Local-scale Habitat Components Driving Bird Abundance in Eastern Deciduous Forests.
Bird populations face increasing threats of both natural and anthropogenic origin (Loss et al., 2013; Rosenberg et al., 2016). For breeding birds of eastern North American deciduous forests, habitat loss and fragmentation have negatively impacted population abundance and reproductive success (Robbins et al., 1989; Robinson et al, 1995; Bender et al, 1998; Manolis et al, 2002; Sauer et al, 2015). As urbanization and other sources of anthropogenic disturbance continue to expand throughout the eastern U.S., remnant patches of mature forest are increasingly important for maintenance of the breeding bird community. Accordingly, understanding how habitat characteristics within these remnant forest patches affect breeding bird abundance and community composition is crucial for effective management (Hayden et al., 1985).
Bird populations are impacted by habitat characteristics at multiple scales (Block and Brennan, 1993). At the landscape scale, habitat composition and configuration (Howell et al., 2000; Lichstein et al, 2002), patch size (Askins et al, 2007; Modest et al., 2016), edge length (Heikkinen et al., 2004), and isolation (Modest et al., 2016) drive patterns in the bird Community. However, local-scale habitat characteristics also can explain a significant proportion of variability in bird abundance and community composition (Lichstein et al., 2002; Askins et al, 2007). Vegetation structure and terrain characteristics are among the habitat components that potentially influence the bird community at the local scale (Hagan and Meehan, 2002; Smith et al, 2008).
When characterizing forest vegetation, canopy structure is a dominant component. Canopy structure can be quantified in different ways, each with implications for breeding bird habitat suitability. For example, canopy height reflects multiple structural metrics including tree diameter, stand basal area, species composition, and potentially stand age (McElhinney et al., 2005). Variability in canopy height within a forest stand, therefore, reflects corresponding variability in potential habitat for birds, particularly for canopy-dwelling species. Accordingly, canopy height has previously been associated with both the abundance of individual species (Roth and Islam, 2008; Seavy et al., 2009; Jenkins, 2017) and overall community richness (Goetz et al, 2007). In addition to horizontal variability in canopy height across a stand, vertical structure (i.e., understory, midstory, and canopy stratification) influences bird community diversity (MacArthur and MacArthur, 1961).
Below the canopy, abundance of ground-nesting birds associated with mature forest habitat (hereafter mature forest birds; e.g., worm-eating warbler (Helmitheros vermivorum Gmelin) and ovenbird (Seiurus aurocapilla L.) is impacted by structural attributes, such as density of understory vegetation (Smith et al., 2008; Vitz and Rodewald, 2013). However, these birds also depend on microhabitat characteristics provided by the forest canopy, such as leaf litter depth and composition (Ruhl et al., in review).
In addition to vegetation characteristics, terrain variables including elevation, slope, and aspect also impact the bird community (Lichstein et al., 2002). Ground-nesting warbler species in the Central Hardwood Region demonstrate ecological segregation, utilizing slightly different microhabitats present along a slope gradient (Wenny et al., 1993). Some of the perceived impacts on bird distributions with respect to slope or aspect may be in response to the underlying effects of soil type, moisture, and solar exposure on plant biomass and diversity (Olivero and Hix, 1998). Regardless of underlying drivers of distributional segregation, managers can be faced with important conservation decisions involving breeding bird associations with specific aspect and slope combinations.
Past work has frequently focused on the impact of only a subset of these variables and/or only a small number of mature forest bird species (e.g., Noon, 1981a). Additional research examining the relative impacts of a wider range of local-scale habitat variables on multiple bird species is helpful for robust multi-species management considerations. Our objective was to assess the relative effects of several key habitat variables in driving abundance of breeding birds: maximum canopy height, variability in tree height, stem density in multiple strata, elevation, slope, and aspect. Of these variables, we expected canopy height would affect the largest number of species and have the largest effect size. We also expected elevation and slope would be important predictors of abundance given the steep, hilly terrain in our study area.
The study area was located in Morgan-Monroe (39[degrees]25'N, 86[degrees]25'W) and Yellowwood (38[degrees]50'N, 86[degrees]30'W) state forests in south-central Indiana, U.S.A. The state forests together cover >19,000 ha and are managed for multiple uses including timber production and outdoor recreation. The forests are 60-90 y old and have an overstory dominated by oak (Quercus spp.), hickory (Carya spp.), and tulip poplar (Liriodendron tulipifera L.) with an understory of shade-tolerant species including sugar maple (Acer saccharum Marshall) and American beech (Fagus grandifolia Ehrh.; Saunders and Arseneault, 2013). In 2006, the Hardwood Ecosystem Experiment (HEE), a study of forest ecosystem responses to active timber management, was established in the two state forests (Kalb and Mycroft, 2013). The HEE is composed of nine research cores (78-110 ha in size). Total basal area of trees >1.5 cm dbh in the cores ranged from 21.7-29.9 [m.sup.2]/ha, and total tree densities from 923-1,527 trees/ha (Saunders and Arseneault, 2013). Within a 3 km buffer around each individual core, forest cover, of which the majority was deciduous, averaged 91 [+ or -] 3.7% (mean [+ or -] SD), agricultural area averaged 4.1 [+ or -] 3.2%, and developed/urban area averaged 2.7 [+ or -] 1.0% of the total area (Homer et aL, 2015).
