107 BROWSE SELECTION BY MOOSE IN THE ADIRONDACK PARK, NEW YORK Samuel Peterson1, David Kramer2, Jeremy Hurst2, and Jacqueline Frair1 1State University of New York College of Environmental Science and Forestry, 1 Forestry Drive, Syracuse, New York, USA 13210; 2New York Department of Environmental Conservation, Division of Fish and Wildlife, 625 Broadway, Albany, New York, USA 12233 ABSTRACT: Moose (Alces alces americana), a large-bodied and cold-adapted forest herbivore, may be vulnerable to environmental change especially along their southern range in the northeastern United States. Better understanding of moose foraging patterns and resource constraints in this region, which moose recolonized over the past several decades, is needed to anticipate factors that may influence the long-term viability of the regional moose population. We quantified browse selection, intensity and nutritional quality, and the impact of other vegetation potentially interfering with browse availability for moose within the Adirondack Park, New York. We backtracked GPS-collared female moose (n = 23) to assess the seasonal composition of selected browse from 2016 to 2017, compared browse selection to plant nutritional quality, and modeled local browsing intensity. Moose demonstrated a generalist feeding strategy in summer, but in winter selected browse species largely in order of digest- ible dry matter. Red maple (Acer rubrum) was the most heavily used species in both seasons. Areas having a high proportion of beech (Fagus grandifolia), which in this region regenerates in dense thickets in the aftermath of beech bark disease and thwarts timber regeneration, were associated with reduced browsing intensity by moose in both seasons. Given the limited amount of timber harvest within the Adirondack Park, thoughtful management of harvested stands may increase marketable timber while also benefitting moose and ensuring the longevity of the New York population. ALCES VOL. 56: 107–126 (2020) Key words: Alces alces, browse selection, dry matter digestibility, foraging ecology, New York, tannins Prior to European settlement, the geo- graphic range of moose (Alces alces ameri- cana) in the northeastern United States extended south into northern Pennsylvania. During the 18th and 19th centuries, the range of moose receded northward as populations were decimated from unregulated harvests and given broad-scale conversion of forests to agricultural lands (Alexander 1993, Foster et al. 2002). Successful natural resource pro- tection ultimately restored forest habitat for moose and recovered populations of native species such as beaver (Castor canadensis), whose maintenance of wetlands enhanced habitat conditions for moose. By the mid- 1980s, moose recolonized their eastern range as far south as northernmost Connecticut and the Adirondack Park in New York (Hicks 1986). With a population of ~700 moose (J. Frair, unpubl. data), the 5.8 million-acre Adirondack Park and Forest Preserve in northern New York supports the lowest den- sity of moose across comparable latitudes in their contemporary range in the Northeast (Wattles and DeStefano 2011). Following recolonization, New Hampshire and Vermont documented rapid growth in moose numbers ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 108 through the 1990s, with population stabili- zation observed through the mid-2000s, and more recently, declines in some areas. In contrast, the Adirondack moose population appears to have remained at low density since recolonization (Wattles and DeStefano 2011). Of the potentially limiting factors for moose within New York, the most influen- tial are likely to include parasites (i.e., Parelaphostrongylus tenuis and Fascioloides magna) and limited abundance of quality food resources due to forest age. Although winter tick (Dermacentor albipictus) has been observed on moose in New York, the high levels of ticks infestation, tick-induced mortalities, and subsequent population declines observed in neighboring states has not yet been anecdotally documented likely owing to low moose density. Yet, in comparison to the larger populations in neighboring states, the Adirondack popu- lation is likely to be less resilient to chang- ing environmental conditions given that numerical size is a key determinant in the viability of a population over time (Amos and Balmford 2001). The forests inhabited by northeastern moose have experienced dramatic changes in canopy dominance due to invasive agents causing chestnut blight (early 1900s), Dutch elm disease (1920–1940s), and beech bark disease (1960s; Giencke et al. 2014). Shifts in canopy dominance precipitate cascading changes in the understory plant communities that, in turn, affect the herbivore community. Although moose successfully exploit non-traditional habitats in the Northeast, such as oak-dominated forests in Massachusetts (Wattles and DeStefano 2013), their behavioral plasticity to chang- ing environmental conditions may be out- paced by that of white-tailed deer (Odocoileus virginianus; Post and Stenseth 1999). A high degree of behavioral plasticity in white-tailed deer has been evidenced by their expansion into human-dominated land- scapes (VerCauteren 2003), as well as north- ward expansion into the little disturbed boreal forests long considered primary moose habitat (Latham et al. 2011). Broad- scale overlap between moose and deer, espe- cially along their southern range margin, poses concern for moose persistence owing to increased interspecies disease transmis- sion and competition for resources. Given differences in body size, mor- phology and energetic requirements, moose and deer have adopted differing foraging strategies (Irwin 1975, Ludewig and Bowyer 1985). Deer have relatively higher energy requirements than moose, and