MULTICOHORT MANAGEMENT AND LIDAR: NEW FOREST MANAGEMENT TOOLS FOR NORTHEASTERN ONTARIO BOREAL MIXEDWOOD BIRD COMMUNITIES

Transcription

1 MULTICOHORT MANAGEMENT AND LIDAR: NEW FOREST MANAGEMENT TOOLS FOR NORTHEASTERN ONTARIO BOREAL MIXEDWOOD BIRD COMMUNITIES by Michael Vernon Ashley Burrell A thesis submitted in conformity with the requirements for the degree of Master of Science in Forestry, Faculty of Forestry University of Toronto Michael Vernon Ashley Burrell 2009

2 Burrell, Michael Vernon Ashley Multicohort management and LiDAR: new forest management tools for northeastern Ontario boreal mixedwood bird communities. Master of Science in Forestry, Faculty of Forestry, University of Toronto. ABSTRACT While traditional management of the boreal forests results in even-aged forests with low landscape scale variability, recent work has suggested that much of the eastern boreal forest of North America is subject to long natural fire return-intervals. This has led to the development of new management strategies to maintain a mosaic of even and multi-aged stands. In this context I investigated the relationships between diameterdistributions, stand age, forest structure and bird communities. Results showed weak associations of the bird community with cohort classes, but that diameter-distributions can work to succinctly describe some of the variation in stand structure and bird communities. I also explored the utility of LiDAR to measure important structural features for bird communities. Results showed that LiDAR can outperform traditional measures of stand structure at explaining bird communities at differing scales. ii

3 ACKNOWLEDGEMENTS I would like to take this opportunity to thank my supervisors, Dr. Jay Malcolm and Dr. Pierre Drapeau and my committee member, Dr. Sean Thomas for their guidance and feedback throughout this project; your help will make the final product special. I would also like to thank the entire faculty of forestry for the unique and positive learning environment. Thanks especially to the other students at the faculty for being so interesting and supportive. I especially want to thank Ben Kuttner, who helped me immensely by always being willing to give me some tips, whether it be on MCM or LiDAR, or great field camps. Charlotte Sharkey s help with MCM is also greatly appreciated. The rest of the wildlife lab also deserves special thanks for their companionship in Toronto and in the field. Thank you to Lyle Walton, and the MNR/Trent wolf crew for your hospitality at Horwood Lake. A huge thanks goes out to my field assistant, Devin Turner, who did an excellent job of measuring trees, not to mention the fresh fish, moose racks, and companionship you provided during fieldwork. I would like to thank my family, for getting me interested in birds first of all, but ultimately for providing me with the opportunity to do what I desire. Finally, I would like to thank my fiancée, Erica Barkley, for your understanding and support through the good and bad times of this project, and of course for your constant feedback and discussions regarding the project. I would certainly not been able to complete this work without the generous funding of several bodies: Forestry futures trust, Tembec Inc., the National Science and Engineering Research Council of Canada, the Faculty of Forestry, Ontario Centers of Excellence, and the Ontario Graduate Scholarship. Thank you to everyone. iii

4 TABLE OF CONTENTS ABSTRACT...ii ACKNOWLEDGEMENTS.iii LIST OF TABLES... v-vi LIST OF FIGURES...vii-viii LIST OF APPENDICES......ix GENERAL INTRODUCTION.1-4 CHAPTER 1 RELATIONSHIPS AMONG HABITAT STRUCTURAL FEATURES, TREE DIAMETER DISTRIBUTIONS, AND AGE IN BOREAL MIXEDWOOD FORESTS OF NORTHEASTERN ONTARIO.5-31 INTRODUCTION 5-7 METHODS.7-15 RESULTS DISCUSSION CHAPTER 2- COMPARATIVE PERFORMANCE OF FOREST STRUCTURE, TREE SPECIES COMPOSITION, LIVE-STEM DIAMETER DISTRIBUTIONS, AND STAND AGE IN PREDICTING BOREAL MIXEDWOOD BIRD COMMUNITIES OF NORTHEASTERN ONTARIO INTRODUCTION METHODS RESULTS DISCUSSION CHAPTER 3 MULTISCALE LIDAR AS A CORRELATE OF BOREAL BIRD COMMUNITY STRUCTURE INTRODUCTION METHODS RESULTS DISCUSSION CONCLUSIONS LITERATURE CITED APPENDICES iv

5 LIST OF TABLES Table 1.1. Weibull scale and shape, Cramer-von Mises (W 2 ) goodness-of-fit (and associated p-value), cohort class, and stand age for the mixedwood study sites in boreal northeastern Ontario. Table 1.2 Spearman rank correlations coefficients (r s ) and associated P-values between Weibull curve parameters and age versus structural habitat features of boreal mixedwood stands in northeastern Ontario. Table 1.3. Decomposition of variance of Weibull parameters (shape and scale) of the live-stem diameter distribution and age for boreal mixedwood stands in northeastern Ontario. Table 1.4. As table 1.3, except that decomposition of variance is just for the Weibull scale and shape parameters. Table 2.1. Bird species detected on point counts and playback surveys and their relative abundance in each of the three cohort classes in boreal mixedwood forests of northeastern Ontario. Table 2.2Comparative results of playback and point count surveys for cavity nesting bird species in boreal mixedwood forests of northeastern Ontario Table 2.3. Structural habitat variables and the variance (and associated p-value) that they explained singly for the entire bird community and habitat and feeding guilds in boreal mixedwood stands of northeastern Ontario. Only significant relationships are shown. See appendix 1.2 for variable code definitions. Table 2.4. As table 2.3 except that values for variables in a forward selection are shown and cohort class and FEC site types are included (as dummy variables). Table 2.5. Decomposition of variance of bird community structure for entire bird community and various habitat and feeding guilds explained between Weibull parameters (shape and scale) and age (stand age) of boreal mixedwood stands in northeastern Ontario. Table 2.6. Decomposition of variance of bird community structure for entire bird community and various habitat and feeding guilds explained between Weibull shape and Weibull scale of boreal mixedwood stands in northeastern Ontario. Table 3.1. Decomposition of variance of bird community structure explained between ground-based structure and LiDAR for mixedwood sites in boreal northeastern Ontario. Table 3.2. Decomposition of variance of bird community structure explained between landscape-scale FRI data and landscape-scale LiDAR data v

6 Table 3.3. Decomposition of variance of bird community structure explained between stand-level and landscape-level habitat variables as measured using traditional groundbased methods and LiDAR for mixedwood sites in boreal northeastern Ontario. vi

7 LIST OF FIGURES Figure 1.1 Locations of 45 study sites within the Romeo-Malette forest management unit (shaded area) in northeastern Ontario. See appendix 1.1 for geographic coordinates of sites. Figure 1.2 Habitat sampling design used in boreal mixedwood sites of northeastern Ontario. Shown are the centre station (C) and the three satellite stations (X, Y, and Z). Figure 1.3: All sites plotted by Weibull scale vs. shape. Symbols correspond as follows: cohort class 1; cohort class 2; cohort class 3; cohort class 4. Cohort means are shown by shaded squares with the cohort number inside. Example histograms with fitted Weibull curves are shown for each cohort. Figure 1.4. Principal components analysis on habitat structure variables in mixedwood stands of northeastern Ontario. A) all sites classified by cohort. Symbols correspond as follows: cohort class 1; cohort class 2; cohort class 3; cohort class 4. B) vectors of the habitat structure variables. In both A) and B), Weibull scale and shape, cohort class, and age are plotted passively and are represented with dashed arrows (Weibull scale and shape and age) and shaded upside-down triangles (cohort class centroids). Variable acronyms are identified in Appendix 1.2. Figure 2.1 Mean ( 1 SE) a) richness b) abundance and c) diversity of all birds for cohort classes in boreal mixedwood forests of northeastern Ontario. Letters above means represent classes with significant differences. Figure 2.2. As figure 2.1 except that deciduous habitat guild is shown. Figure 2.3. As figure 2.1 except that mixedwood habitat guild is shown. Figure 2.4. As figure 2.1 except that coniferous habitat guild is shown. Figure 2.5. As figure 2.1 except that ground-feeding guild is shown. Figure 2.6. As figure 2.1 except that shrub-feeding guild is shown. Figure 2.7. As figure 2.1 except that canopy-feeding guild is shown. Figure 2.8. As figure 2.1 except that bark-feeding guild is shown. Figure 2.9. Principal components analysis on entire bird community in all mixedwood sites in boreal northeastern Ontario plotted according to cohort class with species vectors for the fifteen species with longest vectors. Symbols correspond as follows: cohort class 1; cohort class 2; cohort class 3; cohort class 4. In part b), significant structural variables are plotted passively (* = variable significant on its own; + = variable vii