In each of the nine research cores, we established nine to thirteen permanent bird survey points (99 total points, mean 11 points per core). Individual survey points were evenly spaced across the core area, separated by approximately 150 m, and were at least 100 m away from recent (<5 y) harvest openings. A subset of the 99 points were sampled in each study year: 87 points in 2012 and 91 in 2014. Each point sampled in a given year was surveyed twice between 20 May and 20 June, with visits separated by 2-3 wk. Sampling occurred between 0600 and 1100 and only under ideal conditions for detecting birds (i.e., no precipitation and Beaufort wind scale <4). On each sampling occasion, a single observer arrived, waited 1 min, and then conducted a 10 min survey. During the survey period, the observer recorded the species, sex (if possible), time of observation, and distance to each bird detected (to the nearest 10 m) within 75 m of the survey point.
We included eight habitat variables in the model. Variables were selected to characterize forest structure and terrain at each point while minimizing multicollinearity (variance inflation factors for all variables <3; Zuur et al., 2010). Variables were obtained from three different data sources. First, we obtained a raster of maximum canopy height in each research core (3 m resolution), derived from aerial LiDAR data collected from 2009-2011 (IndianaMap, http://www.indianamap.org; Barnes et al., 2016). Using this raster, we calculated mean maximum canopy height (MCH) in a 75 m radius at each bird survey point.
Second we used forest inventor)' data collected at the HEE in 2013-2014 (Saunders and Arseneault, 2013). Each bird point was paired with a 0.1 ha inventory plot located within a 75 m radius. Inventory data for each point were then used to calculate coefficient of variation in tree heights (CVH), overstory stem density (OSD; defined as density of trees with most of the live tree crown at or above the general canopy), midstory stem density (MSD; below general canopy and dbh [greater than or equal to] 5 cm) and understory stem density (USD; dbh < 5 cm).
Finally, we obtained terrain variables from a digital elevation model (DEM) of the study area. Using the DEM we generated maps of elevation, slope, and aspect. We converted aspect from units of degrees to Beers' aspect which takes on values from 0-2 where 0 represents southwestern-facing slopes and 2 represents northeastern-facing slopes (Barnes et al., 2016). We then calculated mean elevation (ELE), mean slope (SLO), and mean Beers' aspect (ASP) in a 75-m radius around each bird survey point. We standardized all habitat covariates to have a mean of 0 and a standard deviation of one prior to analysis.
We fit an N-mixture model (Royle, 2004) for each bird species with [greater than or equal to] 50 total observations (18 total species; Table 1). The true abundance [N.sub.ij] of bird species i during survey j was modeled as [N.sub.ij] ~ Poisson ([[lambda].sub.ij]). Mean abundance [[lambda.sub.]ij] was a function of an intercept [alpha], random effects of research core (RQ and survey point (SP), and the eight habitat covariates:
log([[lambda].sub.ij]) = [alpha] + RC + SP + MCH + CVH + OSD + MSD + USD + ELE + SLO + ASP
According to a model framework described by Amundson et al (2014), the detection model was separated into two components: the probability [P.sub.a] that a bird was available to be sampled (i.e., visible or singing), and the probability [P.sub.d] that the bird was detected by an observer. The two probabilities were estimated independently. First, the number of birds of species i that were available to be sampled during survey j was modeled as [N.sub.aij] ~ Binomial ([P.sub.aij], [N.sub.ij]). Probability of availability [P.sub.aij] was estimated using a time removal model, with each 10 min sample period divided into five 2 min intervals (Famsworth et al, 2002). The ordinal date of sampling was included as a covariate on [p.sub.aij]. Next, the observed counts were modeled as [y.sub.ij] ~ Binomial([P.sub.dij], [N.sub.aij]), with [p.sub.dij] estimated using a distance-sampling approach (Farnsworth et al, 2005). Wind speed (Beaufort scale) and a random observer effect were included as covariates on [P.sub.dij]. The overall detection probability P was calculated as the product of [P.sub.a] and [P.sub.d].
Each model was fit in a Bayesian framework in R (R Development Core Team, 2015) using JAGS (Plummer, 2003) called from package jagsUI (Kellner, 2015). Each JAGS model was run with three chains of 70,000 iterations each, a burn-in period of 60,000 iterations, and a thin rate of 100. We considered models to have converged when the Brooks-Gelman-Rubin statistic for all parameters <1.1 (Brooks and Gelman, 1998).
