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A Cultch Presentation A highly theatrical case for the creation of art as a means to survive. Because of its strong gradients in vegetation and climate with latitude, longitude, and elevation, the Seward Peninsula contains many diverse habitats in close proximity [ 25 ], making it an ideal laboratory for studying avian responses to habitat gradients across the boreal forest-tundra ecotone.

We conducted surveys at a subset of 35 survey blocks that had been randomly selected on the Seward Peninsula and surveyed during the s for bristle-thighed curlews Numenius tahitiensis and other breeding birds C.

This study took place on a combination of public and private lands; permission was obtained from all land managers and private owners before the study commenced.

For the current study — , we selected 14 of the 35 study blocks that maximized accessibility and variation among sites. We did not complete ground-based vegetation measurements for points in two survey blocks and thus dropped all bird surveys from those blocks for this analysis Fig 1.

Bird surveys consisted of a min point count wherein observers recorded all birds detected by sight or sound within a m radius [ 27 ].

We conducted surveys from 21 May to 21 June, — All observers were experienced with point-count techniques and underwent 1—2 weeks of intensive training, including visual and aural identification of birds and distance estimation, before conducting surveys.

We repeated surveys at some locations up to three times per season and we recorded distance to detected birds in distance bands 0—50, 50—, and — m to assess detection probability [ 28 , 29 ].

This study did not involve endangered or protected species, nor did it involve handling or disturbance of wildlife. We measured various aspects of vegetation at each survey point once during the 3-year study.

These four measurements were combined into an average distance for each shrub-height category. When there was no shrub of a specific height class within visible range of the point in any direction, we imputed a maximum value of 5 m, m, and m for dwarf, low, or tall shrubs, respectively, and we truncated all greater values to these distances.

Maximum distances were based on natural breakpoints in data; i. We measured additional vegetative characteristics at 10—15 subsampling points within each bird survey area.

Subsample points were placed at 5-m intervals along two or three m transects with randomly selected orientations 0— degrees.

The first transect originated at the bird survey point and the second and third, in a few areas was randomly placed within m of the survey point.

At each subsample point, we measured visual obstruction, an index of vegetation height and density, from a distance of 2 m and at a height of 0.

Additionally, within a 0. At one study block with low diversity in structure and composition of plants, subsample vegetation surveys were not completed at 10 of 39 survey points.

For these points, we imputed the mean value from the other 29 points in the block. Several bird species were observed in high numbers during a small number of point-count surveys.

In these instances, we truncated counts to the next lowest number to reduce the influence of uncommonly observed large flocks [ 31 ]: We had a large number of potential habitat variables and endeavored to reduce this set before commencing with analysis [ 32 ].

To reduce the number of potential covariates, we combined related measurements into single variables and eliminated variables that had limited modeling utility e.

Ultimately, we included four covariates for bird abundance as follows. Because this calculation will generate asymptotically high values when distances to shrubs are low e.

When a shrub from a particular group ericaceous, willow, alder, or dwarf birch was not present in a sample frame, height was reported as a missing value and omitted from the mean calculation; this naturally weighted the data by the more common classes of shrubs.

We truncated the mean value at cm, reducing 8 high values. The maximum correlation coefficient among these four covariates was 0.

We considered five covariates for inclusion in the detection model: We conducted a series of preliminary assessments using both repeat surveys and distance-sampling methods for assessing factors that influenced detection [ 28 , 35 ].

Distance-sampling methods assess the likelihood of detecting a bird that is available to be detected i. Distance methods suggested limited or inconsistent support for the influence of observer skill level and shrub height, and we subsequently chose to omit these variables from consideration.

We ultimately included three variables that consistently influenced detection for all species: While biologically appropriate to consider, the quadratic term for date was problematic for many species inducing non-convergence and was therefore excluded.

Given the short seasonal period of the surveys designed to coincide with maximum cue production, we judged the linear effect of date sufficient for consideration.

We modeled relationships between bird abundance and habitat characteristics with N-mixture models, which use repeated counts to assess imperfect detection while concurrently analyzing factors that influence abundance [ 29 ].

