HETEROGENEITY AND POWER TO DETECT TRENDS IN MOOSE BROWSE UTILIZATION OF WILLOW COMMUNITIES
Braden O. Burkholder1,4, Nicholas J. DeCesare2, Robert A. Garrott1, and Sylvanna J. Boccadori3
1Fish and Wildlife Ecology and Management Program, Department of Ecology, Montana State University, 310 Lewis Hall, Bozeman, Montana, 59717 USA; 2Montana Fish, Wildlife and Parks, 3201 Spurgin Road, Missoula, Montana, 59804 USA; 3Montana Fish, Wildlife and Parks, 1820 Meadowlark Lane, Butte, Montana, 59701 USA.
ABSTRACT: Monitoring of browse utilization of plant communities is consistently recommended as an important component of monitoring moose (Alces alces) populations across regions. We moni- tored winter browse utilization by moose within a willow (Salix spp.) -dominated winter range of Montana in 2008–2010. We sought to improve our understanding of: 1) spatiotemporal hetero- geneity of intensity of moose browsing across the winter range, 2) species-specific selection of willow by moose during winter, and 3) appropriate sample sizes, placement, and stratification of monitoring sites for estimating browse utilization. During 3 consecutive winters we monitored 108–111 transect segments, each 50 m in length, in a systematic distribution across willow communities and assessed the effects of covariates potentially predictive of variation in browsing. Mean annual estimated browse utilization across all segments was 11.5% of sampled twigs in 2008 (95% CI = 9.4 – 13.7%), 8.0% in 2009 (95% CI = 6.2 – 9.8%), and 8.3% in 2010 (95% CI = 6.5 – 10.1%). Modeling of variation in browse utilization revealed positive relationships with the proportion of preferred species (β = 0.44, P = 0.05) and previously browsed willow plants (β = 3.13, P < 0.001), and a negative relationship with willow patch width (β = 0.002, P < 0.001). We found that planeleaf (Salix planifolia), Wolfʼs (S. wolfii), and Boothʼs willow (S. boothii) were the most consistently preferred species, whereas Drummondʼs(S. drummondiana) and Geyer willow (S. geyeriana) willow were moderately preferred; Lemmonʼs willow (S. lemmonii) was used less than expected. Power analyses indicated that detecting a 10% increase in browse utilization with 95% confidence in consecutive years required measuring 38–41, 50-m segments. Because systems with low and heterogeneous browse utilization of willow pre- sent challenges for efficient monitoring, we encourage power analyses as a means of evaluating sam- pling protocols, in addition to consideration of covariates predictive of spatiotemporal heterogeneity.
ALCES VOL. 53: 23–39 (2017)
Key Words: Alces alces, browse utilization, forage, habitat selection, monitoring, moose, power ana- lysis, Salix, ungulate
The dynamics of herbivores are inherent- population growth rates, and population ly tied to those of the plant communities in density (Sæther and Andersen 1990, Vucetich which they forage (White 1983, Sinclair et al. and Peterson 2004, Boertje et al. 2007). 1985, Crête 1989). Large herbivores such as Relative density of ungulate herbivores, in moose (Alces alces) are affected by the quan- turn, affects dynamics of plant communities, tity, quality, and diversity of forage available with further implications across trophic levels to them, as evidenced by effects upon move- (Berger et al. 2001, Pedersen et al. 2007). ment patterns, digestible intake, fecundity, Thus, monitoring browse utilization of plant
4Present address: Montana Natural Heritage Program, 1515 E 6th Ave, Helena, Montana, USA 59620 23 MOOSE BROWSING HETEROGENEITY – BURKHOLDER ET AL. ALCES VOL. 53, 2017 communities is recommended as an import- browse selection of willows include: 1) the ant component of monitoring moose popula- species of willow, with potential differences tions across regions (Crête 1989, Keigley in nutritional content and preference by moose and Fager 2006, Seaton et al. 2011). (Risenhoover 1989, Stolter et al. 2005, McArt Moose are typically browsers, eating the et al. 2009); 2) prior browsing history, with current annual growth of shrubs and trees increased preference for previously browsed when foraging, though they occasionally plants or branches (Bowyer and Bowyer consume forbs and grasses. In North America 1997, Stolter 2008); 3) plant density (Shipley moose forage upon 221 different plant spe- and Spalinger 1995, Palo et al. 2015); 4) cies and/or genera (Renecker and Schwartz browse biomass (Bowyer et al. 2001); 2007, Shipley 2010). However, individual 5) snow depth (Lundmark and Ball 2008); moose browse predominantly on a few spe- and 6) distance to conifer edge (Dussault et al. cies, suggesting a degree of local specializa- 2006). Lastly, and related to the above, habi- tion (Gillingham and Parker 2008, Portinga tat use and consumption of browse are gener- and Moen 2015). For the Shiras moose (A. a. ally heterogeneous and concentrated in local shirasi) of the Rocky Mountains and other patches (Månsson 2009, Palo et al. 2015). populations worldwide, willow (Salix spp.) Heterogeneity in browse utilization pat- species are both highly preferred and abun- terns creates a challenge