Christopher M. Harwood1,2, Robert E. Gill, Jr.3 & Abby N. Powell1,4
1Department of Biology and Wildlife, University of Alaska Fairbanks, Fairbanks, Alaska 99775, USA
2Kanuti National Wildlife Refuge, U.S. Fish and Wildlife Service, Fairbanks, Alaska 99701, USA
christopher_harwood@fws.gov
3U.S. Geological Survey, Alaska Science Center, 4210 University Drive, Anchorage, Alaska 99508, USA
4U.S. Geological Survey, Florida Cooperative Fish and Wildlife Research Unit, University of Florida, Gainesville,
Florida 32611, USA (current address)
Harwood, C.M., R.E. Gill, Jr. & A.N. Powell. 2016. Nesting ecology of Whimbrels in boreal Alaska. Wader Study
123(2): 99–113.doi:10.18194/ws.00037
Abstract
Breeding ecology studies of boreal waders have been relatively scarce in North America. This paucity is due in part to boreal habitats being difficult to access,and boreal waders being widely dispersed and thus difficult to monitor. Between2008 and 2014 we studied the nesting ecology of Whimbrels Numenius phaeopushudsonicus in interior Alaska, a region characterized by an active wildfire regime.Our objectives were to (1) describe the nesting ecology of Whimbrels in tundrapatches within the boreal forest, (2) assess the influence of habitat features atmultiple scales on nest-site selection, and (3) characterize factors affecting nestsurvival. Whimbrels nested in the largest patches and exhibited a consistentlycompressed annual breeding schedule. We hypothesized that these Whimbrelswould exhibit synchronous and clustered nesting, but observed synchronousnesting in only 2009 and 2011, and evidence of clustered nesting at just onestudy area in 2009, providing limited support for the hypothesis. Nests tendedto be on hummocks and exhibited lateral concealment around the bowl,suggesting a trade-off between a greater view from the nest and concealment.However, our analysis failed to identify other important habitat features at scalesfrom 1–400 m from the nest. Our best-supported nest survival model showed astrong difference between our two main study areas, but this difference remainslargely unexplained. Given the increased frequency, severity, and extent ofwildfires predicted under climate change climate change Learn more about climate change
Climate change includes both global warming driven by human-induced emissions of greenhouse gases and the resulting large-scale shifts in weather patterns. Though there have been previous periods of climatic change, since the mid-20th century humans have had an unprecedented impact on Earth's climate system and caused change on a global scale.
INTRODUCTION
Whimbrels Numenius phaeopus breed throughout theHolarctic, mostly in treeless, open habitats (Cramp &Simmons 1983). The North American subspecies N. p.hudsonicus (AOU 1998) nests in two disjunct regions,one confined mostly to Alaska and W Canada (i.e., theproposed N. p. rufiventris; Engelmoer & Roselaar 1998),and the other around Hudson Bay, Canada (Skeel &Mallory 1996). In northern and western Alaska, Whimbrelsare considered widespread, discontinuous breeders beyondthe treeline (Gotthardt et al. 2013). Within interior Alaskaand NW Canada, however, Whimbrels appear to breedprimarily in tundra-like patches, either in contiguousstretches (e.g., subalpine) or interspersed among borealforests (Sinclair et al. 2003, Gibson 2011, Gotthardt et al.2013). This area is characterized by a boreal forest-tundraecotone comprising a vast mosaic of postfire communities,including lichen-shrub tundra and lichen-spruce woodlands(Payette et al. 2001). Yet despite the limited extent oftundra here (Jorgenson & Meidinger 2015) compared tosites in W and N Alaska, 50% (perhaps 20,000) of thewestern population of North American Whimbrels arethought to breed within the boreal forest biome (Wells &Blancher 2011, Andres et al. 2012).
This biome is characterized by disturbance, with wildfiresarguably the most important factor shaping habitats, bothspatially (local) and temporally (annual to decadal; Kasischkeet al. 2010). The boreal region, however, is increasinglythreatened by disturbances related to a warming climateand these act on larger (landscape-level) and longer(decades to centuries) scales (Grosse et al. 2011). Advancingtree line (Lloyd 2005), wetland drying (Riordan et al.2006, Roach 2011), peatland loss (Frolking et al. 2011),increased shrubification (Tape et al. 2006), and moreactive fire regimes (Kasischke & Turetsky 2006, Kasischkeet al. 2010) are all hypothesized broad-based, long-termdisturbances to this region that could further impactboreal tundra-like habitats in which Whimbrels currentlybreed.
In light of these predicted changes to boreal forest habitats,we studied the nesting ecology of Whimbrels breeding ininterior Alaska, a region characterized by a continentalclimate and having an active wildfire regime (Kasischkeet al. 2006). Indeed, this is the most comprehensive studyof Whimbrels breeding in Alaska, and the first extensivebreeding study of any boreal wader species in interiorAlaska. We wanted to identify factors that may limit thedistribution and nesting success of Whimbrels in theirpatchily distributed breeding habitats within the borealforest biome. Our primary objective was to describe thenesting ecology of Whimbrels in tundra patches withinthe boreal forest, including metrics of phenology (arrivalthrough hatch), nest density, and nest success. We alsoassessed habitat features at multiple spatial scales to determinetheir importance in the selection of nest sites (Jones& Robertson 2001, Bailey & Thompson 2007).
The Whimbrel is an aggressive attack-mobbing speciesthat relies on early detection of predators (Skeel 1983,Skeel & Mallory 1996). We hypothesized that boreal breedingWhimbrels would nest synchronously and inclusters to enhance joint nest defense. Further, becausethe placement of nests for many open-nesting bird species(including waders; Götmark et al. 1995, van der Vliet etal. 2008, Gómez-Serrano & López-López 2014) mayrepresent a trade-off between concealment (e.g., landform,complexity of vegetative cover) and providing the incubatingbird a clear view of its surroundings, we hypothesizedthat Whimbrels would select nest sites that were elevatedfor view, yet still inconspicuous. We predicted that nestingearlier, nearer to conspecifics, and with fewer largeobstacles (i.e., medium and tall shrubs, trees; Ballantyne & Nol 2011) to limit view from the nest, would increaseWhimbrels’ nest survival. Finally, we measured habitatpreferences to predict how Whimbrels might respond tomore woody environments projected under future climates(Lloyd 2005, Tape et al. 2006).