with their smaller muzzles can be selective for high energy and nutrient-rich plant parts. In contrast, moose are bulk feeders that require large bite sizes to meet their ener- getic needs, a feeding strategy that requires dense concentrations of browse. Landscapes that have a higher degree of heterogeneity may reduce spatial overlap, and therefore resource competition between moose and deer, by providing a multitude of foraging opportunities that meet their different for- aging strategies. Across the northeastern states, the highest concentrations of browse, and by extension the highest densities of moose, occur on regenerating forests fol- lowing timber harvest (Dunfey-Ball 2009), a cover type that remains relatively uncom- mon within the largely “Forever Wild” Adirondack forests. Where moose are con- centrated within the Adirondack Park now and into the future, how many moose can be supported by the landscape, and the degree to which moose overlap white-tailed deer will be driven in large part by the structure and composition of suitable for- aging habitat. To assess moose foraging in the Adirondacks, we focused solely on browse because tree and shrub species compose ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 109 up to 90% of moose diets (Van Dyne et al. 1980, Belovsky 1981, Dungan and Wright 2005). We further focused on beech (Fagus grandifolia) as a potentially interfering species because in the aftermath of beech bark disease (Houston 1994), beech exhib- its a “thicket” like growth form shown to suppress seedling survival by sugar maple (Hane 2003) and reduce floral species diversity (Giencke et al. 2014). Beech may be an important factor influencing avail- able forage for moose due to is high abun- dance and large distribution within the region. Moreover, beech thickets form visually dense pockets of foliage poten- tially perceived wrongly by moose as suit- able foraging habitat and costing them valuable foraging time. Lastly, we evalu- ated how much time moose spend brows- ing in foraging patches given the local abundance of both principle browse spe- cies and beech. By identifying key forage considerations for moose, this work provides insights to habitat management for moose in the Adirondack region of New York. Current restraints on active resource extraction throughout large portions of the region limit the ability of wildlife managers and foresters to manipulate the landcover to meet environ- mental objectives. Therefore, our work can help inform management decisions in the limited areas where timber harvest is allowed. Herein, we sought to 1) investigate the seasonal composition of browse used by moose along their southern range boundary in the Adirondack Park and identify the prin- ciple species browsed by moose in the sum- mer and winter, 2) quantify diet selection relative to plant nutritional quality and com- pare species-specific values of energy and digestible protein per season, and 3) model local browsing pressure as a function of the availability of desirable and potentially interfering woody species. STUDY AREA Established by the New York State Legislature in 1885, the Adirondack Park (Park) (43°57’08.9”N 74°16’57.5”W) encompasses ~5.8 million acres in northern New York consisting of both publicly (45%) and privately-managed land (49%; Fig. 1). The majority of public land is protected by Article XIV of the New York State Constitution as “forever wild forest,” which precludes resource extraction or develop- ment of any kind. In contrast, the majority of private land is designated for resource management and owned by timber compa- nies that focus on harvest of white ash (Fraxinus americana), sugar maple (Acer saccharum), red maple (A. rubrum), red oak (Quercus rubra), black cherry (Prunus serotina), and white pine (Pinus strobus) (NYS DEC 2016). The forest community is a patchwork of the northern boreal ecosys- tem interspersed with temperate deciduous forests and large peatland complexes. Lower elevations with fertile soils support a diverse array of tree species dominated by American beech, yellow birch (Betula allegheniensis), paper birch (B. papyrifera), sugar maple, and red maple. Higher eleva- tions are typically more coniferous, domi- nated by red spruce (Picea rubens), balsam fir (Abies balsamea), white pine, and east- ern hemlock (Tsuga canadensis) (Jenkins and Keal 2004). Elevations range from 30 m on the shores of Lake Champlain to over 1,600 m at the highest summit (Mount Marcy). Much of the Park consists of large glacial valleys that gradually rise in eleva- tion to the High Peaks region in the east- central part. Average monthly winter temperatures range from −12 to −6°C, with summer monthly temperatures typically range from 20 to 26°C. Monthly precipita- tion averages 8–10 cm year-round (Jenkins and Keal 2004). Aerial surveys indicate 25 times more white-tailed deer than moose ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 110 across the Park (J. Frair, unpubl. data). The region also supports sustainably harvested populations of two potential moose preda- tors, black bear (Ursus americanus) and coyote (Canis latrans). METHODS Sampling Moose Browse Adult female moose (n = 23) were captured in January 2015 to 2017, fitted with a GPS radio-collar (BASIC Iridium Track M 3D, Fig. 1. Adirondack Park study area in northeastern New York, USA showing public lands (light gray) and private lands (white) along with water bodies (dark gray). Locations where GPS-moose were back-tracked are indicated (black circles) along with locations where nutritional samples were collected (white circles) and the grouping of nutritional samples (large boxes) to test for geographic variation in plant quality. ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 111 Lotek Wireless, Newmarket, ON or TGW- 4670-3, Telonics, Mesa, AZ), and released on site (SUNY ESF IACUC protocol 140901). We programmed collars to attempt a GPS fix every 2 hours for 2 years, and col- lars achieved a mean fix rate of 98.7 ± 1.1%. Following deployment, we back-tracked collared moose to quantify browsing pat- terns using procedures similar to Dungan et al. (2010), Seaton (2002), and Wilson (1971). We conducted summer sampling 15 Jun – 15 Jul 2016 and 2017, and winter sampling during the intervening Dec-Jan period. We concentrated search efforts on clusters of consecutive moose locations (~10–15 GPS locations/ha) established by an individual over the previous 10-day win- dow, and field visited clusters only after the animal had moved >500 m away. Two-three person field crews searched the vicinity of each GPS location cluster to record evidence of moose and deer. To avoid confusing browse by moose and deer, we did not sam- ple areas for moose browse if deer scat or tracks were detected within 15 m of any por- tion of the subplots described below. We defined the perimeter of the browsed patch following Bailey et al. (1996). Starting at a point central to the GPS location cluster, we walked in each cardinal direction until no browsed stems were observed on 5 consecu- tive individual trees or shrubs. Within that patch boundary, we established one transect having 3, 2- × 4-m sub-plots spaced 10 m apart (Fig. 2). We defined browsable twigs as those <8 mm diameter that extended >15 cm from a given branch point to terminal bud, and that occurred within a 0.5–3 m height stratum (Crete and Jordan 1982, Raymond et al. 1996). We tallied the total number of browsed and unbrowsed twigs by species within each subplot. We measured basal diameter (10 cm above ground) or quantified volume (tallest height × longest width × per- pendicular width) for each individual plant so as to predict the total browsable biomass on that individual using allometric equations (Peterson 2018). We recorded the cover type of each transect as deciduous/mixed forest, conifer forest, open wetland, or wooded wet- land based on classes derived from The Nature Conservancy’s Terrestrial Habitat Map for the Northeastern US and Atlantic Canada (Ferree and Anderson 2013) or, in forest management patches, as harvested stands ranging in cut age from 6 to 8 years (J. Santamour, LandVest, unpubl. data). Browse Selectivity We calculated proportional representa- tion of each species, Pri, in the collective moose diet as: ∑ = = Pr T T i i i n i1 (1) where T is the number of browsed twigs for each species i. In each season we ranked spe- cies in order by Pri and summed cumulatively across species. We assumed principle browse Fig. 2. Diagram of field sampling layout used to conduct browse selection surveys for moose in Adirondack Park, New York, USA. Sampling design consisted of three 4 m × 2 m plots spaced 10 m apart on a transect. Plots were centered on an observed foraging patch that was located using GPS collared female moose. Edges of the foraging patch were delineated by walking concentric circles around an area of observed browse until no signs of moose browsing remained visible. Foraging Patch Sampling Plots Moose GPS Loca�ons ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 112 species to be those, in rank order, whose cumulative proportional representation summed to 0.95. In the case where a principle browse species was geographically restricted (as described later), we substituted the next species in rank order until the 95% threshold was met to represent park-wide availability of principle browse for moose. We summed the number of browsed and unbrowsed twigs on each transect to represent browse availability for species i, and we calculated two indices of selection for each species at the transect level. Ivlev’s electivity index (Manly et al. 2002, Cook et al. 2016) ranges from -1 to +1 and was calculated as: = − + I % Twigs Browsed % Twigs Available % Twigs Browsed % Twigs Availableij (2) for species i along transect j. Ivlev’s index is symmetric about 0, facilitating direct interpretation of moose selection or avoidance of species i. In contrast, Chesson’s index is bounded by 0 and 1, interpreted as the probability that the next bite will be of species i, and calculated as: ∑ =        = C Proportion twigs browsed Proportion twigs available Proportion twigs browsed Proportion twigs available ij i i i n 1 (3) Nutritional Analysis From principle browse species we col- lected 73 summer samples (Jul–Aug 2016, n = 1–8 per species, 29 locations) and 131 winter samples (Jan–Feb 2017, n = 1–18 per species, 37 locations), selecting individual plants to represent the size distribution measured during park-wide vegetation surveys (Peterson 2018). We clipped 5 browsable twigs (as previously defined) from various heights within the browse stra- tum per each sampled individual. We stored fresh clippings in plastic bags, kept them on ice in the field, and froze the samples as soon as possible. Prior to nutritional analysis, we composited samples from different individu- als by size class, with the included mass from a given size class proportional to the abun- dance of that size class across the landscape. We further organized composite samples to assess for potential variation in plant quality among central, northeastern, and southwest- ern portions of the Park. We analyzed repli- cate composite samples to quantify variation within a species and region (although not all species were sampled in all regions). We sent frozen samples to the Wildlife Habitat and Nutrition Laboratory at Washington State University to determine crude protein (%, CP), gross energy (cal/g, GE), neutral detergent fiber (%, NDF), acid detergent fiber (%, ADF), acid detergent lignin (%, ADL), acid