8 significant and included in forward selection of best model). See appendix 1.2 and 1.3 for variable and bird species codes. Figure Principal components analysis on deciduous bird habitat guild in all mixedwood sites in boreal northeastern Ontario plotted according to cohort class. Symbols correspond as follows: cohort class 1; cohort class 2; cohort class 3; cohort class 4. Significant structural variables are plotted passively (* = variable significant on its own; + = variable significant and included in forward selection of best model). See appendix 1.2 and 1.3 for variable and bird species codes Figure As figure 2.10 except that mixedwood habitat guild is shown. Figure As figure 2.10 except that coniferous habitat guild is shown. Figure As figure 2.10 except that bird community was characterized according to feeding guilds Figure As figure 2.10 except that ground-feeding guild is shown. Figure As figure 2.10 except that shrub-feeding guild is shown. Figure As figure 2.10 except that canopy-feeding guild is shown. Figure As figure 2.10 except that bark-feeding guild is shown. Figure 3.1 Correlations between LiDAR-derived and ground-based measures for (a and b) maximum canopy height and (c and d) mean canopy height for (a and c) 2004 LiDAR and (b and d) 2005 LiDAR for boreal mixedwood stands in northeastern Ontario. Dashed line is for the line y=x. Figure 3.2 Correlations between LiDAR-derived and ground-based measures for foliage height diversity for a) 2004 LiDAR and b) 2005 LiDAR for boreal mixedwood stands in northeastern Ontario. Figure 3.3 Correlations between LiDAR-derived and ground-based measures for (a and b) upper canopy, (c and d) lower canopy, and (e and f) understorey thickness for (a, c, f) 2004 LiDAR and (b, d, e) 2005 LiDAR for boreal mixedwood stands in northeastern Ontario. viii

9 LIST OF APPENDICES Appendix 1.1 GPS coordinates of sites Appendix 1.2 Structure variables used in analysis Appendix 1.3 Bird species codes and guild Appendix 1.4 PCA of height strata Appendix 1.5 Rarefaction curve for tree canopy class richness Appendix 1.6 Rarefaction curves for bird species richness Appendix 1.7 LiDAR year sampled and vegetation return densities ix

10 GENERAL INTRODUCTION The world s forests provide many important services for human populations and are home to much of the planet's biodiversity. Of the world s closed canopy forests, the boreal forest occupies about 25% (Natural Resources Canada 2009) and contains some of the largest intact forest ecosystems in the world (IUCN 2003). It provides globally important services, including massive carbon storage and freshwater production (IUCN 2003). It also is home to globally significant biodiversity and numerous indigenous cultures (IUCN 2003). The boreal forest is an especially important resource in a Canadian context, where it comprises 35% of the total land area and an even more impressive 77% of the total forested area (Natural Resources Canada 2009). In Ontario, about 55% (50 million hectares) of the total land mass is boreal forest (OMNR 2008) of which some 50% (25.8 million hectares) is managed for forest harvesting (OMNR 2008). About 73% (18.8 million hectares) of these managed forests are crown land (OMNR 2008). Any forest harvesting operation on crown land must ensure the longterm health of the forest (OMNR 2008) by following the Crown Forest Sustainability Act (CFSA) (Government of Ontario 1995). One of the goals of the CFSA is to maintain biodiversity while ensuring that forest is available for harvest now and into the future. More generally, maintaining forest biodiversity is a goal for sustainable forest management throughout Canada (CCFM 2003). Since the early 1990s, many authors have popularized coarse-filter strategies to preserve biological diversity (e.g., Franklin 1993). A natural disturbance-based (Attiwil 1994) coarse-filter approach is now the working model for forest management in Ontario 1

11 (OMNR 2001). In the boreal forest, fire is the most important form of natural disturbance; Amiro et al. (2001) estimate fires consume about three million hectares per year in Canada alone. As such, forestry operations in the boreal forest use even-aged management and clearcut silviculture as a surrogate of stand-replacing disturbances in the hopes of providing a suitable framework that allows for both timber production and biodiversity conservation. At the same time, however, even-aged management differs from fires in several key aspects. For instance, compared to post-harvest stands, post-fire stands usually have more retained live trees and many snags (McRae et al. 2001, Drapeau et al. 2002). From a landscape perspective, even-aged management with short rotations ( years) truncates both the age and size distribution of stands (Bergeron et al. 1999, 2002, 2007). These differences may be amplified in regions where the fire cycle is much longer than the rotation age. Recent work suggests, for example, that the eastern boreal forests of North America has much longer average fire-return intervals than typical rotation periods of years (Bergeron et al. 2001, 2004 Gauthier 2002). Moreover, paleoecological studies indicate that this trend has persisted throughout the Holocene postglacial period (Carcaillet et al. 2001). Given the short life span of many boreal tree species, older stands may become dominated by smaller-scale disturbances and multiple cohorts of trees in the absence of a stand-replacing event such as a fire (Kneeshaw and Bergeron 1996, Bergeron et al. 1995). Gauthier et al. (2002) found that almost half of the Lake Abitibi Model forest was composed of such stands with multiple cohorts of trees. This all has important implications. Perhaps most importantly, current even-aged management might only partly simulate the natural disturbance regime, and hence may 2

12 not fully be an efficient coarse filter approach for maintaining biodiversity. A possible alternative management scheme known as multi-cohort management (MCM) has been proposed as a way to bridge the gap between these naturally structurally complex stands and modern managed stands through the use of more varied silvicultural tools in emulating this structural complexity (Bergeron and Harvey 1997, Bergeron et al. 2002). Specifically, Bergeron et al. (1999) and Harvey et al. (2002) further developed MCM, defining three developmental stages or cohorts, from even-aged, single cohort stands to multi-aged multi-cohort stands that could be managed with differing silvicultural techniques. Development of MCM as a forest management tool requires understanding the variability of forest structure within and among stands, especially in the context of biodiversity maintenance. Of particular interest is the importance of multicohort stand structures for biodiversity as a whole. In Old World boreal forests, especially Fennoscandia, structural simplification of boreal forests through long-term, even-aged management has already been implicated in the decline of a wide range of taxa associated with old growth and over mature stands (Ecke et al. 2002, Edman et al. 2004, Helle and Jarvinen 1986). Can we expect similar results in Canada? More generally, is an understanding of the cohort structure of stands, especially as envisioned in MCM, likely to be a useful coarse filter strategy in managing for biodiversity in boreal Ontario? In this thesis, I build on the work of Sharkey (2008) and Barkley (2009), who examined small mammal and insect communities, respectively, to examine relationships between multi-cohort stand structures and wildlife communities of the boreal forest, in this case breeding birds. In chapter 1, I examine the abilities of FRI-based stand age and 3

13 the diameter distributions of living trees to describe stand structural features that may be important to wildlife communities. In chapter 2, I examine the cohort concept from the perspective of bird communities. Birds are an ideal group in this regard as they are well known and relatively easy to study. Unlike insects and mammals, which may require intensive trapping efforts (Ecke et al. 2002, Vance et al. 2007), bird community sampling can be done relatively easily through passive point counts or playbacks (Drapeau et al. 2000, Lee and Marsden 2008). Bird communities are also of particular interest in that forest structure, especially the vertical distribution of foliage, has long been recognized as a key variable in understanding variation among forest bird communities (e.g., MacArthur and MacArthur 1961). Finally, in chapter 3, I examine the utility of a relatively new forest management tool, Light detection and ranging (LiDAR), at measuring structural features important for wildlife and it s ability, in particular, to explain bird communities at both stand and landscape scales. 4