METHODS
Study area
We studied the nesting biology of Whimbrels from Mayto July during 2008–2012 and 2014 near the Kanuti Riverin Kanuti National Wildlife Refuge (NWR; 66.18°N,151.74°W), approximately 235 km NW of Fairbanks,Alaska (Fig. 1). This lowland (165–180 m elevation) areafeatures a diverse mosaic of boreal floodplain habitatsincluding lakes and ponds, black spruce Picea marianawoodland, riparian riparian Learn more about riparian
Definition of riparian habitat or riparian areas.
Image Details
During 2008–2010 we visited only the Kanuti Lake andLake Taiholman study areas, with nest searching beginning in 2009 (Fig. 1). During 2011–2012 we expanded oursearch area to investigate tundra patches that were >0.5km² and within a 20-km range along the Kanuti River(boatable) and ≤6 km from the river (walkable). We usedground and aerial reconnaissance, as well as SPOT andLANDSAT imagery, to identify and locate patches. Givenextensive avifaunal reconnaissance of all habitats withinthe greater study area during 2008 –2010, we were confidentthat these identified patches comprised all potentialbreeding habitats for local Whimbrels. We intensivelyand repeatedly surveyed all such areas for Whimbrels in2011 and 2012, ceasing visits once a patch was deemedunoccupied or territorial birds had not bred and hadsince departed. During 2014, our work was limited to theKanuti Lake, Lake Taiholman, and Everglades study areas.
In all years, we arrived at Kanuti Lake no later than 1May, a date prior to the first arrival of Whimbrels. Visitsto Lake Taiholman, the Everglades, and the unnamedpatches were constrained by logistics (e.g., ice-out ofriver) and did not occur until after the arrival of Whimbrelsto those areas; consequences included potentially moreconservative assessments of arrival and nest initiation,and less frequent nest checking, especially near hatch. Inmost years fieldwork extended into mid-July and spannedthe entire nesting period, although biologists generally
departed before the departure of juveniles and anyattending adults.
Nest searching and monitoringWe surveyed the Kanuti Lake study area nearly daily onfoot to document the arrival of Whimbrels and the subsequentoccupancy of nesting territories. We visited LakeTaiholman and Everglades as soon as boat access permitted.Beginning about seven days after Whimbrel arrival andfor ≥ three weeks thereafter, we intensively searchednesting areas by walking the area and either flushingbirds off nests or looking for courtship and nest defenseactivity. We recorded nest locations with a GPS, markedthe nest with bare tree/shrub limbs 3 and 10 m north ofthe nest to minimize visual cues to predators, and notedthe number of eggs. We floated eggs of complete clutches(four eggs) to assess stage of incubation (after Liebezeitet al. 2007, Brown et al. 2014). Incubation was assumedto begin when the third egg was laid (CMH pers. obs.,Skeel & Mallory 1996; and as observed in other Numenius;Marks et al. 2002, Hartman & Oring 2006) and to be 24 dlong (Skeel & Mallory 1996). On average we checkednests every five days and followed protocols from Brown
et al. (2014) for monitoring and assessing status and fatesof nests.
Nesting habitat surveys
In 2011 and 2012 we characterized nesting habitat featuresat four spatial scales: landscape (up to 400 m), territory(10–50 m), nest area (1–10 m), and nest bowl (≤1 m). Weduplicated all measurements at a paired non-nest point(‘random’ hereafter) located at a random bearing anddistance (up to 50 m) from the nest, and avoided locatingthese points in unlikely Whimbrel nesting habitats (e.g.,forest, tall shrub, water). The 50-m maximum distancefor locating random points was to ensure that pointswere within a pair’s territory, based on observations ofrelatively close inter-nest distances from 2009–2010(median = 105 m, range 76–131 m, n = 5). We deployed a16-cm-diameter plastic disk to represent the ‘nest’ locationwhen conducting measurements at random points. Sixteencm closely approximated the size of local nest bowls. Tominimize disturbance to incubating birds, we measuredall habitat variables within one week post-hatch (or estimatedhatch date for failed nests). To minimize temporalbias, we measured habitat features of a given year’s nestswithin a two-week span.
In 2011 we relocated nests found in 2009 and 2010. At these nests we collected the same habitat data as collectedfor the 2011 and 2012 nests to use in our nest survivalanalysis, but did not collect data for paired randompoints. We recognized that only the most persistenthabitat features, such as the presence of a tree or tallershrubs, would likely be appropriate for inclusion inbetween-year comparisons one to two years after actualuse. Thus, we avoided inclusion of more ephemeralfeatures like water, or more dynamic features like plantsthat might exhibit sufficient annual growth to changecover or height categories.
The habitat variables assessed at the four spatial scaleswere:
Landscape (≤400 m). We measured the distance (m) tothe nearest water (including small bogs and fens), and todwarf (<20 cm tall), low (20–50 cm), medium (50–150cm), and tall (>150 cm) shrubs and trees. At sites whereshrubs were too distant to detect with a rangefinder, wesubstituted the maximum distance recorded among anynests/random; this allowed us to include all nests in thelogistic regression nest-site selection analysis. Similarly,for sites where the distance to the nearest tree exceeded400 m, we substituted a minimal value of 400 m.
Territory (10–50 m). We counted the number of trees, aswell as the combined number of medium and tall shrubs,within 30 m of the nest/random for comparison withother studies. We also classified the major (>50%) andminor (<50%) habitat types by percentage within a 50-mradius according to the Alaska Vegetation Classification(Level IV; Viereck et al. 1992).