However, we did not employ open-population models that explicitly account for interannual dynamics at a location because these parameters were not the focus of this analysis and because these models typically require large amounts of data [ 38 , 39 ].

Any additional non-independence of surveys i. Previous analysis of portions of these data also showed lack of spatial autocorrelation [ 40 ].

Finally, some species arrived on breeding grounds after surveys had begun; to avoid improper inference about habitat relationships or detection, we noted the first date that a species was observed each year and omitted any surveys conducted before that date for each year [ 36 ].

We conducted all analyses using R statistical software version 3. We proceeded with model fitting in several stages. First, we used a generally parameterized model, with three detection covariates as previously described and all four habitat covariates all included as linear and quadratic terms for abundance, to select the better-fitting error distribution for each species, either negative binomial or Poisson, as evidenced by lower AIC values [ 34 ].

Next, we selected the formulation of each predictor that generated the best fit, as determined by lowest AIC: All other habitat variables were included as quadratic terms while a single habitat covariate was altered.

The best-fitting formulations for each habitat variable were then combined to generate a single top model for each species. Finally, we assessed model fit using a parametric bootstrap method, wherein the top model was used to generate simulated datasets, allowing comparison of observed and expected values via a chi-square test.

In some instances, the negative binomial distribution will generate very high prediction values, despite indications of acceptable model fit; in such cases, we instead generated predictions with a Poisson distribution, which is considered the better alternative [ 34 ].

During —, we conducted surveys at unique survey points within 12 km 2 blocks. We observed 68 species and selected 17 of the most commonly observed species for analysis, including 12 passerines, 4 shorebirds, and 1 ptarmigan species S2 Table.

We omitted several common species from analysis because they tended to be poorly sampled by point-count methods. For example, redpolls Acanthis spp.

When selecting among the five potential models for each habitat covariate, in many cases there were a number of competitive models S3 Table.

In these cases, we proceeded with the selection that generated the lowest AIC. Overall, models fit data well, with all 17 species having satisfactory model fit Table 1.

For all four shorebird species, negative binomial models passed the fit test, but provided unreasonably high prediction values; thus, predictive plots were generated with a Poisson distribution.

Average shrub height at individual survey points ranged from 0. Survey points with the tallest shrubs were also characterized by less lichen cover 4.

More bird species responded to shrub height than to any other habitat variable; for 16 of 17 species, models reported a significant relationship between bird abundance and shrub height Table 1 , Fig 2 ; all coefficient and standard error estimates are reported in S4 and S5 Tables.

Conversely, abundances of American golden-plover, bluethroat, golden-crowned sparrow, Lapland longspur, western sandpiper, and willow ptarmigan were predicted to decline with increasing shrub height, with several reaching near-zero estimates at relatively low values of shrub height e.

Abundances of arctic warbler, American tree sparrow, Savannah sparrow, and whimbrel were predicted to peak at low to moderate average shrub heights 33, 22, 18, and 12 cm, respectively; Fig 2.

Predicted response abundance of 17 species to increasing shrub height cm; green and density shrubs per m 2 ; gray , both of which varied from 0 to Predictions are based on the final model for each species with all other habitat covariates held at mean values.

Low—tall shrub density i. Points with low and high densities of low—tall shrubs had similar values for bare ground, herbaceous plant, and dwarf birch cover.

Abundances of American tree sparrow and fox sparrow were best described by a quadratic response with maximum values predicted at mean values of low—tall shrub density peaking at 65 and 52 shrubs per m 2 , respectively.

The top model for Lapland longspur also included a negative quadratic fit for shrub density, predicting highest abundance at 0 shrubs per m 2 Fig 2 , Table 1 , S3 Table.

Mean shrub cover ranged from 0 to High shrub cover was associated with taller average shrub height Sites with high shrub cover were also associated with considerably more ericaceous and dwarf birch cover Models indicated that abundances of 13 species were associated with percent shrub cover S4 Table.

