Canadian Journal of Zoology
Bird communities and vegetation associations across a treeline ecotone in the Mealy Mountains, Labrador, an understudied part of the Boreal forest
Journal: Canadian Journal of Zoology
Manuscript ID: cjz-2014-0309.R2
Manuscript Type: Article
Date Submitted by the Author: 20-Apr-2015
Complete List of Authors: Lewis, Keith; Memorial University Starzomski,Draft Brian; University of Victoria, Environment and Conservation vegetation-habitat concept, tree line, avian species richness, forest-tundra Keyword: ecotone, boreal forest, Labrador
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1 2 3 4 5 6 7 8 Bird communities and vegetation associations across a treeline ecotone in the Mealy 9 Mountains, Labrador, an understudied part of the Boreal forest 10 11 12 13 Keith P. Lewis 1,2 and Brian M. Starzomski 3,4 14 15 16 17 18 1 Department of Biology 19 Memorial University of Newfoundland 20 St. John’s NL Draft 21 Canada A1C 3X9 22 23 2 Department of Environment and Conservation 24 Government of Newfoundland and Labrador 25 St. John’s NL 26 Canada A1B 4J6 27 28 3 School for Resource and Environmental Studies 29 Dalhousie University 30 6100 University Avenue, 31 Halifax, Nova Scotia 32 Canada, B3H 3J5 33 34 4School of Environmental Studies 35 University of Victoria 36 3800 Finnerty Road. 37 Victoria, British Columbia 38 Canada. V8P 5C2. 39 [email protected] 40 41 42 43 1 Author for correspondence: [email protected]
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44 Abstract 45 46 We examined the factors structuring bird communities across a complex sub-arctic treeline in the
47 Mealy Mountains, Labrador, Canada. Using point counts of bird abundance in 2007 and 2008,
48 we show that changes in vegetation driven by elevation are strongly correlated with avian
49 community structure in this treeline ecotone system. Overall, avian diversity was higher in the
50 forest compared to other habitat classes (krummholz, deciduous shrub, and alpine). There were
51 strong correlations between avian diversity and vegetation richness as well as structure among
52 and within habitat class in 2008. Numerous habitat types (subset of habitat class) were
53 correlated with avian composition although some species were clearly habitat generalists.
54 Contrary to expectation, avian species composition was associated with physiognomy
55 (vegetation structure) in alpine and deciduousDraft shrub, and with neither physiognomy or floristics
56 (vegetation species composition) in krummholz and forest. Given the strong impact of elevation
57 on vegetation and the demonstrated influence on bird communities, we note that for bird species
58 whose near-southernmost populations are found in the Mealy Mountains, climate change is likely
59 to have a strong negative effect if alpine tundra habitat is lost. Further, forest bird species are
60 likely to benefit from the increased tree cover as treeline moves poleward and upward.
61
62 Keywords : treeline, avian species richness, vegetation volume, forest-tundra ecotone, plant
63 community composition, boreal forest, Labrador
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64 Introduction
65 Ecotones have been extensively studied in large part because of the significant influence they
66 have on biodiversity (Risser 1995; Payette et al. 2001; Harper et al. 2005). This increase in
67 biodiversity is often attributed to the large changes in vegetation structure over short distances or
68 specialization at edges (Ries and Sisk 2004). Changes in vegetation at the forest-tundra ecotone
69 (treeline), an ecotone caused by complex changes at a latitudinal or altitudinal gradient, have
70 been extensively documented, (Payette et al. 2001; Harper et al. 2011), in response to the
71 growing concern that global climate change (GCC) may adversely affect these important areas
72 (Grace et al. 2002). However, the impact of treeline ecotones on other key organisms like
73 passerine songbirds remains relatively unstudied (Terborgh 1985; Archaux 2004; Lloyd et al.
74 2011; Kent et al. 2013), and boreal ecotoneDraft communities are especially understudied.
75 The influence of treelines on biodiversity is of concern in northern regions because the
76 boreal forest is critical for the biodiversity and viability of populations of North American
77 passerine birds (Blancher 2003; 2005). Further, significant impacts of climate change are
78 predicted to occur at boreal latitudes resulting in increased plant growth (infilling, Danby and
79 Hik 2007) or increased recruitment in treeline species, and a subsequent migration of the treeline,
80 latitudinally and altitudinally (Cannone et al. 2007; Rucksuhl et al. 2008; Harsch et al. 2009).
81 The potential for changes in avian distribution in response to a changing climate was suggested
82 by Cumming et al. (2014), who showed that climate variables explained most of the deviance in
83 abundance of boreal songbird distribution. An examination in the boreal region will broaden our
84 understanding of the influence of ecotones on avian diversity and shed light on the influence of
85 GCC on these regions.
86 Relevant to understanding the influence of ecotones and a changing climate on passerine
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87 songbirds is the vegetation-habitat concept, considered a unifying theory in avian biology (Lee
88 and Rotenberry 2005). Vegetation as habitat can influence avian assemblages in terms of
89 structure (physiognomy: MacArthur et al. 1962; James and Wamer 1982; Cody 1985; Bersier
90 and Meyer 1995), and composition (floristics: Rotenberry 1985; Bersier and Meyer 1995).
91 Ecotones, including the treeline, provide an excellent opportunity to examine the vegetation-
92 habitat concept because the changes in physiognomy and floristics across the treeline allow for
93 examination of communities that may differ dramatically over short distances (< 5 km).
94 Therefore, although the debate over the relative roles of physiognomy and floristics on avian
95 assemblages is not new, it should be re-examined in the context of global climate change and
96 expected poleward and upward shifting treeline ecotones.