14 CHAPTER 1 RELATIONSHIPS AMONG HABITAT STRUCTURAL FEATURES, TREE DIAMETER DISTRIBUTIONS, AND AGE IN BOREAL MIXEDWOOD FORESTS OF NORTHEASTERN ONTARIO INTRODUCTION Logging in Ontario is mandated to emulate natural disturbances (OMNR 2003). Clear-cut logging, the most common type of logging in boreal Ontario, is used to mimic natural stand-replacing events such as insect outbreaks and fire (OMNR 2001); however, Bergeron et al. (2001, 2004) found that many natural stands in Quebec s western boreal are older than the average fire-return interval. These over-mature stands develop characteristics much different from younger, even-aged stands that occur from clearcutting; in particular, multiple cohorts of trees become present as small-scale gap dynamics come to dominate local disturbance regimes (Harvey et al. 2002). To provide a better coarse filter approach that incorporates such structural variation, several authors have suggested that forest management in the boreal forest should include strategies to maintain forests in multi-cohort states by broadening the set of silvicultural tools to include partial cutting (Harvey et al. 2002, Bergeron et al. 1999, 2002, 2007). Compared to clearcutting, MCM may not only be better for maintaining over-mature stand characteristics; it also may be better for maintaining stands with characteristics close to the ones late-seral stages experience when natural disturbances of lower intensity occur in other parts of the landscape (Lee 2004). Although Harvey et al. (2002) based their system of cohort classification largely on age since stand-replacing events, others have proposed a structural definition quite independent of age (e.g., Kuttner 2006). As pointed out by Sharkey (2008), there is a need to test our assumptions regarding relationships between cohort structure of a stand 5

15 and other structural features of importance for wildlife communities. To what extent do structural or age-based cohort classification systems capture variation in stand structure? Of particular interest in this regard are two easily defined parameters used in stand cohort classification, the shape and scale from a two-parameter Weibull curve fitted to the diameter distribution of living stems (Kuttner 2006). Kuttner found that the two parameters worked well to capture cohort classes as defined based on a wide variety of diameter distribution-based stand metrics (Kuttner 2006). How well do such diameter distribution-based cohort classification schemes work in capturing other important structural features, especially those important to wildlife? In this chapter, I focus on the value of stand age versus stand diameter distributions in capturing stand structural features important for wildlife. Sharkey (2008) examined the same question for boreal mixedwood sites to the north of my study site; here, I extend the test to a larger set of sites with different underlying geology (Canadian Shield vs. clay belt). I expected similar results as found by Sharkey (2008), such as that Weibull parameters would explain more variance in stand structural data than would age. I predicted that the two Weibull parameters would explain a similar amount of variance, but that Weibull scale would explain more than Weibull shape, as Sharkey (2008) found. I expected that Weibull scale would be especially correlated with structural features associated with stand stature and age because scale represents a measure of the central tendency of the distribution (specifically, the diameter at which 63.2% of the stems are accounted for). On the other hand, because Weibull shape is a measure of how mixedaged a stand is, it should be associated with measures of stand heterogeneity, such as foliage height diversity and spatial variability within the various height strata. Regarding 6

16 structural features associated with diameter distribution-based cohort classes, I expected that early cohort classes (bell-shaped diameter distributions) would be associated with relatively high mean thicknesses for various height strata, while later cohort classes (Jshaped diameter distributions) would be associated with high variance for various height strata, higher richness and diversity of canopy classes, and higher foliage height diversity; essentially, I expected early cohort classes to be more homogeneous regarding various structural features compared with later cohort classes. METHODS Site Selection Study sites were in the Romeo-Malette forest management unit west of the town of Timmins within Ecoregion 3E of northeastern Ontario s boreal forest (Rowe 1972). The area is characterized by exposed Canadian Shield in the south, gradually giving way to glacial deposits in the north as one approaches the clay belt. The forest management unit is characterized by high abundances of birch (Betula spp), jack pine (Pinus banksiana), poplar (Populus spp.), black spruce (Picea mariana), and white spruce (Remmel et al. 2008) Forty-five sites were selected for study during early May, 2007, based on several criteria (Figure 1.1, see appendix 1.1 for geographical coordinates of the sites). Initially, equal numbers of two forest types ("standard forest units") were selected from the digital forest resource inventory (FRI; OMNR, unpublished): MW2 (mixedwoods with an abundant hardwood component) and SF1 (mixedwoods with a greater softwood component). However, after field inspections of potential sites, it became clear that the FRI designation only crudely approximated actual stand composition, hence sites were 7

17 selected such that they were "mixedwoods"; that is, they had a mixed deciduous and coniferous composition. All sites had between 5-79% deciduous basal area (mean = 42%). Selected sites were all closed canopy (at least 25 years post disturbance) and, to the extent possible, spanned a range of ages post-disturbance ( years) and cohort classes based on previous classifications by Kuttner (2006) and on ground-based visual assessments. Stand age was measured from the FRI. FRI stand age is based on interpreting tree heights from aerial photos and calibrating it with cruise plots (Pinto et al. 2007). When available, other sources of information (e.g., harvesting and disturbance history) are used to increase the accuracy of the FRI (Pinto et al. 2007). The area has been under forest management since the early 20 th century. A few (3) sites were assumed to be natural (fire) origin stands: NW026 (106 years), TEM (107), and TEM (147 years). From the early 20 th century to the early 1960s forests were clearcut by use of horses; after this mechanized skidders took over (Radforth 1987). Therefore the vast majority (34) of the sites chosen were horse-logged, and 8 were mechanized logged. 8

18 Figure 1.1 Locations of 45 study sites within the Romeo-Malette forest management unit (shaded area) in northeastern Ontario. See appendix 1.1 for geographic coordinates of sites. 9

19 Site centers were at least 100 m from the nearest road and at least 1 km from nearest site (although two sites, Mike019 and Tem , did no quite meet that criterion in that they were only 929 m apart). Centers were placed randomly within a stand as delineated on the FRI with the proviso that they fell in the centre of a 100-mradius circle within the stand. To take advantage of existing habitat data where possible, sites centers coincided with the centers of existing OMNR Permanent Growth Plots (PGP; Hayden et al. 1995). When PGP centers fell within 100 m of a road, the PGP center was used as a "satellite" habitat site (see below) and the actual site center was projected 50 m from that point at a predetermined angle to ensure a distance of at least 100 m from nearest road. Habitat Sampling Habitat variables to be measured were chosen for direct comparison with those of Sharkey (2008) and Barkley (2009). Habitat measurements were completed during July- August Live trees were sampled at four stations at each site (the site centre and three satellite stations). At the centre station, all live and dead (standing) trees (live woody stems 2.5cm in diameter at breast height [DBH]) were sampled within a circle of radius m. At the three satellite stations each 50-m from the centre (see Figure1.2), all live and dead (standing) trees >10-cm DBH were sampled that fell in a BAF 2.0 prism sweep. Each live tree was identified to species and had its DBH and canopy class measured (Hayden et al. 1995). Standing dead trees (snags) were identified to species (where possible), had their DBH measured, and were assigned a decomposition class (Hayden et al. 1995). 10

20 Figure 1.2 Habitat sampling design used in boreal mixedwood sites of northeastern Ontario. Shown are the centre station (C) and the three satellite stations (X, Y, and Z). 11

