Management impacts on Carex assiniboinensis and Cardamine concatenata on the Ottawa National Forest

Monitoring of herbaceous plants on the Ottawa National Forest (ONF) is used to understand the impact of forest management on understory composition and site conditions. In their planning, national forests are required to take into account management impacts on diversity and ecosystem health. The effect of management on understory species is dependent on various factors, including the intensity of disturbance and the biology of the plant. In the first study in this report, a population of Carex assiniboinensis, a Michigan state threatened species, was monitored for seven seasons including before logging commenced, in order to determine the sedge’s response to a single-tree selection harvest. Analyses provided insights for management of C. assiniboinensis at the stand level over the short-term. In the second study in this report, the use of the cutleaf toothwort (Cardamine concatenata) as a Management Indicator Species on the ONF was reviewed. Data were analyzed to determine the suitability of using C. concatenata to monitor impacts of forest management on site conditions. The various factors that affect understory species population dynamics illuminated the challenges of using indicator species to monitor site conditions. Insights from the study provide a greater understanding of management impacts on understory species across the Ottawa National Forest.

VOLUME YIELDS OF SMALLHOLDER Eucalyptus grandis W. HILL EX MAIDEN STANDS IN EASTERN PARAGUAY

Smallholders in eastern Paraguay plant small stands of Eucalyptus grandis W. Hill ex Maiden intended for sale on the local market. Smallholders have been encouraged to plant E. grandis by local forestry extension agents who offer both forestry education and incentive programs. Smallholders who practice recommended forestry techniques geared towards growing large diameter trees of good form are financially rewarded by the local markets which desire saw log quality trees. The question was posed, are smallholders engaging in recommended silvicultural practices and producing reasonable volume yields? It was hypothesized that smallholders, having received forestry education and having financial incentives from the local market, would engage in silvicultural practices resulting in trees of good form and volume yields that were reasonable for the local climate and soil characteristics. Yield volume results from this study support this hypothesis. Mean volume yield was estimated at 70 cubic meters per hectare at age four and 225 cubic meters per hectare at age eight. These volume yields compare favorably to volume yields from other studies of E. grandis grown in similar climates, with similar stocking levels and site qualities.

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.