to effectively moni- dant, sometimes representing >90% of tor browse utilization as an index of moose winter browse consumed (Dorn 1970, Shipley population dynamics. Despite underlying 2010). changes in moose density, certain local Peek (1974) described 3 common patches or even individual plants may be types of Shiras moose winter range: conifer, chronically browsed, whereas other patches riparian, and floodplain riparian. All 3 types or plants remain unbrowsed (Bowyer and of winter range are utilized by moose in Bowyer 1997, Stolter 2008). Monitoring Montana (Dorn 1970, Stevens 1970, Matchett programs founded on estimation of browse 1985), with floodplain riparian systems sup- utilization require careful attention to sam- porting substantial populations and hunter opportunity in the southwest portion of pling in terms of distribution and quantity the state (e.g., Big Hole Valley, Centennial of measurements necessary to accurately Valley; DeCesare et al. 2014). Floodplain capture trends in browsing and health of riparian systems yield expansive willow plant communities. communities on low gradient streams that We evaluated patterns of browse utiliza- often provide the majority of winter browse tion across multiple species of willows with- consumption and may support year-round in southwestern Montana, and paid special moose populations. In this study, we focus attention to the drivers of heterogeneity in on monitoring browse utilization during browse utilization and the ramifications of winter within a willow-dominated, floodplain such heterogeneity on statistical power to riparian winter range in Montana. Moose in detect trends. Specifically, we sought to im- our study population averaged >75% of prove our understanding of: 1) levels and their winter space-use within floodplain drivers of heterogeneity of browsing across vegetation types (Burkholder 2012). the winter range, 2) species-specific selec- Moose browsing of willows during win- tion of willow during winter, and 3) appro- ter has been well-studied across all winter priate sample sizes of sites for monitoring range types. Factors potentially influencing browse utilization of willows.
24 ALCES VOL. 53, 2017 BURKHOLDER ET AL. – MOOSE BROWSING HETEROGENEITY
STUDY AREA were present in limited quantity within We studied winter browse utilization by these lower elevation forest communities. moose during 2008–2010 in a 50 km2 study Stands of whitebark pine (Pinus albicaulis), area within the Mount Haggin Wildlife Engelmann spruce, and subalpine fir (Abies Management Area (MHWMA; 45° 57’ N, lasiocarpa) were dominant at higher 113° 4’ W), a portion of the Upper Big Hole elevations. River Valley in southwestern Montana. The Moose were the only ungulate species MHWMA straddles the Continental Divide, wintering on the study area (January to with streams that flow into the Big Hole and mid-May; Keigley et al. 2003, Burkholder Clark Fork River drainages. Elevations 2012). Other ungulate species present ranged from 1750 to 2250 m and the during summer and fall included pronghorn topography varied from rolling hills and flats (Antilocapra americana), elk (Cervus ela- with meandering streams to steeper slopes at phus), and mule (Odocoileus hemionus) and the bases of high mountains (>3200 m). white-tailed deer (O. virginianus). Cattle Mean daily average temperature at nearby (Bos taurus) were also in the study area Wise River, Montana during 1981–2010 was during summer (15 June–10 Oct) under a –8°CinJanuaryand14°CinJuly.Average strictly managed, rest-rotation grazing sched- annual precipitation during 1980–2010 was ule and stocking rate. Predators included 48 cm (Calvert Creek SNOTEL site, 1965 m wolves (Canis lupus) and black bears (Ursus a.s.l), and mean February snow depth during americanus). 2004–2011 was 68 cm. Aerial surveys conducted mid-winter Willow communities are the primary in the study area yielded an average mini- cover type used by moose during winter, mum count of 51 moose per year in given that an average of 69% of winter tel- 1983–2014; the trend was generally stable, emetry locations occurred there (Burkholder though with low precision (DeCesare et al. 2012). Mixed willow communities included 2016). The number of annual moose hunting 6 primary species (Keigley and Fager licenses declined from an average of 19 2006): Geyer willow (Salix geyeriana), (11 antlered, 8 antlerless) in 2002–2009 to Lemmonʼs willow (S. lemmonii), Boothʼs 6 antlered licenses in 2012. willow (S. boothii), Drummondʼswillow(S. drumondiana), planeleaf willow (S. planifo- METHODS lia), and Wolfʼswillow(S. wolfii). Bebb (S. Browse utilization bebbiana) and sandbar willow (S. exigua), We established browse transects in a and red-osier dogwood (Cornus sericea) systematic distribution across willow com- were more sparsely distributed and occurred munities (Fig. 1). Transects 50 to 500 m at lower elevations; snowberry (Symphoricar- in length were placed perpendicular to pos albus) and bog birch (Betula pumila) also streams at 1 km intervals. Transects were occurred in riparian communities. Lodgepole divided into 50 m segments that were treated pine (Pinus contorta) dominated the forests as the unit of analysis; no segments were at lower elevations, with patches of quaking <50 m. In total, we established 40 transects aspen (Populus tremuloides), and occasional (5.55 km in total length) comprised of 111 Douglas fir (Psuedotsuga menziesii) and segments. Engelmann spruce (Picea engelmannii)stands Browse utilization was measured during in mesic areas. Scoulerʼs willow (Salix scou- early May which allowed easy identifica‐ leriana) and thinleaf alder (Alnus incana) tion of the previous winter’s browsing and
