POLÍTICAS CONTABLES SIGNIFICATIVAS

The number of birds counted was averaged over the three visits for each bird species at each point, and those averages were used as the dependent variables in the analyses. Because bird detections from the point counts are actually a function of abundance and detectibility, some have argued that raw counts should be corrected for detectibility (Rosenstock et al. 2002). Since the goal of this research was to compare the relative performance of the two habitat sampling methods, and we used the same bird data for both analyses, we did not anticipate that detectibility would affect our results, and thus did not adjust the counts for detectibility (Johnson 2008). Only focal scrub-shrub species, as defined by Schlossberg and King 2007, encountered at ≥65% of points were included in the analyses. Six bird species were included in the analysis: blue-winged warbler (Vermivora pinus), common yellowthroat (Geothylpis trichas), eastern towhee (Piplio eurythropthalmus), gray catbird (Dumetella carolinenesis), indigo bunting (Passerina cyanea) and yellow warbler (Dendroica petechia). These six scrub-shrub birds are of concern because they are birds that are showing general trends of decline according to the Breeding Bird Survey (BBS) and are dependent upon early-

successional habitats that are declining regionally (Trani et al. 2001).

Habitat data for the point intercept method was placed into groups based upon

commonalities in growth and structure (King et al. 2008). Six categories of cover were created including low broadleaved shrubs (<2 m tall), broadleaved shrubs (2-5m tall), broadleaved trees (>5 m tall), cover of conifers (trees and shrubs combined), forbs (including ferns) and graminoids (including sedges and rushes) (King et al. 2008). For each data set, we calculated the average height, so a total of twelve independent

variables were used for the point intercept method portion of the analysis.

The BBIRD data was grouped according to the different types of habitat data that were collected. Variables collected were sum of shrub stems (>50 cm in height and then counted at 10 cm), average cover of green vegetation, average cover of graminoids, average cover of shrubs, average cover of forbs and sum of tree stems. Other variables listed in the BBIRD protocol were also measured but were either combined with similar variables (e.g. average cover of graminoids= average cover of grass,sedge and rush) because of low occurrence across sites or left out for this analysis because of the lack of power from the low occurrence of data within sites (e.g. average cover of water).

Additional variables obtained from BBIRD protocol include the following, which are from the point centered quarter method (PCQM) of estimating density (Mueller-Dombois and Ellenberg 1974): Density of shrubs, average height of shrubs, average width of shrubs, average perpendicular width of shrubs, density of trees,

average height of trees, average canopy cover of trees, average diameter at breast height (DBH) and average tree crown width. Because of the large number of variables

obtained from the BBIRD sampling procedure, I computed Pearson correlations, and if variables were found to be correlated (r2 ≥ .60), I retained the variable with the lower

AICc value. Variables that were included, as well as the variables that were dropped

from the BBIRD are presented in Table 2.1. The way in which I interpreted and grouped the point intercept method data was baed off of the work of King and was an arbitrary decision. Had I grouped the point intercept method variables that were analogs for the BBIRD variables from Table 2.1, it is possible that the habitat conditions

indicated by the models for each species would have been more similar.

To address the question of whether BBIRD and point intercept method measure the same information, a table was created, Table 2.1, to determine which variables from both vegetation methods where analogous to each other, and the BBIRD habitat

variables were correlated with analogous variables from the point intercept method. BBIRD variables were compared with the smaller number of the point intercept method variables and then variables that were determined to measure the same basic habitat information were selected as analogous pairs. Analogous variables for the two methods where than plotted against each other in the program R, using the scatter plot function and analyzed using a Pearson correlation matrix, to determine how closely the variables were correlated.

A principle components analysis (PCA) was conducted separately on the BBIRD and point intercept method data to compare the structure of each data set and how the variables grouped within the loadings. The PCA analysis was run using the free software package R (McGarigal 2008).

To compare the relative effectiveness of each method in summarizing bird habitat data, regressions of mean avian abundance with independent variables from point intercept method and BBIRD were run separately in an all subsets general linear

model (McGarigal, 2008) using the statistical package R. The all subsets function runs a series of regressions with the independent variables and narrows down the models to find a minimally adequate model. The all subsets function also selects the predictors which drive the model selection (McGarigal 2008). My analysis bases the model comparison on the Akaike Information Criterion corrected for small sample size (AICc) (Anderson et al 2001). AICc is a preferable method to select models because it accounts for both fit and model complexity (Franklin et al 2001, Johnson and Omland 2004). Once I obtained a minimally adequate model for each of the six birds in the study for each of the habitat methods, I compared the AICc between the BBIRD and point intercept method for each of the six scrub-shrub birds, as well as the number of terms included in the minimally adequate model.

Next, I looked at the importance of the predictors to find out what variables were driving the relationships between the birds and habitat. The variable importance

selection for this analysis was based on the weight of evidence across all models containing each variable (McGarigal 2008).