21 To assess vertical and horizontal heterogeneity of foliage, I used the method developed by Hubbel and Foster (1986) and modified by Malcolm (1994). This was done by setting up three 100-m long transects separated by 120º. Each was walked to assess vertical stratification of foliage. Sighting straight upwards along a 2.5-m long wooden pole at 2.5-m intervals, a foliage density score was given for each of the following height intervals: 0-2.5, 2.5-5, 5-10, 15-20, 20-25, and m. The density scores were as follows: 0=0-10%, 1=10-50%, 2=50-75%, and 3=75-100%. Heights were periodically checked using an optical rangefinder. Density scores were converted to thickness (in meters) by multiplying the midpoint of the score s range (%) with the total thickness of the height interval in question. For example, a density score of 1 (10-50%, midpoint = 30%) in the height interval (total thickness of interval = 2.5 m) corresponded to a thickness of 0.75 m. Shrubs were defined as woody-stemmed plants less than 2.5 cm in DBH (or shorter than 1.3 m tall) and shrub stems were counted by species within 2.5-m radius circular plots at each of the four stations (Figure 1.2). Coarse woody debris sampling was conducted along four 15-m long transects originating from the site centre following cardinal directions (Figure 1.2). Along each transect, any coarse woody debris intercepting the transect line was identified to species (where possible) and its diameter at the point of intersection measured and its decay class assessed (Hayden et al. 1995). Vegetation and site type as defined by the Northeast Forest Ecosystem Classification system (Taylor et al. 2000) were determined for the site center. 12

22 Data reduction To reduce the number of variables from the vertical stratification data, I conducted a Principal Components Analysis (PCA) on the mean thicknesses of the various height intervals across the sites (on the correlation matrix). In this matrix, rows were sites and columns were heights. The first two PCA axes accounted for 55% of the variance. A biplot showed that heights within three strata were correlated: 0-10 m (understorey), m (lower canopy), and m (upper canopy). Accordingly, I summed scores in these three strata (see appendix 1.4). For each stratum, I calculated the mean thickness and the within-site variance and semivariance. Because the means were highly correlated with the latter two measurements, I used regression across the sites to partial out the effects of the mean. The residual variance and semivariance was then used in analyses rather than the raw measurements. I also calculated mean canopy height for each site and, as was true of the previous measurements, calculated the residual variance and semivariance. From the mean thicknesses of each measured strata for each site, foliage height diversity was measured as the Shannon index of diversity (H ; Magurran 1988) using: H = -Σ p i ln p i, where p i represents the proportion of the foliage thickness at height i. From the data collected on trees, canopy class diversity was calculated using the Shannon diversity index (H ). Canopy class richness was calculated using simple rarefaction in which residuals were used as taken from the relationship between the number of canopy classes at a site and the number of stems (see appendix 1.6). 13

23 Tree densities were calculated at each of the four stations in 1-cm DBH classes and averaged across the four stations (for stems >10cm; for stems < 10cm only the fixed radius sample was used). Prism data were converted to densities following Thompson et al. (2006). Because only trees with DBH >2.5 cm were measured, 2.5 cm was subtracted from each DBH to account for the empty probability space (see Sharkey 2008). The Weibull function was fit to the distribution of diameters (for 1 cm DBH bins) for each site using the CAPABILITY procedure in SAS, and scale and shape of the curve were estimated using maximum likelihood (location parameter was set to zero). Sites were then classified into 4 cohort classes using Weibull shape and scale cutoffs identified by Sharkey (2008) for mixedwood sites: Cohort class 1 sites had shape 1.01, and scale 9.02; cohort class 2 sites had shape 1.17 and scale 9.02; cohort class 3 sites had shape 1.17 and scale 7.29; cohort class 4 sites had shape 1.01 and scale Because these cutoffs resulted in a small amount of overlap (when shape was and scale was ) between cohort class 1 and 3, those sites (NW027 and Mike021) in the overlapping area were visually inspected and placed in cohort class 3, as there diameter distributions most closely resembled the cohort class 3 curve in Sharkey (2008). In total, from the various habitat measurements, I calculated 24 habitat structure variables (see appendix 1.2). Data Analysis To compare the relative strength of Weibull parameters (shape and scale) and age at explaining other structural features, spearman rank correlation coefficients were calculated. To test for differences in age among cohort classes I conducted a one-way ANOVA. A PCA on the correlation matrix of the various structural habitat measurements 14

24 was conducted across the sites. To visualize the effects of Weibull shape and scale and stand age, these variables were plotted passively (Leps and Smilaur 2003). In addition, sites were classified by cohort in the ordination biplot. When tests failed the assumption of equal variances I conducted ANOVAs on ranks (SAS Institute Inc. 1985) To test the effectiveness of Weibull parameters and age to explain the structural habitat variables, I used Redundancy Analyses (RDA) to constrain the structure variables by Weibull parameters or stand age. Decomposition of variances in CANOCO, to partial out the effect of the two Weibull parameters and stand age on stand structure (ter Braak and Smilauer 1998), was undertaken. Significance tests were from Monte Carlo permutation tests with 9999 permutations. All univariate tests were conducted in SAS (SAS v8.2) and multivariate analyses were conducted in CANOCO for Windows v4.5. RESULTS Cohort classification The clustering identified four groups that matched with that of Sharkey (2008). Cohort class 1 sites exhibited a relatively normal distribution of diameters. Class 2 sites showed a very right-skewed normal distribution of diameters. Class 3 and 4 sites had distributions approaching or representing an inverse-j shaped curve, with the main difference between the two classes being the longer tail shown by class 3 sites. To test how well the Weibull curve fitted the actual diameter distributions, I used a Cramer-von Mises (W 2 ) goodness of fit statistic. As Sharkey (2008) points out this allows a comparison between curves of different lengths (a result of sites having different numbers of DBH classes). Cohort class 2 sites had the best goodness-of-fit (mean W 2 = 15

25 2.11), followed by cohort class 3 (mean W 2 = 3.93), cohort class 1 (mean W 2 = 5.25) and last were cohort class 4 s (mean W 2 = 8.93). An ANOVA on W 2 ranks was significant (F 3,44 = 10.36, p 0.001) and a Tukey s Studentized range test found differences between cohort classes 1 and 2 and 2 and 4. Cohort class 4 sites had the highest average FRI-based age (mean age = 84.7), followed by Cohort class 3 (mean age = 73.5), Cohort class 2 (mean age = 72.4) and cohort class 1 (mean age = 52.2). An ANOVA on age ranks was significant among cohort classes (F 3,44 = 0.9, p=0.008). A Tukey s Studentized range test found differences between cohort classes 1 and 2 and 1 and 4. Cohort class 3 sites had the highest mean deciduous percentage by basal area (45.29%), followed by cohort class 1 (42.94), cohort class 4 (42.68), and cohort class 1 (40.07). However an ANOVA on percent deciduous basal area ranks was not significant (F 3, 44 = 0.28, p=0.837). 16

26 Figure 1.3: All sites plotted by Weibull scale vs. shape. Symbols correspond as follows: cohort class 1; cohort class 2; cohort class 3; cohort class 4. Cohort means are shown by shaded squares with the cohort number inside. Example histograms with fitted Weibull curves are shown for each cohort. 17

27 Table 1.1. Weibull scale and shape, Cramer-von Mises (W 2 ) goodness-of-fit (and associated p-value), cohort class, and stand age for the mixedwood study sites in boreal northeastern Ontario. Site Weibull Scale Weibull Shape Goodnessof-fit W 2 statistic P- value Cohort class Stand age CHT < b CHT < b Mike < b Mike < b Mike < b Mike < b Mike < b Mike < b Mike < b Mike < b Mike < b NW < b NW < b NW < b NW < b NW < b NW < a NW < b NW < b NW < b NW < b NW < c NW < b NW < b Tem < b Tem < b Tem < b Tem < b Tem < b Tem < b Tem < c Tem < a Tem < b Tem < b Tem < b Tem < a Tem < b Tem < b FEC site type 18

28 Table 1.1. (continued) Site Weibull Scale Weibull Shape Goodnessof-fit W 2 statistic P- value Cohort class Stand age Tem < b Tem < b Tem < b Tem < b Tem < b Tem < b Tim < b FEC site type 19