25 MOOSE BROWSING HETEROGENEITY – BURKHOLDER ET AL. ALCES VOL. 53, 2017
Fig. 1. Study area and sampling locations for measuring browse utilization surveys on the Mount Haggin Wildlife Management Area in southwestern Montana, 2008–2010. preceded the arrival of other ungulates and browse zone. We defined a twig as an un- spring green-up (Keigley et al. 2003, Seaton branched portion of branch where apical or et al. 2011). All 111 segments were surveyed lateral growth is occurring, often consisting in 2009 and 2010, and 108 in 2008. We of the current and previous year’s growth. adapted a branch/twig sampling method Five twigs from each branch were selected developed by Stickney (1966) to estimate according to those twigs for which the the percent of twigs browsed across a large base of the current-year-growth of the twig sample of willow plants. To maximize the was the closest to the leader, or tallest point extent of sampling, we did not estimate of the branch. A detailed key for selecting biomass consumption. plants, twigs, and branches is available in The willow community was systematic- Burkholder (2012; pp. 240–243). ally sampled at 20 points on each 50-m seg- We marked sampled branches with alu- ment, starting at the origin and at every minum tags to facilitate measuring the same 2.5 m thereafter. At each point, we used a willows and twigs across years. For each of graduated 2.5 m pole to locate the nearest 20 twigs sampled per willow, we noted willow plant, regardless of species; the sam- them as either unbrowsed, browsed (only ple point was skipped if no plant was within previous yearʼs growth removed by brows- 2.5 m. The number of browsed twigs was ing), or heavily browsed (more than previous counted on each plant from the 5 most yearʼs growth removed by browsing). distal twigs on each of 4 sampled branches. We excluded willows for which 4 branches Branches were sampled following a key with 5 twigs were not available for browsing that prioritized branches nearest to the (4.4% of sampled plants). We only counted transect that were within a 0.75–2.0 m browsing from the previous winter, as
26 ALCES VOL. 53, 2017 BURKHOLDER ET AL. – MOOSE BROWSING HETEROGENEITY determined by the color, weathering, and as opposed to floral or catkin characteristics) new growth on the browsed twig. Each 50-m based on the work of Dorn (1970). We sought segment resulted in a maximum number of additional guidance to distinctly identifying 400 twig examinations, if suitable plants Salix geyeriana and S. lemmonii (Brunsfeld were found at all sample points. and Johnson 1985, Heinze 1994, Fertig and We adjusted the traditional estimator Markow 2001, Hoag 2005), and in June 2009 of browse utilization as the proportion of we examined pistillate catkins from a sub- twigs browsed per segment (Jensen and sample of willows under a light microscope Scotter 1977) to include increased weight or hand lens to confirm prior classifications. for heavily browsed twigs with multiple The proportion of preferred willow spe- years of growth removal. Field observations cies along each segment was estimated by suggested that the average heavily browsed calculating the proportion of 3 willow spe- twig included the consumption of 3 previous cies (planeleaf, Boothʼs, and Drummondʼs). year’s growth based on comparison with Species preference was ascertained from unbrowsed twigs on the same plants. Thus, palatability studies with moose and other we multiplied the count of heavily browsed ungulates in western North America (Dorn twigs by 3 to account for the influence of 1970, Stevens 1970, Tyers 2003). Growth heavy browsing on the counts of twigs forms of willow classified in this study browsed and sampled, such that: were uninterrupted, released, arrested, and N þ ðÞN 3 retrogressed (Keigley et al. 2003, Bur- B ¼ b hb ðEquation 1Þ kholder 2012: 244–245). The proportions adj N þ N þ ðÞN 3 ub b hb of heavily browsed willows along each seg- ment were estimated from growth form data where Badj was the intensity-adjusted browse according to the amount of arrested and utilization (i.e., proportion of twigs browsed) retrogressed types. Height of the sampled per segment, and Nub, Nb, and Nhb were the willows was measured in July 2008 to estab- counts of unbrowsed, browsed, and heavily lish an average height within each segment. browsed twigs, respectively. Estimates of The estimated width of each willow patch Badj ranged between 0 and 1 and reflected was used as a proxy for the general abun- the