Vascular plants and plant habitats of Brush Creek Island, Lewis County, Kentucky, U.S.A.

A descriptive survey of the vascular flora and plant habitats of Brush Creek Island, a 6.7-ha Ohio River island in Lewis County, Kentucky, was conducted during 1995–1996 and 2012. Brush Creek Island (BC), one of three Ohio River islands politically a part of Kentucky, is currently under private ownership and projected as a future part of the Ohio River Islands National Wildlife Refuge. Two major habitats in 2012 were Vegetated Unconsolidated Shoreline and Bottomland Hardwood Forest, a final sere of Late Old Field and Immature Bottomland Hardwood Forest. Two additional 1996 habitats, a seasonal Riverine Emergent Wetland and Late Old Field, were altered through fluvial action and secondary succession processes by 2012. An annotated list consists of 330 species in 220 genera from 82 families. Taxa are composed of one Monilophyte, four Magnoliids, 76 Monocots, and 249 Eudicots. Ninety-three taxa (28.2%) are non-native adventive or naturalized spe-cies. Forty-eight taxa (54%) are classified as Kentucky invasive plants. A total of 189 species (57.3%) are hydrophytes. Ninety-five native or non-native taxa (28.8%) are Lewis County distribution records.

Rhizosphere Priming Effects On Soil Extracellular Enzymatic Activity And Microbial Abundance During The Low-Temperature Dormant Season In A Northern Hardwood Forest

PurposePlant roots alter nutrient cycling within the soil surrounding them (rhizosphere). Recent studies have focused on nutrient uptake by plants in low-temperature seasons. This study aimed to reveal the nutrient dynamics in rhizosphere during low-temperature seasons in a northern hardwood forest in Japan.MethodsThe potential extracellular enzymatic activity, bacterial, fungal, and archaeal abundances, and soil chemical properties in the rhizosphere of canopy trees and understory vegetation and non-rhizosphere bulk soil were measured at the beginning of the dormant season (November), end of the dormant season (April and May), and middle of the growing season (August) in a northern hardwood forest in Japan.ResultsThe abundance of fungi and the activity of nitrogen- and phosphorus-degrading enzymes were higher in the rhizosphere than in non-rhizosphere bulk soil regardless of the season. The concentration of extractable organic and inorganic N was higher in the rhizosphere than in the non-rhizosphere bulk soil at the beginning and end of the dormant season, but this trend was not observed in the middle of the growing season. ConclusionSince the concentration of nutrients in the rhizosphere is determined by the balance between nutrient uptake by fine roots and root-induced acceleration of decomposition, our results suggest that plant roots would accelerate N and P cycles during the dormant season, even though the amount of nutrient uptake by plants was lower during the season.

Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.