29 Structural habitat variables In a PCA of sites classified according to cohort class, moderate separation was observed between the four cohorts (Figure 1.5). Cohort class 1 sites were quite well clustered in the lower left of the biplot, while cohort class 2 sites were fairly widely scattered mainly across the top half of the biplot. Cohort class 3 sites were well clustered in the upper right while cohort class 4 sites were a bit more scattered but generally in the lower right of the biplot. When plotted passively, the age vector was correlated positively with the cohort class 3 centroid and negatively with the cohort class 1 centroid. In general, the Weibull shape vector (plotted passively) was associated with cohort class 1 and 2 sites. Weibull scale was associated with cohort classes 2 and 3. Looking at structural habitat vectors, the longest vectors were foliage height diversity (Euclidean distance = 0.94), mean upper canopy thickness (0.88), canopy height variance (0.81), mean canopy height (0.74), and shrub stem density (0.73)(Figure 1.5). Weibull scale, Weibull shape and age, when plotted passively were fairly strong vectors associated with both axis. The cohort class 1 centroid was associated with relatively low mean canopy height, mean canopy thickness, lower canopy thickness semivariance, and foliage height diversity. In other words, a typical cohort class 1 site would have low/thin canopies, low vertical heterogeneity, and a small grain size in reference to the lower canopy strata. The cohort class 2 centroid was associated with high basal area of living trees, high semivariance of canopy height, upper canopy thickness, and lower canopy thickness, and with low values of old coarse woody debris, and mean understorey thickness. Typical cohort class 2 stands would thus tend to have large grain sizes for the upper part of the forest, but be characterized by fairly dense middle-aged trees. The cohort class 3 centroid 20

30 was most strongly associated with high values of mean canopy height, mean upper canopy thickness, and foliage height diversity. So, typical cohort class 3 stands had very well developed upper canopies, and increasing heterogeneity (compared with classes 1 and 2). The cohort class 4 centroid was associated positively with the shrub stem, lower canopy thickness variance, canopy height variance, upper canopy thickness variance, canopy class diversity, canopy class richness, and understorey thickness variance vectors and negatively with the mean lower canopy thickness vector. Thus, cohort class 4 stands were characterized by being very vertically and horizontally heterogeneous; they were the true multi-cohort stands. The cohort class 4 centroid (and most of the structural habitat features associated with heterogeneity) was strongly negatively correlated with the Weibull shape parameter (plotted passively). The Weibull scale parameter and stand age were correlated (plotted passively) with each other and with structural habitat variables associated with stand stature (mean canopy height, mean upper canopy thickness). 21

31 Figure 1.4. Principal components analysis on habitat structure variables in mixedwood stands of northeastern Ontario. A) all sites classified by cohort. Symbols correspond as follows: cohort class 1; cohort class 2; cohort class 3; cohort class 4. B) vectors of the habitat structure variables. In both A) and B), Weibull scale and shape, cohort class, and age are plotted passively and are represented with dashed arrows (Weibull scale and shape and age) and shaded upside-down triangles (cohort class centroids). Variable acronyms are identified in Appendix

32 Weibull parameters and age as predictors of stand habitat structure On average, age slightly outperformed the two Weibull parameters at explaining other structural data (Table 1.2). Of the 20 structural variables measured, the mean of the r s values squared for age was 0.08 and r s was significant for 7 variables; corresponding values for Weibull scale and shape, respectively, were 0.03, 2, 0.05, and 6. Age was a significant positive correlate of mean, and variance of canopy height, semivariance of lower canopy thickness, mean and variance of canopy thickness, and foliage height diversity. It was a significant negative correlate of mean understorey thickness. Weibull scale was a significant positive correlate of stand age, mean canopy height, and mean upper canopy thickness. Weibull shape was a significant negative correlate of canopy height variance, lower canopy thickness variance, mean and variance of upper canopy thickness, foliage height diversity, and canopy class diversity. 23

33 Table 1.2 Spearman rank correlations coefficients (rs) and associated P-values between Weibull curve parameters and age versus structural habitat features of boreal mixedwood stands in northeastern Ontario. Stand Age Weibull scale Weibull shape Structural variable r s P r s P r s P Stand Age Canopy Height mean residual variance residual semivariance Understorey thickness mean residual variance residual semivariance Lower canopy thickness mean residual variance residual semivariance 0.48 < Upper canopy thickness mean 0.59 < residual variance residual semivariance Foliage height diversity 0.63 < Canopy Classes richness diversity Basal area of live trees Basal area of snags Downed woody debris new DWD late-decay DWD Shrub stems Mean r s

34 Decomposition of variance Together, the Weibull parameters and age was a significant predictor of the habitat structural features and explained almost 20% of the variance (Table 1.3). On their own, Weibull and age were both significant and explained 15.4% and 9.3% of the variance of structural habitat variables, respectively. Even when the other was partialled out (Borcard et al. 1992), both remained significant. Age and Weibull parameters shared 4.9% (or about 31% and 53% of variance explained by Weibull and age on their own) of the total variance of structural data. When Weibull scale was considered on its own, it was significant and explained some 4.7% of the variance (Table 1.4). Weibull shape was also significant on its own and explained 6.3% of the variance. However, when either Weibull parameter was partialled out, the other was not only still significant but it explained a higher amount of variance of structural data then when just considered on its own. This is the result of Weibull scale and shape sharing a negative amount of variance explained a situation in which the two variables together better explain the structural data than sum of their individual effects (Legendre and Legendre 1998, p. 533). 25

35 Table 1.3. Decomposition of variance of Weibull parameters (shape and scale) of the live-stem diameter distribution and age for boreal mixedwood stands in northeastern Ontario. Variance explained (%) P-value Together 19.7 <0.001 Weibull 15.4 <0.001 Age 9.3 <0.001 Unique to Weibull 10.5 <0.001 Unique to Age Shared

36 Table 1.4. As table 1.3, except that decomposition of variance is just for the Weibull scale and shape parameters. Variance explained (%) P-value Together 15.4 <0.001 Scale Shape Unique to Scale 9.2 <0.001 Unique to Shape 10.7 <0.001 Shared

37 DISCUSSION Cohort classification As expected, Weibull curves described diameter distributions well. This is not surprising given that they are well established in the forestry literature for this purpose (e.g., Rennolls et al. 1985, Maltamo et al. 2000). We saw a range of Weibull curves, from distinctly bell-shaped to inverse-j. When sites were clustered using Weibull parameters they clustered into groups based on curves similar to those found in other studies (Kuttner 2006, Sharkey 2008). Of importance to MCM, within the age gradient I analyzed, age per se only partially explained structural variation in the cohort classification. This has implications for management as it suggests that stands could be guided into a multi-cohort state through the use of varying silvicultural tools. However, at the same time there was an element of succession seen in the cohort classes, as evidenced by differences in mean stand age among some of the cohort classes. Cohort classes did not differ in percent of deciduous composition, which suggests that structural differences detected were not artifacts of tree species composition or site productivity, although I did not test for differences at the tree species level. In general, structural features expected to associate with single cohort stands, such as lack of heterogeneity and snags, were indeed found to associate with these structurally-simple stands (cohort classes 1 and 2). The results largely agreed with my expectations, such as increasing canopy height and thickness in cohort classes 2 and 3, and a gradual increase in stand heterogeneity, particular in cohort class 4, and somewhat in cohort class 3. 28

38 Weibull scale and age were, as expected, correlated, since Weibull scale represent the diameter at which 63.3% of the stems have accumulated (Abernethy 1996). Weibull and age as predictors of stand structure Contrary to my predictions, stand age was a stronger correlate of more structural variables than either Weibull scale or shape, although in the variance decomposition, Weibull parameters outperformed age. Sharkey (2008) found that for mixedwood stands age and Weibull parameters performed similarly to each other, but that Weibull scale was the best correlate of structural habitat features. Compared with her work, in general I found that Weibull scale and shape and stand age performed more poorly as correlates of structural habitat features. Unlike Sharkey s (2008) work, however, Weibull shape performed better than Weibull scale. It appears that from a univariate perspective Weibull scale is basically a proxy for stand age (they are highly correlated and Weibull scale basically is a measure of how far to the right the diameter distribution extends). It is of interest that Weibull shape was correlated with several structural habitat features. More specifically, it was significantly correlated with measures of stand heterogeneity (canopy height variance, lower canopy thickness variance, upper canopy thickness variance, foliage height diversity, and canopy class diversity.). From a MCM perspective, Weibull shape may thus be important because it measures stand heterogeneity independently of stand age. Weibull shaped was negatively correlated with stand age (sites tend to become heterogeneous with time) but this relationship was not significant. The importance of considering both Weibull parameters becomes clear when you consider that they share a negative fraction of various explained, indicating they are 29