proportion of twigs browsed, while dance of browse plants within a given area. accounting for the added effects of heavy The average elevation (m) of each seg- browsing. ment was calculated using a digital elevation model from the National Elevation Dataset Hypothesized covariates to browse (Gesch et al. 2002). Elevation was used as utilization a potential proxy for snow depth since deep‐ To explain patterns in browse utilization, er snows were expected at higher eleva‐ we collected data on potentially influential tions as is typical in the Rocky Mountains covariates both in the field and using remote‐ (Brennan et al. 2013). Distance (m) to the ly sensed data. We documented willow nearest willow and coniferous forest cover species, growth form (see below) per plant, types was measured for each segment as and patch width per transect. Sampled the distance from the segment centroid to the willows were revisited and identified to spe- nearest edge of each cover type using cies in July 2008 to estimate species compos- ArcGIS Desktop 9.3 (Environmental Systems ition within each segment. Species were Research Institute, Redlands, California) initially classified by vegetative separations and Montana Landcover Map data (MTNHP (i.e., differences in branch and leaf structure 2010).
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Statistical analyses correlations due to repeated visits. This ap- Investigation of dependence. — Our proach accounted for clustering of repeated sampling design of consecutive 50-m seg- data within segments, while also accounting ments along the same transect created high for overdispersion because no distribution is potential for autocorrelation or a lack of in- assumed. GEE also offered population-level, dependence among sample units (segments). or marginal coefficient results rather than We used a suite of plots and statistical tests individual- or segment-level inferences, such to examine the effects of this potential as would be achieved with a generalized liner autocorrelation problem. First, we created mixed-effects models (GLMM) approach linked-segment profile plots to display how (Koper and Manseau 2009). The quasi-likeli- patterns of browse utilization (the response) hood under the independence model criterion varied from segment to segment across trans- (QIC) developed by Pan (2001) was used to ects (Ramsey and Schafer 2012). Linked- facilitate model selection in an information segment profile plots display the utilization criteria framework. Analyses were completed of browse (0-100%) at each segment across in R 2.13.1 with additional packages APE, the entire transect and allow for an examin- bbmle, MuMIn, and geepack (Paradis et al. ation of notable drifts from the mean of a 2004, Bolker 2010, Barton 2011, Yan transect. Serial and spatial correlation detec- et al. 2011). tion techniques were then employed to quan- All possible candidate models were eval- tify correlation. A partial autocorrelation uated including main effects for 7 candidate function (PACF) was applied to the longest variables: proportionate preferred species, transects in the study (9-10 segments) to proportionate previously browsed plants, wil- examine 1-dimensional serial correlation low patch width (m), elevation (m), distance (Ramsey and Schafer 2012). Lastly, we to willow (m), distance to conifer (m), and examined spatial correlation (2-dimensional) study year. No covariates had correlations by constructing correlograms of browse util- >0.6 nor variation inflation factors >5, thus ization per segment across the study area. All multicollinearity was not considered further. analyses were completed in R 2.13.1with the spatial package (Ripley 2011, R Core We estimated QICc model weights for each Team 2014). model and considered top models as those Δ Modeling variation in browse with QICc scores <4. Nested models with utilization. — We used model selection uninformative parameters were removed Δ approaches to evaluate whether measured from the model set by evaluating QICc, covariates explained patterns in browse coefficient estimates, and standard errors utilization across our study area. Browse (Arnold 2010). Using model weights, utilization data fit a binomial distribution coefficients of covariates in remaining top with repeated Bernoulli trials resulting in models were averaged to weight individual unbrowsed (0) and browsed (1) responses. estimates and calculate adjusted standard Data contained additional nuances of being errors (Burnham and Anderson 2002). overdispersed (i.e., containing more variation We conducted additional post-hoc ana- than expected within a binomial distribution) lyses that included 1 additional variable and containing correlation due to repeated (willow height), quadratic forms of each measures of the same segments across years. covariate, and various interactions between We used generalized estimating equa- year and each covariate. There was some tions (GEE) to simultaneously model all statistical support for certain more com- 3 years of data while accounting for the plex models, but here we focus on those
