Nicholas L. Crookston

Empirical analyses of plant-climate relationships for the western United States

The Random Forests multiple-regression tree was used to model climate profiles of 25 biotic communities of the western United States and nine of their constituent species. Analyses of the communities were based on a gridded sample of ca. 140,000 points, while those for the species used presence‐absence data from ca. 120,000 locations. Independent variables included 35 simple expressions of temperature and precipitation and their interactions. Classification errors for community models averaged 19%, but the errors were reduced by half when adjusted for misalignment between geographic data sets. Errors of omission for species‐specific models approached 0, while errors of commission were less than 9%. Mapped climate profiles of the species were in solid agreement with range maps. Climate variables of most importance for segregating the communities were those that generally differentiate maritime, continental, and monsoonal climates, while those of importance for predicting the occurrence of species varied among species but consistently implicated the periodicity of precipitation and temperature‐precipitation interactions. Projections showed that unmitigated global warming should increase the abundance primarily of the montane forest and grassland community profiles at the expense largely of those of the subalpine, alpine, and tundra communities but also that of the arid woodlands. However, the climate of 47% of the future landscape may be extramural to contemporary community profiles. Effects projected on the spatial distribution of species‐specific profiles were varied, but shifts in space and altitude would be extensive. Species‐specific projections were not necessarily consistent with those of their communities.

Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data

We describe and evaluate a new analysis technique, spatial wavelet analysis (SWA), to automatically estimate the location, height, and crown diameter of individual trees within mixed conifer open canopy stands from light detection and ranging (lidar) data. Two-dimensional Mexican hat wavelets, over a range of likely tree crown diameters, were convolved with lidar canopy height models. Identification of local maxima within the resultant wavelet transformation image then allowed determination of the location, height, and crown diameters of individual trees. In this analysis, which focused solely on individual trees within open canopy forests, 30 trees incorporating seven dominant North American tree species were assessed. Two-dimensional (2D) wavelet-derived estimates were well correlated with field measures of tree height (r = 0.97) and crown diameter (r = 0.86). The 2D wavelet-derived estimates compared favorably with estimates derived using an established method that uses variable window filters (VWF) to estimate the same variables but relies on a priori knowledge of the tree height – crown diameter relationship. The 2D spatial wavelet analysis presented herein could potentially allow automated, large-scale, remote estimation of timber board feet, foliar biomass, canopy volume, and aboveground carbon, although further research testing the limitations of the method in a variety of forest types with increasing canopy closures is warranted.

Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data

We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived from discrete-return lidar data (2 m post spacing), advanced land imager (ALI) multispectral (30 m) and panchromatic (10 m) data, or geographic X, Y, and Z location. In general, the lidar-derived variables had greater utility than the ALI variables for predicting the response variables, especially basal area. The variables most useful for predicting basal area were lidar height variables, followed by lidar intensity; those most useful for predicting tree density were lidar canopy cover variables, again followed by lidar intensity. The best integrated models selected via a best-subsets procedure explained ~90% of variance in both response variables. Natural-logarithm-transformed response variables were modeled. Predictions were then transformed from the natural logarithm scale back to the natural scale, corrected for transformation bias, and mapped across the two study areas. This study demonstrates that fundamental forest structure attributes can be modeled to acceptable accuracy and mapped with currently available remote sensing technologies.

North American vegetation model for land-use planning in a changing climate: a solution to large classification problems.

Data points intensively sampling 46 North American biomes were used to predict the geographic distribution of biomes from climate variables using the Random Forests classification tree. Techniques were incorporated to accommodate a large number of classes and to predict the future occurrence of climates beyond the contemporary climatic range of the biomes. Errors of prediction from the statistical model averaged 3.7%, but for individual biomes, ranged from 0% to 21.5%. In validating the ability of the model to identify climates without analogs, 78% of 1528 locations outside North America and 81% of land area of the Caribbean Islands were predicted to have no analogs among the 46 biomes. Biome climates were projected into the future according to low and high greenhouse gas emission scenarios of three General Circulation Models for three periods, the decades surrounding 2030, 2060, and 2090. Prominent in the projections were (1) expansion of climates suitable for the tropical dry deciduous forests of Mexico, (2) expansion of climates typifying desertscrub biomes of western USA and northern Mexico, (3) stability of climates typifying the evergreen-deciduous forests of eastern USA, and (4) northward expansion of climates suited to temperate forests, Great Plains grasslands, and montane forests to the detriment of taiga and tundra climates. Maps indicating either poor agreement among projections or climates without contemporary analogs identify geographic areas where land management programs would be most equivocal. Concentrating efforts and resources where projections are more certain can assure land managers a greater likelihood of success.

