the PNW; (2) identify potential linked disturbance interactions with an empirical wildfire likelihood index; (3) quantify this insect‐fire likelihood across different insect agents, time lags, ecoregions, and fire sizes. All three disturbance agents have occurred primarily in the drier, interior conifer forests east of the Cascade Range. In general, WSB extent exceeds MPB extent, which in turn exceeds wildfire extent, and each disturbance typically affects less than 2% annually of a given ecoregion. In recent decades across the PNW, wildfire likelihood does not consistently increase or decrease following insect outbreaks. There is evidence, however, of linked interactions that vary across insect agent (MPB, WSB), space (ecoregion), and time (interval since insect onset). Specifically, in most cases following MPB activity, fire likelihood is neither higher nor lower than in non‐MPB‐affected forests. In contrast, fire likelihood is lower following WSB activity across multiple ecoregions and time lags. In addition, insect‐fire likelihood is not consistently associated with interannual fire extent, suggesting that other factors (e.g., climate) control the disproportionately large fire years accounting for regional fire dynamics. Thus, although both bark beetles and defoliators alter fuels and associated fire potential, the windows of opportunity for increased or decreased fire likelihood are too narrow—or the phenomena themselves too rare—for a consistent signal to emerge across PNW conifer forests. These findings suggest that strategic plans should recognize (1) the relative rarity of insect‐fire interactions and (2) the potential ecosystem restoration benefits of native insect outbreaks, when they do occur

Does wildfire likelihood increase following insect outbreaks in conifer forests?

Although there is acute concern that insect‐caused tree mortality increases the likelihood or severity of subsequent wildfire, previous studies have been mixed, with findings typically based on stand‐scale simulations or individual events. This study investigates landscape‐ and regional‐scale wildfire likelihood following outbreaks of the two most prevalent native insect pests in the US Pacific Northwest (PNW): mountain pine beetle (MPB; Dendroctonus ponderosae) and western spruce budworm (WSB; Choristoneura freemani). We leverage seamless census data across numerous insect and fire events to (1) summarize the interannual dynamics of insects (1970–2012) and wildfires (1984–2012) across forested ecoregions of the PNW; (2) identify potential linked disturbance interactions with an empirical wildfire likelihood index; (3) quantify this insect‐fire likelihood across different insect agents, time lags, ecoregions, and fire sizes. All three disturbance agents have occurred primarily in the drier, interior conifer forests east of the Cascade Range. In general, WSB extent exceeds MPB extent, which in turn exceeds wildfire extent, and each disturbance typically affects less than 2% annually of a given ecoregion. In recent decades across the PNW, wildfire likelihood does not consistently increase or decrease following insect outbreaks. There is evidence, however, of linked interactions that vary across insect agent (MPB, WSB), space (ecoregion), and time (interval since insect onset). Specifically, in most cases following MPB activity, fire likelihood is neither higher nor lower than in non‐MPB‐affected forests. In contrast, fire likelihood is lower following WSB activity across multiple ecoregions and time lags. In addition, insect‐fire likelihood is not consistently associated with interannual fire extent, suggesting that other factors (e.g., climate) control the disproportionately large fire years accounting for regional fire dynamics. Thus, although both bark beetles and defoliators alter fuels and associated fire potential, the windows of opportunity for increased or decreased fire likelihood are too narrow—or the phenomena themselves too rare—for a consistent signal to emerge across PNW conifer forests. These findings suggest that strategic plans should recognize (1) the relative rarity of insect‐fire interactions and (2) the potential ecosystem restoration benefits of native insect outbreaks, when they do occur

The Problem of Experience in the Study of Organizations

This paper deals with the fact that we cannot experience large organizations directly, in the same way as we can experience individuals or small groups, and that this non-experientiability has certain implications for our scientific theories of organizations. Whereas a science is animated by a constructive interplay of theory concepts and experience concepts, the study of organizations has been confined to theory concepts alone. Implications of this analysis for developing a science of organizations are considered.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68303/2/10.1177_017084069301400102.pd