97 We examined avian community Draftstructure and its relationship to vegetation across a
98 treeline ecotone in the Mealy Mountains (Akamiuapishkua ), a highland area of south-central
99 Labrador, Canada. This region is projected to warm by 2.05-4.32 oC over the next eighty years
100 (Bell et al. 2008) and climate-habitat simulations show that the treeline is expected to move both
101 poleward and upward (Loader 2007). Due to topography and edaphic factors, the treeline
102 ecotone is complex, resulting in a highly variable deciduous shrub-krummholz zone bounded by
103 largely contiguous forest, and capped by a small amount of alpine tundra (currently less than
104 10% of the total area [Loader 2007]). The relatively species-poor avifauna (composed of both
105 habitat specialists and generalists) and high variation in physiognomy and floristics over a small
106 area allowed us to test the following questions: A) how do measures of avian diversity vary with
107 elevation, vegetation richness, and vegetation structure across the ecotone?; and B) to what
108 degree are elevation, floristics, and physiognomy associated with avian composition across the
109 ecotone? Answers to these questions are critical to our ability to predict shifts in avian
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110 communities due to climate change. Finally, we report the first systematic study on avian
111 diversity in this rarely studied part of North America.
112
113 Methods
114 Study Area
115 The Mealy Mountains are the dominant highlands of southern Labrador, occupying an
116 area of approximately 2000 km 2 directly south of Lake Melville (Fig. 1). Mountain summits are
117 broad and rounded, with an elevation just over 1100 m. The highlands (above 800 m) are
118 dominated by alpine vegetation, are surrounded by boreal forest and wetlands at lower
119 elevations, and have been described as southernmost mountainous outliers of the High Subarctic
120 Tundra Ecoregion (Meades 1990). LocalDraft glacial and glaciofluvial deposits are conspicuous and
121 important components of the landscape, influencing position and structure of the vegetation .
122 The Labrador Highlands Research Group base camp is located approximately 20 km south of
123 Lake Melville, in an eastward trending valley south of an unnamed mountain (Mt. 1057, N 53 o
124 36’ W 58 o 50’, Fig. 1), and west of an unnamed lake forming the headwaters of the Eagle River
125 (N53 o 36’ W58 o 47). Our total study region comprises approximately 64 km 2, between 500 –
126 1000 m in elevation. Following the terminology of Körner (2007), the timberline occurs at ~ 550
127 m, the treeline up to 700 m in sheltered areas, and the tree species limit at ~ 900 m. Conifer
128 species include black spruce (Picea mariana (Mill.) Britton,Sterns & Poggenb.), white spruce
129 (Picea glauca (Moench) Voss), balsam fir ( Abies balsamea (L.) Mill.),) and larch ( Larix laricina
130 (DuRoi) K. Koch) that become increasingly stunted (i.e. krummholz), and occur in smaller, more
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131 isolated, and sheltered patches above 640 m. See Munier et al. (2010) and Trant et al. (in
132 review)1 for a more complete description of the vegetation.
133
134 Bird sampling
135 Birds were surveyed during 2007-2008 using standard point count methods (100 m
136 radius; Bibby et al. 2000). Point counts were placed along transects every 300 m by following a
137 compass bearing from an arbitrary point parallel to the trend of the valley. A total of 76 point
138 counts were surveyed from 4:20-10:00am from 2 July - 13 July, 2007. During 2008, we added
139 38 point counts (total = 114) surveyed from 25 June to 10 July. Point counts were visited twice
140 per season although twelve were only surveyed once in 2007 due to logistical constraints. We
141 attempted to minimize intra- and inter-observerDraft reliability for bird detection. Both observers
142 were experts with boreal songbirds, trained together to minimize inter-observer differences prior
143 to the initiation of surveys, and had previous experience using point count methods. Finally, we
144 conducted point counts in a different order and by a different observer on the second visit where
145 possible (Bibby et al. 2000). Due to safety and logistical issues associated with working in this
146 location, point counts were conducted along routes in close proximity to one another (i.e.,
147 observers were usually ~ 300 m apart). Point counts were conducted from forest to alpine
148 habitat between 510 - 982 m. The relatively few point counts between 760 and 900 m were
149 largely due to very steep topography in these areas.
150 For each point count site, we calculated species richness and abundance. Abundance was
151 calculated by taking the mean number of individuals of each species for each visit (Betts et al.
1 Trant, A.J., Lewis, K.P., Cranston, B.H., Wheeler, J.A., Jameson, R.G., Jacobs, J.D., Hermanutz, L., and Starzomski, B.M. Submitted. Complex changes in plant communities across a subarctic alpine treeline in Labrador, Canada. Arctic.
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152 2005).
153 The influence of detectability during avian surveys and how it varies by species, distance,
154 and land cover is well known (Nichols et al. 2000; Rosenstock et al. 2002). However, we believe
155 that many of these concerns were minimized in our system due to the visibility of singing and
156 foraging birds and the simple, easily detected avifauna. Visibility was very good on most of the
157 point counts sites due to low, well-spaced vegetation. Therefore, we believe differences in
158 detectability among these sites should be minor. We acknowledge that more modern methods
159 such as distance estimation (Rosenstock et al. 2002) and the double observer approach (Nichols
160 et al. 2000), incorporate detectability and that these methods might increase the precision of the
161 point count method and improve estimates of density. However, while we normally advocate the
162 use of these methods, we believe that theirDraft utility in the studied habitat is small, for the above
163 reasons. Methods such as spot mapping (Verner 1985) and measures like breeding productivity
164 or nesting success (Martin and Geupel 1993) would enhance our knowledge of this system,
165 though all of these methods are impractical in this study due to the logistical constraints of
166 working in such a remote location.
167
168 Vegetation
169 Due to the extreme heterogeneity of the treeline ecotone and influence of glacial and
170 other topographic features on vegetation, we used a modification of Emlen’s method (Emlen
171 1967) to characterize vegetation on point count circles (hereafter Emlen points; see Simon et al.