Nest area (1–10m). We quantitatively assessed microrelief,or roughness (Rodrigues 1994), within 10 m of the nest/random by stringing a level line over the nest/random inboth east-west and north-south orientations. At 1-mintervals along each line (40 points in total), we measuredthe vertical distance (0.5-cm precision) to the surfacebelow or above the string with a 2-m folding rule. Weassessed three features along the four 10-m radii: (1)surface roughness, defined as the standard deviation ofthe differences in heights between adjacent points(‘AdjHt’); (2) height of the nest/random site relative to themean height of the points (‘RelCup’); and (3) percentcovers based on surface type (e.g., plant form, water) atthe 40 sample points (‘Cover’). In addition, we summedthe combined number of medium (50–150 cm) and tall(>150 cm) shrubs within 10 m of the nest/random(‘Shrub’). To assess visibility of the nest we estimated thepercentage (to nearest 5%) of a 16-cm plastic disk, placedat the nest/random, that was visible when observed at aheight of 1 m and a distance of 3 m from each cardinaland intercardinal direction (see vegetation densityestimation in Ballantyne & Nol 2011). Finally, wemeasured the absolute relief of the area, defined as thedifference between the highest and lowest surface heights.
Nest bowl (0–1 m). We recorded if nests and random pointswere located on top of a hummock (‘Hummock’). Wecalculated nest concealment (‘Conceal’) by adopting Skeel’s(1983) assessment of ‘nest protection’ (percentage of timesthe nest cup/random had an adjacent mound [includingtussock] or shrub >8 cm above nest cup/random in thefour cardinal and four intercardinal directions). Wephotographed a 1-m² quadrat centered on the nest/random to estimate the non-overlapping percent cover ofseven categories of cover: shrub, graminoid, forb, moss,lichen, dead organic matter, and water. Where needed, wephotorectified images to remove any image distortion. All photos were then analyzed with the software ‘SamplePoint’(Booth et al. 2006), which features an automated, pixelbasedpoint-intercept sampling procedure and summarycalculation of percentages. We used a systematic samplingof 100 point-intercepts for each image. To assess covercomplexity, we calculated the standard deviation inpercent cover among the observed cover types for eachnest/random (‘Cover’). Finally, we assessed roughness atthis scale by sampling points at 10-cm intervals out to 1 min each cardinal direction. We defined roughness (‘Rough’)here as the standard deviation of the differences in heightsof the 40 points relative to the nest cup/random pointheights; positive and negative values reflected heightsabove and below the nest/random, respectively.
Analyses
Breeding phenology. We used calculated initiation (i.e.,laying of first egg) dates based on observed clutchcompletion dates (assuming 1 egg per day) where nestswere found during laying, backdating from observed hatch dates using a 26-d exposure period where possible,or by using float angle data (Liebezeit et al. 2007) for neststhat did not hatch or that were not revisited to determinefate (e.g., all nests in 2014). We used standard deviationsto characterize heterogeneity or synchrony in dates (Nolet al. 1997, Smith et al. 2010).
Nest distribution. To assess the distribution of nests, wecreated study area polygons in ArcMap (ver. 10.1;Environmental Systems Research Institute, Redlands, CA)based on our GPS search track histories and ecotonesindicated in our SPOT imagery basemaps, allowing us toestimate nest densities per areas searched. We followedrecommendations in Fortin & Dale (2005) and usedmultiple tools to test whether Whimbrel nests wereclustered or dispersed. When sample sizes allowed, weused the ‘Multi-distance Spatial Cluster Analysis (Ripley’sK)’ and ‘Average Nearest Neighbor’ (ANN) tools inArcMap’s ‘Spatial Analyst’ extension. Ripley’s K assessesif the average number of neighboring nests for a particulardistance band is higher than the average concentration ofnests throughout the study area; if so, the nests areconsidered clustered at that distance. ANN comparesobserved mean distance among nests to the expectedmean distance (i.e., random distribution of nests).
Nest-site selection. We used an information-theoreticapproach (Burnham & Anderson 2002) and logisticregression to evaluate support for specific habitat featuresin predicting nest location at the landscape, nest-area, andnest-bowl scales. We omitted a similar analysis at theterritory scale because of redundancy in the variablesmeasured. We ran correlation analyses on all two-waycombinations of predictor variables and detected noproblematic collinearity (all r < 0.5 and all P > 0.05). Weselected variables for further analyses that (1) explicitlyaddressed our hypotheses, (2) allowed for comparisonwith other Whimbrel habitat selection studies, or (3)assessed habitat features previously undescribed oruntested. This resulted in candidate sets of 16 models with0–4 predictors for each spatial scale. We centered allcovariates to improve interpretation of the relativestrength of parameter estimates (Grueber et al. 2011)using a standard Z-transformation. Because the data setwas small and we did not want to over-paramaterize the models, we did not fit interaction models and we pooledresults across years. We used the Hosmer-Lemeshow teststatistic to confirm goodness-of-fit. We calculated AICcweights for each supported model (i.e., those withoutuninformative parameters; Arnold 2010) in the candidate
set. We summed the model weights (Ʃwi) for each variableusing the individual weights of those models containingthe respective variable. When model-selectionuncertainty was high, we model-averaged parameters togenerate estimates, their 95% confidence intervals, andrelative importance values. We considered modelaveragedparameter estimates with 95% confidenceintervals that did not overlap zero to be biologicallymeaningful, and we assessed effect size on a probabilityscale. Analyses were conducted using Program R.3.1.1 (RDevelopment Core Team 2014) and packages MuMIn(Bartón 2014) and Resource Selection (Lele et al. 2014).
Nest survival. We used a similar information-theoreticapproach to evaluate the relative support for potentialfactors influencing daily nest survival rate (DSR). We usedProgram MARK (White & Burnham 1999) to build a setof competing models following Rotella (2015). We firststandardized the dates among all years such that thenumbering started with the first nest found and endedwith the last nest checked across all years (19 May–25June; 38 d). We censored the only three nests (two in 2009,one in 2010) we located at Lake Taiholman because of thesmall sample size at that site. We first created modelswhere DSR varied by ‘Year,’ ‘Site’ (Kanuti Lake vs.Everglades), and their interaction. We then considered 4time- or stage-related models: (1) constant DSR throughtime (‘Constant’), (2) DSR varying across the nestingseason (i.e., linear trend on the logit scale; ‘Season’), (3)DSR varying with nest age (‘NestAge’), and (4) DSRvarying by nest age at the time of finding (‘FoundAge’).
