Moose Alces alces are large and conspicuous animals valued for wildlife watching and hunting opportunities. However, near urban areas they can cause collisions with vehicles and damage to garden and ornamental plants. We studied a population of adult female moose that lives in and around both urban and industrial development on an active Army and Air Force base adjacent to Anchorage, Alaska, to evaluate nutrition and diet, map habitat quality, and model how habitat development affects the number of moose the landscape can support. Population density was moderate and hunter harvest was high in our study area, so we hypothesized that moose in our study area would be in similar condition to other healthy populations in Alaska. We also hypothesized that, in our study area, shrublands would support more moose than any other habitat type and that areas disturbed for urban development would be crucial to maintaining the local moose population. Rump fat depths, blood chemistries, and pregnancy rates in November and March for moose in our study area were consistent with populations in good to moderate condition. Microhistology of composite fecal samples indicated that willows Salix spp. dominated the summer diet, whereas the winter diet was divided among willows, birch Betula spp., and cottonwood Populus balsamifera. Low concentrations of available nitrogen in winter stems limited the number of moose that could be supported in our study area. Shrublands were the most valuable habitat type for moose, theoretically supporting 11–81 times more moose per hectare than any other habitat type. Shrublands were more concentrated within the developed portion of our study area than the surrounding undeveloped portions of the military base; and the access to shrublands in clearings, greenbelts, and parks sustains the productivity of this moose population despite the many disturbances of an urbanized landscape. Our habitat values can be used to model potential impacts of habitat modification on the number of moose the landscape can support.
The movement and productivity of wildlife populations are affected by the quality, quantity, and spatial distribution of foods throughout the year. Urban development changes the accessibility, extent, and quality of foraging areas, especially for large mammals with commensurately large appetites and patterns of movement (McLeery 2010; Johnson and St-Laurent 2011). Mammals that persist in urban areas often modify their foraging behavior to tolerate or avoid human disturbance (Bateman et al. 2012; Lowry et al. 2013). Ungulates are the most conspicuous wildlife near roads and urban areas. As a consequence, they provide communities with opportunities for wildlife watching and hunting. However, ungulate populations can reach high densities near urban areas that provide foraging opportunities and refuge from predators (Côté et al. 2004; Berger 2007; Harveson et al. 2007). Overabundance of ungulates near urban areas increases the frequency of conflicts with humans (Adams et al. 2006; McDonald et al. 2012) and the likelihood of collisions with vehicles, especially when animals are attracted to forages or salt along the roads (Dussault et al. 2005; Coffin 2007; Laurian et al. 2008; Litvaitis and Tash 2008; Sullivan 2011).
Moose Alces alces are the largest urban animals in the northern hemisphere. Moose habituate to human disturbance (Belant et al. 2006) and also use the woody browse in early successional habitats on lands that have been altered for urban development. Moose harvests are important for both rural and urban communities in Alaska and Scandinavia (Kofinas et al. 2010), but high fecundity of moose when predation or harvest are low can lead to large populations that can cause overutilization of the forage base (Whittaker et al. 2001; McLaren et al. 2004; Boertje et al. 2007). Management of moose must therefore balance the benefits of harvest and nonconsumptive uses with the negative effects that high moose densities have on cultivated plants, overbrowsing native plant communities, and the increased likelihood of vehicle collisions (Olaussen and Skonhoft 2011).