172 2000; Schwab et al. 2001; Simon et al. 2002). At each Emlen point we randomly placed a 2 m
173 pole held vertically at arms length, and classified the dominant plant species that the pole
174 touched as well as the surrounding habitat based on species, plant form, and height. Emlen
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175 points were taken every 5 m on a series of transects connected in the following manner (assume
176 that the observer is walking east-west): the observer determined a point 80 m from the centre of
177 the circle, walked north 40 m, turned west for 20 m, and turned south for 80 m. The 20 m and 80
178 m transects were continued a further five times, snaking across the point count and ending with a
179 40 m transect in a southern direction. In this way, we were able to efficiently but systematically
180 obtain habitat classifications for all point count circles.
181 On every twelfth Emlen point ( n = 12/point count), we further recorded species richness
182 (i.e., all plant species that touched the pole), and all vegetation contacts with the pole at 0.25 m
183 increments. We calculated the average vegetation richness (AVR) by averaging the species
184 richness at the twelve Emlen points per point count circle. As a proxy for vegetation structure,
185 we estimated total vegetation volume (TVV;Draft Mills et al. 1991; Fleishman et al. 2003) at each
186 sampling point as h/10p where h = the total number of ‘hits’ (intervals that contained vegetation)
187 and p = the number of points at which vegetation volumes were measured. In the forest, the
188 height of the pole was increased as was required to handle the higher vegetation layers.
189 Collectively, these methods allowed us to build an index of habitat types (e.g. alpine,
190 deciduous shrub, fen, krummholz, forest, rock, late snow melt), AVR, and TVV per point count
191 site, as well as a matrix of plant community composition (floristics) and vegetative height classes
192 (physiognomy).
193
194 Analyses
195 Elevation strongly influences climate and vegetation and is clearly the most important
196 determinant of vegetation in montane regions (Ruggiero and Hawkins 2008). Therefore, to
197 control for the influence of elevation, we performed ANCOVAs with measures of avian diversity
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198 as the response variable and vegetation richness (AVR) and structure (TVV) as covariates. Point
199 counts were divided into four broad habitat classes along an elevational gradient: alpine,
200 deciduous shrub, krummholz, and forest based on the data from the Emlen points (Appendix 1).
201 These classes are the most obvious features of the landscape and this division of habitat is similar
202 to a classification of this region using remote sensing data (Loader 2007; habitat types are a
203 subset of these broader classes). Habitat class was included in the model as a main effect and as
204 two-way interaction terms with the other covariates. The overall influence of a variable was
205 assessed using the ANOVA table, but we based most of our interpretation on the coefficient
206 estimates to determine if there was an effect of vegetation species richness or structure on avian
207 diversity within the four habitat classes. Since deciduous shrub serves as an effective transition
208 between treed and alpine habitat, it wasDraft used as the baseline. Years were analyzed separately.
209 Normality of residuals, linearity, multicollinearity, and homogeneity of variance was assessed for
210 all analyses. When computing the altitudinal range, we used observations of all birds detected at
211 a point count.
212 We used non-metric multidimensional scaling (NMDS) with fitted vectors to explore the
213 relationships between avian species composition, habitat types (based on Emlen points), and
214 elevation. These tests were chosen due to the large number of zeros in the species composition
215 matrix (Oksanen et al. 2007; Zuur et al. 2007). NMDS was conducted with species regularly
216 detected (i.e., the summed average detections of a species across point counts per year was
217 greater than five) within the point count radius, e.g. Common Redpoll were often seen flying
218 over but only rarely on the ground. After conducting the initial NMDS analyses, we followed
219 with Analysis of Similarities (ANOSIM) to quantify differences in avian communities across
220 habitat and elevation classes. We chose three broad elevation classes, based on a combination of
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221 observations in the field and the clear separation in the NMDS itself: <700m representing forest
222 habitat, 700-799m representing the forest-tundra transition, and 800m+, representing alpine
223 habitat. We followed the ANOSIM analyses with Similarity Percentages (SIMPER) routines:
224 these show the avian species responsible for the differences seen between habitat and elevation
225 classes (Clarke and Gorley 2006). Multiple regression on distance matrices was used to assess
226 the association between physiognomy, floristics, and avian species composition (Lichstein 2007).
227 For the linear models, we used the Rcmdr package (Fox et al. 2007) in R (R Development
228 Core Team 2007). NMDS were conducted using package vegan, the former using function
229 “metaMDS” (Oksanen et al. 2007) in R. Primer 6 (Clarke and Gorley 2006) was also used to
230 conduct NMDS as well as ANOSIM and SIMPER. To evaluate the relationship between the
231 dominant vegetative types and the avianDraft species ordination, we used the function “envfit” in R.
232 Envfit calculates vectors that maximize the correlation between ordination scores and habitat
233 variables. A goodness-of-fit statistic (r 2) was computed for each vector by calculating 1,000
234 random permutations of the maximum correlation between NMDS ordination scores and habitat
235 variables (Oksanen et al. 2007). Multiple regression on distance matrices was performed using
236 package ecodist (Goslee and Urban 2007) in R.
237
238 Results
239 Bird and vegetation community composition
240 In 2007, we observed 763 individuals and 15 bird species, while in 2008, 1369 individuals and
241 24 bird species were observed. Common Redpoll (Acanthis cabaret (Statius Muller, 1776)) and
242 White-Crowned Sparrow (Zonotrichia leucophrys (Forster, JR, 1772)) were the most widespread
243 and ubiquitous species in both years, and were found over a 470 m range in elevation (Table 1).
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244 Willow Ptarmigan (Lagopus lagopus, L., 1758) was the only abundant non-passerine species.
245 We observed 65 different categories of plants and ground cover. These included 46 plant
246 species and 9 genera or higher taxa (e.g., sphagnum mosses). Other categories included water,
247 rock, and leaf litter. Average vegetation richness (AVR) differed significantly among habitat
248 classes ( F3,110 = 11.6, p <0.0001). AVR did not differ between alpine and deciduous shrub but
249 was significantly greater in forest and krummholz than in deciduous shrub ( t110 = 5.5, p < 0.0001;
250 t110 = 2.2, p = 0.03) . Overall, total vegetation volume (TVV) differed significantly among habitat
251 classes ( F3,110 = 31.43, p < 0.0001). Specifically, TVV did not differ between alpine and
252 deciduous shrub, but was significantly higher in forest and krummholz ( t110 = 8.7, p < 0.0001;
253 t110 = 2.9, p = <0.005; Table 2).
