Landscape and forest structure at moose mortality sites on Isle Royale National Park. A LiDAR based assessment

Landscape structure and heterogeneity play a potentially important, but little understood role in predator-prey interactions and behaviourally-mediated habitat selection. For example, habitat complexity may either reduce or enhance the efficiency of a predator's efforts to search, track, capture, kill and consume prey. For prey, structural heterogeneity may affect predator detection, avoidance and defense, escape tactics, and the ability to exploit refuges. This study, investigates whether and how vegetation and topographic structure influence the spatial patterns and distribution of moose (Alces alces) mortality due to predation and malnutrition at the local and landscape levels on Isle Royale National Park. 230 locations where wolves (Canis lupus) killed moose during the winters between 2002 and 2010, and 182 moose starvation death sites for the period 1996-2010, were selected from the extensive Isle Royale Wolf-Moose Project carcass database. A variety of LiDAR-derived metrics were generated and used in an algorithm model (Random Forest) to identify, characterize, and classify three-dimensional variables significant to each of the mortality classes. Furthermore, spatial models to predict and assess the likelihood at the landscape scale of moose mortality were developed. This research found that the patterns of moose mortality by predation and malnutrition across the landscape are non-random, have a high degree of spatial variability, and that both mechanisms operate in contexts of comparable physiographic and vegetation structure. Wolf winter hunting locations on Isle Royale are more likely to be a result of its prey habitat selection, although they seem to prioritize the overall areas with higher moose density in the winter. Furthermore, the findings suggest that the distribution of moose mortality by predation is habitat-specific to moose, and not to wolves. In addition, moose sex, age, and health condition also affect mortality site selection, as revealed by subtle differences between sites in vegetation heights, vegetation density, and topography. Vegetation density in particular appears to differentiate mortality locations for distinct classes of moose. The results also emphasize the significance of fine-scale landscape and habitat features when addressing predator-prey interactions. These finer scale findings would be easily missed if analyses were limited to the broader landscape scale alone.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Spatial and temporal analysis of land cover and landscape structure change in Zagros Forests

The Zagros oak forests in Western Iran are critically important to the sustainability of the region. These forests have undergone dramatic declines in recent decades. We evaluated the utility of the non-parametric Random Forest classification algorithm for land cover classification of Zagros landscapes, and selected the best spatial and spectral predictive variables. The algorithm resulted in high overall classification accuracies (>85%) and also equivalent classification accuracies for the datasets from the three different sensors. We evaluated the associations between trends in forest area and structure with trends in socioeconomic and climatic conditions, to identify the most likely driving forces creating deforestation and landscape structure change. We used available socioeconomic (urban and rural population, and rural income), and climatic (mean annual rainfall and mean annual temperature) data for two provinces in northern Zagros. The most correlated driving force of forest area loss was urban population, and climatic variables to a lesser extent. Landscape structure changes were more closely associated with rural population. We examined the effects of scale changes on the results from spatial pattern analysis. We assessed the impacts of eight years of protection in a protected area in northern Zagros at two different scales (both grain and extent). The effects of protection on the amount and structure of forests was scale dependent. We evaluated the nature and magnitude of changes in forest area and structure over the entire Zagros region from 1972 to 2009. We divided the Zagros region in 167 Landscape Units and developed two measures— Deforestation Sensitivity (DS) and Connectivity Sensitivity (CS) — for each landscape unit as the percent of the time steps that forest area and ECA experienced a decrease of greater than 10% in either measure. A considerable loss in forest area and connectivity was detected, but no sudden (nonlinear) changes were detected at the spatial and temporal scale of the study. Connectivity loss occurred more rapidly than forest loss due to the loss of connecting patches. More connectivity was lost in southern Zagros due to climatic differences and different forms of traditional land use.

Assessing visual disturbance conditions on the Custer National Forest

Assessment of soil disturbance on the Custer National Forest was conducted during two summers to determine if the U.S. Forest Service Forest Soil Disturbance Monitoring Protocol (FSDMP) was able to distinguish post-harvest soil conditions in a chronological sequence of sites harvested using different ground-based logging systems. Results from the first year of sampling suggested that the FSDMP point sampling method may not be sensitive enough to measure post-harvest disturbance in stands with low levels of disturbance. Therefore, a revised random transect method was used during the second sampling season to determine the actual extent of soil disturbance in these cutting units. Using combined data collected from both summers I detected statistically significant differences (p < 0.05) in fine fraction bulk density measurements between FSDMP disturbance classes across all sites. Disturbance class 3 (most severe) had the highest reported bulk density, which suggest that the FSDMP visual class estimates are defined adequately allowing for correlations to be made between visual disturbance and actual soil physical characteristics. Forest site productivity can be defined by its ability to retain carbon and convert it to above- and belowground biomass. However, forest management activities that alter basic site characteristics have the potential to alter productivity. Soil compaction is one critical management impact that is important to understand; compaction has been shown to impede the root growth potential of plants, reduce water infiltration rates increasing erosion potential, and alter plant available water and nutrients, depending on soil texture. A new method to assess ground cover, erosion, and other soil disturbances was recently published by the U.S. Forest Service, as the Forest Soil Disturbance Protocol (FSDMP). The FSDMP allows soil scientists to visually assign a disturbance class estimate (0 – none, 1, 2, 3 – severe) from field measures of consistently defined soil disturbance indicators (erosion, fire, rutting, compaction, and platy/massive/puddled structure) in small circular (15 cm) plots to compare soil quality properties pre- and post- harvest condition. Using this protocol we were able to determine that ground-based timber harvesting activities occurring on the Custer National Forest are not reaching the 15% maximum threshold for detrimental soil disturbance outlined by the Region 1 Soil Quality Standards.