We considered a habitat variable to have a significant effect on the abundance of a given species when the 95% credible interval around the estimated co-efficient did not overlap 0. We calculated a mean absolute effect size and 95% credible interval for each habitat covariate across all species using a bootstrap approach. For each of 1000 bootstrap samples, we randomly selected one posterior realization and calculated the mean of the absolute values of a given habitat variable co-efficient (i.e., the effect size) across all species. We then used the resulting distribution of mean absolute effect sizes to generate a 95% credible interval.
We observed substantial variation in habitat covariates across the nine research cores. Mean maximum canopy height was 22.1 [+ or -] 4.00 m (mean [+ or -] SD) and the co-efficient of variation in tree height was 0.30 [+ or -] 0.10. Overstory stem density was 146 [+ or -] 66.3 stems/ha, midstory stem density was 198 [+ or -] 123, and understory stem density was 839 [+ or -] 534. Mean elevation was 238 [+ or -] 19.1 m, slope was 13.2 [+ or -] 3.01 degrees, and Beers' aspect was 0.52 [+ or -] 0.60.
Across the 2 y of the study, we recorded 2566 total observations of 43 different bird species (Table 1). The most commonly detected bird species were the red-eyed vireo, Acadian flycatcher, worm-eating warbler, and eastern wood-pewee (counts and scientific names found in Table 1). For the 18 species included in the abundance analysis, the average estimated probability of detection was 0.56 and ranged from 0.08-0.83 (Table 1). Full output from the fitted models can be found in Appendix 1.
Of the eight habitat variables, canopy height MCH and terrain variables ELE and SIX) had a significant relationship with abundance for the largest number of bird species (4/18, or 22% of species for each; Fig. la). ASP additionally had a significant effect on 3/18 species (17%). The remaining variables were significantly related to the abundance of one (MSD, USD), or no species (CVH, OSD). Most species (13/18, 72%), had a significant relationship beUveen abundance and at least one habitat variable, but only three species (wood thrush, cerulean warbler, and indigo bunting) were affected by more than one of the eight variables (Fig. la).
With the exception of ASP, all habitat variables that had a significant effect on the abundance of >1 species had both positive and negative effects on abundance (Fig. la).
Ignoring the direction of the effect, absolute mean effect sizes for the habitat covariates reflected the number of significant relationships with abundance (Fig. 1). Because habitat variables were standardized prior to analysis, these effect sizes are directly comparable; absolute effect size on abundance was largest for MCH and ELE (Fig. lb). A one-standard deviation change in MCH (equivalent to a change in canopy height of 4 m) corresponded to an average 34% absolute change in abundance across all species. For ELE a one-standard-deviation change (19.1 m) resulted in a 31% absolute change in abundance.
Across the 18 species we modeled, canopy height was among the most important predictors of breeding bird abundance. Although we observed a limited range in variation of canopy height across our study site, due to similarities in age and site quality (Saunders and Arseneault, 2013), canopy height was an important predictor of abundance for several species and had the largest mean effect size on abundance of any of the habitat variables. Previous studies have connected bird diversity in deciduous forests to canopy height and its influence on the diversification of vegetation within various strata (MacArthur and MacArthur, 1961). Therefore, our observations of the effect of canopy height on species abundance paralleled previously described habitat associations of individual species (Robbins et al, 1989; Poole, 2005; Nemes and Islam, 2016). For example, Acadian flycatcher, a mature forest species with increased nest success and concealment rates in higher strata of the forest canopy (Wilson and Cooper, 1998) was positively associated with canopy height, whereas shrubland species, including the indigo bunting and eastern towhee, had the opposite relationship. The effect of canopy height on bird communities has also been emphasized in studies considering multiple species (MacArthur and MacArthur, 1961). Seavy et al. (2009) described a relationship between canopy height and occurrence of 10 riparian bird species and Goetz et al. (2007) reported a relationship between canopy height and bird community composition. While this study did not directly test different forest management approaches, our results support prior findings that maintenance of a diversity of canopy heights and structures across the landscape is likely to promote a corresponding diversity in the forest bird community (Kendrick et al., 2015; Kellner et al., 2016).
In addition to canopy height, terrain variables derived from the digital deviation model were important predictors of bird habitat use in this study. As with canopy height, responses to elevation occurred at a fine spatial scale, because range in elevation across the landscape was relatively small (182-271 m). Variability in elevation at our study sites generally represented the contrast between adjacent xeric uplands (ridgetops) and mesic bottomland areas (steep ravines and valleys; Kalb and Mycroft, 2013). This difference in site moisture seems the most parsimonious explanation for the positive association of eastern wood-pewee with higher elevation sites in our study system (McCarty, 1996). These higher elevation xeric sites also likely had lower vegetation density, facilitating eastern wood-pewee foraging. Among species more common at lower elevations, cerulean warblers had a particularly strong response. Past research is inconclusive, suggesting potential regional variation in cerulean warbler breeding habitat preference. Barnes et al. (2016) found a similar negative relationship for cerulean warblers between elevation and occurrence in our study area, but Buehler et al, (2006) observed the opposite relationship in a mountainous region of Tennessee.