28 ALCES VOL. 53, 2017 BURKHOLDER ET AL. – MOOSE BROWSING HETEROGENEITY representing a priori hypotheses (for complex for standard power methods (Cohen 1988, models see Burkholder 2012). Manly 2006). Instead, we adapted a 1-sample Selection of willow species. — Selec- method using techniques for testing sample tion ratios (wi) were estimated by comparing means and sequential sample size testing the proportionate counts of browsed and total (Bros and Cowell 1987, Manly 2006). We twigs sampled for each of 6 species (i)of resampled data sets ranging in sample size willows. The same multiplier as in Equation from 4 to 100. For each set of simulations, 1 was used to inflate counts of heavily 10,000 samples of responses of a set sample browsed twigs within the sums of both size were randomly drawn (with replacement) browsed and total twigs sampled. We ana- from our observed data. From this bootstrap lysed both for each year and with data pooled t-distribution, we estimated an observed t ¼ Specified difference inpffiffi browse utilization across all 3 years using methods of Manly SD= n . Estimates et al. (2002) in Program R with the package of t were compared to the 95% confidence adehabitat (Calenge 2006). Selection results interval created from the bootstrap-t distribu- per species from our study area were com- tion for each sample size to determine at pared with those used in our a priori classifi- which sample size the detection of an actual cation of preferred species within our difference would become significant with modeling of factors influencing browse either 90 or 95% confidence. We completed utilization. Our top browse utilization this analysis independently for each year’s model with a post-hoc recategorization of data to assess if annual distribution of the proportionate preferred species covariate browse utilization data affected results. (according to these results) was run again to assess the importance of species-specific RESULTS preferences within our particular study area. Assessing browse utilization Power analysis of sampling effort. — We sampled 35,320 twigs along 108, Accurately quantifying trends in ungulate 50-m segments in 2008, 35,560 twigs along browse utilization or willow community 111 segments in 2009, and 35,320 twigs health may require increased sample sizes along 111 segments in 2010. Mean annual when faced with a large amount of heterogen- estimated browse utilization across all eity in browse utilization. A power analysis segments was 11.5% in 2008 (95% CI = 9.4 was conducted using our browse utilization – 13.7%, median = 8.9%, range = 0–54%), data to estimate the range of sample sizes 8.0% in 2009 (95% CI = 6.2 – 9.8%, median that would be required to detect trends under = 4.6%, range = 0–48%), and 8.3% in 2010 future monitoring scenarios; additional cov- (95% CI = 6.5 – 10.1%, median = 4.4%, ariates were not used in this analysis. We range = 0–48%). The distributions of browse assessed the sample sizes necessary to detect utilization per segment were heavily skewed increases of 5–25% in browse utilization over each year (Fig. 2). Several segments had 0% a given monitoring period with either 90 or use each year; 14 in 2008, 27 in 2009, and 95% confidence. For example, an increase 23 in 2010. A total of 1826 individual wil- of 10% in browse utilization (e.g., from 10 lows were measured over the course of this to 20%) was identified as a target level for study, 1766–1778 annually; slight annual effective monitoring (Dobkin et al. 2002). variation was due to tag attrition, loss to Non-parametric bootstrapping was beavers (Castor canadensis), and the absence employed to simulate data because our of 3 segments in 2008. The number of measure of interest (i.e., browse utilization) plants classified to species (n = 1910) con- failed to meet assumptions of normality sisted of 700 Lemmon’s, 459 Geyer, 348
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Booth’s, 340 Drummond’s, 35 planeleaf, and 28 Wolf’s willows.
Investigation of dependence Analyses designed to assess dependence among segment data did not indicate signifi- cant levels of correlation among segments. We examined a total of 75 linked-segment profile plots for all transects with >1 segment (n = 25) and each of 3 years, and they gener- ally indicated minimal possible serial correl- ation (plots available in Burkholder 2012, pp. 251–256). Applying a PACF to the 3 Fig. 2. Histogram of willow browse utilization longest transects revealed virtually no correl- per segment-year during 2008–2010 within ation; the largest lag-1 serial correlation co- Mount Haggin Wildlife Management Area, efficient was <0.25 and most were <0 Montana. (Fig. 3; Ramsey and Shafer 2012). While PACF-based tests of significance were hampered by the small sample size, the lack of a consistent trend suggesting higher
Fig. 3. Partial autocorrelations among browse utilization of willows per segment along the 3 longest transects (9–10 segments each), by lag, and 95% confidence bands (dashed lines) within the Mount Haggin Wildlife Management Area in southwestern Montana, 2008–2010.