254 Draft
255 Bird-vegetation relationships - Diversity
256 Elevation plays a strong role in determining avian community composition (Fig. 2). Multivariate
257 tests showed a strong difference in avian community structure with elevation (ANOSIM Global
258 R = 0.326, p = 0.001), with the avian community of each elevation differing from each of the
259 other elevation classes (Appendix 2). This difference was driven to a large extent by forest
260 generalist bird species (e.g., AMRO, BLPW) declining with elevation (SIMPER: Appendix 3 and
261 below). Richness and abundance were generally highest in the forest but similar in the other
262 habitat types.
263 Overall, avian richness increased with AVR, TVV, and among habitat classes (F1,102 =
264 5.09, p = 0.026, F1,102 = 7.03, p = 0.009, F3,102 = 6.5, p = 0.005) in 2008. Within each habitat
265 class, richness did not significantly change with AVR but increased with TVV (t102 = 2.7, p =
266 0.009 for deciduous shrub); slopes for other habitat classes did not differ from deciduous shrub
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267 (Fig. 3). Avian richness was significantly higher in the forest compared to other habitats ( t102 =
268 2.85, p = 0.006). In 2007, the richness did not differ with AVR and TVV but did by habitat class
269 (F3,64 = 3.34, p = 0.03). Richness was significantly lower in alpine than in deciduous shrubs ( t64
270 = -2.25, p = 0.03; Table 2).
271 Avian abundance increased with AVR and TVV ( F1,102 = 15.26, p = 0.0002; F1,102 = 12.18,
272 p = 0.0007) in 2008; these increases were consistent across habitat class. Although the overall
273 term was not significant ( F3,102 = 1.8, p = 0.15), abundance in 2008 was significantly higher in
274 the forest compared to other habitat classes ( t102 = 2.4, p = 0.018). In 2007, none of the
275 explanatory variables or coefficients was significant (Table 2).
276
277 Bird-vegetation relationships - CompositionDraft
278 The correlation of the avian species distribution with the habitat variables is summarized
279 in Fig. 4A and B. Results were largely consistent between years for the 17 types of habitat we
280 identified, with most of the major habitat type / avian assemblage correlations conserved (Table
281 3). Alder, late snow melt areas, Salix spp., scrub spruce, and water (i.e., ponds) habitat were not
282 significantly correlated with the avian assemblage in either year. Fen and krummholz forest
283 were not significantly correlated with the avian assemblage in 2007. Elevation was the most
284 robust variable explaining much of the variance in both years.
285 Habitat associations are typical of most species and are generally consistent among years
286 (Fig. 4 A and B). American Pipit (Anthus rubescens (Tunstall, 1771)) was strongly associated
287 with elevation and also alpine. The abundances of American Tree Sparrow and Willow
288 Ptarmigan were strongly correlated with deciduous shrub and krummholz. Lincoln’s Sparrow
289 (Melospiza lincolnii (Audubon, 1834)) and American Robin (Turdus migratorius L., 1766) were
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290 correlated with krummholz. Many species, such as Gray Cheeked Thrush (Catharus minimus
291 (Lafresnaye, 1848)) and Yellow-rumped Warbler (Setophaga coronate (L., 1766)), were strongly
292 correlated with forest habitat, especially in 2008. Some species such as Blackpoll Warbler
293 (Setophaga striata (Forster, JR, 1772)) were not strongly associated with any habitat but were
294 negatively correlated with elevation and alpine. White-crowned Sparrow was weakly correlated
295 with rock and alpine habitat in 2008 but strongly correlated with forest in 2007. This
296 discrepancy is likely due to the large elevational range of this species and differences in sample
297 sizes between years.
298 The association between vegetation and avian species composition varied with habitat
299 class. Avian species composition was correlated with physiognomy on alpine sites and
300 deciduous shrub site and with floristics Drafton alpine sites in 2007 (Table 4). There were no
301 significant correlations between avian composition, floristics, or physiognomy in krummholz or
302 forest habitat in either year.
303
304 Discussion
305 Elevation and its subsequent impact on vegetation is the strongest driver of species
306 composition across the treeline ecotone. This trend was reflected in the difference in avian
307 richness among habitat classes and a strong negative relationship with avian composition (Fig. 2,
308 4).
309 Although many studies have examined avian diversity in relation to elevation, these
310 studies are usually conducted from a biogeographic perspective, concerned with testing
311 hypotheses that explain species richness over long elevational gradients (e.g. Herzog et al. 2005;
312 Rugerio and Hawkins 2008). These studies do not examine the vegetation-habitat concept nor do
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313 they explicitly examine ecotones. Other studies have examined these concepts, but this is the
314 first study, to the best of our knowledge, to do so across a boreal treeline ecotone (see Terborgh
315 1985; Archaux 2004; Lloyd et al. 2011; Kent et al. 2013 for non-boreal treeline examples), and
316 consider the potential impacts of global climate change (see Conservation Implications - but also
317 Cumming et al. 2014). Further, while we are in general agreement with Cumming et al. (2014)
318 that most studies of boreal song birds occur over too small a spatial extent to detect the influence
319 of climate variation, ecotone studies ameliorate this to a large degree. Indeed one of the
320 strengths of this study is the documented change in climate (Jacobs et al. 2014), vegetation
321 (Trant et al. in review 2), and avian composition over the short elevational gradient.