Towards greater accuracy in individual-tree mortality regression

Background mortality is an essential component of any forest growth and yield model. Forecasts of mortality contribute largely to the variability and accuracy of model predictions at the tree, stand and forest level. In the present study, I implement and evaluate state-of-the-art techniques to increase the accuracy of individual tree mortality models, similar to those used in many of the current variants of the Forest Vegetation Simulator, using data from North Idaho and Montana. The first technique addresses methods to correct for bias induced by measurement error typically present in competition variables. The second implements survival regression and evaluates its performance against the traditional logistic regression approach. I selected the regression calibration (RC) algorithm as a good candidate for addressing the measurement error problem. Two logistic regression models for each species were fitted, one ignoring the measurement error, which is the “naïve” approach, and the other applying RC. The models fitted with RC outperformed the naïve models in terms of discrimination when the competition variable was found to be statistically significant. The effect of RC was more obvious where measurement error variance was large and for more shade-intolerant species. The process of model fitting and variable selection revealed that past emphasis on DBH as a predictor variable for mortality, while producing models with strong metrics of fit, may make models less generalizable. The evaluation of the error variance estimator developed by Stage and Wykoff (1998), and core to the implementation of RC, in different spatial patterns and diameter distributions, revealed that the Stage and Wykoff estimate notably overestimated the true variance in all simulated stands, but those that are clustered. Results show a systematic bias even when all the assumptions made by the authors are guaranteed. I argue that this is the result of the Poisson-based estimate ignoring the overlapping area of potential plots around a tree. Effects, especially in the application phase, of the variance estimate justify suggested future efforts of improving the accuracy of the variance estimate. The second technique implemented and evaluated is a survival regression model that accounts for the time dependent nature of variables, such as diameter and competition variables, and the interval-censored nature of data collected from remeasured plots. The performance of the model is compared with the traditional logistic regression model as a tool to predict individual tree mortality. Validation of both approaches shows that the survival regression approach discriminates better between dead and alive trees for all species. In conclusion, I showed that the proposed techniques do increase the accuracy of individual tree mortality models, and are a promising first step towards the next generation of background mortality models. I have also identified the next steps to undertake in order to advance mortality models further.

CHARACTERIZING AND IMPROVING PRODUCTION OF FERMENTABLE SUGARS AND CO-PRODUCTS FROM A FOREST PRODUCT INDUSTRY WASTEWATER STREAM

Hardboard processing wastewater was evaluated as a feedstock in a bio refinery co-located with the hardboard facility for the production of fuel grade ethanol. A thorough characterization was conducted on the wastewater and the composition changes of which during the process in the bio refinery were tracked. It was determined that the wastewater had a low solid content (1.4%), and hemicellulose was the main component in the solid, accounting for up to 70%. Acid pretreatment alone can hydrolyze the majority of the hemicellulose as well as oligomers, and over 50% of the monomer sugars generated were xylose. The percentage of lignin remained in the liquid increased after acid pretreatment. The characterization results showed that hardboard processing wastewater is a feasible feedstock for the production of ethanol. The optimum conditions to hydrolyze hemicellulose into fermentable sugars were evaluated with a two-stage experiment, which includes acid pretreatment and enzymatic hydrolysis. The experimental data were fitted into second order regression models and Response Surface Methodology (RSM) was employed. The results of the experiment showed that for this type of feedstock enzymatic hydrolysis is not that necessary. In order to reach a comparatively high total sugar concentration (over 45g/l) and low furfural concentration (less than 0.5g/l), the optimum conditions were reached when acid concentration was between 1.41 to 1.81%, and reaction time was 48 to 76 minutes. The two products produced from the bio refinery were compared with traditional products, petroleum gasoline and traditional potassium acetate, in the perspective of sustainability, with greenhouse gas (GHG) emission as an indicator. Three allocation methods, system expansion, mass allocation and market value allocation methods were employed in this assessment. It was determined that the life cycle GHG emissions of ethanol were -27.1, 20.8 and 16 g CO2 eq/MJ, respectively, in the three allocation methods, whereas that of petroleum gasoline is 90 g CO2 eq/MJ. The life cycle GHG emissions of potassium acetate in mass allocation and market value allocation method were 555.7 and 716.0 g CO2 eq/kg, whereas that of traditional potassium acetate is 1020 g CO2/kg.