30 ALCES VOL. 53, 2017 BURKHOLDER ET AL. – MOOSE BROWSING HETEROGENEITY correlation at low lags also suggests a lack of willow edge. Willow twigs within segments relevant serial autocorrelation in the data. entirely consisting of preferred species were Spatial correlograms also indicated minimal 1.6 more likely to be browsed than those spatial correlation for segments within the within segments with no preferred species. same transect (plots available in Burkholder Additionally, twigs within a segment within 2012, pp. 251–256). Given these analyses, which all plants were previously browsed we concluded that segments were functional- were 23 more likely to be browsed than ly independent, and proceeded with all subse- those within segments composed entirely of quent analyses assuming that the segments unbrowsed plants. Twigs within a narrow, were uncorrelated sample units. 50-m wide willow patch were approximately 3 more likely to be browsed than those Modeling variation in browse utilization within a patch 500 m wide. After screening models for uninforma- Selection of willow species tive parameters and ΔQICc scores <4, we found a single top GEE model to best explain Species-specific browse preferences “ the effects of covariates on browse utilization were examined to identify preferred ” across the study area and time period species within our modeling framework. (Table 1). The proportion of preferred species Counts of used and available twigs per plant (β = 0.44, z = 3.86, P = 0.05) and the and the resulting selection ratios (wi) differed proportion of previously browsed willow significantly among species. Planeleaf, ʼ ʼ (β = 3.13, z = 80.3, P < 0.001) were positively Wolf s, and Booth s willow were the most associated with browse utilization, whereas consistently preferred (selection ratio (wi) ʼ willow patch width (β = 0.002, z = 26.5, >1.50), Drummond s and Geyer willow P < 0.001) had negative association with were moderately preferred (1.50 > wi > ʼ browse utilization (Fig. 4). An indicator 1.00), and Lemmon s willow was used sub- variable for year was also included. Model stantially less than expected (wi < 0.50) selection results did not support associa- (Table 2). These patterns existed to varying ʼ tions between browse utilization and eleva- degree in all years, although Drummond s tion, distance to forest edge, or distance to and Geyer willow were used in proportion to their availability in 2010.
Table 1. Coefficient values (βi) and 95% con- A post-hoc analysis of our browse util- fidence intervals (in parentheses) for the best ization model was conducted due to the GEE binomial regression model explaining discrepancies between the a priori species winter browse utilization of willow twigs by preference classifications and our observed moose during 2008–2010, Montana. preferences. All species except Lemmonʼs Parameter β SE zPwillow were reclassified as preferred. This Intercept –2.63 0.18 214.1 <0.001 covariate was then used to replace the Year (2009) –0.362 0.13 7.65 0.006 previous parameter within the top model Year (2010) –0.322 0.12 6.89 0.009 which substantially improved the strength β Proportionate 0.444 0.23 3.86 0.050 of this covariate ( = 0.92, z = 13.6, P < preferred species 0.001). The predicted effect of a willow Proportionate 3.13 0.35 80.3 <0.001 segment consisting entirely of preferred spe- previously cies being browsed compared to a segment browsed with no preferred species increased from Willow patch –0.002 0.0005 26.5 <0.001 1.6 more likely in the a priori model to width (m) 2.5 more likely in this reformulated model.
31 MOOSE BROWSING HETEROGENEITY – BURKHOLDER ET AL. ALCES VOL. 53, 2017
Fig. 4. Predicted values from GEE binomial regression models of the proportionate willow browse utilization by moose within a given transect segment as a function of proportion of willow plants previously browsed, proportion of preferred willow species, and willow patch width (m) during the 2008 winter season, Mount Haggin Wildlife Management Area, Montana.
