Alison Maye - Invasion pattern of the exotic shrub, Lonicera maackii: Composition of forest stands with high vs. low L. maackii cover. Mentor: Dr. David Gorchov, Botany.
Invasive species are a major threat to biodiversity and costs of controlling them can heavily impact local and regional economies. Invasions by these species can occur via two modes of dispersion, diffusion dispersal and long-distance dispersal. Remote sensing has been used in multiple studies to detect invasive plants; however, none have studied a forest understory species or the mode of dispersal of the invasive species. Amur honeysuckle (Lonicera maackii) is a non-indigenous invasive shrub that can dominate the understory of deciduous forests in the eastern U.S. It has been found to reduce survival, growth, and fecundity of native herbs and to decrease survival of native tree seedlings. The early leaf expansion and late leaf retention of L. maackii compared to phenologies of native tree and shrub species provides temporal opportunities for detection with satellite imagery. As an initial stage in a larger project to determine whether L. maackii is spread by diffusion or long distance dispersal, this study’s objective was to collect field data on sites with varying densities of L. maackii. Six deciduous forest stands were surveyed for forest composition and L. maackii density in Butler and Preble counties, Ohio. All stands were dominated by sugar maple (Acer saccharum). L. maackii cover ranged from 37% to 57% in high density sites and 0% to 1.7% in low density sites. A joint study obtained satellite images from spring and autumn to determine if the high and low density sites could be distinguished from each other. If detection of L. maackii in satellite images is successful, its invasion path over time could be reconstructed using older satellite images, allowing for determination of mode of dispersal. This information would assist land managers in focusing on currently invaded areas and on areas most susceptible to future invasion.
Julian Resasco – Invasion pattern of the exotic shrub, Lonicera maackii: Using remote sensing to discriminate stands with high vs. low L. maackii cover. Mentor: Dr. Mary Henry, Geography.
Plant invasions can occur via two modes of dispersion, diffusion dispersal and long-distance dispersal. Knowing the mode of dispersal is extremely important for effective land management strategies to combat invasives. Remote sensing has been used in multiple studies to detect invasive plants; however, none have studied a forest understory species. Amur honeysuckle (Lonicera maackii) is an invasive shrub that can dominate the understory of deciduous forests in the eastern U.S. Negative impacts of L. maackii on native flora include a reduction of survivability and fecundity of native herbs and tree seedlings. The objective of this study was to distinguish L. maacki plots of high density from plots of low density using Landsat imagery. The early leaf expansion and late leaf retention of L. maackii compared to phenologies of native tree and shrub species provides temporal opportunities for detection with satellite imagery in spring and autumn. Fifteen Landsat Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+) images were obtained from spring and autumn dates from 1999-2006. A concurrent investigation located plots of high and low density of L. maackii with similar tree canopy composition. A quantitative spectral separability analysis showed greater separability between density classes (high and low) than within density classes on all November images. A Spectral Vegetation Index (SVI) revealed higher levels of green biomass in high L. maackii density plots than low density plots for November images only. This study is a pilot study for a larger project to map historical spread of L. maackii and to determine its mode of dispersal.
Will Tardy - Using Satellite Imagery to Predict Forest Community Composition and Lepidopteran Biodiversity: A Multifaceted Approach to Estimating Biodiversity. Mentor: Dr. John Maingi, Geography.
Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms (Hill and Thomson 2005). In the past, techniques such as biological surveys have been to elucidate attributes of these communities. However, as the need for rapid conservation assessment increases the application of field surveys can become restrictive. To address this problem this study explores the application of Landsat 5 TM data to detect and therefore predict attributes of 5 forest stand communities in Southwest Ohio. The area of interest for this investigation lie in Bachelor Reserve, a natural area maintained and owned by Miami University, and Hueston Woods State Park and Nature Preserve. Within each of these areas qualitative surveys were conducted to locate distinct forest stands reflecting different disturbance regimes and ecological niches. Once located, vegetative surveys were conducted in each of the communities and GPS coordinates were cataloged. Landsat 5 TM images were than acquired for the survey areas and processing using vegetative indices including SARVI 2, MVI 5, NDVI, and the Kauth-Thomas transformation. From these enhanced images spectral signatures for each of our five community types were generalized and a Jeffries-Matusita test for separability was conducted. To detect relationships between the forest communities and spectral data other than species composition, Spermam’s Rho bivariate correlation analysis was also conducted. The results from these analyses indicate while it is not yet possible to predict the species composition of hardwood forests using Landsat 5 TM data, it is possible to distinguish hardwood communities from coniferous stands. Moreover, distinct correlations exist between forest stand attribute, such as density, diversity, and basal area, and spectrally derived data, such TM 2, TM 4, and MVI 5.
Ashley Wick - Forest Lepidoptera as Indicator Taxa of Forest Community Composition. Mentor: Dr. Thomas Crist, Zoology.
Assessing biodiversity is imperative in enabling land managers to recognize areas of high conservation value. Furthermore, biodiversity must be measured at multiple levels of organization (Noss 1990) and complete biological assessments are resource intensive. Therefore, attention has turned to the use of indicator taxa in order to estimate biodiversity. Forest Lepidoptera are taxonomically well known and speciose in temperate forests. Species richness and abundance of two families of Lepidoptera have been recognized as indicators of habitat disturbance (Notodontidae) and overall Lepidopteran species richness (Arctiidae) (Summerville et all 2004). We predicted that the diversity and composition of forest Lepidoptera communities could be predicted from forest stand attributes (ie age and composition). Preliminary results suggest that species abundance, although not species richness, is significantly correlated with changes in forest attributes.