In 2009, we lacked information for six nests for assessingage so we assigned them the mean initiation date for thatyear to estimate relative nest age and age when found, andretained them in the models; we felt that was reasonablegiven the synchrony we observed that year. We built onemodel where DSR varied by inter-nest distance to explorethe possible influence of intraspecific neighbors(‘InterDist’). Finally, we created two models withcovariates for the number of medium and tall shrubswithin 30 m (‘Shrub’) and the presence of trees within 30m of the nest (‘Tree’) to evaluate possible influence ofgreater woody growth on DSR (e.g., Ballantyne & Nol2011). To estimate nest survival and its 95% confidenceinterval (CI), we used the estimates for DSR andlower/upper CI bounds each multiplied over the lengthof the exposure period (i.e., 26 d for this study).Unless otherwise noted, means are presented ± SD.
RESULTS
Breeding phenology
Mean first detection of Whimbrels among all yearsoccurred on 6 May (Table 1). The first nest(s) wereinitiated 11.2 d (range: 8–14 d) after the first Whimbrelswere detected. Mean nest initiation varied among yearsby less than one week, but there were up to 17 d betweenthe earliest and latest recorded nests across years (Table1). Nine nests that were found during laying were subsequentlyobserved hatching, with mean and modal nestexposure lengths of 26.7 d and 26.0 d, respectively. Basedon the modal exposure length and observed incubationpatterns from nests with < four eggs, we inferred a 24-dincubation period beginning with the laying of the thirdegg. Hatch generally occurred in the third week of June(17 June ± 3.3 d, n = 70 nests; Table 1).
Table1.Breeding phenology of Whimbrels nesting on the Kanuti River study area, Alaska, 2008–2014.
Year | # Nests found | First detection | Mean initiation date1 | Mean hatch date1 |
2008 | NA2 | 6 May | NA | NA |
2009 | 19 | 4 May | 20 May ± 1 18–21 May (11) | 15 June ± 1 13–16 June (11) |
2010 | 14 | 8 May | 24 May ± 4 17–31 May (14) | 19 June ± 4 12–26 June (14) |
2011 | 17 | 6 May | 22 May ± 1 20–24 May (17) | 17 June ± 1 15–19 June (17) |
2012 | 22 | 7 May | 23 May ± 4 18–31 May (22) | 18 June ± 4 13–26 June (22) |
2014 | 8 | 6 May | 20 May ± 3 14–23 May (6) | 15 June ± 3 9–18 June (6) |
1mean ± SD, range, and (n). 2NA = not available (Nests were not searched for but broods were observed).
Nesting distribution and densities
During 2010–2012 we visited all or most of nine tundraareas (mean size: 2.64 ± 2.35 km²) to identify potentialhabitat for Whimbrel breeding (Fig. 1). We observed noWhimbrels in the three smallest patches (0.54–0.87 km²),which were also areas with the largest perimeter-to-arearatios (5.8–10.2). We observed at least one displayingmale in the six largest patches (1.49–7.20 km²), includingconfirmed nesting at Kanuti Lake (2008–2012, 2014),Lake Taiholman (2008–2010), and Everglades (2011–2012,2014; Fig. 2). The areas with confirmed nesting had threeof the four lowest perimeter-to-area ratios (2.0–4.4).
Nesting density at Kanuti Lake declined by at least 65%between 2009 and 2011 (from 2.67 to 0.94 nests/km²)before rebounding in 2012 (1.57 nests/km²). At Lake Taiholmanat least two pairs nested in 2008 and 2009, andone pair nested in 2010, but no birds were detected therein 2011–2014, despite repeated annual visits. The Evergladespopulation densities (1.53–1.67 nests/km²) were similarto that of Kanuti Lake in 2012. Overall, nests at KanutiLake (mean 318 m, range 64–926 m, n = 46 nests, 2009–2012) were closer together than at the Everglades (mean372 m, range 160–694 m, n = 23 nests, 2011–2012).
Because our sample sizes fell short of those recommended(i.e., <30) for the ANN and ‘Ripley’s K’ cluster analyses,we restricted final interpretation to each area’s mostpopulous and densest nesting year. Clustered nesting wassuggested for Kanuti Lake in 2009 (ANN nearest neighborratio = 0.72, Z-score = –2.179, P = 0.029; Ripley’s K‘clustered’ distance beginning at ~300 m).Dispersednesting, however, was weakly suggested at the Evergladesin 2012 (nearest neighbor ratio = 0.99, Z-score = –0.040,P = 0.968; Ripley’s K ‘clustered’ distance = ≥850 m).
Breeding habitat characterization
We characterized habitat features at 17 and 22 confirmednesting territories in 2011 and 2012, respectively. ‘Mixedshrub-sedge tussock bog’ (Viereck et al. 1992; level IIC2b)was the primary habitat characterizing Whimbrel territories(≤50 m from the nest) in the study area, composing onaverage 69% of each territory. Territories included sixother habitat classifications, including three more of the‘open low scrub’ type (level IIC2, but non-tussock) andthree ‘wet graminoid herbaceous’ types (level IIIA3),accounting for 14% and 17% of the territories, respectively.
Trees and tall (>150 cm tall) shrubs tended to be bothdistant and scarce; 41% of nests had no trees and 36%had no tall shrubs within 100 m. Nests were typicallynear water (Table 2). Non-overlapping percent coverwithin 1 m and 10 m of nests was similar for the sevenfunctional ‘vegetative’ types, despite the difference inscales, with shrubs and graminoids as most abundant in
each (Table 2).
Nest-site selection
Landscape (0–400 m) and nest area (1–10 m) scales. At thelandscape scale, none of the models with factorsrepresenting distances from the nest/random to nearestwoody vegetation (i.e., low, medium, and tall shrubs; tree)were supported (Table 3). At the nest area scale, the modelwith the lowest AICc included only the surface roughnessvariable ‘AdjHt’ (Table 4). This was the only model rankedhigher, albeit only slightly (ΔAICc = 0.79; coefficientestimate ± SE: 0.23 ± 0.27), than the null model, and itpassed the Hosmer-Lemeshow goodness-of-fit test (χ2 =8.6, df = 8, P = 0.38). The competing models at these twoscales of habitat selection generally showed little separation.