322
323 Ecotones Draft
324 Avian diversity did not increase across the ecotone but was highest in the forest (Fig. 4, Table 2,
325 Appendix 3), likely because of the increase in nesting and foraging opportunities. Further, while
326 some species were clearly associated with the treeline (Fig. 4), similar to Lloyd et al. (2011),
327 they are clearly not treeline specialists. The birds in this study are all quite common across
328 North America (e.g. American Robin), and the ecotone is far too narrow in these mountains to
329 promote speciation; in all likelihood, these birds were simply utilizing the area along the
330 elevational gradient with their preferred floristics and physiognomy. Simple relationships have
331 been hypothesized for the influence of ecotones including increased diversity (Odum 1958), but
332 further study has often produced little consensus because the relationship is far more complex
333 than originally realized (Murica 1995; Baker et al. 2002).
2 Trant, A.J., Lewis, K.P., Cranston, B.H., Wheeler, J.A., Jameson, R.G., Jacobs, J.D., Hermanutz, L., and Starzomski, B.M. Submitted. Complex changes in plant communities across a subarctic alpine treeline in Labrador, Canada. Arctic.
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335 Bird-vegetation relationships - Diversity and composition
336 Overall, richness and abundance generally increased with Average Vegetation Richness
337 (AVR) and Total Vegetation Volume (TVV; Fig. 3). The result of TVV, but not AVR, being
338 associated with avian diversity is consistent with Fleishman et al. (2003). The discrepancies
339 between 2007 and 2008, i.e., non-significant as opposed to significant findings respectively, are
340 likely due to the low number of point counts and lesser coverage in some habitat types in 2007.
341 As with measures of diversity, elevation is the most robust predictor of avian
342 composition. However, elevation is just a surrogate for other variables; it does not control avian
343 composition per se. Most species correlate well with one or two of the dominant habitat types,
344 justifying our division into habitat classDraft (Table 3, Fig. 4). Those few that do not can be
345 considered habitat generalists, like American Robin, found from forest to deciduous shrub, and
346 White-Crowned Sparrow, found from the timberline to alpine. Discrepancies between years in
347 correlation coefficients for Fen and Forest (Table 3) are likely due to the greater number of
348 species detected in 2008 and their association with these habitat types (Table 1, Fig. 4).
349 This is the first study of avian diversity in the highlands of Labrador and of highlands of
350 eastern Canada in general. Although a number of avian studies have been conducted in the
351 boreal forests of Labrador (Simon et al. 2000; Schwab et al. 2001; Simon et al. 2002) and
352 Quebec (Imbeau et al. 1999), as well as the low Arctic of Quebec (Andres 2006), these studies
353 have largely been focused on forests and forestry issues. Abundance, type, and diversity of
354 species were similar to results from these studies. Although these studies were not conducted
355 near the treeline, many of them occurred on burned, clearcut, or early successional plots.
356 Abundances of White-crowned and White-throated Sparrows (Zonotrichia albicollis (Gmelin, JF,
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357 1789)) were highest in these habitats and are likely to be structurally similar to our deciduous
358 shrub and krummholz sites. However, there were some differences. American Pipit (Anthus
359 rubescens (Tunstall, 1771)), Horned Lark (Eremophila alpestris L., 1758), Savannah Sparrow
360 (Passerculus sandwichensis (Gmelin, JF, 1789)), and Rock Ptarmigan (Lagopus mutus (Montin,
361 1781)) are not reported in the above-mentioned Labrador studies since they are found almost
362 strictly above the treeline. We also found Grey-cheeked Thrush rather than Hermit ( Catharus
363 guttatus (Pallas, 1811)) and Swainson’s Thrush ( Catharus ustulatus (Nuttall, 1840)). We suggest
364 that this is due to the dense woody undergrowth and alder thickets in these higher elevation
365 forests that Gray-cheeked Thrush favour (Lowther et al. 2001). We note here that Rock
366 Ptarmigan, known to occur in Northern Labrador, have not been reported breeding in this area
367 before, and this southern outlier of a moreDraft northern species may be expected to be lost as treeline
368 advances over alpine habitat (Montgomerie and Holder 2008).
369 Results of the multiple regression on distance matrices were not consistent with
370 Rotenberry’s hypothesis that floristics are more important for avian assemblages within a habitat
371 (Table 4; Rotenberry 1985; Bersier and Meyer 1994; Fleischman et al. 2003; Lee and Rotenberry
372 2005 and references therein). In alpine and deciduous shrub habitat, with the exception of 2007,
373 the results were opposite. One explanation for this finding is that bare rock is a significant
374 feature in alpine habitat and to a lesser degree in deciduous shrub. Rock clearly does not
375 contribute to the physiognomy of the system. This suggests that some vegetation structure is
376 better than none at all, and that physiognomy is more important than floristics. In addition, some
377 small, isolated krummholz stands were found in areas that are predominantly shrub habitat and
378 this may have a similar effect. Other studies have found a similar pattern (Shahabuddin and
379 Kumar 2007), which is more consistent with the original hypotheses that avian diversity is more
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380 strongly correlated with vegetation habitat diversity rather than vegetation species (MacArthur et
381 al. 1962). However, avian diversity studies beyond the treeline ecotone are rare and similarity
382 between alpine and other habitat classes is low, suggesting that these species respond to
383 vegetation in different ways than others (Catsadorakis 1997). In many places, alpine habitat is
384 predicted to be lost with changing climate (Rozenzweig et al. 2007).
385 Our results were inconclusive for Rotenberry’s hypothesis in krummholz and forest
386 habitat. Krummholz habitat contains stunted spruce and balsam fir trees that form dense patches
387 on the landscape, much different in structure than either the vegetation higher in elevation (alpine
388 tundra) and lower (open canopy forest). It is not clear why changes in floristics and
389 physiognomy were not associated with avian composition in krummholz, but there are two
390 possible explanations. First, birds may Draftbe responding to other habitat features within this zone
391 (Knick et al. 2008). Krummholz were simply the dominant vegetation in a transition zone from
392 relatively unbroken forest to deciduous shrub with a few tree-islands to treeless alpine habitat.