39 more powerful when they are considered together rather than separately (Legendre and Legendre 1998). Management implications Overall I found support for the concept of using Weibull parameters as coarse-filter features for describing structural habitat variation. However, there still exists a need to test these concepts using natural (fire) origin stands of varying ages, something that is difficult in the study region because of fire suppression. Kuttner s (2006) strategy of combining Weibull parameters with a number of other stand structural parameters may be the best method to ensure that the classification system does not leave out important structural features, such as availability of snags. While Weibull parameters captured multivariate structural data well, stand age was also significant and so cannot be disregarded. Stand age has in particular been shown to be important regarding snags and coarse woody debris availability in eastern boreal mixedwoods (Harper et al. 2007, Brassard and Chen 2008, Drapeau et al. 2003, 2009), although in this study snags were not explained well by Weibull parameters or age. Perhaps the most important aspect to consider is that we do not really know how boreal stands will regenerate after varying silvicultural treatments and to what extent such treatments will emulate the structural variation important to biodiversity (see Deans et al. 2005, for example). The Weibull parameters of diameter distributions did explain stand structure better than FRI-based stand age. We must acknowledge that while stand age (based on FRI data) may be relatively inaccurate, reconstructing fire histories as a means to obtain accurate stand ages may not be feasible over large areas. Therefore we can say that 30

40 Weibull parameters out-performed readily available measures of stand age. Given the relatively small range of ages of sites in this study we cannot necessarily extend our conclusions that stand age is less important that Weibull parameters for very old stands (i.e. stands older than approximately 100 years). However, it is of note that the sites used in this study had a similar range of diameter distributions as found by Sharkey (2008) for mixedwood stands with an age range of years post-disturbance. Additionally, it is of note that even within relatively young stands there is a large amount of variation in diameter distributions (as measured by Weibull parameters) and overall structural heterogeneity. This may indicate that boreal mixedwood stands in northeastern Ontario are subject to small-scale natural disturbances frequently enough to generate structural diversity. Alternatively, the comparatively low-intensity harvesting associated with horse-logging may have left enough residual structure to allow stands to progress into varied structural cohort classes. It remains to be seen whether this is the case for mechanized clear cutting, or whether the higher intensity harvesting will result in the elimination of structurally complex stands from the landscape. As such, this work supports (but does not discount an age-based) a structural (Weibull-based) cohort classification system, but for such a system to work best regionally informed cut-offs for the two parameters need to be developed based on a large number of sites and a large number of trees at each site. 31

41 CHAPTER 2- COMPARATIVE PERFORMANCE OF FOREST STRUCTURE, TREE SPECIES COMPOSITION, LIVE-STEM DIAMETER DISTRIBUTIONS, AND STAND AGE IN PREDICTING BOREAL MIXEDWOOD BIRD COMMUNITIES OF NORTHEASTERN ONTARIO INTRODUCTION The boreal forest covers a vast area of land in North America, nearly 25% of the continental landmass, and is home, during the breeding season, to an estimated three to five billion birds of over 300 species (Boreal Songbird Initiative 2007). This importance to birds and other wildlife highlights the importance of sustainable forest management in the region. Unfortunately, traditional even-aged management of the boreal forest based on clearcutting, particularly in the eastern boreal forest, has been implicated in the potential loss of older forests and, in corollary, structural and compositional variability associated with these older stands (Drapeau et al. 2000, 2003, Bergeron et al. 2007, see chapter 1). Such structural features appear to be important for wildlife communities. In Scandinavia, the gradual simplification of boreal forests through even-aged management has been implicated in changes to a wide assortment of wildlife communities. For example, Ecke et al. (2002) found that declines in populations of three species of voles were strongly associated with declines in forest heterogeneity, a feature that was being eliminated from the landscape due to even-aged management. Edman et al. (2004) found that two species of fungi in Finland were not only becoming restricted to increasing rare old growth boreal forests, but also that they appeared to be suffering from negative genetic effects of isolation, resulting in low spore germination. Similarly in Finland, Helle and Jarvinen (1986) found that certain birds, particularly resident species in the families Paridae, Certhiidae, and Corvidae, were negatively impacted by logging- 32

42 associated changes in forest structure. Imbeau et al. (2001) argued that such changes in Fennoscandia had the potential to apply in Canada as well. Multicohort management (MCM) has been proposed as a way to maintain current levels of harvests while doing a better job of acting as a coarse-filter strategy than even-age management for biodiversity conservation (Bergeron and Harvey 1997). While the concepts behind MCM have been subject to several studies, there still needs to be further tests of the MCM concept from wildlife perspectives. Sharkey (2008) and Barkley (2009) found some support for a structural cohort concept in explaining variation in small mammal and insect communities, respectively; however, from a forest bird perspective, we are still lacking information. In many ways, forest birds may be an ideal group to assess wildlife responses in relation to forest structure. Since MacArthur s (1958) seminal work, forest structure has been linked to niche partitioning among birds within forested landscapes. Bradbury et al. (2005) provided a review of the importance of habitat heterogeneity for birds, which may select breeding habitat based on a mix of structural features. As Helle and Jarvinen (1986) demonstrated, bird communities respond to changes in the physical structure and ages of boreal forests. Similarly, Vernier and Pearce (2005) showed marked differences in bird communities among jack pine stands with varying structural characteristics associated with age. Unlike insects and mammals, which typically require intensive trapping efforts (e.g., Ecke et al. 2002, Vance et al. 2007), bird community sampling can be done relatively easily via passive point counts or playbacks (Lee and Marsden 2008). Birds may thus represent an ideal group for testing the utility of structural habitat features associated with multi-cohort stand development in assessing wildlife habitat. 33

43 In this chapter I set out to explore the potential value of a structural cohort definition in MCM from a bird community perspective. Specifically, I wanted to determine to what extent simple descriptions of the live-stem diameter distribution, various other structural habitat features, and stand age work to explain variation among bird communities in boreal mixedwood forests. I tested the value of these various explanatory variables for the overall bird community and for individual habitat and feeding guilds. The key questions being addressed here are do bird communities differ between cohort classes? How do age and structure compare in explaining bird communities? Are specific habitat or feeding guilds more sensitive to cohort class variation? What other structural features are important for birds that should be considered for incorporation into possible cohort classification systems? I expect that, similar to Sharkey (2008) and Barkley (2009), I would find differences in bird communities among cohort classes, and that these relationships would be especially strong. Alternatively, because of considerable variation in tree species composition even among boreal mixedwood stands, such influences on bird communities might serve to obscure structural relationships. Therefore, I also wanted to compare the relative power of forest composition and Weibull parameters at explaining the bird community and the various habitat and feeding guilds. I expected that the community as a whole would be explained by habitat composition best, but that Weibull parameters would work better for each of the habitat guilds, since in effect I am controlling for habitat composition by defining habitat guilds. Because different species rely on different structural features, I was concerned that species with different habitat requirements would make associations less clear. Therefore, I chose to group species that 34