Nest bowl (0–1 m) scale. The best-supported AICc modelincluded the variables Hummock, Conceal, and Cover and accounted for 35% of the AICc weight (Table 5). Thesimilarly parameterized full model, but also including thevariable Rough, differed only slightly (ΔAICc = 0.09) fromthe former; these two models accounted for 68% of thecumulative AICc weight. However, unlike the top-rankedmodel (χ2 = 10.7, df = 8, P = 0.219), the full model did notpass the Hosmer-Lemeshow goodness-of-fit test (χ2 =15.9, df = 8, P = 0.044). Another seven models were within 10 AICc units of the top-ranked model and captured 100%of the AICc weights; we model-averaged over these ninemodels to test the probability of nest occurrence. Thelocation of a nest on a hummock was the most importantpredictor of nest selection, with percent of nest concealedhaving 93% relative importance to Hummock. Theprobability of a nest occurring on a hummock was 49%higher than it not being on a hummock. Further, theprobability of nest occurrence increased by 0.16 for eachadditional direction (of eight possible) providingconcealment at the nest rim. The 95% confidenceintervals for the estimates of the Cover and Roughcoefficients overlapped zero, suggesting they were notbiologically meaningful predictors of nest bowl location.
Nest survival
We modeled factors affecting nest survival using datafrom 67 nests, including 16, 13, 6, and 10 for Kanuti Lake2009–2012, and 10 and 12 for Everglades 2011–2012,respectively. The model receiving the greatest support(wi = 0.79) among those in the candidate set included thesingle factor Site (Table 6). This model was 5.6 timesmore likely than the second-ranked interaction modelSite*Year, which was based on very small sample sizesper site per year; other candidate models had little support.Using DSR estimates derived from the top-ranked model,overall nest survival during the 26-d exposure periodwas estimated to be 41% (95% CI: 26–55%) at KanutiLake and 92% (95% CI: 55–99%) at the Everglades acrossthe years monitored at each site.
DISCUSSION
Distribution and timing of nesting
The discreteness and relatively small area of tundrapatches in our study area appeared to spatially limitWhimbrels’ breeding. In other ecosystems, the occurrenceand abundance of birds within habitat patches is a functionof multiple factors, including patch-scale variables likesize, habitat condition, shape, and perimeter, and landscape-scale variables like configuration of patches andthe habitat matrix surrounding patches (Mazerolle &Villard 1999, With & King 2001, Fleishman et al. 2002,Blevins & With 2011). The absence of Whimbrels occupyingthe smallest patches of tundra in our study area suggestsa possible threshold. Patches where Whimbrels displayedbut did not nest may suggest inferior habitat conditions,including: (1) high perimeter-to-area ratios deemed byprospective breeding females as insufficient distancebetween a nest and possible predators in the ecotone(Andren & Angelstam 1988); (2) increasingly higher anddenser shrub structure structure Learn more about structure
Something temporarily or permanently constructed, built, or placed; and constructed of natural or manufactured parts including, but not limited to, a building, shed, cabin, porch, bridge, walkway, stair steps, sign, landing, platform, dock, rack, fence, telecommunication device, antennae, fish cleaning table, satellite dish/mount, or well head.
Table 2. Selected habitat measurements (mean, SD, range) at four scales of distance from Whimbrel nests (n = 39) and from random points within the nest territory, Kanuti Lake and Everglades study areas, Alaska, 2011–2012.
Scale | Variable | Nest | Random |
0–400 m | Distance to water (m) | 2.3 ± 5.3 0.3–31.0 | 3.3 ± 2.9 0.0–10.9 |
Distance to dwarf shrub (m) | 0.0 ± 0.0 0.0–0.1 | 0.0 ± 0.0 0.0–0.1 | |
Distance to low shrub (m) | 1.0 ± 0.7 0.0–3.0 | 1.1 ± 1.1 0.1–5.6 | |
Distance to medium shrub (m) | 17.3 ± 21.9 1.4–109.0 | 14.1 ± 17.4 1.2–80.0 | |
Distance to tall shrub (m) | 60.3 ± 56.6 5.9–208.0 | 49.9 ± 48.9 2.2–185.0 | |
Distance to tree (m) | 130.4 ± 138.0 9.0–400.0 | 133.2 ± 137.9 10.5–400.0 | |
10–50 m | # of medium & tall shrubs | 18.6 ± 25.5 0–95 | 18.5 ± 26.2 0–125 |
# of trees | 0.6 ± 1.6 0–9 | 0.4 ± 1.1 0–6 | |
1–10 m | Percent visible | 54.2 ± 20.4 6.4–90.6 | 82.5 ± 17.5 31.9–100.0 |
Absolute relief (m) | 0.67 ± 0.45 0.03–2.1 | 0.69 ± 0.38 0.3–1.9 | |
# of medium & tall shrubs | 3.6 ± 5.3 0–22 | 3.5 ± 5.6 0–25 | |
Percent graminoid | 24.3 ± 14.9 5.0–65.0 | 21.1 ± 9.9 0.0–45.0 | |
Percent forb | 2.6 ± 3.6 0.0–12.5 | 2.6 ± 3.8 0.0–20.0 | |
Percent lichen | 14.5 ± 10.2 0.0–32.5 | 16.0 ± 10.8 0.0–40.0 | |
Percent moss | 17.2 ± 9.8 0.0–40.0 | 16.4 ± 8.6 2.5–37.5 | |
Percent organic matter | 3.5 ± 5.0 0.0–22.5 | 4.2 ± 5.7 0.0–22.5 | |
Percent shrub | 35.1 ± 10.7 15.0–55.0 | 38.5 ± 9.5 25.0–57.5 | |
Percent water | 2.8 ± 5.4 0.0–27.5 | 1.4 ± 3.4 0.0–15.0 | |
0–1 m | Concealment | 0.4 ± 0.2 0.0–0.9 | 0.3 ± 0.2 0.0–0.8 |
SD of percent cover | 14.2 ± 2.7 9.1– 21.5 | 15.0 ± 3.2 8.9–24.2 | |
Height diff.: nest vs. intercept (cm) | -2.5 ± 3.2 -9.8–6.0 | -0.9 ± 4.4 -12.1–8.4 | |
Percent graminoid | 24.5 ± 13.8 3.1– 60.0 | 21.9 ± 13.8 0.0–64.9 | |
Percent forb | 3.6 ± 4.5 0.0–18.1 | 3.5 ± 4.3 0.0–14.4 | |
Percent lichen | 18.9 ± 12.1 0.0–44.3 | 18.8 ± 13.4 0.0–49.0 | |
Percent moss | 16.0 ± 8.0 0.0–34.0 | 16.7 ± 9.4 1.0–39.0 | |
Percent organic matter | 7.7 ± 5.2 0.0–28.9 | 7.7 ± 8.2 0.0–48.5 | |
Percent shrub | 29.1 ± 10.1 7.5–58.5 | 31.5 ± 11.1 4.3–50.0 | |
Percent water | 0.2 ± 1.0 0.0–6.2 | 0.0 ± 0.0 0.0–0.0 |
Image Details
The breeding phenology of Whimbrels in our study areashowed pronounced annual consistency, with arrival,mean nest initiation and mean date of hatch varying byonly 5 d across years. Further, mean hatch occurred onlyabout six weeks after the first Whimbrels arrived,suggesting a compressed schedule; no nests hatched (orwere scheduled to hatch) after 26 June. Grant (1989)documented annual initiation ranging over periods of26–31 d for Whimbrels nesting in temperate Shetland, more than twice the longest initiation period that we observed. In 2010 and 2012, in which initiation in ourstudy area occurred over about two weeks, the latestnests (n = 4) all failed, including three that were abandoned.This suggests that late nesting at this site is not generallysuccessful (e.g., Smith et al. 2010), perhaps because thisnorthern latitude and boreal climate impose a shorterwindow for successful breeding.