393 The krummholz zone forms a very fragmented, narrow band across the ecotonal gradient
394 between forest and alpine habitat types, and is often embedded within a matrix of secondary
395 habitat types (late snow melt areas, fen, streams). Alternately, Norment (1991) found that avian
396 diversity in the subalpine forest-alpine tundra ecotone was positively correlated with forest patch
397 size and edge, though we are unable to test for this in our system. It is possible that this latter
398 explanation accounts for the lack of correlation of avian composition and vegetation in the forest
399 habitat. As with most ecological patterns, multiple factors structure avian communities.
400
401 Conservation implications
402 This is the first study to examine avian communities at a treeline in Labrador. There are
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403 two general hypotheses for how treeline may change with climate change: infilling of presently
404 patchy habitat, and general advance of the leading edge of treeline, both poleward and upward in
405 elevation (Danby and Hik 2007). In the infilling scenario, physiognomy changes dramatically,
406 but floristics may not. In the advance scenario, both physiognomy and floristics may change as
407 treeline advances. Each scenario will have different impacts on local and regional avian
408 communities. In an infilling scenario, alpine and shrub habitat may not decrease in area, leading
409 to maintenance of the habitat for alpine and shrub-associated species. In general, the regional
410 community will not change but some species may fare poorly, such as Blackpoll Warbler and
411 American Robin that do well in patchy tree-shrub habitats. In the advance scenario, alpine and
412 shrub habitat may be lost, with little change in the proportion of below treeline habitat (Danby
413 and Hik 2007). This may lead to importantDraft local and regional changes in communities of birds
414 found above the treeline: alpine species such as American Pipit, Horned Lark, and Rock
415 Ptarmigan would be lost, eliminating these southern outposts of more northerly species.
416
417 Conclusion:
418 We examined the impact of elevation, as well as floristics and physiognomy of vegetation on
419 avian communities across a treeline ecotone in the Mealy Mountains, Labrador, Canada. Our
420 findings suggest that elevation plays a strong role in structuring avian communities, and are
421 generally inconsistent with the vegetation-habitat concept, i.e. that birds are influenced by
422 vegetation structure and by composition at more local scales. Given the strong impact of
423 elevation on vegetation structure and avian communities, we note that for bird species whose
424 near-southernmost populations are found in the Mealy Mountains, climate change is likely to
425 have a strong negative effect as alpine tundra habitat is lost. Further, forest-associated species
426 are likely to benefit from the increased tree cover as treeline moves poleward and upward.
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427
428 Acknowledgments
429 We would like to thank Danielle Fequet for her tireless help in the field, as well as thank other
430 members of the Labrador Highlands Research Group, especially John Jacobs and Luise
431 Hermanutz. We are grateful to Ian Warkentin for reviewing a previous draft of the manuscript.
432 We acknowledge funding from the Government of Canada Program for International Polar Year
433 as part of the project PPS Arctic Canada. This project is a product under the IPY core project
434 PPS Arctic as part of the International Polar Year 2007-2008, which was sponsored by the
435 International Council for Science and the World Meteorological Organisation. We dedicate this 436 paper to the memory of our friend NealDraft P. P. Simon and his work on avian ecology in Labrador.
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575 ground disturbance, and herbivory on seedling establishment: implications for treeline 576 advance with climate warming. Plant Ecol. 210 : 19-20. 577 578 Murica, C. 1995. Edge effects in fragmented forests.: implications for conservation. Trends Ecol. 579 Evol. 10: 58-62. 580 581 Nichols, J.D, Hines, J.E., Sauer, J.R., Fallon, F.W., Fallon, J.E. and Heglund, P.J. 2000. A 582 double-observer approach for estimating detection probability and abundance from point 583 counts. Auk, 117 : 393-408. 584 585 Norment, C.J. 1991. Bird use of forest patches in the subalpine forest-alpine tundra ecotone of 586 the Beartooth Mountains, Wyoming. Northwest Sci. 65 : 1-9. 587 588 Odum, E.P. 1958. Fundamentals of ecology. Second edition. Saunders, Philadelphia, 589 Pennsylvania, USA. 590 591 Oksanen, J., Kindt, R., Legendre, P., O'Hara, B., Henry, M. and Stevens, H. 2007. Vegan: 592 Community Ecology Package. R package version 1.8-8. http://cran.r-project.org/. 593 594 Payette, S., M.-J. Fortin, and I. Gamache. 2001. The Subarctic Forest–Tundra: The Structure of a 595 Biome in a Changing Climate The shiftingDraft of local subarctic tree lines throughout the forest– 596 tundra biome, which is linked to ecological processes at different spatiotemporal scales, will 597 reflect future global changes in climate. BioScience, 51 : 709–718. 598 599 R Development Core Team 2007. R: A language and environment for statistical computing. R 600 Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL 601 http://www.R-project.org. 602 603 Reynolds, C.E. and Houle, G. 2002. Mantel and partial Mantel tests suggest some factors that 604 may control the local distribution of Aster laurentianus at Îles de la Madeleine, Québec. Plant 605 Ecol. 164 : 29-27. 606 607 Robert, K., Garant, D., Vaner Wal, E., and Pelletier, F. 2013. Context-dependent social behavior: 608 testing the interplay between season and kinship with raccoons. J. Zool. (Lond.) 290: 199- 609 207. 610 611 Ries, L., and T. D. Sisk. 2004. A predictive model of edge effects. Ecology, 85 : 2917–2926. 612 613 Risser, P. G. 1995. The status of the science examining ecotones. BioScience, 45 : 318–325. 614 615 Rosenstock, S.S, Anderson, D.R., Giesen, K.M., Leukering, T., and Carter, M. F. 2002. Landbird 616 counting techniques: current practices and an alternative. Auk, 119 : 46-53. 617 618 Rotenberry, J.T. 1985. The role of habitat in avian community composition: physiognomy or 619 floristics? Oecologia, 67 : 213-217. 620