Power analysis of sampling effort. Monitoring browse condition has been sug- As expected, the required sample sizes gested as a potential tool for monitoring increased as the level of change of interest and managing the abundance and condition decreased (e.g., from 20 to 5%) and as the of wild ungulate populations, including desired level of confidence increased (e.g., moose (Keigley et al. 2002, Seaton et al. from 90 to 95%; Fig. 5). For example, 2011). However, implementation of this tool detecting an increase of 10% in browse util- would benefit from increased understanding ization from one year to the next with 95% of spatial drivers of variation in browse util- confidence required 38–41 segments (with ization and the amount of effort required to up to 20 plants sampled in each) based on detect meaningful changes. Our results in- the distributions of data collected in 2008– clude the identification of several factors 2010; 32–35 sites would be required for that were predictive of variation in browse 90% confidence. Results differed somewhat use by moose, as well as a demonstration of across years due to the among-year variation power analysis to identify sample sizes neces- in the mean and variation of browse utiliza- sary to monitor trends in our study area. tion. Slightly larger sample sizes were The annual winter browse utilization was recommended to detect a given increase low–moderate (~10%) on our study area, based on data from 2008 when the mean and willows increase new growth in specific and variation of browse utilization was response to winter browsing (Molvar et al. highest. 1993, Guillet and Bergström 2006). This complicates our understanding of optimal DISCUSSION browse utilization for the health of willow Browse utilization has been measured plants and ecological communities. For for decades to quantify the effects of ungu- willows, browsing thresholds that define lates on shrubs and trees (Stickney 1966, overutilization, sufficient to cause negative Jensen and Scotter 1977, Singer et al. 1994). effects, range from 20% (Dobkin et al. 2002)
32 ALCES VOL. 53, 2017 BURKHOLDER ET AL. – MOOSE BROWSING HETEROGENEITY i w 2010) – Pooled (2008 Used Available i w
Fig. 5. Average required sample sizes and standard deviations (among years of data) for
spp.) surveyed for winter browse utilization by moose detecting changes in willow browse utilization with either 90% or 95% confidence according
Salix to power analyses based on the empirical distribution of browsing within the Mount Haggin Wildlife Management Area in south- Used Available western Montana during 3 years of sampling, 2008–2010. Note: Changes for these purposes
i are additive rather than proportional, e.g., a w 10% change would be from 10% to 20%, rather than from 10% to 11%. =0.05. α to upwards of 50–75% (Wolff 1978). While ) for different species of willow ( i
w browse utilization exceeded 40% at certain sites each year, we found no evidence, over- all, that willows were overutilized. Further,
Used Available we did not detect spatial autocorrelation in browsing among segments, which suggests
i a high degree of heterogeneity in browsing w across fine and broad spatial scales. This heterogeneous consumption of browse is consistent with that observed in previous studies (e.g., Tyers 2003, Månsson 2009, 2008 2009 2010 Palo et al. 2015), and adds to the challenge of detecting meaningful differences from a 2010) of sampling, Montana. Selection ratios >1 indicate selection for a species, and selection ratios <1 indicate selection against that – given sample of measurements.
Used Available Browsing heterogeneity presents a problem for sampling efficiency, but vari- s 1,090 7,088 1.27* 665 6,912 1.14* 615 6,804 1.07 2,370 20,804 1.14* ’ ation in browse utilization provides some s 568 12,894 0.36* 347 13,432 0.31* 453 13,486 0.40* 1,368 39,812 0.31* ’ . Counts of used and available twigs and selection ratios ( level of predictability, as indicated by our s 1267 7,376 1.42* 908 7,018 1.53* 951 7,072 1.59* 3,126 21,466 1.53* ’ s 41 182 1.86* 53 174 3.61* 39 172 2.67* 133 528 3.61* ’ analysis. The complexity of plant species species, with * indicating values significantly different from 1.0 at across 3 years (2008
Table 2 Species PlaneleafWolf Booth Geyer 171Drummond Lemmon 540 1360 2.61*being 9,038 204 foraged 1.24* 604 936 represents 4.01* 8,780 288 1.26* one 750 596 axis 5.70* 8,368 of 663 1.06 3,046 1,740 26,186 4.01* 1.26*
33 MOOSE BROWSING HETEROGENEITY – BURKHOLDER ET AL. ALCES VOL. 53, 2017 potential predictability. We sampled 6 spe- means of improving the efficiency of sam- cies of willow in proportion to their avail- pling, assuming that previous browsing sur- ability, and found significant differences in veys can accurately identify and stratify the browse utilization by species (Tables 1 and 2). landscape of interest. At a broader scale, In our case, focus on a subset of preferred Seaton et al. (2011) used estimates of moose species or stratification of effort according density or space use from aerial survey and to species may allow more efficient sampling telemetry data to stratify sites prior to browse at the current density of animals and inten- sampling. The cross-scale spatial correlations sity of browsing. However, heterogeneity in between broad-scale patterns of animal browse utilization is likely to vary among density or space use and fine-scale browse se- and within plant species with changes in lection is an area worthy of future research density, making sampling of less preferred with application to vegetation-based moni‐ species necessary (Connor et al. 2000, Speed toring of wildlife populations. Generally et al. 2013). Identification of preferred species speaking, scale-dependent patterns of re- in new study areas or over time would also source selection are common (DeCesare et al. require multiple years of winter sampling 2012), which suggests that a high level of and associated summer data before any form fine-scale heterogeneity may still exist within of species-based subsampling or stratification strata developed at broader scales. Prelimin- would be possible. To provide a robust depic- ary analyses of our browse utilization data tion of browse utilization and willow commu- with sample units pooled to the level of nity health across multiple levels of moose the transect (averaging adjacent segments) density, monitoring all species of potential resulted in a “washed-out” model with no cov- relevance in a random or stratified-random ariates predictive of browsing (Burkholder manner may be the best option. 