Given the spatial and temporal constraints imposed onWhimbrels breeding in our study area, we had hypothesizedthat predators might more efficiently target nestingWhimbrels here; in turn, these Whimbrels might nest inways that facilitate cooperative nest and chick defense(i.e., clustered and synchronized nesting). Support forthis premise was equivocal. We documented fairly synchronousnesting in 2009 and 2011, but less so in 2010and 2012; however, we recognize that the compressedbreeding schedule here necessarily increases synchronymore so than at sites with longer seasons. Clusterednesting was suggested for Kanuti Lake in 2009, but notfor the Everglades. Despite these inconsistent results,nests may still be close enough for neighboring pairs tojointly mob effectively.
Nesting habitat and nest-site selection
Whimbrels nesting in our study area encounter a diversesuite of avian and terrestrial predators that vary inhunting behavior. This diversity of predators coulddemand potentially conflicting nest protection strategies,such as timely predator detection and nest crypsis. Ourhypothesis that Whimbrels here would optimize a tradeoffbetween nest concealment and view to limit predationwas partly supported: nesting on a hummock (thus providinggreater view) and greater lateral nest concealmentwere both shown to be important factors in nest-siteselection at the smallest scale. Hummock use has beenwidely documented in Whimbrels (in Ballantyne & Nol2011). Our result provides further support that hummocksmay be important for early detection of aerial predators.However, Ballantyne & Nol (2011) proposed an alternatehypothesis that hummocky sites melt out earlier, as istrue in our study area, and this is advantageous to earlynesting species such as Whimbrels. Unlike Skeel (1983),who attributed lateral protrusions of vegetation at neststo protection from prevailing winds, we believe this attributeto be more locally important as camouflage. Ourfinding of marginal support for nesting areas havinggreater surface roughness may be further evidence forthe importance of habitat complexity (e.g., pattern disruption,increased shadow) in nest concealment, as alsosuggested by Skeel (1983). Several other measures ofcomplexity, however, were not shown to be importantpredictors of habitat use in our study.
We found little support for predictors of nest-site selectionat larger spatial scales. This could be explained in severalways. For example, the 50-m ‘territorial’ radius may havebeen too small to reveal differences between nest andrandom point locations, given the relative hom*ogeneityof the habitat.
Other studies (Pirie 2008, Ballantyne & Nol2011) used larger distances (250 and 150 m, respectively)in their nest-site selection investigations. Another factorcould have been the timing of our habitat measurements.While collecting habitat measurements after nests havehatched is a common practice, this delay risks missingearly habitat distinctions evident to Whimbrels duringnest-prospecting, such as patterns of snow melt and preleaf-out vegetative cover.
Alternatively, some of our seemingly equivocal habitatselection results may actually be representative for Whimbrelsbreeding within tundra patches in boreal forest.These birds may simply be generalists at certain scaleswhen selecting a nest site within a suitable patch ofhabitat. The species has a demonstrated flexibility innesting habitat selection at the landscape or patch scalethroughout other parts of its range. Whimbrels outsideof Alaska have been documented nesting in multiplehabitat types including hummock-bog, sedge meadow,heathland tundra, riverplain, and even mountain birchforest (Skeel 1983, Pulliainen & Saari 1993, Katrínardóttiret al. 2015). We observed Whimbrels using sites withvarying levels of surface roughness, cover heterogeneity,and woody vegetation near the nest, but these mayrepresent minor variations within the nest-site selectionrepertoire of this widely distributed species.
Table 3. Logistic regression model selection results used to predict Whimbrel nest-site selection (nest vs. random point within the territory) as a function of four habitat variables measured within a scale of 0–400 m from the nest or random point, Kanuti Lake and Everglades study areas, Kanuti National Wildlife Refuge, Alaska, 2011–2012. Models are ordered by Akaike’s Information Criterion, corrected for small sample size (AICc). K is the number of parameters, ∆AICc is the AIC difference from the top model, and –LL is the negative log-likelihood, a measure of deviance. The four variables considered were distances to nearest low shrub (Low), medium shrub (Medium), tall shrub (Tall), and tree (Tree). The 16 candidate models were ultimately averaged. No models received greater support than the null model, so model weights were not calculated/ shown.