insoluble ash (%, AIA), and bovine serum albumin protein precipitate (mg ppt./mg feed, BSA) (Goering and Van Soest 1970, Martin and Martin 1982, Robbins et al. 1987a). We used the BSA values to account for reductions in digestibility due to the tannin content of forage. Duplicate runs of NDF, ADF, ADL and AIA were conducted for each composite sample, with final values for analysis averaged across duplicates. Dry matter digestibility (%, DMD) and digestible protein (g/100 g feed, DP) were calculated following Robbins et al. (1987a, 1987b). Digestible energy (kcal/g, DE) was determined as the product of GE and DMD for a given species. We used a one-way ANOVA to compare the mean values of DMD, DE, DP, CP, and BSA for species in which samples were available in multiple regions and Pearson’s correlation coefficients to assess nutritional values for each species (DE, DP, DMD, CP, NDF, ADF, and BSA) and moose diet metrics (Pri, Iij and Cij) averaged across transects. Modeling Browse Intensity We used the number of stems browsed in plot k as a measure of local browse ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 113 intensity. We expected browse intensity at an individual plot to be influenced fundamen- tally by the availability of preferred browse within the larger patch. Total browsable bio- mass of all principle species on plot k (Bailey et al. 1996) was quantified using allometric equations (Peterson 2018), and summed across individual species along a given tran- sect to provide a patch-level estimate. We modeled local browse intensity using stan- dard and zero-inflated Poisson and negative binomial models using the R package glm- mADMB (Bolker et al. 2012). The global model in each season included fixed effects for available browse biomass (linear and quadratic fits tested), proportion beech, pro- portion conifer, and two-way interactions among these three covariates. Models also included random intercepts for individual transect and moose. Covariates were cen- tered and standardized prior to fitting mod- els (Schielzeth 2010, Dormann et al. 2012). We compared alternative models using Akaike’s Information Criterion with a bias adjustment for small sample size (AICc; Burnham and Anderson 2002). RESULTS Principle Browse Species We identified 13 and 12 principle browse species in summer and winter, respectively (Table 1, Appendix 1). Red maple consti- tuted the largest portion of the diet in both seasons, representing 20.6 and 38.5% of the browse biomass consumed in summer and winter respectively. Yellow birch comprised 19.0% of the summer diet and 10.0% of the winter diet. Gray birch and paper birch made up 8.3 and 6.6% of the summer diet, respec- tively, but accounted for <1% each of the winter diet. In contrast, Balsam fir accounted for 16.5% of the winter diet, but was not detected in the summer diet. Though beech was abundant across the landscape, we found that it was rarely browsed by moose. We included beech in our nutritional analy- sis due to the prevalence on the landscape and the potential for browse interference. Selection Indices For principle browse species, C ranged 0.04–0.39 with yellow birch and grey birch having the first and second highest C scores in summer, though their scores dropped pre- cipitously in winter. Red maple ranked third according to C in summer but first in winter. According to values of I, in summer moose selected for striped maple, avoided sugar maple and hobblebush, and used the other species in proportion to their availability. In winter, moose selected for red maple while avoiding balsam fir, sugar maple, yellow birch, and black cherry. All other principle species were used in proportion to their availability. Among the non-principle browse species, winterberry (Ilex verticil- lata) was selected for in summer and willow (Salix spp.) in winter. Nutritional Analysis Northern wild raisin (Viburnum nudum L. var. cassinoides) and big-tooth aspen (Populus grandidentata) exhibited two of the highest DE values in summer at 2.85 and 2.76 kcal/g, respectively, concurrent with the two lowest DP values at 1.58 and 0.77 g/100 g feed, respectively. Low DP values are caused by relatively low CP con- tent and, in the case of big-tooth aspen, compounded by a high reduction in diges- tion due to tannins (0.08 mg ppt/mg forage). Red maple, the largest component of moose diets in both seasons, exhibited moderate values for all nutrients measured. Perhaps the most nutritious forage in summer was pin cherry (Prunus pensylvanica), exhibit- ing the highest DP (6.20 g/100 g forage), and second highest DE (2.76 kcal/g) and DMD (55.24%; Fig. 3). Quaking aspen (Populus tremuloides; DE = 2.63 kcal/g, ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 114 Ta bl e 1. B ro w se m et ric s, in cl ud in g pr op or tio n of d ie t ( Pr ), Iv le v’ s I nd ex (I ), C he ss on ’s In de x (C ), an d U til iz at io n (U ) a lo ng w ith n ut rit io na l q ua lit y, in cl ud in g D ig es tib le E ne rg y (k ca l/g , D E) , D ig es tib le P ro te in (g /1 00 g fe ed , D P) a nd D ry M at te r D ig es tib ili ty (% , D M D ), fo r p rin ci pl e br ow se s pe ci es c on su m ed b y m oo se in s um m er a nd w in te r i n 20 16 –2 01 7 w ith in th e A di ro nd ac k Pa rk , N ew Y or k, U SA . N 1 i s th e nu m be r o f b ro w se s el ec tio n tra ns ec ts o n w hi ch a g iv en sp ec ie s w as o bs er ve d. N 2 i s th e nu m be r of n ut rit io na l s am pl es c ol le ct ed f or la bo ra to ry a na ly si s of a c er ta in s pe ci es . V al ue s in p ar en th es es a re s ta nd ar d de vi at io ns . F or I an d C , 9 5% c on fid en ce in te rv al s t ha t d o no t o ve rla p 0 in di ca te d by * . B ro w se M et ric s N ut rit io na l Q ua lit y Se as on Pr in ci pl e B ro w se S pe ci es N 1 Pr I C N 2 D E D P D M D Su m m er Ac er p en sy lv an ic um 19 <0 .0 1 +0 .8 1( 0. 