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621 Ruggiero, A., and Hawkins, B.A. 2008. Why do mountains support so many species of birds? – 622 Ecography, 31 : 306-315. 623 624 Ruckstuhl, K.E., Johnson, E.A., and Miyanishi, K. 2008. Introduction. The boreal forest and 625 global change. Philos. Trans. R. Soc. London B Biol. Sci. No. 363: 2243-2247. 626 627 Schwab, F. E., Simon, N.P.P., Carroll, C.G. 2001. Breeding songbird abundance related to 628 secondary succession in the subarctic forests of western Labrador. Ecoscience, 8: 1-7. 629 630 Shahabuddin, G. and Kumar, R. 2007. Effects of extractive disturbance on bird assemblages, 631 vegetation structure and floristics in tropical scrub forest, Sariska Tiger Reserve, India. For. 632 Ecol. Manage. 246 : 175-185. 633 634 Simon, N.P.P., Schwab, F.E., and Diamond, A.W. 2000. Patterns of breeding bird abundance in 635 relation to logging in western Labrador. Can. J. For. Res. 30 : 257-263. 636 637 Simon, N.P.P., Schwab, F.E., and Otto, R.D. 2002. Songbird abundance in clear-cut and burned 638 stands: a comparison of natural disturbance and forest management. Can. J. For. Res. 32 : 639 1343-1350. 640 641 Terborgh, J. 1985. The role of ecotonesDraft in the distribution of Andean birds. Ecology, 66 : 1237- 642 1246. 643 644 Verner, J. 1985. Assessment of counting techniques. Chapter 8. Curr. Ornithol. 2: 247-302. 645 646 Zuur, A.F., Ieno, E.N., and Smith, G.M. 2007. Analysing ecological data. Springer, New York. 647 648 649
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650 Figure legends 651 652 Figure 1. Location of the study area in the Mealy Mountains of Labrador, Newfoundland and 653 Labrador, Canada. 654 655 Figure 2. Non-metric multidimensional scaling plot (NMDS) of bird community composition 656 by elevation in the Mealy Mountains (Akamiuapishkua), Labrador, for the year 2008. BinElev 657 includes the bird community in the 50m including and above the stated value (e.g., 800 = 800 658 to 849m). 659 660 Figure 3. The influence of total vegetation volume (m3/m2) on bird species richness in 2008 in 661 four habitat types. The regression lines for each habitat (dashed lines) and overall regression 662 line (solid line) are shown. Box-and-whisker plots denote the spread of the variable along the 663 axis. 664 665 Figure 4. A two-dimensional, non-metric multidimensional scaling (NMDS) ordination of 666 species scores for A) 2007 and B) 2008 (see Table 2). Stress <0.15 for both years. The “+” 667 indicates the location of the species scores. Codes for bird species and habitat are in Table 1 668 and Table 2 respectively. ‘Envfit’ was used to create and overlay vectors on the ordination. 669 For clarity, only vectors with p <0.01Draft in 2007 and p<0.006 in 2008 are shown.
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Table 1. Elevation range of species observed on100 m radius point counts in the Mealy Mountains (2007 and 2008): Minimum (Min), median, maximum (Max), n (number of point counts), and range (Max – Min). a Found only in 2007. b Found only in 2008.
Common Name Scientific Name Code mean sd Min Max n Range
Red-breasted Merganser a Mergus serrator L. 1758 RBME 683 NA 683 683 1
Rock Ptarmigan Lagopus mutus (Montin, 1781) ROPT 790 137 618 969 6 351 Willow Ptarmigan (Lagopus lagopus, L., Draft1758) WIPT 627 55 510 740 49 230 Greater Yellowlegs b Catoptrophorus semipalmatus GRYE 515 8 507 527 5 20
(Gmelin, JF, 1789)
Solitary Sandpiper b Tringa solitaria (Wilson, A, 1813) SOSA 513 NA 513 513 1
American Pipit Anthus rubescens (Tunstall, 1771) AMPI 738 96 603 982 37 379
American Robin Turdus migratorius L., 1766 AMRO 609 70 507 740 67 233
American Tree Sparrow Spizella arborea (Wilson, A, 1810) AMTR 633 68 510 815 86 305
Blackpoll Warbler Setophaga striata (Forster, JR, 1772) BLPW 584 55 503 720 78 217
Common Redpoll Acanthis cabaret (Statius Muller, 1776) CORE 631 89 508 982 92 474
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Common Name Scientific Name Code mean sd Min Max n Range
Dark-eyed Junco Junco hyemalis L., 1758 DEJU 568 52 507 709 53 202
Fox Sparrow Passerella iliaca (Merrem, 1786) FOSP 578 55 503 718 70 215
Gray-cheeked Thrush Catharus minimus (Lafresnaye, 1848) GCTH 529 20 503 585 23 82
Gray Jay b Perisoreus Canadensis L., 1766 GRJA 533 NA 533 533 1
Horned Lark Eremophila alpestris L., 1758 HOLA 814 110 683 982 12 299 Lincoln's Sparrow b Melospiza lincolnii (Audubon,Draft 1834) LISP 595 53 503 685 26 182 Northern Shrike b Lanius borealis (Vieillot, 1808) NOSH 517 10 510 524 2 14
Northern Waterthrush Parkesia noveboracensis (Gmelin, JF, 1789) NOWA 555 49 503 650 25 147
Pine Grosbeak b Pinicola enucleator L., 1758 PIGR 524 16 503 558 8 55
Pine Siskin b Carduelis pinus (Wilson, A, 1810) PISI 541 9 534 547 2 13
Ruby-Crowned Kinglet Regulus calendula L., 1766 RCKI 530 17 503 567 17 64
Savannah Sparrow Passerculus sandwichensis (Gmelin, JF, 1789) SASP 590 65 510 709 15 199
Tennessee Warbler a Vermivora peregrina (Wilson,A, 1811) TEWA 551 32 510 605 10 95
Tree Swallow Tachucineta bicolor (Vieillot, 1808) TRSW 538 34 517 577 3 60
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Common Name Scientific Name Code mean sd Min Max n Range
White-crowned Sparrow Zonotrichia leucophrys (Forster, JR, 1772) WCSP 637 102 507 982 111 475
Wilson's Warbler Wilsonia pusilla (Wilson, A, 1811) WIWA 567 42 503 646 43 143
White-Throated Sparrow b Zonotrichia albicollis (Gmelin, JF, 1789) WTSP 565 39 516 646 13 130
White-winged Crossbill b Loxia leucoptera Gmelin, JF, 1789 WWCR 525 17 503 560 11 57
Yellow-bellied Flycatcher b Empidonax flaviventris (Baird,WM & YBFL 539 21 517 558 3 41 Baird,SF, 1843) Draft Yellow Warbler a Setophaga petechia (L., 1766) YEWA 606 30 586 641 3 55
Yellow-Rumped Warbler Setophaga coronate L., 1766 YRWA 529 21 508 605 22 97
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Table 2. The average (± standard deviations) of avian species richness and abundance as well as vegetation species richness and total vegetation volume by habitat type. Avian values are presented by year.