Other terrain variables, slope and aspect, also had a relatively large influence on species abundance. This relationship was expected for some species (e.g., worm-eating warbler is known to prefer steep slopes for nesting; Vitz et al, 2013; Ruhl et al, in press). However, inconsistent responses to slope and aspect in other species within our study system may also be explained by correlations between aspect and odier unmodeled habitat characteristics (e.g., vegetation composition or distance to forest edge; Wenny et al, 1993; Olivero and Hix, 1998). Reynolds-Hogland et al. (2006) found percent cover in berry plants was highest on northwestern-facing aspects, and Marquis and Le Corff (1997) found reduced insect abundance on southeastern-facing aspects. Therefore, slope and aspect may influence numerous factors potentially affecting food availability or preferred breeding habitat for birds.
We observed fewer and generally weaker habitat associations for variables characterizing canopy stratification. The pioneering study of MacArthur and MacArthur (1961) emphasized the importance of canopy stratification for bird community structure. In addition, when vegetation measurements and bird observations were considered at a similar scale, vegetation volume was an important predictor of breeding bird density (Mills et al., 1991). The forest stratification metrics we used, obtained from overstory plots, had a high degree of spatial variability and were collected at a smaller spatial scale (<0.1 ha) than our paired observations of bird abundance (~1.8 ha; Noon, 1981b; Chandler et al, 2009). Therefore, we suspect our ability to detect effects of canopy stratification variables on bird abundance may be a function of the somewhat mismatched scales at which the forest structure data and bird abundance data were collected (Fearer et al., 2007).
In contrast to forest stratification data, canopy height data were collected via LiDAR at a much higher spatial resolution and across the entire extent of the area sampled for birds, likely improving our ability to detect effects of canopy height on abundance (Goertz et al, 2007). Recent advancements in characterizing forest structure below the canopy with LiDAR (Martinuzzi et al., 2009; Wing et al, 2012, Gang et al, in press), and simultaneously identifying tree species composition using spectral data (e.g., Hill and Thomson, 2005; Cho et al., 2012) would allow future analyses to better assess the impact of below-canopy structure on bird abundance at fine spatial scales. Our work emphasizes the value of utilizing aerial LiDAR as a tool for collecting forest structure information at a fine resolution over relatively large spatial scales (Lesak et al., 2011; Wulder et al, 2012).