42 )* 0. 04 (0 .1 0) 8 2. 62 (0 .1 5) 2. 40 (1 .0 3) 53 .6 5( 3. 21 ) Ac er ru br um 59 0. 21 - 0. 06 (0 .6 3) 0. 28 (0 .2 9) * 6 2. 36 (0 .1 6) 2. 24 (0 .8 0) 49 .0 7( 2. 80 ) Ac er sa cc ha ru m 23 0. 05 - 0. 27 (0 .5 7) * 0. 23 (0 .2 6) * 2 2. 47 (0 .0 1) 2. 79 (0 .1 0) 52 .5 6( 1. 02 ) Ac er sp ic at um 3 <0 .0 1 - 0. 06 (0 .8 3) 0. 22 (0 .2 2) - - - Be tu la a lle gh an ie ns is 48 0. 19 +0 .1 1( 0. 52 ) 0. 36 (0 .3 0) * 4 2. 00 (0 .1 4) 3. 12 (1 .1 2) 39 .9 6( 2. 68 ) Be tu la p ap yr ife ra 15 0. 07 +0 .0 5( 0. 66 ) 0. 25 (0 .2 3) * 3 2. 12 (0 .3 0) 3. 68 (0 .8 2) 40 .9 7( 4. 79 ) Be tu la p op ul ifo lia 11 0. 08 +0 .2 5( 0. 43 ) 0. 33 (0 .2 7) * 7 2. 23 (0 .2 1) 2. 66 (1 .0 0) 43 .1 3( 3. 87 ) O st ry a vi rg in ia na 7 0. 01 - 0. 37 (0 .8 0) 0. 24 (0 .3 8) - - - Po pu lu s g ra nd id en ta ta 6 0. 02 +0 .3 0( 0. 45 ) 0. 28 (0 .1 9) * 1 2. 76 (n .a .) 0. 77 (n .a .) 53 .0 0( n. a. ) Po pu lu s t re m ul oi de s 13 0. 05 - 0. 00 (0 .5 0) 0. 20 (0 .1 3) * - - - Pr un us p en sy lv an ic a 13 0. 06 - 0. 22 (0 .5 5) 0. 22 (0 .3 0) * 3 2. 76 (0 .1 2) 6. 20 (1 .2 4) 55 .2 5( 2. 38 ) Vi bu rn um la nt an oi de s 17 0. 02 - 0. 53 (0 .5 9) * 0. 17 (0 .2 7) * 2 2. 31 (0 .1 3) 3. 30 (0 .5 4) 48 .2 1( 2. 40 ) Vi bu rn um n ud um c as si no id es 18 0. 09 - 0. 03 (0 .5 8) 0. 23 (0 .1 9) * 5 2. 85 (0 .2 1) 1. 58 (0 .2 8) 56 .1 5( 4. 04 ) W in te r Ab ie s b al sa m ea 24 0. 17 - 0. 31 (0 .5 0) * 0. 22 (0 .3 0) * 18 2. 68 (0 .2 0) 2. 80 (0 .9 5) 49 .6 0( 3. 54 ) Ac er p en sy lv an ic um 20 0. 06 - 0. 15 (0 .4 9) 0. 18 (0 .2 1) * 5 2. 18 (0 .0 9) -0 .3 5( 0. 24 ) 44 .3 6( 1. 18 ) Ac er ru br um 38 0. 39 +0 .1 7( 0. 52 )* 0. 39 (0 .2 8) * 3 2. 02 (0 .0 8) 0. 19 (0 .4 6) 42 .2 7( 2. 25 ) Ac er sa cc ha ru m 14 0. 01 - 0. 58 (0 .5 1) * 0. 09 (0 .1 4) * 3 1. 66 (0 .0 5) 0. 37 (0 .4 4) 33 .8 0( 1. 06 ) Ac er sp ic at um 5 0. 01 - 0. 31 (0 .7 2) 0. 22 (0 .3 0) 3 - - - Be tu la a lle gh an ie ns is 17 0. 10 - 0. 57 (0 .4 4) * 0. 06 (0 .0 6) * 2 1. 67 (0 .1 9) 2. 89 (1 .9 1) 32 .8 2( 3. 39 ) Po pu lu s g ra nd id en ta ta 6 0. 01 +0 .0 9( 0. 63 ) 0. 21 (0 .1 3) * 3 2. 23 (0 .0 2) 2. 61 (0 .5 2) 43 .8 2( 0. 62 ) Po pu lu s t re m ul oi de s 11 0. 03 +0 .2 0( 0. 55 ) 0. 31 (0 .2 7) * 4 2. 63 (0 .1 5) 3. 78 (1 .5 0) 50 .8 5( 2. 74 ) Pr un us p en sy lv an ic a 9 0. 02 - 0. 40 (0 .6 3) 0. 10 (0 .1 3) * 2 1. 69 (0 .0 0) 0. 96 (0 .9 1) 33 .7 3( 0. 47 ) Pr un us se ro tin e 21 0. 07 - 0. 54 (0 .5 0) * 0. 10 (0 .1 5) * 1 1. 85 (n .a .) 3. 23 (n .a .) 34 .7 6( n. a. ) Vi bu rn um la nt an oi de s 17 0. 03 - 0. 13 (0 .6 5) 0. 22 (0 .2 6) * 3 2. 17 (0 .0 9) 1. 62 (0 .5 7) 45 .2 7( 1. 15 ) Vi bu rn um sp . 9 0. 06 - 0. 01 (0 .4 6) 0. 21 (0 .2 4) * 2 2. 43 (0 .0 7) 0. 34 (0 .0 6) 46 .6 2( 1. 20 ) ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 115 Fig. 3. Nutritional values, showing mean with 95% confidence interval, of principle browse species consumed by moose during the summer (Jun–Aug 2016; denoted by solid circle) and the winter (Dec–Feb 2017; denoted by outlined circle) in Adirondack Park, New York, USA. American beech, a potentially interfering browse type, is shown for comparison. ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 116 Fig. 4. Ivlev’s Electivity Index (top panels) and Chesson’s Index (bottom panels), with 95% confidence intervals, plotted as a function of Dry Matter Digestibility (DMD) in summer (left panels) and winter (right panels) for moose principle browse species and American Beech (FaGr; non-principle browse species) in 2016–2017, Adirondack Park, New York, USA. Species are labeled using the first two letters of their genus and species (see Table 2 for scientific names), beech plotted only for comparison and not used in statistical analyses. DP = 3.78 g/100 g feed, DMD = 50.85%, BSA = 0.00) and balsam fir (DE = 2.68 kcal/g, DP = 2.80 g/100 g forage, DMD = 49.60%, BSA = 0.06 mg ppt/mg forage) were among the highest quality browse species in winter. For species collected in both seasons, DE, DP, and DMD were on average 18.65, 23.12 and 19.31% lower, respectively, across spe- cies in winter compared to summer. No regional differences in DMD, DE, DP, CP, or BSA were detected for any individual species (summer p = 0.08–0.96; winter p = 0.15–0.98). We found that striped maple had a DP value of less than 0 in winter, given the low average CP level (4.46%) was inhibited by the level of tannins (BSA = 0.052 mg ppt/mg forage). American beech exhibited relatively low values for DE (1.77 kcal/g), DMD (36.4%), and CP (7.40%) in summer. However, there was no observed reduction in digestibility due to tannins (BSA = 0.00) for this species resulting in a relatively large DP (3.00 g/100 g feed). In winter, American beech exhibited low DE (1.66 kcal/g) and DMD (33.60%) values with a moderate DP (2.30 g/100 g forage). Both I and C were positively correlated with DE and DMD in winter (0.62 < r < 0.76, p < 0.05; Fig. 4). In contrast, nutritional metrics were not significantly related to diet metrics in summer (−0.55 < r < 0.49, p > 0.05). Browse Utilization Models Local browse intensity, as measured by the number of stems browsed on at plot, was best fitted in both seasons by a zero-inflated negative binomial model (α, the dispersion ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 117 Table 2. Comparison of top 10 candidate models (summer cumulative weight = 0.58, winter cumulative weight = 0.96) predicting local browse intensity by moose in 2016–2017 in the Adirondack Park, New York, USA as a function of the principle browse biomass (M; fitted as a linear or polynomial), proportion stems beech (B), proportion stems conifer (C), and interactions as indicated. For each model, the model degrees of freedom (df), estimated log-likelihood (LL), difference