Model | K | ∆AICc1 | –LL |
Null | 1 | 0.00 | 54.07 |
Low | 2 | 1.63 | 53.83 |
Medium | 2 | 1.66 | 53.84 |
Tall | 2 | 1.98 | 54.00 |
Tree | 2 | 2.10 | 54.06 |
Med + Low | 3 | 3.10 | 53.48 |
Tree + Medium | 3 | 3.61 | 53.74 |
Tall + Low | 3 | 3.69 | 53.77 |
Tree + Low | 3 | 3.80 | 53.83 |
Tall + Medium | 3 | 3.81 | 53.83 |
Tree + Tall | 3 | 4.07 | 53.96 |
Tree + Medium + Low | 4 | 5.13 | 53.38 |
Tall + Medium + Low | 4 | 5.33 | 53.48 |
Tree + Tall + Medium | 4 | 5.74 | 53.69 |
Tree + Tall + Low | 4 | 5.88 | 53.76 |
Tree + Tall + Medium + Low | 5 | 7.39 | 53.37 |
1AICc value of the top model is 110.18. |
Table4.Logistic regression model selection results used to predict Whimbrel nest-site selection (nest vs. random point within the territory) as a function of four habitat variables measured at a scale of 0–10 m from the nest or random point, Kanuti Lake and Everglades study areas, Kanuti National Wildlife Refuge, Alaska, 2011–2012. Models are ordered by Akaike’s Information Criterion, corrected for small sample size (AICc). K is the number of parameters, ∆AICcis the AIC difference from the top model, and –LL is the negative log-likelihood, a measure of deviance. The four variables tested included measures of (1) surface roughness (AdjHt) and (2) relative height of nest to surrounding surfaces (RelCup), (3) cover hetero- geneity (Cover), and (4) number of medium or tall shrubs (Shrub). The 16 candidate models were ultimately averaged. Only the model with ‘AdjHt’ variable received even marginal support over the null model; thus, model weights were not calculated/shown.
Model | K | ∆AICc1 | –LL |
AdjHt | 2 | 0.00 | 52.62 |
Null | 1 | 0.79 | 54.07 |
RelCup + AdjHt | 3 | 1.95 | 52.51 |
Shrub + AdjHt | 3 | 2.16 | 52.62 |
Cover + AdjHt | 3 | 2.16 | 52.62 |
RelCup | 2 | 2.59 | 53.91 |
Cover | 2 | 2.78 | 54.01 |
Shrub | 2 | 2.89 | 54.06 |
Shrub + RelCup + AdjHt | 4 | 4.17 | 52.51 |
RelCup + Cover + AdjHt | 4 | 4.18 | 52.51 |
Shrub + Cover + AdjHt | 4 | 4.38 | 52.62 |
RelCup + Cover | 3 | 4.65 | 53.86 |
Shrub + RelCup | 3 | 4.75 | 53.91 |
Shrub + Cover | 3 | 4.94 | 54.01 |
Shrub + RelCup + Cover + AdjHt | 5 | 6.45 | 52.51 |
Shrub + RelCup + Cover | 4 | 6.87 | 53.86 |
1AICcvalue of the top model is 109.39. |
Nest survival
Ultimately we found little support for our hypothesesthat nesting earlier, nearer to conspecifics, and with fewerlarge obstacles near the nest were important factors fornest survival. However, we did find a very strong siteeffect between our two main study areas; DSR was consistently higher at the Everglades than at Kanuti Lake.
Other studies have documented that Whimbrels nestingin different habitat types may experience different levelsof nest success (Skeel 1983, Pulliainen & Saari 1993,Katrínardóttir et al. 2015). Although we characterizedWhimbrel territories similarly at the two sites, we didnote coarse differences not necessarily captured by ourassessments; for example, we noted a dominance of stringbogs at Everglades, but less so at Kanuti Lake. Further,the most recent wildfires at Everglades and Kanuti Lakeoccurred in 1977 and 2005, respectively. Burn perimetersand unburned inclusions are evident at both sites, withWhimbrels nesting among them; however, vegetationrecovery at Everglades was more advanced (e.g., nomineral soil visible, less burned duff, more lichen).Whether the likely wetter and less recently burned habitatsof the Everglades impart advantages in nest survival isunknown.
Contrary to our expectations, the presence of nearbytrees and large shrubs did not influence nest site selectionor impair nest survival. Whimbrels breeding in our studyarea appeared to tolerate, and to some extent even exploit(e.g., as sentry perches; Fig. 3), scattered black spruce inthe tundra. Indeed, the three major breeding concentrations(‘east’ and ‘west’ Kanuti Lake, Everglades) partly surroundconspicuous, isolated black spruce groves (ca. 0.01–0.03km²) within the tundra patches, with nests as close as 10m to the groves. Scattered medium and tall shrubs alsowere not strongly avoided. While we do not know whatthreshold of tree and shrub cover will be tolerated, wehave observed Whimbrel occupying much shrubbier siteselsewhere in the species’ range (e.g., Donnelly TrainingArea, Alaska; CMH unpubl. data). However, for Whimbrelsattempting to breed in small tundra patches like those atKanuti NWR, increased woody vegetation within thepatch and encroachment of trees and shrubs inward fromthe edge could jeopardize the persistence of these patchesas open habitats. The increases in shrub and tree coverrecently documented at Churchill, Manitoba, Canada,have likely contributed to a decline of Whimbrels there(Ballantyne & Nol 2015), and similar habitat changeshave also been predicted for Alaska under a warmingclimate scenario (Lloyd 2005, Tape et al. 2006).
Table 5. Logistic regression model selection results used to predict Whimbrel nest-site selection (nest vs. random point within the territory) as a function of four habitat variables measured at a scale of 0–1 m from the nest or random point, Kanuti Lake and Everglades study areas, Kanuti National Wildlife Refuge, Alaska, 2011–2012. Models are ordered by Akaike’s Information Criterion, corrected for small sample size (AICc). K is the number of parameters, ∆AICc is the AIC difference from the top model, wi is AICc weight, and –LL is the negative log-likelihood, a measure of deviance. The four variables tested included (1) whether nest was on a hummock (Hummock), (2) nest concealment (Conceal), (3) cover heterogeneity (Cover), and (4) an alternate measure of roughness (Rough). The candidate models contributing to the cumulative AICc were ultimately averaged. Model weight for model named ‘Cover’ (ranked lower than Null) was not calculated.