Although Anchorage is the largest city in Alaska (291,826 people, United States Census 2010), moose and their predators (black bears, Ursus americanus; brown bears, U. arctos; and wolves, Canis lupus) use adjacent public lands, as well as municipal greenbelts, parks, and yards within the urban areas. Moose are also common on Joint Base Elmendorf-Richardson (JBER; Figure 1), an active Army and Air Force base adjacent to Anchorage. Management of wildlife on urban lands, including the urbanized portions of JBER, is dynamic because land development and use are constantly in flux. Changes in land use and development can quickly alter forage abundance and distribution. Moose also concentrate in low-elevation habitats near urban areas in winter largely because Anchorage is surrounded by alpine habitat with scarce forage and deep winter snows (Garrett and Conway 1999; Sinnott 2008). Winter movement of moose out of higher elevations into the Anchorage area increases the density of moose, and therefore the risk of collisions with vehicles and the risk of starvation in winter when snow depths are high (Sinnott 2008). However, high moose densities are valued by many people that enjoy viewing, photographing, and harvesting moose in the immediate Anchorage area. Joint Base Elmendorf-Richardson and the adjacent Ship Creek drainage support a high harvest rate of moose each year (7%–16% of the population; Gasaway et al. 1983, 1992; Sinnott 2008).
The purpose of this study was to evaluate moose population health and habitat quality on the urbanized landscape of JBER. Evaluating habitat quality requires segregating the landscape into discrete habitat types important to moose, calculating available nutrients within those habitats, determining moose diets and nutrient intakes, calculating seasonal requirements of nutrients, and determining which habitat provides most for those requirements. Our objectives for this study were to 1) assess the nutritional condition of female moose on JBER; 2) determine the diet of female moose on JBER; 3) model the relative nutritional value of habitat types available on JBER to adult female moose, expressed as the hypothetical number of animal units (AUs; female moose-days per hectare) each habitat could support; and 4) map the distribution of AUs in JBER and model the potential impacts of habitat modification to AUs. We hypothesized that moose in our study area would be in good condition because population densities were moderate and the annual harvest was sustained at a high level. We also hypothesized that shrublands would support the greatest number of AUs, that areas disturbed for urban development would be crucial to maintaining the moose population, and that our results could help quantify the potential impacts of habitat modification to the population.
We studied moose on JBER, a 30,400-ha military installation adjacent to Anchorage, Alaska (61.25°N, 149.75°W) with a climate transitional between the maritime Gulf of Alaska and the continental interior (Figure 1). Average daily air temperatures were +16°C and +14°C in July 2009 and 2010, respectively, and −7.5°C in January 2010 (Alaska Climate Research Center 2012). Average annual precipitation was 40.1 cm, of which 58% was rainfall between July and October (Western Regional Climate Center 2011). We compared precipitation during our 2-y study period with the records for this area from 1971 to 2000 (Natural Resources Conservation Service 2012). In this study, summer rain was 28% below normal for May–August 2009, winter snowfall was 1% below normal for November 2009–March 2010, and summer rain was 12% above normal for May–August 2010.
Approximately 40% of JBER consisted of undeveloped lands >0.5 km from a road. Development was localized in the central part of the base. This central developed area (Figure 1) was 28% of the study area and consisted of housing, office buildings, warehouses, storage lots, runways, golf courses, and firing ranges. The central developed area was interspersed with greenbelts and small (<0.25-ha) to large (>25-ha) woodlands. The woodlands consisted of paper birch Betula papyrifera, white spruce Picea glauca, black spruce Picea mariana, quaking aspen Populus tremuloides, balsam poplar Populus balsamifera, and black cottonwood Populus trichocarpa. Shrubs were primarily willow Salix spp., alder Alnus spp., and high-bush cranberry Viburnum edule. Independent of proximity to development, most of the study area (66% or 19,900 ha) was woodland (mixed, deciduous, coniferous, and shrub) that ranged from recent (<5 y) burns or clearings to mature communities without signs of recent logging or fire (Figure 2). Shrublands (3,580 ha) were nearly equally dominated by alder (1,611 ha) or willow (1,969 ha). The majority of willow shrublands (55%) were early successional hardwoods that were created by fire and clearing.