44 feed in similar parts of the forest into feeding guilds. I expect that when each feeding guild is analyzed independently structural habitat associations and cohort class associations will be clearer. Because Weibull parameters explained other structural features better than age (Chapter 1) I expect that they will perform better than age at explaining bird communities, both as a whole and when simplified to habitat and feeding guilds. I expected the ground-feeding guild would be related most to the structural features associated with undergrowth- understorey thickness mean, variance, and semivariance, shrub density, and coarse woody debris. Because shrub density and understorey thickness where associated with cohort classes 4 and 1, respectively, I expected that ground-feeding birds may be associated with these cohort classes. I expected the understorey and shrub density to play a role because most of the groundforaging birds likely also spend some time in the shrub layer, and because it represents cover while they are on the ground foraging. Schneider (1984) found this to be true for White-throated Sparrows, which reduced predation risk at the cost of foraging efficiency by foraging under dense cover. I expected the shrub-feeding guild would be most closely related to understorey thickness, shrub density, and lower canopy thickness since those strata represent the amount of available habitat for foraging. Because many of the shrubfeeding species are early-successional species or associated with openings in the canopy (e.g., Chestnut-sided Warbler, Mourning Warbler, Wilson s Warbler, and Canada Warbler), I expected that measures of understorey, lower canopy, or upper canopy variance may be significant predictors of this feeding guild and that they may be associated with cohort class 3 or 4 sites. For the canopy-feeding guild I expected canopy height and canopy thickness to be significant predictors, since those structural features 35

45 would represent a measure of habitat availability to species feeding in the canopy. Therefore I expected canopy-feeding birds to be associated with later cohort classes 2, 3, and 4, because they were associated with canopy features. Because most of the barkfeeding species specialize on dead trees (and require snags for nesting), I expected snag basal area to be a significant predictor of the bark-feeding guild. On the question of particular feeding guilds being more sensitive to forest structure, some work suggests that the bark-feeding guild may be especially sensitive to forest structure. Imbeau et al. (2001) included cavity nesting and snag foraging as threat factors when ranking species likely to be at risk due to logging in the eastern boreal forest. Much of the work regarding forestry and birds in the boreal forest focuses on woodpeckers and other cavity nesters (Drapeau et al. 2009). METHODS Site Selection Forty-five sites were chosen in mixedwood stands and sampled during the summer of For details on the site selection process, see chapter 1. Bird Community Sampling Bird communities were sampled during the 2007 breeding season. Because of their earlier breeding activity, low detection probabilities on point counts, and special interest from a forest management perspective, cavity nesting species (Downy Woodpecker, Hairy Woodpecker, Yellow-bellied Sapsucker, Northern Flicker, Pileated Woodpecker, Brown Creeper, Red-breasted Nuthatch, Boreal Chickadee, and Blackcapped Chickadee) were surveyed once by playback during the period May 21, 2007 June 5, 2007, inclusive. Other cavity-nesting species, such as Black-backed 36

46 Woodpecker, American Three-toed Woodpecker, and Eastern Bluebird were not surveyed via playbacks because the sites used for this study were judged to be unsuitable. Black-backed Woodpecker and American Three-toed Woodpecker use heavily coniferous stands or recently burned areas (Hutto 1995, Murphy and Lenhaussen 1998, Dixon and Saab 2000, Leonard 2001, Nappi et al. 2003, Hannon and Drapeau 2005, Nappi and Drapeau 2009), while Eastern Bluebirds use extensive open areas (Gowaty and Plissner 1998). Playback was conducted on calm days (wind speed <15 km h -1 ) with no precipitation between sunrise and noon. A playback session consisted of arriving on site, setting up a portable Pignose guitar amplifier (model 7-100) on the ground with a standard volume level (I tested the volume of the amplifier before surveys began to a level that I could easily hear from 100m away) attached to a portable CD player and waiting five minutes. Subsequently, for each species, approximately two minutes of recorded calls and drumming (woodpeckers only) was followed by approximately two minutes of silence. Only birds encountered during the species designated time (playback or silence) were recorded. For each species the number of individuals detected within and beyond 100 m was recorded. Because distant birds were usually not visible through the forest, birds that were obviously very far away were included as being greater than 100m away. Bird communities were sampled with passive point counts between June 6 and July 4, 2007, inclusive. Each site was visited three times between dawn and 9:30 am on calm mornings (windspeed <15 km h -1 ) with no precipitation. Each site was visited once during each third of the sampling season. In addition, efforts were made to sample each site during early, mid, and late parts of the morning sampling session as well as early, 37

47 mid, and late during each third of the sampling season in order to avoid biases. Point counts were made up of two, back-to-back five minute listening periods during which time, species and numbers of birds seen and heard were recorded. Each bird s distance from the point count centre was estimated and only those estimated as within 100 m from the point count centre were recorded. Because distant birds were usually not visible through the forest, birds that were obviously very far away were included as being greater than 100m away. For each species, the maximum number of individuals detected on any of the six five-minute point counts and the playback (for cavity nesters) was calculated and this was used as an estimate of the maximum number of territories overlapping the 100 m circle at each site for each species. Species not associated with forested habitats such as Common Loon and Belted Kingfisher (mostly associated with water) were dropped from the analyses. Community metrics were calculated as species richness (corrected for abundance using rarefaction), abundance (total number of forest birds), and diversity (Shannon diversity index, H ). Species with similar life history traits were grouped with regards to a feeding guild. To guard against mistakenly inferring results from common guild members to the entire guild each species abundance was centered and standardized for each column, and were added together to get guild abundance. Guild richness was calculated correcting for abundance using rarefaction, and guild diversity was calculated using the Shannon diversity index (H ) with the non-standardized abundances. The guilds used in this study were based on primary feeding locations on the breeding grounds within a forest. Information on primary feeding locations was gathered 38

48 from each species account from Bird s of North America online (Poole 2009). The following feeding guilds were used (see appendix 1.3 for guild members): ground (9 species; insectivorous species that feed on or near the ground); shrub (14 species; insectivorous species that feed in the shrub layer); canopy (14 species; insectivorous species that feed in the canopy); bark (7 species; insectivorous species that feed on the trunks or limbs of trees); general (5 species; species that don t spend most of their feeding time in any particular area); seed (5 species; granivorous species that feed on tree seed crops). Because of their relatively low sample size, the generalist and seed-feeding guilds were excluded from analysis. To control for variation in tree species composition, bird species also were grouped by general habitat preference using habitat preferences found in each species account from Bird s of North America online (Poole 2009): deciduous (13 species; birds favouring forests dominated by deciduous tree species); mixedwood (16 species; birds favouring mixedwood stands or with no preference for either deciduous or coniferousdominated forests); and coniferous (24 species; birds favouring forests dominated by coniferous tree species). See appendix 1.3 for guild members. Habitat data Twenty structural habitat features were measured at each site (for details see chapter 1 and appendix 1.2). In addition, as detailed in Chapter 1, for each stand I determined stand age and cohort class and Weibull scale and shape from the live-stem DBH distribution. In addition to those structural habitat features from chapter 1, I included percent deciduous composition (percent of basal area of live trees that was deciduous) and FEC site type, coded as dummy variables (nine site types). 39

49 Data Analysis To test for cohort class effects, one-way ANOVAs were conducted on abundance, richness, and diversity for the entire bird community and separately for each habitat and feeding guild. Because they did not meet the assumption of equal variances, ANOVAs on ranks (sorted descending) were conducted for ground-feeding abundance and diversity, shrub-feeding abundance and diversity, and bark-feeding richness and abundance (SAS Institute Inc. 1985). For the entire bird community and each feeding and habitat guild, a PCA of species abundances (species were centered and standardized) was conducted. Preliminary Detrended Correspondence Analyses (detrended by segments) in all cases indicated short gradient lengths (<4), hence my use of linear methods (Leps and Smilauer 2003). In the ordination diagrams, sites were classified by cohort class. Structural variables that had strong relationships (axis scores of 0.4) or that were significant (p 0.05; Monte Carlo Permutation test with 9999 permutations) on their own or in a forward selection in an RDA were plotted passively. For each ordination, sites were considered outliers if the Euclidean distance (on a biplot of the first two axes) did not pass Chauvenet's criterion (Taylor 1997). To measure the explanatory power of Weibull parameters, age, and habitat composition (FEC site types coded as dummy variables), RDA s were conducted and the variance was decomposed (Borcard et al. 1992, Drapeau et al. 2000) in CANOCO, using Monte Carlo s Permutation tests with 9999 permutations. 40