in AICc value (∆AICc), and AIC model weight (w) are reported. Season Model Main Effects Interactions df LL ∆AICc w Summer 1 M+M2, B, C MxB, MxC, BxC 12 −872.29 0.00 0.11 2 M, B, C MxB, BxC 10 −874.73 0.42 0.09 3 M, B, C MxB, MxC, BxC 11 −873.75 0.69 0.08 4 M+M2, B+B2, C MxC, BxC 12 −872.76 0.94 0.07 5 M, B, C MxC, BxC 10 −875.25 1.46 0.05 6 M+M2, B+B2, C BxC 11 −874.22 1.62 0.05 7 M+M2, B, C MxC 10 −875.43 1.82 0.04 8 M+M2, B+B2, C MxB, BxC 12 −873.37 2.16 0.04 9 M+M2, B 8 −877.80 2.19 0.04 10 M+M2, B+B2, C MxB, MxC, BxC 13 −872.29 2.26 0.03 Winter 1 M+M2, B 8 −578.66 0.00 0.35 2 M+M2, B, C 9 −578.14 1.27 0.19 3 M+M2, B M×B 9 −578.58 2.14 0.12 4 M+M2, B, C M×C 10 −578.09 3.50 0.06 5 M+M2, B, C M×B 10 −578.11 3.55 0.06 6 M+M2, B, C B×C 10 −578.14 3.61 0.06 7 M+M2 7 −581.70 3.81 0.05 8 M+M2, C 8 −581.08 4.84 0.03 9 M+M2, B, C M×B, M×C 11 −578.08 5.87 0.02 10 M+M2, B, C M×C, B×C 11 −578.09 5.88 0.02 parameter, for global models = 1.10 [SE = 0.16] in summer and 2.39 [SE = 0.88] in winter). In summer, model selection uncertainty (based on ΔAIC < 2.0) involved interaction terms and whether total available browse was better fit with a linear or qua- dratic form (Table 2). The top ranked model in summer indicated the effect of total browse biomass on browse intensity satu- rated at ~31,250 kg/ha in the absence of other covariates (Fig. 5). However model selection uncertainty on the quadratic term suggests that browse intensity may be equally well represented by a linear function of increasing biomass availability. In winter, model selection uncertainty centered on the Fig. 5. Plot of browse intensity by moose in summer across the observed range of standardized browsable biomass values, holding beech and conifer coverage at a value of 0. Maximum browsing intensity is encountered at approximately 7.5 standardized biomass units (2.5 kg/m2; see Appendix 2 for standardization values), Adirondack Park, New York, USA. ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 118 inclusion of conifer as an interfering vegeta- tion type. During winter, browse intensity was predicted to peak at ~4,200 kg/ha of principle browse. The amount of beech in the patch had a negative effect on browse intensity in both seasons, and conifer abun- dance had a negative effect on browse inten- sity in summer (Table 3). The top summer model showed that undesirable woody plants (beech and conifer) negatively affected local browse intensity by moose, an effect not observed in the winter. Although browsing intensity increased with total biomass of principle species, interactions showed a predicted reduction in browse intensity as the proportional coverage of beech or conifer increased. The impact of beech on browsing intensity was greatest at lower levels of browsable biomass and diminished with increasing biomass ( Fig. 6A). In contrast, proportional coverage of conifer increasingly Table 3. Standardized coefficient values for zero-inflated negative binomial regression models describing the local browse intensity (BI) by moose in the Adirondack Park, New York, USA given the amount of browsable biomass as well as potentially interfering species (beech and conifer). Data were standardized within a season prior to model fitting (see Appendix 2 for standardization values). Covariate Summer Winter β SE z P β SE z P Intercept 3.11 0.11 27.51 <0.01 3.67 0.13 27.39 <0.01 Browse Biomass 0.60 0.16 3.67 <0.01 0.71 0.17 4.14 <0.01 Browse Biomass2 -0.04 0.02 -1.73 0.08 -0.14 0.04 -3.62 <0.01 Proportion stems beech -0.95 0.22 -4.29 <0.01 -0.27 0.11 -2.48 0.01 Proportion stems conifer -0.65 0.19 -3.45 <0.01 - - - - Biomass × Beech 0.22 0.24 0.92 0.36 - - - - Biomass × Conifer -0.25 0.17 -1.46 0.14 - - - - Beech × Conifer -1.06 0.39 -2.74 0.01 - - - - Fig. 6. Partial slope plots of biomass, conifer coverage and beech coverage interactions for models predicting browsing intensity (number of stems browsed) of moose during summer at a given location in Adirondack Park, New York, USA. Browsed stems are estimated at the plot (8 m2) level. Biomass values (x-axis, panels A and B) are shown in g/8 m2 plot, and span the range of 95% of observed values of biomass (Peterson 2018). Levels of beech and conifer coverage are also restricted to 95% of observed values for each variable (~75% coverage for each). Vertical dashed lines represent average browse biomass estimated in regenerating forest (400 g/8 m2, or 0.05 kg/ha). ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 119 diminished browsing intensity as total browsable biomass increased (Fig. 6B). Where occurring together, conifer and beech species had a compounding reduction on browse intensity (Fig. 6C). DISCUSSION Iconic of the north woods and wilderness areas, moose are a culturally as well as eco- logically important species in the northeast- ern United States. Along their southern range in this region lies the Adirondack Park – the largest protected area within the contiguous United States. This study is the first to quan- tify moose diets and forage quality within the Park, establishing an important baseline for understanding potential climate-induced threats to moose habitat quality in the future. The composition of moose diets was similar to that observed in New Hampshire (Pruss and Pekins 1992) but quite different from that observed in Maine (Ludewig and Bowyer 1985). Red maple, pin cherry, and quaking aspen were used by New Hampshire and Adirondack moose alike, but speckled alder (Alnus incana), a commonly browsed species in New Hampshire, was not used, although widely available in wetlands. Winter diets in Maine were dominated by balsam fir (70.5%), American beech (11.4%), and hawthorn (Crataegus spp., 9.3%) (Ludewig and Bowyer 1985), versus red maple and balsam fir in the Adriondacks. Generally, herbivore