Whitebark pine (Pinus albicaulis Engelm.) forests play a prominent role throughout high-elevation ecosystems in the northern Rocky Mountains, however, they are vanishing from the high mountain landscape due to three factors: exotic white pine blister rust (Cronartium ribicola Fischer) invasions, mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreaks, and successional replacement by more shade-tolerant tree species historically controlled by wildfire. Land managers are attempting to restore whitebark pine communities using prescribed fire and silvicultural cuttings, but they are unsure if these techniques are effective. The objective of this study was to determine how whitebark pine regeneration responds to selective thinning and prescribed burn treatments. We studied changes in diameter growth after restoration treatments using ring width measurements obtained from 93 trees at four sites in Montana and Idaho that were treated in the late 1990s. Overall, the average annual radial growth rates of the trees in treated areas were greater than those of trees in control areas. Specifically, there were significant increases in the growth ratio (180%) in the two sites that were both thinned and later burned. Younger regeneration showed more response to the treatments than older regeneration. All sites showed high variability in post-treatment growth rates across individual trees, with greater variability for trees in treated areas than in trees from the control areas. Results suggest that whitebark pine regeneration can respond to thin and burn release treatments and that managers may see positive results in areas that are treated similarly.

Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link responses with predictor variable values. Inherently, this linking process introduces measurement error into the response and predictors, which in the latter case causes attenuation bias. Through simulations, our findings indicate that the spatial correlation of response and predictor variables and their corresponding spatial registration (co-registration) errors can have a substantial impact on the bias and accuracy of linear models. Additionally, in this study we evaluate spatial aggregation as a mechanism to minimize the impact of co-registration errors, assess the impact of subsampling within the extent of sample units, and provide a technique that can be used to both determine the extent of an observational unit needed to minimize the impact of co-registration and quantify the amount of error potentially introduced into predictive models.

Effective forest management is predicated on accurate information pertaining to the characteristics and condition of forests. Unfortunately, ground-based information that accurately describes the complex spatial and contextual nature of forests across broad landscapes is cost prohibitive to collect. In this case study we address technical challenges associated with estimating forest characteristics from remotely sensed data by incorporating field plot layouts specifically designed for calibrating models from such data, applying new image normalization procedures to bring images of varying spatial resolutions to a common radiometric scale, and implementing an ensemble generalized additive modeling technique. Image normalization and ensemble models provided accurate estimates of forest types, presence/absence of longleaf pine (Pinus palustris), and tree basal areas and tree densities over a large segment of the panhandle of Florida, USA. This study overcomes several of the major barriers associated with linking remotely sensed imagery with plot data to estimate key forest characteristics over large areas.

Climate change and shifting fire regimes have the potential to alter forest structure and tree growth dynamics in high‐elevation forests; however, the relative role of competitive and facilitative interactions in shaping postfire responses remains unclear. To investigate these dynamics, we studied tree growth across an elevational gradient in the subalpine and treeline forests of the Selway‐Bitterroot Wilderness (Idaho and Montana, USA), where fire atlas data and a history of limited fire suppression enabled the implementation of a 2 × 2 factorial study design. We sampled burned and unburned stands in two elevation strata: within the transition from closed canopy subalpine forest to subalpine parkland (forest–parkland transition; FPT) and near the upper limit of single‐stem upright tree growth (alpine treeline ecotone; ATE), which represent low environmental stress and high environmental stress conditions, respectively. We measured radial growth and tree neighborhood composition for focal trees (≥20 cm diameter at breast height [dbh]) of two functional groups informed by their ecological roles: stress‐tolerant, early successional “benefactor” species (whitebark pine, Pinus albicaulis and subalpine larch, Larix lyallii ), and shade‐tolerant, late successional “beneficiary” species (subalpine fir, Abies lasiocarpa and Engelmann spruce, Picea engelmannii ). Patterns of neighborhood stem density across our sites indicated a shift from competition to facilitation with increasing elevation, while fire served to reduce neighborhood stem density in focal tree plots. Growth responses to fire were group‐specific: spruce/fir showed a clear fire‐induced growth release, particularly at lower elevations, while whitebark pine/larch exhibited a more limited response. Although fire did not alter the sensitivity of tree growth to climate, relationships with winter precipitation shifted from uniformly negative (pre‐1979) to neutral or positive (post‐1979) across all species and elevations. Ultimately, our results suggest that while responses to disturbance are likely to vary across species and elevations, directional climate change is uniformly reducing abiotic limitations on tree growth in the high‐elevation forests of the northern Rocky Mountains.

Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have relied on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy.

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (RBS). Synthetic generalized regression (GREG) estimators are developed, and their properties examined against standard estimators. It is shown that synthetic GREG estimators with zero or low design-bias can be obtained, and that the variance of a design-unbiased class of GREG estimators can be unbiasedly estimated. Simulated sampling from 20 censused crowns of two Rocky Mountain species indicated that improvements in accuracy can be obtained through GREG estimation following RBS. Simulations also showed that synthetic GREG estimators that pool data from multiple trees can stabilize coefficients of multivariate regression models to provide improved accuracy over direct GREG estimators. However, for the univariate regression models that proved most adept for the censused crowns, direct GREG estimation provided the lowest average root mean squared error (RMSE) for RBS. Simulations also showed that model-based branch aggregation estimators have generally low RMSE but can be heavily design-biased. For the crown forms studied, use of branch auxiliary information at both the design and estimation stages through RBS and GREG estimation appears to be more efficient than using the information only at the estimation stage following simple or stratified random sampling.