Assessing the nutritional condition of female moose
We studied animals under approved animal use and care protocols for the Alaska Department of Fish and Game (#90-05) and from the University of Alaska Fairbanks (#148885, 182744). To determine the nutritional condition of urban moose on JBER for comparison with other moose populations, we captured adult female moose in March 2009 (n = 7) and November 2009 (n = 12) within 0.5 km of roads, often in or adjacent to the central developed area. We darted all moose from the ground with a 3-cc dart containing a mixture of 3.0–3.9 mg carfentanil (carfentanil-citrate; Wildlife Pharmaceuticals, Fort Collins, CO) and 100 mg xylazine (xylazine-hydrochloride; Wildlife Pharmaceuticals). Anesthesia was reversed within 30 min of darting by injection of 400 mg Naltrexone (naltrexone-hydrochloride; Wildlife Pharmaceuticals) and 800 mg Tolazoline (tolazoline-hydrochloride; Wildlife Pharmaceuticals). We equipped moose with Global Positioning System (GPS) collars (Telonics, Inc., Mesa, AZ) that were equipped with very high frequency transmitters. We recaptured animals in March and November of 2009, 2010, and 2011 to recover location data stored on collars and to record body condition and pregnancy status.
We measured maximum depth of subcutaneous rump fat via ultrasound by using a Tringa Linear portable ultrasound (Esaote Group, Genova, Italy) along a transect from the spine, at the closest point to the coxal tuber (hip bone), to the ischial tuber (pin bone; Stephenson et al. 1993, 1998). We measured shoulder muscle depth was via ultrasound from a point 5 cm cranial of the posterior process of the scapula. We measured mandible and leg length as an index of skeletal size. Mandible length was the linear distance from base of the gum line of the incisors to the posterior angle of the mandible. Leg length was the linear distance from tip to base of the metatarsus when the limb was retracted and aligned with the torso. We collected blood from the jugular vein into glass tubes without additive (Vacutainer; Becton Dickinson, Franklin Lakes, NJ). Serum was separated by centrifugation at 3,000 × g and stored at −20°C for analysis (described below). We collected fecal pellets directly from the rectum or sampled from a pellet pile defecated during the capture for diet analysis (described below).
We also used reproductive success to assess and compare the nutritional condition of JBER moose with other populations. During the annual calving period (15–30 May), we observed collared females daily until we were certain whether a parturition event occurred and whether twins were present. We estimated twinning rates as the proportion of calving collared females that we observed with twins. We attempted to observe calves twice per month through August to determine calf survival.
We measured blood serum chemistries (blood urea nitrogen, creatinine, phosphorus, calcium, total protein, albumin, globulin, glucose, cholesterol, alanine amino transferase, alkaline phosphatase, gamma glutamyl transferase) with a Heska Fujifilm DRI-CHEM® Analyzer (Heska Corporation, Loveland, CO). We evaluated pregnancy rates with Pregnancy Specific Protein B (Bio-Tracking, Moscow ID) and progesterone (ELISA kit; Cayman, Ann Arbor MI).
Diets were used as indictors of nutritional condition and as inputs for modelling habitat quality. To estimate diet, we collected fresh fecal samples during moose captures (n = 39), and while conducting other field work (n = 90) from known and unidentified individuals from January 2009 through March 2010. We prepared composite fecal samples for microhistological analysis (200 views/sample) at the Wildlife Habitat and Nutrition Laboratory (Washington State University, Pullman, WA) by combining an equal number and mass of random fecal samples collected in each habitat type in winter (n = 10) and in spring, early summer, and late summer (n = 5/season), to reduce any potential bias from collection site or individual moose diet preference. The sex of most moose we collected fecal samples from was not known, but we assumed male and female moose had similar diets (Dungan and Wright 2005).