diet selection correlates with relative nutritional content (Hobbs and Swift 1985, Hanley 1997). Winter moose diets in the Adirondacks correlated positively with dry matter digestibility (and by extension digestible energy) and negatively with fiber concentrations. Balsam fir was one of the highest quality browse species in winter along with quaking aspen. Spatial variation in plant-nutritive quality was not observed across the vast Adirondack Park despite meaningful differences in terrain and soil characteristics (Miller 1914) and the expectation that plant secondary compounds and other components can be spatially diverse within species (Gusewell and Koerselman 2002). Small sample sizes (summer: 1–2 samples/region, winter: 1–11 samples/region) may have precluded detection of differences among regions of the Park. However, our values for crude protein in balsam fir, striped maple, red maple, and hobblebush were 1–2% lower than previous studies elsewhere in the northeastern United States (Mautz et al. 1976, Raymond et al. 1996), potentially indicative of subtle regional differences but perhaps due to differences in laboratory techniques. The small degree of difference in our work compared to previous findings highlights that there is likely a limited difference in regional forage quality, allowing for comparisons in regional moose foraging work. Although comparative studies in the northeastern US are lacking with respect to summer diet of moose, Renecker and Schwartz (1998) listed aspen, birch, and willow as species highly utilized across moose range. In our study, birch species comprised 34% of the summer diet of moose, aspen was relatively rare and generally utilized in proportion to its availability, and willow comprised <1% of the diet in each season. Generally speaking, moose were less selective in summer than winter, and patterns of browse selection did not reflect plant nutritional quality during summer. It is possible that moose base summer diet choices on nutrients or minerals not measured in this study, such as copper, sodium, or selenium (O’Hara et al. 2001, Custer et al. 2004). Moreover, moose make ready use of aquatic vegetation and herbaceous forage during summer, reducing their dependence on browse for meeting nutritional requirements. Importantly, we observed that foraging decisions by moose, in particular local foraging intensity, may be influenced by the relative ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 120 abundance of undesirable species such as American beech that regenerates as shrub thickets as a legacy of beech bark disease. In both seasons, the proportional coverage of beech on a plot was associated with a reduction in local browse intensity. In summer, the difference in browsing intensity predicted between low and high biomass plots increased with increasing coverage of beech, notably by inordinately reducing browsing intensity in lower biomass plots. The interactive effect of beech on forage intensity was predicted to diminish towards zero with increasing amounts of browsable biomass; however, under average biomass conditions, foraging intensity was predicted to be ~12–41% lower in areas given ~25–75% beech coverage, respectively. Summer browse intensity was also predicted to decline as a function of proportional coverage of conifer species, although in contrast to beech, the relative impact of conifer increased within increasing browse biomass. Yet, at average biomass levels, little influence of conifer coverage is expected. Summer foraging sites with both high browse biomass and high conifer dominance (~50%) typically occur in canopy openings within mature conifer forest – those areas moose rely on for thermoregulation (van Beest et al. 2012), possibly explaining the expected reductions in forage intensity under these conditions in our study. As moose are bulk feeders requiring large amounts of nutrients to maintain their large body size, effective use of their available foraging time is critical. Our work suggests that browsing intensity by moose is impacted directly by the availability of the biomass of principle browse species and indirectly by the amount of beech, and potentially conifer, relative to the total amount of browse. Maximum summer browsing intensity was predicted at approximately 2.5 kg/m2, even under the highest value of beech coverage. Yet, managing forest stands to provide this level of browse biomass to support moose is likely not feasible; on average, 0.05 kg/m2 occurred in regenerating hardwood stands during summer (Peterson 2018). Under these average conditions, our models indicate that the value of forage to moose, as evidenced by moose foraging intensity, might be maximized by suppressing beech to <25% coverage. The Adirondack Park in northern New York has a series of unique constraints that complicate managing forests and the fauna within. The government imposed “Forever Wild” status has ensured that the majority of mature forest in the region stays intact. However, small pockets of privately-owned forests can be harvested when enrolled in a state-sponsored easement program that includes state-mandated and approved management plans. As our work suggests, these private forests can provide ideal habitat for moose in that species that tend to thrive in timber openings due to their shade intolerance (Acer spp., Betula spp., and Prunus spp.) were preferred forage of Adirondack moose. Additionally, our work highlights the need for proactive steps to manage the composition of regenerating stands. Beech can often establish and out-compete more marketable tree species (e.g., Acer spp., Prunus spp.) following timber harvest, and by incorporating post- harvest stand management, managers can both increase their marketable yield and reduce the negative impacts of beech on moose browsing intensity. In so doing, forest managers may initially find increased browsing impacts by moose, although long- term impacts at higher moose densities are unsubstantiated in the northeastern United States (Bergeron et al. 2011, Andreozzi et al. 2014). Our findings are useful to wildlife managers working with private landowners to create management plans that can curate high quality moose habitat while mitigating issues associated with overbrowsing of commercially important species. ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 121 ACKNOWLEDGEMENTS We would like to thank our partners at SUNY-ESF, NYSDEC, WCS and Cornell University for their assistance. This project would not have been possible without the cooperation of commercial foresters of the Adirondacks including Lyme Adirondacks, The Forestland Group and Molpus Woodlands. Funding for this project was provided by SUNY-ESF, NYS-DEC (Federal Aid in Wildlife Restoration Grant W-173-G), and the American Wildlife Conservation Society. Additionally, we would like to thank K. Powers, D. Tinklepaugh, R. Rich, D. DeGroff, and R. Tam for their assistance with data collec- tion and field support. REFERENCES AlexAnder, C. E. 1993. 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Carrying capacity of the key browse species for moose on the north slopes of the Uinta Mountains, Utah. M. S. Thesis. Utah State University, Logan, Utah, USA. ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 124 APPENDICES Appendix 1 Browse metrics for all species observed during moose browse selection surveys during summer (2016 and 2017) and winter (2016–2017) in Adirondack Park, New York, USA. The number of plots on which each species was observed is represented by N, Chesson’s index values are represented by C, Ivlev’s electivity index values are repre- sented by I, and the proportion of the total observed diet made up by each species is represented by Pr. Standard deviations for C and I are also displayed. Season Species N C C (SD) I I (SD) Pr Summer Abies balsamea 34 0.00 0.00 −1.00 0.00 0.00 Summer Acer pensylvanicum 19 0.04 0.10 −0.81 0.42 0.00 Summer Acer rubrum 59 0.28 0.29 −0.06 0.63 0.21 Summer Acer saccharum 23 0.23 0.26 −0.27 0.57 0.05 Summer Acer spicatum 3 0.22 0.22 −0.06 0.83 0.00 Summer Alnus incana 7 0.00 0.00 −1.00 0.00 0.00 Summer Amelanchier sp. 7 0.15 0.20 −0.36 0.81 0.01 Summer Betula alleghaniensis 48 0.36 0.30 0.11 0.52 0.19 Summer Betula papyrifera 15 0.25 0.23 0.05 0.66 0.07 Summer Betula populifolia 11 0.33 0.27 0.25 0.43 0.08 Summer Caprinus caroliniana 1 0.30 N/A 0.71 N/A 0.00 Summer Cornus sp. 2 0.05 0.08 −0.55 0.64 0.00 Summer Corylus sp. 3 0.40 0.27 0.39 0.38 0.00 Summer Fagus grandifolia 37 0.02 0.07 −0.83 0.41 0.03 Summer Fraxinus americana 7 0.05 0.09 −0.65 0.59 0.00 Summer Viburnum lantoides 17 0.17 0.27 −0.53 0.59 0.02 Summer Ilex mucronata 18 0.13 0.20 −0.42 0.64 0.06 Summer Ilex vericillata 2 0.63 0.53 0.12 0.08 0.01 Summer Juglans cinerea 1 0.00 N/A −1.00 N/A 0.00 Summer Larix laricina 4 0.00 0.00 −1.00 0.00 0.00 Summer Chamaedaphne calyculata 3 0.00 0.00 −1.00 0.00 0.00 Summer Ostrya virginiana 7 0.24 0.38 −0.36 0.80 0.01 Summer Picea rubens 7 0.00 0.01 −0.91 0.23 0.00 Summer Pinus strobus 1 0.00 N/A −1.00 N/A 0.00 Summer Pinus sylvestris 3 0.00 0.00 −1.00 0.00 0.00 Summer Populus grandidentata 6 0.28 0.19 0.30 0.45 0.02 Summer Populus tremuloides 13 0.20 0.13 0.00 0.50 0.05 ALCES VOL. 56, 2020 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. 125 Summer Prunus pensylvanica 13 0.22 0.30 −0.22 0.55 0.06 Summer Prunus serotina 19 0.13 0.17 −0.37 0.60 0.01 Summer Prunus virginiana 8 0.05 0.09 −0.71 0.42 0.00 Summer Salix sp. 5 0.17 0.15 −0.15 0.59 0.01 Summer Spirea sp. 4 0.00 0.00 −1.00 0.00 0.00 Summer Tsuga canadensis 1 0.00 N/A −1.00 N/A 0.00 Summer Vaccinium corymbosum 3 0.00 0.00 −1.00 0.00 0.00 Summer Viburnum cassinoides 18 0.23 0.19 −0.03 0.58 0.09 Summer Viburnum lentago 2 0.16 0.19 0.02 0.67 0.00 Winter Abies balsamea 24 0.22 0.29 −0.31 0.50 0.17 Winter Acer pensylvanicum 20 0.18 0.21 −0.15 0.49 0.06 Winter Acer rubrum 38 0.39 0.28 0.17 0.52 0.39 Winter Acer saccharum 14 0.09 0.14 −0.58 0.51 0.01 Winter Acer spicatum 5 0.22 0.30 −0.31 0.72 0.01 Winter Alnus incana 2 0.06 0.05 −0.42 0.46 0.01 Winter Amelanchier sp. 5 0.19 0.17 −0.02 0.59 0.00 Winter Betula alleghaniensis 17 0.06 0.06 −0.57 0.44 0.10 Winter Betula papyrifera 9 0.04 0.07 −0.67 0.50 0.01 Winter Betula populifolia 4 0.07 0.12 −0.64 0.56 0.01 Winter Cornus sp. 1 0.13 N/A −0.05 N/A 0.00 Winter Corylus sp. 1 0.00 N/A −1.00 N/A 0.00 Winter Fagus grandifolia 30 0.01 0.03 −0.95 0.16 0.01 Winter Fraxinus americana 1 0.00 N/A −1.00 N/A 0.00 Winter Viburnum lantoides 17 0.22 0.26 −0.13 0.65 0.03 Winter Ilex mucronata 3 0.13 0.07 −0.01 0.18 0.01 Winter Larix laricina 1 0.00 N/A −1.00 N/A 0.00 Winter Picea rubens 13 0.02 0.09 −0.92 0.29 0.00 Winter Populus grandidentata 6 0.21 0.13 0.09 0.63 0.01 Winter Populus tremuloides 11 0.31 0.27 0.20 0.54 0.03 Winter Prunus pensylvanica 9 0.10 0.13 −0.40 0.63 0.02 Winter Prunus serotina 21 0.10 0.14 −0.54 0.50 0.07 Winter Prunus virginiana 3 0.06 0.05 −0.46 0.47 0.00 Winter Salix sp. 2 0.22 0.01 0.36 0.01 0.00 Winter Viburnum sp.* 9 0.21 0.24 −0.01 0.46 0.06 ADIRONDACK BROWSE SELECTION – PETERSON ET AL. ALCES VOL. 56, 2020 126 Appendix 2 Standardization values used to center and standardize data for zero-inflated negative binomial regression to model browse utilization as a function of biomass availabil- ity, beech coverage, and conifer coverage, Adirondack Park, New York, USA. Summer Winter Mean Standard Deviation Mean Standard Deviation Principle Browse Biomass (g/8 m2) 1288.776 2476.36 640.4 898.61 Beech Coverage 0.107 0.23 0.086 0.17 Conifer Coverage 0.143 0.245 0.136 0.252 a b _Hlk14949768