Acknowledgments.--J. Riegel, A. Meier, P. Ma, and C. Owings helped to coordinate this study. We thank the many technicians that contributed to data collection. This paper is a contribution of the Hardwood Ecosystem Experiment, a partnership of the Indiana Department of Natural Resources, Purdue University, Ball State University, Indiana State University, Drake University, Indiana University, and The Nature Conservancy. Funding for the project was provided by the Indiana Division of Forestry, the Department of Forestry and Natural Resources at Purdue University, the Purdue University Graduate School, and U.S. Department of Agriculture National Institute of Food and Agriculture Mclntire-Stennis Project 011557 (to RKS).
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APPENDIX 1.--Full set of parameter estimates from the abundance model for all included species. For each parameter the posterior mean and 95% credible interval are provided Parameter Metric REVI ACFL WEWA EAWP alpha.time mean 0.18 0.28 0.12 0.39 alpha.time q2.5 0.04 0.06 -0.11 0.11 alpha.time q97.5 0.3 0.52 0.35 0.66 beta.jd mean 0.08 0.03 -0.17 0.21 beta.jd q2.5 -0.05 -0.2 -0.39 -0.06 beta.jd q97.5 0.23 0.24 0.06 0.53 alpha.dist mean 42.2 33.99 44.93 81.34 alpha.dist q2.5 33.54 27.39 34.42 61.09 alpha.dist q97.5 51.98 42.92 57.78 99.19 observer.sd mean 0.37 0.37 0.37 0.37 observer.sd q2.5 0.16 0.16 0.16 0.16 observer.sd q97.5 0.88 0.88 0.88 0.88 beta.wind mean -0.01 0.06 -0.03 0.01 beta.wind q2.5 -0.05 0.02 -0.11 -0.13 beta.wind q97.5 0.04 0.11 0.04 0.13 mean.sigma mean 37.45 30.15 39.94 72.48 mean.sigma q2.5 35.03 28.22 35.52 59.4 mean.sigma q97.5 40.43 32.11 45.74 86.41 pperc mean 0.45 0.31 0.49 0.84 pperc q2.5 0.4 0.27 0.41 0.74 pperc q97.5 0.5 0.35 0.57 0.92 pavail mean 0.98 0.98 0.97 0.99 pavail q2.5 0.97 0.97 0.95 0.97 pavail q97.5 0.99 0.99 0.99 0.99 alpha.N mean 1.3 1.03 0.38 -0.59 alpha.N q2.5 1.16 0.85 0.14 -0.91 alpha.N q97.5 1.45 1.22 0.61 -0.35 unit.sd mean 0.12 0.1 0.15 0.29 unit.sd q2.5 0.01 0.01 0 0.02 unit.sd q97.5 0.32 0.31 0.41 0.7 point.sd mean 0.07 0.07 0.11 0.14 point.sd q2.5 0 0 0.01 0.01 point.sd q97.5 0.18 0.2 0.27 0.39 beta.ht mean 0.09 0.19 0.05 0.03 beta.ht q2.5 -0.04 0.06 -0.09 -0.17 beta.ht q97.5 0.18 0.35 0.2 0.22 beta.covarht mean -0.09 0.03 0.03 -0.03 beta.covarht q2.5 -0.18 -0.09 -0.12 -0.2 beta.covarht q97.5 0.02 0.14 0.18 0.16 beta.ovdens niean -0.03 -0.14 -0.02 -0.02 beta.ovdens q2.5 -0.12 -0.28 -0.17 -0.21 beta.ovdens q97.5 0.08 0.01 0.12 0.15 beta.middens mean 0.07 -0.01 0.1 0.03 beta.middens q2.5 -0.03 -0.14 -0.05 -0.17 beta.middens q97.5 0.16 0.1 0.23 0.23 beta.middens mean -0.08 -0.05 0.06 -0.04 beta.middens q2.5 -0.16 -0.19 -0.08 -0.23 beta.middens q97.5 0.01 0.08 0.21 0.13 beta.elev mean -0.02 0 0.07 0.23 beta.elev q2.5 -0.14 -0.13 -0.08 0.01 beta.elev q97.5 0.08 0.14 0.25 0.45 beta.slope mean 0.01 0.07 0.17 -0.01 beta.slope q2.5 -0.08 -0.07 0 -0.22 beta.slope q97.5 0.11 0.22 0.33 0.21 beta.aspect mean 0.01 -0.05 -0.16 -0.01 beta.aspect q2.5 -0.1 -0.19 -0.33 -0.21 beta.aspect q97.5 0.11 0.06 0.02 0.2 Parameter Metric WOTH BHCO YTVI INBU alpha.time mean 0.1 -3 -0.28 -0.38 alpha.time q2.5 -0.33 -4.3 -0.78 -1.33 alpha.time q97.5 0.49 -2.04 0.22 0.27 beta.jd mean -0.65 0.92 0.09 0.03 beta.jd q2.5 -1.1 0.57 -0.37 -0.74 beta.jd q97.5 -0.17 1.27 0.56 0.53 alpha.dist mean 72.58 32.39 36.85 56.61 alpha.dist q2.5 49.8 25.04 27.78 38.58 alpha.dist q97.5 95.68 