Model | K | ∆AICc1 | wi | –LL |
Hummock + Conceal + Cover | 4 | 0.35 | 38.1 | |
Hummock + Conceal + Cover + Rough | 5 | 0.09 | 0.33 | 37 |
Hummock + Conceal | 3 | 1.68 | 0.15 | 40.06 |
Hummock + Conceal + Rough | 4 | 2.65 | 0.09 | 39.42 |
Hummock | 2 | 5.55 | 0.02 | 43.07 |
Hummock + Cover + Rough | 4 | 5.89 | 0.02 | 41.05 |
Hummock + Rough | 3 | 6.13 | 0.02 | 42.28 |
Hummock + Cover | 3 | 6.22 | 0.02 | 42.32 |
Conceal + Cover + Rough | 4 | 8.77 | 42.49 | |
Conceal + Rough | 3 | 13.11 | 45.77 | |
Conceal + Cover | 3 | 16.4 | 47.41 | |
Cover + Rough | 3 | 16.48 | 47.45 | |
Rough | 2 | 18 | 49.3 | |
Conceal | 2 | 18.4 | 49.5 | |
Null | 1 | 25.43 | 54.07 | |
Cover | 2 | 26.28 | – | 53.43 |
1AICc value of the top model is 84.75. |
Boreal-nesting Whimbrels and wildfire
The active fire history that regularly and dynamicallyaffects our area (e.g., 27% of Kanuti NWR burned in2004–2005; USFWS 2008) has not been documented inother areas where Whimbrels have been studied. Althoughwildfires are frequent within the greater Hudson BayLowlands (Brook 2006), Whimbrels breeding near Churchillappear to use areas just outside this fire regime (Fig. 2.2in Ballantyne 2009), perhaps a result of the proximity ofthese areas to the maritime influence of Hudson Bay. Ingeneral, we lack a perspective on how Whimbrels respondto major stochastic events that have impacted their landscapes(but see Katrínardóttir et al. 2015). During ourstudy, we observed annual fluctuations in numbers anddistribution, but we do not know how Whimbrels in theEverglades and Kanuti Lake areas responded immediatelyafter wildfires in 1977 and 2005, respectively.
Table 6. Model selection results for analysis testing potential factors of daily survival rate (DSR) for Whimbrel nests at Kanuti Lake and Everglades study areas, Kanuti National Wildlife Refuge, Alaska, 2009–2012. Models are ordered by Akaike’s Information Criterion, corrected for small sample size (AICc). K is the number of parameters, ∆AICc is the AIC difference from the top model, wiis AICc weight. The ten models tested DSR of nests (1) ‘Constant’ through season; and then varying (2) through‘Season’, (3) by‘Year’, (4) by study area (Site), (5) by Site*Year interaction, (6) by age of nest (NestAge), (7) by age of nest when found (AgeFound), (8) by nearest inter-nest distance (InterDist), (9) by number of nearby large shrubs (Shrub), and (10) by presence of nearby trees. Model weights were not calculated for unsupported models (i.e., ranked lower than the constant model).
Model | K | ∆AICc 1 | wi | Deviance |
Site | 2 | 0.79 | 162.01 | |
Site*Year | 6 | 3.44 | 0.14 | 157.37 |
Season | 2 | 5.46 | 0.05 | 167.47 |
AgeFound | 2 | 9.08 | 0.01 | 171.09 |
Constant | 1 | 9.28 | 0.01 | 173.29 |
NestAge | 2 | 9.6 | – | 171.6 |
Year | 4 | 9.97 | – | 167.95 |
Tree | 2 | 10.19 | – | 172.2 |
Shrub | 2 | 10.98 | – | 172.99 |
InterDist | 2 | 11.16 | – | 173.16 |
1AICc value of the top model is 84.75. |
Within the already dynamic landscape of boreal Alaska, we may be witnessing additional effects on habitats fromthe projected increase in landscape flammability acrossthe boreal forest during the coming century (Rupp &Springsteen 2009, Johnstone et al. 2011). The possibleamplification of the area’s historical wildfire regime by awarming climate may pose a major threat to not onlyforested habitats (e.g., conversion of spruce to deciduous),but could result in the loss or modification (e.g., increasedshrubs) of boreal tundra patches suitable for Whimbrelbreeding. Studies of predicted changes in the boreal biome have focused on forests proper, and studies of tundrafires have targeted areas beyond the treeline (Higuera etal. 2011), not tundra patches within the boreal. Studieslike ours can serve as baselines for monitoring these scatteredtundra patches, and the persistence of Whimbrelstherein. Further, replication of our study in other areasoffers an approach to improve inference from local studieslike ours (‘metareplication’; Johnson 2002). Our researchclearly shows the benefit of conducting regular surveysof historical Whimbrel breeding areas accessible withinthe boreal forest to document local persistence of thespecies and to characterize its habitat. We especially urgea timely survey of a recently burned breeding areas toasses any changes to habitats and responses by the localWhimbrel population to wildfire effects. In time we canbegin to better assess the vulnerability of Whimbrels andtheir habitats in a rapidly changing boreal biome.
ACKNOWLEDGEMENTS
We thank the staff of Kanuti NWR for their generous andpersistent support of this project. We especially thankthe following field assistants: L. Smithwick, P. Valle, L.Tibbitts, N. Senner, S. Warnock, D. Kaleta, E. Garrett, R.Dugan, D. Smith, J. McLaughlin, and N. Wepking. Thispaper benefited from reviews by J. Perz, D. Ruthrauff, P.Smith, and D. Verbyla. A.-M. Benson and C. Handel providedvaluable analytical assistance. This study was performedunder the auspices of U.S. Fish and WildlifeService, Region 7, Institutional Animal Care and UseCommittee (IACUC) protocol #2010007. Unpublishedreports cited in this paper are available at Alaska ResourcesLibrary and Information Services, www.arlis.org. Any useof trade, product, or firm names in this publication is fordescriptive purposes only and does not imply endorsem*ntby the U.S. Government.
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