John M. Kabrick

Adjusting Forest Density Estimates for Surveyor Bias in Historical Tree Surveys

Abstract The U.S. General Land Office surveys, conducted between the late 1700s to early 1900s, provide records of trees prior to widespread European and American colonial settlement. However, potential and documented surveyor bias raises questions about the reliability of historical tree density estimates and other metrics based on density estimated from these records. In this study, we present two complementary approaches to adjust density estimates for possible surveyor bias. We addressed the problem of surveyor bias of density estimates by simulating the effects of (1) rank of selected trees (compared to assuming the nearest trees were selected) and (2) specific surveyor bias in selection of (a) quadrant location, (b) quadrant configuration, (c) azimuth, and (d) combined species and diameter. We then developed regression equations to calculate adjustment quotients for these biases, making the adjustment quotients transferable to any similar datasets. For the rank-based approach, an unvarying rank of 2 (selection of the second nearest tree instead of always the nearest tree) decreased density estimates to about 25 to 45% of the actual density, depending on number of trees per survey point, resulting in corrected density estimates that are 2.2 to 4 times greater than uncorrected density estimates. However, constant selection of the second nearest tree did not occur; varying ranks decreased density estimates to around 55 to 65% of the density, resulting in corrected density estimates about 1.5 to 1.8 times greater than uncorrected values. For the bias-based approach, depending on the specific General Land Office dataset, bias for tree species and diameter alone may decrease density estimates by about 35%. Quadrant configuration and azimuth preference may decrease density estimates by about 15% each. The quadrant location bias has negligible effects on the density estimates. The overall density estimates may be about 35 to 55% of the actual density and correction of the density estimate will approximately double the value. These methods can provide a range of estimates, from low values of uncorrected density to high values of corrected density, about the amount that varying surveyor bias may have decreased density estimates for any areas where bias is detected (i.e., non-random frequencies) in point-centered quarter surveys. Adjustments will increase reliability of historical forest density estimates and their applications for restoration.

Historical Open Forest Ecosystems in the Missouri Ozarks: Reconstruction and Restoration Targets

Current forests no longer resemble historical open forest ecosystems in the eastern United States. In the absence of representative forest ecosystems under a continuous surface fire regime at a large scale, reconstruction of historical landscapes can provide a reference for restoration efforts. For initial expert-assigned vegetation phases ranging from prairie to forest across the Missouri Ozarks landscape, we reconstructed historical (1815 to 1850) forest densities, basal area, percent stocking or growing space, and canopy cover. After examination of structural means and ranges by initial expected vegetation phases, we classified vegetation phases based on percent stocking boundaries of 30–55% for open woodlands and 55–75% for closed woodlands (diameters ≥ 12.7 cm). We suggest that a percent stocking boundary of 10% may separate prairie and savannas, but we did not identify any large scale prairies in Missouri. We provided structure of each vegetation phase for restoration targets; mean historical densities of vegetation phases ranged from 81 trees/ha in savannas to 285 trees/ha in non-oak/non-pine forests (diameters ≥ 12.7 cm). Due to greater densities than expected and larger diameter trees than current forests, historical forests may have been primarily (about 65%) woodlands with nearly closed canopies, unlike the open canopies presumed during settlement in the Missouri Ozarks. However, a closed yet single canopy layer can transmit enough light to sustain an herbaceous ground cover, given an open midstory due to frequent surface fires. Restoration of open woodlands across all public lands is not practical, but restoration of lower density forests composed of drought-tolerant tree species should translate to management for changing climate.

Innovations in afforestation of agricultural bottomlands to restore native forests in the eastern USA

Abstract Establishing trees in agricultural bottomlands is challenging because of intense competition, flooding and herbivory. A summary is presented of new practices and management systems for regenerating trees in former agricultural fields in the eastern USA. Innovations have come from improvements in planting stock and new silvicultural systems that restore ecological function more quickly than traditional afforestation with single-species stands. Advances in nursery production of large (e.g. 1–2 m tall; 1.5–2.0 cm basal diameter) bareroot and container seedlings with well-developed root systems have led to increases in survival and growth, and early seed production. In addition to planting high-quality seedlings, managing vegetation is critical to regeneration success. Planting seedlings with cover crops such as redtop grass (Agrostis gigantea Roth) may improve tree survival and growth by controlling competing vegetation and reducing animal herbivory. An innovative strategy that simulates natural succession involves interplanting later seral species such as Nuttall oak (Quercus nuttallii Palm.) in young plantations of pioneer species such as Populus deltoides Bartr. ex Marsh. Populus L. acts as a nurse crop for Quercus L. by reducing biomass of competing vegetation without seriously limiting Quercus L. seedling growth or function. Harvest of the short-rotation Populus L. crop releases the well-established Quercus L. trees. Success in afforestation requires planting high-quality seedlings using management practices that promote survival and growth. Restoration based on ecosystem processes, using tree species that have complementary ecological requirements, will be more successful and affordable than other methods.