43.27 49.32 87.33 observer.sd mean 0.37 0.37 0.37 0.37 observer.sd q2.5 0.16 0.16 0.16 0.16 observer.sd q97.5 0.88 0.88 0.88 0.88 beta.wind mean 0.06 -0.12 0.01 0.06 beta.wind q2.5 -0.11 -0.23 -0.12 -0.22 beta.wind q97.5 0.23 -0.01 0.13 0.27 mean.sigma mean 64.87 29.09 32.75 50.51 mean.sigma q2.5 47.97 24.73 27.05 37.13 mean.sigma q97.5 83.99 34.69 40.76 73.23 pperc mean 0.77 0.29 0.36 0.62 pperc q2.5 0.59 0.21 0.25 0.44 pperc q97.5 0.91 0.39 0.5 0.84 pavail mean 0.94 0.28 0.93 0.9 pavail q2.5 0.88 0.09 0.84 0.68 pavail q97.5 0.98 0.48 0.98 0.98 alpha.N mean -1.52 0.64 -0.79 -2.51 alpha.N q2.5 -2.17 -0.44 -1.34 -3.57 alpha.N q97.5 -0.99 1.99 -0.12 -1.64 unit.sd mean 0.54 0.61 0.44 0.87 unit.sd q2.5 0.1 0.07 0.02 0.1 unit.sd q97.5 1.1 1.46 1.28 2.12 point.sd mean 0.28 0.24 0.31 0.64 point.sd q2.5 0.02 0.02 0.02 0.1 point.sd q97.5 0.74 0.59 0.8 1.34 beta.lu mean -0.07 -0.12 -0.03 -0.99 beta.ht q2.5 -0.38 -0.45 -0.38 -1.55 beta.ht q97.5 0.23 0.25 0.29 -0.46 beta.covarht mean 0.21 -0.06 0.14 -0.17 beta.covarht q2.5 -0.08 -0.36 -0.18 -0.56 beta.covarht q97.5 0.51 0.3 0.43 0.18 beta.ovdens mean 0.21 0.02 -0.08 0.21 beta.ovdens q2.5 -0.1 -0.25 -0.43 -0.09 beta.ovdens q97.5 0.48 0.28 0.2 0.54 beta.middens mean -0.36 -0.1 0 -0.49 beta.middens q2.5 -0.64 -0.45 -0.31 -1.06 beta.middens q97.5 -0.08 0.24 0.31 0.04 beta.unddens mean 0.02 -0.09 0 0.16 beta.unddens q2.5 -0.26 -0.38 -0.27 -0.28 beta.unddens q97.5 0.29 0.17 0.29 0.57 beta.elev mean -0.26 -0.11 -0.21 0.03 beta.elev q2.5 -0.64 -0.49 -0.51 -0.45 beta.elev q97.5 0.12 0.25 0.12 0.54 beta.slope mean 0.46 -0.05 0.26 -0.53 beta.slope q2.5 0.1 -0.39 -0.1 -1.02 beta.slope q97.5 0.82 0.3 0.62 -0.06 beta.aspect mean -0.11 0.05 -0.11 0.6 beta.aspect q2.5 -0.42 -0.3 -0.42 0.07 beta.aspect q97.5 0.23 0.35 0.22 1.05 Parameter SCTA OVEN HOWA TUTI RBWO alpha.time 0.28 -0.02 -0.17 -0.17 -0.64 alpha.time 0 -0.34 -0.45 -0.52 -1.08 alpha.time 0.59 0.25 0.12 0.13 -0.22 beta.jd 0.25 0.16 0.29 0.15 -0.59 beta.jd -0.03 -0.1 -0.01 -0.26 -0.98 beta.jd 0.54 0.44 0.59 0.57 -0.18 alpha.dist 77.89 79.81 73.2 76.6 68.69 alpha.dist 59.09 58.97 50.92 57.84 47.13 alpha.dist 96.22 98.3 93.97 96.06 93.58 observer.sd 0.37 0.37 0.37 0.37 0.37 observer.sd 0.16 0.16 0.16 0.16 0.16 observer.sd 0.88 0.88 0.88 0.88 0.88 beta.wind -0.15 0 -0.02 -0.14 -0.05 beta.wind -0.29 -0.16 -0.17 -0.3 -0.24 beta.wind -- 0.14 0.13 0.04 0.15 mean.sigma 70.64 71.29 65.32 69.35 61.53 mean.sigma 57.29 56.02 50.91 54.81 44.92 mean.sigma 82.63 86.75 82.89 81.41 79.38 pperc 0.81 0.83 0.78 0.8 0.74 pperc 0.7 0.7 0.64 0.69 0.57 pperc 0.9 0.92 0.9 0.89 0.88 pavail 0.98 0.96 0.94 0.94 0.84 pavail 0.96 0.92 0.9 0.89 0.75 pavail 0.99 0.98 0.97 0.97 0.92 alpha.N -0.56 -0.78 -0.85 -0.94 -1.24 alpha.N -0.79 -1.4 -1.62 -1.59 -1.69 alpha.N -0.31 -0.14 -0.03 -0.52 -0.79 unit.sd 0.18 0.81 0.92 0.48 0.33 unit.sd 0.01 0.38 0.14 0.05 0.01 unit.sd 0.55 1.46 2.29 1.24 0.91 point.sd 0.12 0.11 0.16 0.26 0.25 point.sd 0.01 0 0 0.03 0 point.sd 0.35 0.3 0.45 0.61 0.64 beta.ht 0 0.06 -0.06 -0.11 0.02 beta.ht -0.18 -0.16 -0.32 -0.38 -0.32 beta.ht 0.2 0.31 0.26 0.12 