Performing Department
School of Forest Resources and Environmental Science
Non Technical Summary
The Karner Blue (KBB; Lycaeides melissa samuelis) is a federally-endangered butterfly native to sparsely-wooded pine barrens and oak savanna in the Great Lakes and Northeastern regions of the United States. Recently, there has been a centralized effort to manage and restore habitat for the KBB. The focus on KBB management and restoration is important not only because the KBB itself is endangered, but also because the KBB has been demonstrated to act as an effective indicator of high quality barrens and savanna habitat (sensu Fleishman et al. 2000; see also Chan and Packer 2006; USFWS 1992). This means that KBB management and habitat restoration benefits not only the butterfly, but an entire suite of plants, insects, arachnids, reptiles, birds, and mammals associated with barrens and savanna habitats. In addition to preserving and creating habitat for a wide variety of wildlife and plants, the restoration of barrens and savanna habitats will also increase regional habitat diversity, which has the potential to further benefit both plants and wildlife, especially birds (Freemark and Merriam 1986; Wichmann et al. 2004). In this study, we will determine which environmental and physical variables are important for a habitat to support successful KBB populations. We will use those data to make predictions as to where habitat restoration for the KBB will be most successful both today and under various future climate scenarios. The predictive framework developed in this study will be useful not only to KBB management, but also to conservation agencies, National Forests, and ecologists working with similar systems.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
The Karner Blue (KBB; Lycaeides melissa samuelis) is a federally-endangered butterfly native to pine barrens and oak savanna in the Great Lakes and Northeastern regions of the United States. The KBB is considered to be an indicator taxon for high quality barrens and savanna habitat (sensu Fleishman et al. 2000; see also Chan and Packer 2006; USFWS 1992). Due to the species' endangered status and importance as an ecological indicator, there has recently been a coordinated effort to manage and restore high quality barrens and savanna habitat (USFWS 2003). The overarching objective of this project is to create a spatial model to predict the location of high quality restoration sites under current and future climate conditions in the Huron-Manistee National Forests. Specifically, our objectives are to: Determine the key predictors of high quality habitat for both the KBB and its obligate larval food source, blue lupine (Lupinus perennis). Determine where habitat restoration will be most successful under current climate conditions. Determine where habitat restoration will be most successful under a series of various future climate scenarios.
Project Methods
Data will be collected from the Huron-Manistee National Forests (HMNF), a focal point for the management and conservation of the KBB. The HMNF currently contains approximately 2,200 acres of high quality savanna habitat that supports KBB populations, as well as a large amount of land historically associated with savanna ecosystems. To identify key predictors of high quality habitat, data will be collected from sites currently occupied by the KBB, savannas previously occupied by the KBB, and random sites which are occupied some years, but not others. Sampling will be based around the four major KBB metapopulation areas in the HMNF, with 10 randomly selected 5-m2 sites of each type (occupied, unoccupied, random) in each metapopulation area. At each site we will measure several soil and microclimate characteristics. Soil pH will be recorded at the plot center and each cardinal direction using an electronic pH meter. To determine soil macro- and micronutrient content, soil samples will be collected in the form of 20-cm cores from the center of the site and each cardinal direction and analyzed using chemical assays and an elemental analyzer in a lab on Michigan Tech's campus. Identity of canopy and understory species, as well as litter type will be recorded in order to determine organic matter type. The slope and aspect of each site will be determined using a compass and clinometer. These field data will be combined with data regularly collected by the HMNF Karner Blue management team on KBB population and distributions, lupine abundance, habitat characteristics, and climatic conditions for analysis. Logistic regression will be used to determine which variables are the strongest predictors of habitat use by the KBB and blue lupine. Logistic regression analysis will be performed using the R statistical environment (version 2.14.1, R Development Core Team 2011). In order to predict where habitat restoration will be most successful, a continuous map of the important variables must first be created. Continuous maps will be produced using geostatistical analysis in the form of spatial interpolation by ordinary kriging using the R statistical environment and ArcGIS 10 (ESRI 2011). Based on the distributions and relative importance of these variables, a predictive geographic model will be constructed using the R statistical environment and ArcGIS 10 to locate areas with a high probability of being able to support KBB and blue lupine populations. The predictive geographic model will be integrated with several climate change models (e.g., Hadley, PCM, GCM averages) in order to predict future locations of potential high quality KBB and blue lupine habitat. The integration of our predictive geographic model and climate change models will be performed in the R statistical environment and ArcGIS 10.