Changing tree composition by life history strategy in a grassland-forest landscape

After rapid deforestation in the eastern United States, which generally occurred during the period of 1850-1920, forests did not return to historical composition and structure. We examined forest compositional change and then considered how historical land use and current land use may influence forests in a grassland-forest landscape, the Missouri Plains, where frequent surface fire was the historical land use and intensive agricultural is the current land use. We compared composition, distribution, and environmental relationships during historical (1813-1860) and current (2004-2008) forest surveys. We also examined changing composition of life history strategies of (1) stress tolerators based on fire tolerance, (2) colonizers based on shade intolerance, and (3) competitors based on shade tolerance. Open forest ecosystems of fire-tolerant oaks have been replaced by forests of fire-sensitive species, such as ashes, hackberry, and maples that expanded from riparian firebreaks and osage-orange and eastern redcedar that expanded from planted windbreaks and rocky firebreaks. Colonizing species increased from 7% to 32% of total composition, with assisted tree migration from planting; we expect continued expansion particularly by eastern redcedar into areas unoccupied by trees. Competitive species have increased slightly to 38% of total composition although the trajectory of current forests suggested competitors may increase to 56% of total composition by replacing oaks in forest ecosystems. Changed success of life history strategies in an agricultural landscape without fire resulted in increased composition and extent of fire-sensitive colonizers compared to fire-tolerant oaks. We suggest that patterns of loss of fire-tolerant oaks and increased distribution of fire-sensitive species reflect suppression of fire, the historical land use. In addition, we suggest that subsequent land use dictates the success of either shade-intolerant colonizers or shade-tolerant competitors in current forests. Forests will be composed of shade-intolerant colonizers where land use disturbance is frequent, such as in agricultural landscapes, and forests will be composed of species with greater shade tolerance where land use disturbance is less frequent.

A Remote Sensing-Assisted Risk Rating Study to Predict Oak Decline and Recovery in the Missouri Ozark Highlands, USA

Forests in the Ozark Highlands underwent widespread oak decline affected by severe droughts in 1999-2000. In this study, the differential normalized difference water index was calculated to detect crown dieback. A multi-factor risk rating system was built to map risk levels of stands. As a quick response to drought, decline in 2000 mostly occurred in stands at low to medium risk, which often recovered within a few years. Decline in 2003, as longer-term response to drought, dominated in stands at medium to high risk. This study demonstrates that remote sensing can be applied to predict oak decline and to mitigate damage before another stressor event occurs.

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availability, we select three variables, the aspect class, the soil depth and the land type association as covariates for analysis. To allow great flexibility of the smoothness of the random field, we choose the Matern family as the correlation function. We adopt the reference prior as an appropriate prior because there is no previous knowledge of the parameters in the model. An efficient algorithm based on the generalized Ratio-of-Uniforms method is developed for the posterior simulation. One advantage of the algorithm is that it generates independent samples from the required posterior distribution, which is much more efficient for both statistical inference of the parameters and prediction of the site indexes at unsampled locations. Our results show that the aspect class and the soil depth are both significant while the land type association is less significant. The model validation is briefly discussed. In addition, our simulation method allows easy realization for computing quantities from the posterior predictive distributions.

Bayesian spatial models with repeated measurements: with application to the herbaceous data analysis

This paper proposes a new statistical spatial model to analyze and predict the coverage percentage of the upland ground flora in the Missouri Ozark Forest Ecosystem Project (MOFEP). The flora coverage percentages are collected from clustered locations, which requires a new spatial model other than the traditional kriging method. The proposed model handles this special data structure by treating the flora coverage percentages collected from the clustered locations as repeated measurements in a Bayesian hierarchical setting. The correlation among the observations from the clustered locations are considered as well. The total vegetation coverage data in MOFEP is analyzed in this study. An Markov chain Monte Carlo algorithm based on the shrinkage slice sampler is developed for simulation from the posterior densities. The total vegetation coverage is modeled by three components, including the covariates, random spatial effect and correlated random errors. Prediction of the total vegetation coverage at unmeasured locations is developed.

East and central farming and forest region and Atlantic basin diversified farming region: LRRs N and S

The East and Central Farming and Forest Region (Land Resource Region N) and the Atlantic Basin Diversified Farming Region (Land Resource Region S) of the central and eastern USA comprise the Interior Highlands, Interior Plains, Appalachian Highlands, and the Northern Coastal Plains. These regions include nearly level to gently rolling plains in the Interior Plains and the Northern Coastal Plains and rugged hills and mountains in the Interior Highlands and Appalachian Highlands. The climate is mild, and rainfall is plentiful for agriculture and the growth of forests. The underlying bedrock includes sedimentary rocks in the Interior Highlands and in the Interior Plains, metamorphic rocks in the Appalachian Highlands, and unconsolidated coastal plain sediments and glacial deposits in the Northern Coastal Plains. The soils of this region comprise Ultisols, Alfisols, and Inceptisols. Other important soil orders include Mollisols and Entisols. Hardwood and softwood forests are important throughout this region, and forests are the single most important land use followed by cropland and grassland. Urban land is more extensive near major metropolitan areas in the Northern Coastal Plains.