0.32 beta.covarht -0.04 -0.11 0.11 0.14 -0.03 beta.covarht -0.23 -0.32 -0.12 -0.07 -0.31 beta.covarht 0.14 0.09 0.32 0.35 0.23 beta.ovdens 0.09 -0.04 0.06 0.04 -0.06 beta.ovdens -0.08 -0.22 -0.1 -0.17 -0.32 beta.ovdens 0.26 0.13 0.24 0.27 0.19 beta.middens -0.05 0.13 -0.02 0.12 0.07 beta.middens -0.27 -0.06 -0.29 -0.11 -0.21 beta.middens 0.14 0.29 0.23 0.37 0.33 beta.middens 0 -0.09 -0.12 0.12 -0.35 beta.middens -0.2 -0.28 -0.32 -0.1 -0.63 beta.middens 0.15 0.1 0.07 0.35 -0.08 beta.elev 0.05 -0.14 0.25 0.08 0.15 beta.elev -0.15 -0.39 -0.08 -0.36 -0.14 beta.elev 0.25 0.15 0.65 0.39 0.52 beta.slope 0.06 -0.22 -0.18 0.24 0.06 beta.slope -0.14 -0.44 -0.41 -0.04 -0.25 beta.slope 0.23 -0.02 0.06 0.5 0.36 beta.aspect 0.02 0 0.03 0.06 -0.05 beta.aspect -0.18 -0.24 -0.2 -0.19 -0.37 beta.aspect 0.2 0.23 0.29 0.28 0.27 Parameter KEWA EATO YTWA BAWW CERW alpha.time -1.1 0.15 -0.47 -0.51 -0.38 alpha.time -2.01 -0.37 -1.09 -1.13 -1.1 alpha.time -0.31 0.64 0.05 0.04 0.17 beta.jd 0.44 -0.07 -0.34 -0.37 -0.09 beta.jd -0.24 -0.53 -0.92 -1.05 -0.78 beta.jd 0.95 0.45 0.2 0.29 0.45 alpha.dist 71.32 67.75 54.54 33.37 47.97 alpha.dist 49.74 43.05 37.72 24.63 32.97 alpha.dist 94.19 95.48 74.36 44.68 67.43 observer.sd 0.37 0.37 0.37 0.37 0.37 observer.sd 0.16 0.16 0.16 0.16 0.16 observer.sd 0.88 0.88 0.88 0.88 0.88 beta.wind -0.21 -0.03 -0.26 0.04 -0.39 beta.wind -0.39 -0.22 -0.46 -0.08 -0.63 beta.wind 0.03 0.14 -0.07 0.19 -0.15 mean.sigma 65.35 60.53 50.65 29.64 46.46 mean.sigma 50 42.58 36.37 24.46 34.27 mean.sigma 81.67 82.35 67.57 36.86 61.76 pperc 0.76 0.73 0.6 0.3 0.53 pperc 0.62 0.53 0.42 0.2 0.38 pperc 0.89 0.9 0.78 0.43 0.7 pavail 0.74 0.97 0.88 0.87 0.91 pavail 0.48 0.92 0.74 0.71 0.75 pavail 0.93 0.99 0.97 0.96 0.98 alpha.N -1.46 -2.55 -1.87 -1.78 -1.96 alpha.N -2.04 -3.54 -2.77 -3.41 -3 alpha.N -0.85 -1.65 -0.92 -0.62 -1.08 unit.sd 0.38 0.86 1.52 1.09 0.86 unit.sd 0.01 0.06 0.38 0.09 0.13 unit.sd 1.15 2.97 4.61 2.46 2.11 point.sd 0.29 0.55 0.2 1.02 0.56 point.sd 0.02 0.01 0.01 0.16 0.06 point.sd 0.84 1.37 0.63 1.82 1.16 beta.lu -0.16 -1.2 0.29 -0.8 -0.32 beta.ht -0.49 -1.73 -0.21 -1.47 -0.87 beta.ht 0.19 -0.64 0.76 -0.24 0.17 beta.covarht 0.04 -0.22 -0.07 0.36 0.11 beta.covarht -0.26 -0.63 -0.44 -0.15 -0.27 beta.covarht 0.35 0.13 0.3 0.88 0.49 beta.ovdens 0.19 -0.05 0.1 0.05 -0.09 beta.ovdens -0.11 -0.45 -0.21 -0.41 -0.5 beta.ovdens 0.48 0.29 0.39 0.51 0.29 beta.middens -0.23 -0.08 -0.14 -0.01 0.28 beta.middens -0.57 -0.54 -0.65 -0.52 -0.11 beta.middens 0.11 0.37 0.25 0.56 0.65 beta.unddens -0.01 0.13 0.12 -0.03 0 beta.unddens -0.29 -0.25 -0.17 -0.57 -0.36 beta.unddens 0.26 0.55 0.44 0.43 0.34 beta.elev -0.41 -0.19 -0.86 -0.52 -0.6 beta.elev -0.73 -0.69 -1.38 -1.14 -1.12 beta.elev -0.11 0.27 -0.37 0.08 -0.13 beta.slope 0.27 -0.27 -0.04 0.39 0.03 beta.slope -0.15 -0.79 -0.43 -0.1 -0.47 beta.slope 0.63 0.22 0.41 0.94 0.51 beta.aspect 0 0.58 0.21 -0.05 0.42 beta.aspect -0.34 0.13 -0.12 -0.67 0.06 beta.aspect 0.31 1.04 0.59 0.46 0.82
SUBMITTED 4 DECEMBER 2017
ACCEPTED 21 MARCH 2018
KENNETH F. KELLNER, (1,2) PATRICK J. RUHL and JOHN B. DUNNING JR.
Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907
KEVIN W. BARNES (3)
Department of Biology, Ball State University, Muncie, Indiana 47306
MICHAEL R. SAUNDERS and ROBERT K. SWIHART
Department of Forestry and Natural Resources, Purdue University, West Lafayette, Indiana 47907
(1) Corresponding author: e-mail: firstname.lastname@example.org
(2) Present address: Division of Forestry and Natural Resources, West Virginia University, 333 Evansdale Dr. Morgan town. WV 26505
(3) Present address: U.S. Fish and Wildlife Service, 992 Bootlegger Trail, Great Falls, MT 59104
Caption: Fig. 1.--(A) Number of bird species on which habitat covariates had a significant effect on abundance, based on Admixture models. We defined an effect as significant when the 95% credible interval did not overlap 0. Alpha codes of the species are shown inside each bar, along with the direction of the effect (positive or negative). Common and scientific names corresponding lo alpha codes are in Table 1. (B) Mean absolute effect size (defined as the mean of the absolute value of the regression coefficient) for each habitat covariate across all species in the analysis. Error bars represent 95% credible intervals obtained via bootstrapping
TABLE 1.--Information about the species observed in the study including common names, scientific names, alpha codes, total number of detections, and if the species was included in the analysis. For species in the analysis, estimated detection probability (P) is provided Common name Scientific name Code Acadian flycatcher Empidonax virescens ACFL American redstart Setophaga rutirilla AMRE Black-and-white warbler Mniotilta varia BAWW Brown-headed cowbird Molothrus ater BHCO Blue-winged warbler Vermivora cyanoptera BWWA Carolina chickadee Puerile carolinensis CACH Carolina wren Thryothorus ludovirianus CARW Cerulean warbler Setophaga cerulea CERW Chipping sparrow Spizella passerina CHSP Common yellowthroat Geothlypis Irirlias cove Chestnut-sided warbler Setophaga pensylvanica CSWA Downy woodpecker Piiviiles pubescens DOWO Eastern towhee Pipilo erythrophlhahnus EATO Eastern wood-pewee Contopus virens EAWP Gray catbird Dumetella carolinensis GRCA Hairy woodpecker Picoides villosus HAWO Hooded warbler Setophaga citrina HOWA Indigo bunting Passerina cyanea INBU Kentucky warbler Geothlypis formosa KEWA Louisiana waterthrush Parkesia motacilla LOWA Mourning dove Zenaida macroura MODO Northern cardinal Cardinalis cardinalis NOCA Northern parula Setophaga americana NOPA Ovenbird Seiu rus au roca pilla OVEN Pine warbler Setophaga pinus PIWA Pileated woodpecker Dryocopus pileatus PIWO Prairie warbler Setophaga discolor PRAW Rose-breasted grosbeak Pheucticus ludovicianus RBGR Red-bellied woodpecker Melanerpes Carolinas RBWO Red-eyed vireo Vireo olivaceus REVI Red-headed woodpecker Melanerpes erythrocephalus RHWO Ruby-throated hummingbird Archilochus colubris RTHU Scarlet tanager Piranga olivaceo SCTA Summer tanager Piranga rubra SUTA Tufted titmouse Hueolophus bicolor TUTI White-breasted nuthatch Sato carolinensis WBNU White-eyed vireo Vireo griseus WEVI Worm-eating warbler Helmitheros vermivorum WEWA Wood thrush Hylocichla mustelina WOTH Yellow-breasted chat Icteria virens YBCH Yellow-billed cuckoo Cocryzus americanus YBCU Yellow-throated vireo Vireo flavifrons YTVI Yellow-throated warbler Setophaga dominica YTWA Common name Detections In analysis? P Acadian flycatcher 313 YES 0.30 American redstart 9 NO -- Black-and-white warbler 51 YES 0.26 Brown-headed cowbird 66 YES 0.08 Blue-winged warbler 8 NO -- Carolina chickadee 17 NO -- Carolina wren 40 NO -- Cerulean warbler 51 YES 0.48 Chipping sparrow 4 NO -- Common yellowthroat 8 NO -- Chestnut-sided warbler 1 NO -- Downy woodpecker 27 NO -- Eastern towhee 60 YES 0.71 Eastern wood-pewee 182 YES 0.83 Gray catbird 1 NO -- Hairy woodpecker 14 NO -- Hooded warbler 142 YES 0.74 Indigo bunting 62 YES 0.56 Kentucky warbler 62 YES 0.56 Louisiana waterthrush 3 NO -- Mourning dove 16 NO -- Northern cardinal 46 NO -- Northern parula 9 NO -- Ovenbird 160 YES 0.80 Pine warbler 10 NO -- Pileated woodpecker 38 NO -- Prairie warbler 2 NO -- Rose-breasted grosbeak 5 NO -- Red-bellied woodpecker 80 YES 0.62 Red-eyed vireo 580 yes 0.44 Red-headed woodpecker 1 NO -- Ruby-throated hummingbird 4 NO -- Scarlet tanager 172 YES 0.79 Summer tanager 17 NO -- Tufted titmouse 129 YES 0.76 White-breasted nuthatch 9 NO -- White-eyed vireo 14 NO -- Worm-eating warbler 259 YES 0.47 Wood thrush 78 YES 0.73 Yellow-breasted chat 14 NO -- Yellow-billed cuckoo 13 NO -- Yellow-throated vireo 63 YES 0.33 Yellow-throated warbler 56 YES 0.53
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|Author:||Kellner, Kenneth F.; Ruhl, Patrick J.; Dunning, John B., Jr.; Barnes, Kevin W.; Saunders, Michael R.|
|Publication:||The American Midland Naturalist|
|Date:||Jul 1, 2018|
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