Abundances of arctic warbler and bristle-thighed curlew were predicted to decline with increasing shrub cover Fig 3. Predicted abundance of 17 bird species with increasing percent herbaceous cover green and percent shrub cover gray.

Average percent herbaceous cover ranged from 1. Points with high herbaceous cover had less bare ground 0.

Points with more herbaceous cover had taller herbaceous plants Abundance of 11 species was associated with herbaceous cover.

Lapland longspur, Savannah sparrow, western sandpiper, and willow ptarmigan were positively associated with percent herbaceous cover.

Abundances of American golden-plover, American tree sparrow, bluethroat, golden-crowned sparrow, white-crowned sparrow, and whimbrel had no significant associations with herbaceous cover Fig 3 , S4 Table.

Wind had a significant negative effect on detection for 9 of 17 species and a positive effect on detection for whimbrel all coefficient estimates, standard errors, and p-values are reported in S5 Table.

Date was a significant predictor for 10 species, with a negative relationship estimated for bluethroat, northern waterthrush, white-crowned sparrow, whimbrel, and willow ptarmigan and a positive relationship for arctic warbler, American tree sparrow, gray-cheeked thrush, Savannah sparrow, western sandpiper, and yellow warbler S5 Table.

Detection probability was related to time of day for five species, with models supporting a quadratic relationship for all of these species.

Detection probabilities of American tree sparrow, bluethroat, fox sparrow, golden-crowned sparrow, gray-cheeked thrush, and Savannah sparrow were predicted to peak at The relationship between bird species richness and vertical structural complexity of vegetation in an area has long been recognized [ 19 , 44 ].

The nature of this relationship and ecological processes driving it are of increasing interest to ecologists attempting to predict effects of climate change on avian diversity across broad landscapes and environmental gradients [ 43 — 47 ].

In tundra ecosystems, shrubs of varying stature are a key component of the flora [ 14 , 48 ] and numerous breeding birds rely on them for forage, shelter, and nesting sites [ 25 ].

Increasing growth and dominance of shrubby vegetation are associated with warmer, longer growing seasons and possibly overall increases in productivity [ 10 , 11 ].

Thus, one might predict that more productive tundra systems in the future would be characterized by taller and more abundant shrubs, supporting more individual birds and a greater diversity of bird species, with perhaps the loss of a few shrub-intolerant species.

Our findings, however, suggest that even species that prefer or rely on shrub habitats have limits. Furthermore, changes in biodiversity in tundra ecosystems over time as structural vegetation complexity increases are likely to reflect a constantly changing array of avian species.

Among 17 species considered in our study, 12 were responsive to at least one characteristic of shrubs, with predicted abundance declining either continuously or as shrubs crossed certain thresholds in height, density, or cover.

Lapland longspurs prefer to nest in low-stature vegetation throughout their range [ 49 ], and the expansion of taller shrubs into herbaceous and dwarf shrub habitats is likely to have strong negative effects on their abundance in affected regions.

The remaining species, however, appear to be responsive to thresholds of vegetative characteristics. Even species that had positive relationships with shrubs often displayed plateaus where increasing shrub values eventually led to no further predicted increases in abundance e.

As the climate continues to warm and shrub encroachment progresses, our results suggest that we will see short-term increases in abundance of many bird species, but as shrubs become increasingly dominant, many shrub-tolerant species may eventually be displaced.

Our models also suggest that bird species are responsive to different aspects of increasing shrub dominance in tundra ecosystems.

For example, the top model for golden-crowned sparrow predicted a positive association with percent shrub cover and a negative association with shrub height.

It may seem counterintuitive that a bird would be averse to tall shrubs, yet tolerate areas with high shrub density. In many tundra areas, however, dwarf or prostrate shrubs rarely grow taller than 0.

Given that birds responded variably to metrics of shrub cover, height, and density, it may be more difficult to predict which species will be most affected by climate-induced changes to vegetation.

Will shrubs grow larger and taller or will low-growing shrub ground cover become increasingly dominant?