Modeling the relative value of habitat types
Determining habitat types
We modeled seven discrete moose habitats for nutritional quality. We used 1:20,000 high-resolution (∼10-m) ecotype shapefiles to determine moose habitat types in ArcGIS 10 (ESRI Inc., Redlands, CA). Ecotypes on JBER were delineated from an ecological land survey in 2001 (Jorgenson et al. 2003). Jorgenson et al. (2003) identified 52 vegetation classes that we subsequently collapsed into seven habitat types based on original vegetation class descriptions (Table S1; Jorgenson et al. 2003). We grouped forested (>25% tree cover) classes as follows: deciduous forests (dominated by only deciduous trees), mixed forests (codominated by deciduous and conifer trees), and conifer forests (dominated by only conifer trees). We grouped nonforested habitats as follows: barrens (alpine tundra, pavement, floodplains, mudflats, landscaping, sites with <30% ground cover, open water); shrublands (willow, alder, and seral scrub communities); shrubby wetlands (bogs and wetlands commonly containing an understory of sweetgale Myrica gale, willow, and dwarf birch); and grasslands (wetland and upland graminoid vegetation classes lacking a shrub understory). Open black spruce vegetation classes were classified as either shrubby wetlands or conifer forests depending on habitat characteristics: stands with a boggy substrate with intermixed shrubs were classified as shrubby wetlands, and stands with a nonboggy substrate in more upland zones were classified as conifer forests. We verified habitat-type delineations near roads and development by driving all roads to confirm or update the classification.
Moose-forage biomass estimation
To estimate limiting nutrients within habitats, we estimated total available stem and leaf current annual growth (CAG) biomass for moose browse at the end of the growing season in August 2009 and 2010 in all habitat types. Plants identified a priori as probable moose browse included paper birch, quaking aspen, balsam poplar–black cottonwood (hereafter referred to as poplar), high-bush cranberry, Barclay willow Salix barclayi, Bebb willow S. bebbiana, diamond-leaf willow Salix pulchra, gray-leaf willow S. glauca, and Scouler willow S. scouleriana. We used GPS coordinates randomly generated in Geographic Information Systems (GIS) to locate sampling plots within conifer forests, deciduous forests, grasslands, mixed forests, shrublands, and shrubby wetlands (total n = 30). We assumed that barrens had no available biomass. Browse biomass was estimated in five circular plots with a radius of 15 m. We used a smaller survey radius of 5 m or 10 m when biomass of a browse species was very high and evenly distributed across the plot.
We followed the approach of Seaton et al. (2011) to estimate shrub CAG. To estimate average stem basal diameter per plot, we randomly sampled 30 total stems per species from at least three plants with no >10 CAG stems/plant. We then counted the total number of CAG stems and leaf clusters (any leaves or leaf groups not originating from a CAG stem) 0.5–3.0 m above ground for each species in each plot. This range corresponds to the normal browsing height of Alaskan moose (Weixelman et al. 1998; Seaton et al. 2011).
We collected stems with leaves and leaf clusters for measurement. Basal diameter of fresh stems was measured (±1 mm) before stems and leaves were dried at 100°C for 24 h to determine dry biomass. We used data to establish relationships of dry stem and leaf biomass to live basal diameter and to determine mean leaf cluster biomass (Oldemeyer 1982; Seaton et al. 2011, Table S2). We square-root transformed stem biomass (Zar 1999) to meet assumptions of normality and homogeneity of variance for linear regression. We estimated the average total available CAG biomass of moose browse in each plot from our stem and leaf cluster count and our estimated biomass from the stem diameter–biomass relationships and the mean stem diameter of each species in each plot. We assumed that CAG leaf and stem biomass reflected availability of summer browse and that CAG stem biomass indicated winter browse availability.
Determining the nutritional composition of forages and diets
We calculated available nutrients by multiplying forage CAG biomass per habitat by the nutritional composition of each forage. We collected up to 200 g of each forage species in four seasons: spring (20 May), early summer (20 June), late summer (15 August), and winter (1 January). During the spring collections, we simulated leaf stripping by moose to collect both leaves and new growth stems. Winter samples were only collected in 2010, whereas spring and summer samples were collected in 2009, 2010, and 2011. We froze samples on dry ice in the field and stored them at −20°C until analysis.