50 RESULTS Community Characteristics in relation to cohort class In total, 53 species of forest birds were detected during point counts and playback samples. The top ten species overall, by mean abundance, were Red-eyed Vireo (2.29), Red-breasted Nuthatch (1.78), Ovenbird (1.36), Magnolia Warbler (1.11), Yellow-bellied Sapsucker (1.09), Black-capped Chickadee (1.02), Yellow-rumped Warbler (0.76), Pine Siskin (0.73), Hermit Thrush (0.73), and Golden-crowned Kinglet (0.73) (Table 2.1). In cohort class 1 sites, the top ten were Red-eyed Vireo (2.18), Red-breasted Nuthatch (1.35), Ovenbird (1.24), Magnolia Warbler (1.12), Black-capped Chickadee (0.94), Hermit Thrush and Golden-crowned Kinglet (0.82), Yellow-rumped Warbler (0.71), and Pine Siskin, Bay-breasted Warbler, and Yellow-bellied Sapsucker (0.65) (Table 2.1). In cohort class 2 sites, the top ten were Red-eyed Vireo (2.40), Red-breasted Nuthatch (1.93), Yellow-bellied Sapsucker (1.40), Ovenbird (1.33), Magnolia Warbler (1.20), Pine Siskin (1.13), Blackburnian Warbler (1.07), Black-capped Chickadee (1.00), Goldencrowned Kinglet (0.80), and Swainson s Thrush (0.73) (Table 2.1). In cohort class 3 sites, the top ten were Red-eyed Vireo (3.50), Ovenbird (2.25), Red-breasted Nuthatch, Yellow-bellied Sapsucker, and Black-capped Chickadee (1.50), White-winged Crossbill (1.00), and Magnolia Warbler, Blackburnian Warbler, Swainson s Thrush, Northern Parula, Winter Wren, White-throated Sparrow, and Black-throated Green Warbler (0.75) (Table 2.1). In cohort class 4 sites, the top ten were Red-breasted Nuthatch (2.44), Redeyed Vireo (1.78), Yellow-rumped Warbler (1.44), Ovenbird and Yellow-bellied Sapsucker (1.22), Magnolia Warbler and Hermit Thrush (1.11), Black-capped Chickadee and Nashville Warbler (1.00), and White-throated Sparrow (0.78). 41

51 Of the nine cavity-nesting species sampled by both playback and point counts, seven were detected at more sites using playback versus point counts (Downy Woodpecker, Hairy Woodpecker, Yellow-bellied Sapsucker, Northern Flicker, Blackcapped Chickadee, Boreal Chickadee, and Red-breasted Nuthatch), the remaining two (Pileated Woodpecker and Brown Creeper) were detected at more sites with point counts than with playback (Table 2.2). For all species except Pileated Woodpecker and Brown Creeper the mean number of individuals detected was greater using playbacks than point counts (Table 2.2). The difference was significant for Downy Woodpecker (t 44 = 2.93, p = 0.005), Yellow-bellied Sapsucker (t 44 = 2.36, p = 0.023), Black-capped Chickadee (t 44 = 3.24, p = 0.002), and Red-breasted Nuthatch (t 44 = 4.33, p < 0.001) (Table 2.2). Generally, species mean abundances were similar among cohort classes with a few exceptions. Two species, Red-eyed Vireo and Ovenbird, associated with deciduous habitats (Cimprich et al. 2000, Van Horn and Donovan 1994) were found in higher abundances in cohort class 3 sites, while they were found in lower abundances in cohort class 4 sites (Table 2.1). Conversely, Yellow-rumped Warbler, Hermit Thrush, and Nashville Warbler, all species associated with coniferous habitats (Hunt and Flaspohler 1998, Jones and Donovan 1996, Williams 1996) showed the opposite trend. Overall, Cohort class 4 sites had the highest mean abundance, followed by cohort class 2, and cohort class 3, and cohort class 1 (Figure 2.1b); an ANOVA was significant: (F 3,44 = 3.11, p=0.037), but a Tukey s Studentized range test did not find differences among any of the cohort classes. Cohort classes showed the same trend for overall species richness as they did for abundance (Figure 2.1a), and an ANOVA was significant (F 3,44 = 3.69, p=0.019). A Tukey s Studentized range test found that the mean richness 42

52 for cohort class 1 was significantly different than that of cohort classes 2 and 4. Mean diversity for all species was highest for cohort class 4, followed by cohort class 2, cohort class 1, and cohort class 3 (Figure 2.1c). However, an ANOVA was not significant (F 3,44 = 1.87, p=0.150). When species were grouped into habitat guilds the general trend was towards increasing richness with increasing cohort class (Figure ). However, only an ANOVA on the deciduous habitat guild was significant (F 3,44 = 3.45, p=0.025), while a Tukey s Studentized range test did not find significant differences among the cohort classes. Habitat guild abundance also showed an increasing trend with cohort class (Figures ). ANOVAs were conducted on the three habitat guilds, but only that for the deciduous guild was significant (F 3,44 = 3.55, p=0.022). A Tukey s Studentized range test found significant differences between cohort classes 1 and 2. Mean diversity of habitat guilds showed a similar increasing trend with cohort class as richness and abundance did (Figures ). However, ANOVAs conducted on mean diversity did not show a significant cohort class effect in any of the habitat guilds. When species were grouped into feeding guilds, the ground (Figure 2.5) and shrub-feeding guilds (Figure 2.6) did not show significant cohort class effect for mean richness, abundance, or diversity. However, the canopy-feeding guild showed a significant class effect for mean diversity (F 3,44 = 3.12, p=0.036). A Tukey s Studentized range test revealed a significant difference between cohort class 1 and 2. Mean richness (F 3,44 = 6.87, p<0.001) and abundance (F 3,44 = 6.68, p<0.001) for the bark-feeding guild also showed a significant cohort class effect. Tukey s Studentized range test revealed that cohort class 1 means were lower than cohort classes 2 and 4 for both metrics. 43

53 Table 2.1. Bird species detected on point counts and playback surveys and their relative abundance in each of the three cohort classes in boreal mixedwood forests of northeastern Ontario. Species Mean Abundance Code 1 Sites All sites Cohort 1 Cohort 2 Cohort 3 Cohort 4 BBCU YBSA DOWO HAWO NOFL PIWO LEFL YBFL PHVI REVI BHVI BLJA GRJA CORA BCCH BOCH BRCR RBNU WIWR GCKI RCKI SWTH VEER HETH AMRO CEDW TEWA NAWA NOPA CSWA MAWA CMWA BLBW BTBW BTNW MYWA WPWA

54 Table 2.1 (continued) Species Mean abundance Code 1 Sites All sites Cohort 1 Cohort 2 Cohort 3 Cohort 4 BBWA BAWW A;MRE MOWA OVEN CAWA WIWA SCTA WTSP DEJU RUBL PISI AMGO PUFI WWCR RECR see appendix 1.3 for species code definitions. 45

55 Table 2.2. Comparative results of playback and point count surveys for cavity nesting bird species in boreal mixedwood forests of northeastern Ontario Species Number of sites detected Mean individuals detected Results from paired t-test Code 1 Playback Point counts Playback Point counts t p DOWO HAWO YBSA NOFL PIWO BCCH BOCH BRCR RBNU < see appendix 1.3 for species code definitions. 46

56 Figure 2.1 Mean ( 1 SE) a) richness b) abundance and c) diversity of all birds for cohort classes in boreal mixedwood forests of northeastern Ontario. Letters above means represent classes with significant differences. 47

57 Figure 2.2. As figure 2.1 except that deciduous habitat guild is shown. 48

58 Figure 2.3. As figure 2.1 except that mixedwood habitat guild is shown. 49

59 Figure 2.4. As figure 2.1 except that coniferous habitat guild is shown. 50

60 Figure 2.5. As figure 2.1 except that ground-feeding guild is shown. 51

61 Figure 2.6. As figure 2.1 except that shrub-feeding guild is shown. 52

62 Figure 2.7. As figure 2.1 except that canopy-feeding guild is shown. 53