Each of our shrub metrics can be related to one or more mechanisms of shrub expansion: Given that studies from across northern latitudes have described increases in shrub height and cover, range expansions, infilling, and movement upslope and along water drainages [ 11 ], we believe that all of our shrub metrics height, cover, and density of low—tall shrubs will continue to increase as they have over the past century, thereby prompting continued and varied responses from more bird species.

Of our three shrub metrics, the average height of shrubs was negatively associated with abundance of more species than shrub density or cover.

Among their study species, they found that arctic warbler, Savannah sparrow and golden-crowned sparrow demonstrated more dramatic shifts in occupancy and elevation than other species.

Our results similarly show that these species were all responsive to increasing shrub height, again suggesting that shrub height is a particularly important driver of avian habitat selection.

A number of ecological mechanisms may cause birds to be responsive to areas with increasingly tall shrubs. Tall shrubs can outcompete other vegetation lower in stature, including low-growing shrubs, lichen, moss, and herbaceous plants [ 12 ].

This reduction in some types of low-growing ground cover around tall shrubs may deter birds that tolerate shrubs, but nest on the ground, and prefer certain types of low-growing vegetation for their nest sites.

Essentially all of our study species, with the exception of gray-cheeked thrush and yellow warbler, nest on or near the ground, and these were two species that were particularly shrub-tolerant [ 25 ].

In addition to the availability of adequate nesting sites, birds require habitats that provide preferred food sources and types of foraging habitat.

Research suggests that shrub dominance in tundra systems is linked with changes in insect abundance and species composition. In Alaskan tundra, shrub-dominated tundra had a greater biomass of canopy dwelling arthropods, whereas graminoid-dominated tundra sites had a greater biomass of ground-dwelling arthropods [ 24 , 50 ].

The majority of our study species prefer to forage for insects on the ground and these birds may be at a disadvantage as shrub dominance increases beyond some threshold best signified by shrub height in this analysis.

Kessel [ 25 ] parsed upland bird habitat of the Seward Peninsula into categories defined by dominant vegetation types and prevailing shrub height and often described avian preferences for habitat based on shrub height.

Our results largely corroborate the importance of shrub height to birds in this region: Kessel [ 25 ] described American golden-plover, whimbrel, bristle-thighed curlew, western sandpiper, and Lapland longspur as species associated with the shortest shrubs or least shrubby habitats: We found all of these species to be particularly responsive to shrubs, with very narrow tolerances for at least one shrub metric.

Kessel noted that willow ptarmigan and Savannah sparrow prefer low shrub thicket dominated by shrubs 0. Our shrub metrics represented average shrub conditions within a m radius around the survey point and thus did not reflect the heights of individual shrub patches available to birds within the sampled area.

As a result, our average shrub heights are not directly comparable to the shrub categories described by Kessel [ 25 ]. Studies investigating mechanistic relationships between shrub characteristics and bird abundance should be certain to account for the spatial scale at which such relationships are being measured.

Based on current avian habitat associations across subarctic tundra landscapes, our findings suggest that increases in shrub cover and density will negatively affect abundance of only a few bird species arctic warbler, bristle-thighed curlew and may potentially be beneficial for many e.

At this time we know very little about how tundra shrub characteristics and related geophysical and climactic factors might affect avian productivity and survival, the primary demographic processes that govern changes in breeding abundance.

Increasing shrub cover or density may be beneficial to more generalist species, but may also signify changes in the timing, quality, quantity, or types of important foods, potentially attracting birds, but providing suboptimal conditions [ 52 ].

Increases in tall woody vegetation could draw in novel predators to which tundra birds may be ill-adapted, reducing survival or fecundity [ 53 ].

As avian assemblages shift from those preferring open tundra to more shrub-preferring communities, interspecific competitive forces may also affect productivity and survival of many species [ 54 ].

As in other regions, generalists are likely to benefit, at least in the near future [ 55 ]. This work is part of the U. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.

We are grateful to everyone who helped collect bird and habitat data for this project: Van Hemert, and S. Matsuoka for helpful comments on an earlier draft of this paper and C.

Amundson for guidance with data analysis. Funding was received by CMH from U. All relevant data are within the paper and hosted at the following DOI:

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