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

Abstract 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.

Height-growth response to climatic changes differs among populations of Douglas-fir: a novel analysis of historic data

Projected climate change will affect existing forests, as substantial changes are predicted to occur during their life spans. Species that have ample intraspecific genetic differentiation, such as Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), are expected to display population-specific growth responses to climate change. Using a mixed-effects modeling approach, we describe three-year height (HT) growth response to changes in climate of interior Douglas-fir populations. We incorporate climate information at the population level, yielding a model that is specific to both species and population. We use data from provenance tests from previous studies that comprised 236 populations from Idaho, Montana, and eastern Washington, USA. The most sensitive indicator of climate was the mean temperature of the coldest month. Population maximum HT and HT growth response to changes in climate were dependent on seed source climate. All populations had optimum HT growth when transferred to climates with warmer winters; those originating in sites with the warmest winters were taller across sites and had highest HT growth at transfer distances closest to zero; those from colder climates were shortest and had optimum HT growth when transferred the farthest. Although this differential response damped the height growth differences among populations, cold-climate populations still achieved their maximum growth at lower temperatures than warm-climate populations. The results highlight the relevance of understanding climate change impacts at the population level, particularly in a species with ample genetic variation among populations.

Quantifying the abundance of co-occurring conifers along Inland Northwest (USA) climate gradients

The occurrence and abundance of conifers along climate gradients in the Inland Northwest (USA) was assessed using data from 5082 field plots, 81% of which were forested. Analyses using the Random Forests classification tree revealed that the sequential distribution of species along an altitudinal gradient could be predicted with reasonable accuracy from a single climate variable, a growing-season dryness index, calculated from the ratio of degree-days >5 degrees C that accumulate in the frost-free season to the summer precipitation. While the appearance and departure of species in an ascending altitudinal sequence were closely related to the dryness index, the departure was most easily visualized in relation to negative degree-days (degree-days < 0 degrees C). The results were in close agreement with the works of descriptive ecologists. A Weibull response function was used to predict from climate variables the abundance and occurrence probabilities of each species, using binned data. The fit of the models was excellent, generally accounting for >90% of the variance among 100 classes.

Using climate-FVS to project landscape-level forest carbon stores for 100 years from field and LiDAR measures of initial conditions

BackgroundForest resources supply a wide range of environmental services like mitigation of increasing levels of atmospheric carbon dioxide (CO2). As climate is changing, forest managers have added pressure to obtain forest resources by following stand management alternatives that are biologically sustainable and economically profitable. The goal of this study is to project the effect of typical forest management actions on forest C levels, given a changing climate, in the Moscow Mountain area of north-central Idaho, USA. Harvest and prescribed fire management treatments followed by plantings of one of four regionally important commercial tree species were simulated, using the climate-sensitive version of the Forest Vegetation Simulator, to estimate the biomass of four different planted species and their C sequestration response to three climate change scenarios.ResultsResults show that anticipated climate change induces a substantial decrease in C sequestration potential regardless of which of the four tree species tested are planted. It was also found that Pinus monticola has the highest capacity to sequester C by 2110, followed by Pinus ponderosa, then Pseudotsuga menziesii, and lastly Larix occidentalis.ConclusionsVariability in the growth responses to climate change exhibited by the four planted species considered in this study points to the importance to forest managers of considering how well adapted seedlings may be to predicted climate change, before the seedlings are planted, and particularly if maximizing C sequestration is the management goal.

An aggregation algorithm for increasing the efficiency of population models

Abstract An algorithm (called COMPRESS) is presented to efficiently combine (aggregate) similar individuals during a simulation of population dynamics, while retaining the basic behavior and overall variation in the system. The algorithm speeds up computing time and makes room for new individuals created by birth processes. First, a linear combination of the important attributes is calculated for each individual, based on the first principal component of the correlation matrix of attributes. After sorting these linear combinations, the largest gaps in the list are found and used as the basis for aggregation. During a second stage, the clusters with the largest ranges left the first stage are split. The first stage maximizes variation among clusters, while the second stage reduces variation within clusters. Although the algorithm has more within-class variation than Fishers optimal algorithm, in our applications this difference was only 1% of the total variation. Furthermore, computing time is reduced by an order of magnitude or more compared to Fishers procedure. The effect of the algorithm on model behavior (e.g., bias) is minimal,comparing favorably with optimal procedures. The algorithm should be useful in any ecologically-based population model that simulates the development of a large number of individuals, such as trees.