2. Methods 2.1. Study area We will conduct our study at the UMBS (45° 35N 84° 43W) (Fig. 1). The surrounding forest is a northern hardwood forest with a mean temperature of 5.5° C and a mean annual precipitation of 817 mm (He et al. 2013). Prior to disturbances, the dominant tree species were bigtooth Aspen (Populus grandidentata), American beech (Fagus grandifolia), sugar maple (Acer rubrum), sugar maple (Acer saccharum), red oak (Quercus rubra), white birch (Betula papyrifera), and white pine (Pinus strobus) (Stuart-Haentjens et al. 2015). In the spring of 2008, the Forest Accelerated Succession Experiment (FASET) was initiated to study succession and disturbance. The FASET site is a 39 ha area with approximately 35% of the basal area removed due to the selective girdling of Aspen (Populus) and Paper birch (Betula) that occurred in 2008. The twenty-five plots of FASET area each have their own degree of disturbance severity which ranges from 0 – 70% disturbances based on the fraction of basal area senesced (Appendix 1). For details about the FASET site description, see source. 2.2. Data collection 2.2.1. Environmental variables We will quantify various habitat characteristics in each plot using previous data (Stuart-Haentjens et al. 2015). These include percent disturbance severity, the richness and evenness of the upper canopy trees, the number of dead trees, and leaf litter composition. In addition, we will record the distance of each pitfall to nearest fallen tree with diameter of at least 15cm, the number of fallen trees at least 15cm within each plot, temperature, precipitation and we will quantify canopy cover. An I-button will take continuous measures of temperature throughout the day and canopy cover will be determined by taking a one-time measure with a densitometer at the center pole of each plot. A single observer will perform these variables to reduce observer bias. 2.2.2. Ground-dwelling spiders Spiders will be sampled in eighteen of the twenty-one plots; these eighteen plots are each 16m2. We will sample spiders with pitfall traps from 21 July to 25 July 2015. Four pitfall traps will be systematically placed within each of the eighteen plots. Each of the plots have a center pole, and we will place a pitfall trap 5m in the four cardinal directions from the center pole. In the case that a pitfall trap lands in an obstructed location (rock, live or dead tree), we will place that trap 0.5m due East. The traps will consist of two 266mL (9oz.) cups, with a width of 15.4 cm and 7.5 cm in depth. The first cup will serve as a placeholder and will be dug into the ground using a bulb planter and closed off. On the first day of the trapping, a second cup with 177mL (6 oz.) of 50:50 propylene-glycol and water will be inserted into the placeholder cups, making sure it is flush with the surface. A Styrofoam plate held up with nails will be placed over the traps (about 8 cm above surface level) to exclude leaf litter, rainwater and debris. The traps will be opened 21 July and collected on 25 July. All the contents from the four pitfalls within one plot will be placed into a single jar (~1000mL), for a total of eighteen jars. The contents will be rinsed with water to remove the propylene-glycol solution and spiders will be separated from non-spiders and put into smaller jars (~500mL). Spiders will be identified to family using Ubick et al. (2005) and Bradley (2004), as well as online sources. Juvenile spiders will be identified to family and adult spiders will be identified and sorted by genera into various sized drams with a polyseal cap. Voucher specimens will be kept at the University of Michigan Biological Station. 2.3. Statistical analyses 2.3.1. Analysis of Variance (ANOVA) For each plot, we will use a variety of analysis of variance (ANOVA, ANCOVA, MANOVA) and Tukey’s pairwise comparisons to examine differences of the averages of our continuous dependent variables: Shannon’s Diversity Index (H’), species richness (S), and evenness (E) with our factors being our independent categorical variables: disturbance classes (Low, Med, High), distance to nearest 15cm log (On, Near, Away) and canopy cover (Closed, Med, Open) and when appropriate, our covariates will be the continuous dependent variables: percent disturbance, temperature and number of dead trees in each plot. 2.3.2. Multiple regression We will use multiple regression to look at correlations among our continuous dependent variables: diversity (H’), evenness (E) and species richness (S) with the continuous independent variables: number of dead trees per plot, canopy cover, and percent disturbance. Variable selection will also be processed by the use of stepwise models which include both forward and backward steps to make comparisons by swapping variables in and out of the model (Gotelli and Ellison 2013). 2.3.3. Ordination To reduce dimensionality of multivariate data we will use Principal Component Analysis (PCA). This will create key variables –Principal Components (which are composites of our original variables) - to characterize the variation in our multivariate dataset (Gotelli and Ellison 2013). We will also use non-metric multidimensional scaling (NMDS) to observe any influence on family-level composition from our environmental variables. We will use forty runs with real data and fifty runs with randomized data and a maximum of 200 iterations per run and Bray-Curtis similarity index (Shields et al. 2008). We will use non-metric multi-dimensional scaling (NMDS) and analysis of similarities (ANOISM) to visually and statistically compare composition of spiders. Each plot is considered as a replicate and we summed occurrences of each species over the trapping period to compare similarity using the Bray-Curtis similarity index. Using ANOISM will provide a global P-value to specify differences in composition, as well as reports pair-wise comparisons between the eighteen plots. Prior to tests, a Kolmogorov-Smirnov normality test will be used to check assumptions of normality and homoscadicity (Gotelli and Ellison 2013). 2.3.4. Possible analyses Individual/sample-based rarefaction curves will be standardized to the number of individuals to compare species richness. Rarefaction analysis may be used as a diversity index because allows for comparison of diversity between treatments at similar sample size, considers number of individuals collected and species richness, and verifies that enough samples were collected for appropriate comparisons of diversity (Varady-Szabo and Buddle 2006).