2012). Thus, in our case, detecting heterogen- As evidenced by the Wald (z) statistic eity in browsing was sensitive to spatial scale. that was an order of magnitude greater than We also found that browse utilization those for most other variables, previous was negatively associated with willow patch browsing of willows was another axis of predictability and was highly predictive of width. That patch width is a proxy for forage current browsing (Table 1, Fig. 4). This ten- abundance would, in part, contrast with pre- dency has been identified elsewhere, and vious findings that browse utilization corre- the positive feedback loop related to specific lates positively with biomass availability browsing and preference history likely (Bowyer et al. 2001, Shipley 2010, Herfindal explains the overall spatial heterogeneity in et al. 2015). Certainly browse utilization browsing patterns (Bowyer and Bowyer may correlate with plant abundance or dens- 1997, Stolter 2008). For tea-leaved willow ity at different scales than those measured (Salix phylicifolia), Stolter (2008) found that here (Herfindal et al. 2015). Patch width new growth from previously browsed twigs could be a proxy for an unmeasured, but was more palatable, and had lower concentra- important variable characterizing variation tions of phenolics and higher biomass. in willow communities or individual plants Similarly, Bowyer and Bowyer (1997) found on our study area. An approach for future a significant increase in biomass of previous- work might be to measure plant density ly browsed grayleaf willow twigs (S. glauca) or biomass within the sample unit; we could versus unbrowsed twigs. not make this comparison because width was Fidelity to previously browsed patches a transect-level variable and did not vary may facilitate stratified sampling as one among segments within each transect.
34 ALCES VOL. 53, 2017 BURKHOLDER ET AL. – MOOSE BROWSING HETEROGENEITY
Moose exhibited different patterns of se- modestly sized study area (50 km2)thatserved lection among willow species (Table 2). Not- as winter range for <100 moose. Considerably ably, moose appeared to prefer Geyer willow more resources would be required to employ over Lemmon’s willow despite some debate this method across a broader scale or larger as to these being distinct species (Brunsfeld population, which makes it somewhat challen- and Johnson 1985, Heinze 1994, Hoag ging to use with the numerous, low density 2005). Selection for individual species also moose populations in much of Montana. How- varied somewhat among years (Table 2); ever, our approach could be applied in select for example, moose appeared more selective situations, especially if overpopulation and det- during the milder winter of 2010 (Burkholder rimental impacts to a willow community were 2012). Because snow depth influences browse suspected. Because winter habitat use and availability and foraging behavior, milder con- browse consumption in willow communities ditions may have facilitated more selective for- are directly related to survival and productivity aging (Schwab and Pitt 1991, Nordengren of many western moose populations, adequate et al. 2003, Lundmark and Ball 2008). Our assessment of willow communities is essential post-hoc analysis resulted in better model fit for effective moose management. when compared to the a priori classification of preference based on available literature. ACKNOWLEDGEMENTS We recommend power analysis as an im- Funding for this research was provided by portant component of any wildlife or wildlife Montana Department of Fish, Wildlife and habitat monitoring program (Tanke and Bon- Parks and Montana State University. We ac- ham 1985, Zielinski and Stauffer 1996). Our knowledge the efforts of K. Alt to develop power analysis indicated that 32 segments the project. We thank R. Keigley and D. Tyers were required to detect a 10% difference in for assistance with field methodology. Statis- browse utilization (with 90% confidence) tical analyses were greatly aided by M. Higgs. on the study area. The projected sample Additional support came from M. Becker, S. size is specific to the distribution of browse Dunkley, C. Gower, J. Mannas, T. McMahon, utilization measured in this study; change in M. O’Reilly, K. Proffitt, and J. Rotella. the abundance/availability of plant species composition would affect the statistical REFERENCES power to detect trends (Frerker et al. 2013). ARNOLD, T. W. 2010. Uninformative para- Our methods employed at a site with similar meters and model selection using access would require ~1.5 person-hours per Akaike’s Information Criterion. Journal – segment, or ~48 h of field time to measure of Wildlife Management 74: 1175 1178. 32 segments. In summer, an additional 0.5 BARTON, K. 2011. MuMIn: Multi-model Infer- h/segment may be needed for subsequent ence. R package Version 1.0.0.
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