Avian species Avian abundance Average Total vegetation richness vegetation species volume Habitat type richness 2007 2008 2007 2008 Alpine 2.2 ± 0.9 3.0 ± 1.3 4.3 ± 2.3 4.8 ± 2.4 1.0 ± 0.4 0.7 ± 0.5 Deciduous shrub 3.2 ± 1.1 3.4 ± 1.2 5.5 ±2.3 5.6 ± 1.8 0.9 ± 0.4 0.6 ± 0.5 Krummholz 3.4 ± 0.9 3.8 ± 1.2 6.0 ± 1.7 5.8 ± 2.0 1.1 ± 0.3 1.0 ± 0.4 Forest 4.4 ± 0.7 6.0 ± 1.7 8.5 ± 1.8 8.6 ± 3.1 1.4 ± 0.5 1.4 ± 0.7 Total 3.1 ± 1.2 4.0 ± 1.8 5.6 ± 2.4 6.2 ±2.8 1.1 ± 0.4 0.9 ± 0.6 Draft
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Table 3. Squared correlation coefficients ( r2) and significance levels (based on 1000 random permutations of the data) of habitat types with the avian assemblages based on the NMDS in 2007 and 2008. Note that habitat variables are independently modeled.
* = p < 0.05, ** p < 0.01, *** p < 0.001
2007 2008
r2 p r2 p
Alder (Ad) 0.08 0.05 . 0.01 0.51
Alpine (Ap) 0.37 <0.001 *** 0.23 <0.001 ***
Fen (Fe) 0.01 0.83 0.12 0.002 **
Forest (Fo) 0.09 0.03 * 0.50 <0.001 ***
Deciduous Shrub (H) 0.14 0.01 ** 0.10 <0.001 ***
Krummholz (K) 0.18 Draft <0.001 *** 0.10 0.006 **
Krummholz Forest (KF) 0.05 0.14 0.06 0.04 *
Late snow melt (LS) 0.01 0.70 0.01 0.67
Open Forest (OF) 0.19 <0.001 *** 0.09 0.009 **
Rock (R) 0.36 <0.001 *** 0.40 <0.001 ***
Salix 0.01 0.62 0.01 0.59
Sedge (Se) 0.07 0.09 . 0.02 0.29
Snow 0.14 <0.01 *** 0.10 0.003 **
Scrub Spruce (SS) 0.04 0.08 .
Stream (STR) 0.00 0.90 0.01 0.51
Water (W) 0.06 0.12 0.02 0.41
Elevation 0.78 <0.001 *** 0.76 <0.001 ***
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Table 4. Multiple regression on distance matrices between avian species composition, vegetation species composition (floristics-
AVR), and vegetation structure (physiognomy - TVV). Results are given as the regression coefficients and p-values (significant
values in bold).
Alpine Deciduous shrub Krummholz Forest Coefficient 2007 2008 2007 Draft 2008 2007 2008 2007 2008 AVR 0.34, 0.03 0.05, 0.67 0.01, 0.41 0.02, 0.82 0.06, 0.71 0.14, 0.29 -0.12, 0.74 -0.11, 0.44
TVV 0.23, 0.01 0.25, < 0.01 0.14, 0.01 0.14, < 0.01 -0.05, 0.74 0.22, 0.05 -0.27, 0.15 0.14, 0.20
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Draft
Location of the study area in the Mealy Mountains of Labrador, Newfoundland and Labrador, Canada. 292x378mm (96 x 96 DPI)
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Draft
Non-metric multidimensional scaling plot (NMDS) of bird community composition by elevation in the Mealy Mountains (Akamiuapishkua), Labrador, for the year 2008. BinElev includes the bird community in the 50m including and above the stated value (e.g., 800 = 800 to 849m). 326x221mm (72 x 72 DPI)
270x203mm (96 x 96 DPI)
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The influence of total vegetation volume (m3/m2) on bird species richness in 2008 in four habitat types. The regression lines for each habitat (dashed lines) and overall regression line (solid line) are shown. Box-and- whisker plots denote the spread of the variable along the axis. 207x207mm (72 x 72 DPI)
155x155mm (96 x 96 DPI)
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Draft
A two-dimensional, non-metric multidimensional scaling (NMDS) ordination of species scores for A) 2007 and B) 2008 (see Table 2). Stress <0.15 for both years. The “+” indicates the location of the species scores. Codes for bird species and habitat are in Table 1 and Table 2 respectively. ‘Envfit’ was used to create and overlay vectors on the ordination. For clarity, only vectors with p <0.01 in 2007 and p<0.006 in 2008 are shown. 190x254mm (96 x 96 DPI)
190x254mm (96 x 96 DPI)
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