Forage samples were freeze-dried, ground through a Wiley mill (Thomas Scientific, Swedesboro, NJ) with a 20-mesh (1.2-mm) screen, and stored at room temperature until analysis. We analyzed only plant species commonly found in our moose diets. We measured total nitrogen (%N) and extracts of both neutral and acid detergent fiber as described by Peltier et al. (2003). We measured in sacco digestibility (Tilley and Terry 1963; Spalinger et al. 2010) in the rumen of two fistulated adult female moose housed at the University of Alaska Experimental Farm in Palmer, Alaska. We followed the methods of Spalinger et al. (2010) for digestibility trials except that we incubated our samples for 30 h (summer forage) or 45 h (winter forage) to mimic seasonal retention times in the rumen (Lechner et al. 2010). We assumed digestible dry matter (DDM) to be equivalent to apparent digestibility. We extracted in sacco residues in neutral detergent to determine digestible neutral detergent fiber content (Spalinger et al. 2010). Total phenols were measured by extraction with acetone and expressed as equivalents of gallic acid (Singleton et al. 1999).
Proportions of each forage in the feces were corrected for indigestibility (1 − DDM) to estimate the proportion of each item in the diet. We estimated neutral detergent fiber, acid detergent fiber, DDM, digestible neutral detergent fiber, phenol concentration, %N, and acid detergent fiber–bound N of the whole diet for each season (spring, early summer, late summer, and winter) from the individual proportions of plants in the diets and the nutritional composition of each plant.
Modeling demands and intakes of energy, biomass, and nitrogen
We estimated energy and nitrogen demands of a reproducing female moose using a factorial approach (Figure 3; Barboza and Bowyer 2001; Barboza et al. 2009) in winter (1 January), spring (20 May; last trimester of pregnancy), early summer (20 June: peak lactation), and late summer (15 August: calf weaned). We used regional measures for moose body size (Schwartz and Hundertmark 1993) and fat mass change (this study). We calculated net energy (kJ/d) demands from previously published metabolic rates (Regelin et al. 1985; Schwartz et al. 1988a; Robbins 1993; Moen and Moen 1998; Barboza et al. 2009), net energy gains and losses due to changes in fat mass (Barboza et al. 2009), and the additional costs for reproduction (Text S1). We used net energy demands to estimate dry matter intake (DMI) demands (Barboza et al. 2009, (Text S1).We calculated the seasonal DMI of each species by multiplying the total seasonal DMI by the proportion of each species in the diet. We did not have biomass estimates for nonbrowse forages, so we calculated adjusted DMI as the sum of the species-specific intakes for only browse. We assumed that woody browses were the limiting forages. The daily N requirement was the sum of endogenous urinary N (0.056 g N/ kgBM0.75; Schwartz et al. 1987b), metabolic fecal nitrogen (5.536 g N/ kg DMI; Robbins et al. 1987) adjusted for available metabolizable dietary N, and N required for reproduction (Text S1). We used the adjusted DMI to calculate the adjusted N intake from shrubs. For more detailed methods, see Text S1.
Calculating animal units
To determine the number of adult reproductive female moose each habitat type could support in each season, we divided the utilizable biomass or N (kg DM/ha or g N/ha) for each habitat type by the adjusted intakes calculated from energy and N requirements in each season (kg DM/day or g N/day) for each female moose. Utilizable biomass is the total biomass of each species of shrub in the habitat type (Table S3) multiplied by the proportion of that species in the diet (based on Table 1). Utilizable N was calculated from the available N concentrations in biomass of each species of shrub (Total N − acid detergent fiber–bound N) and multiplied by the proportion of that species in the diet. The result represents the number of reproductive female moose that could be supported per hectare of habitat type per day (daily animal units; AUs; moose-days/ha). Thus, for any region of JBER, we can multiply AUs by the area (ha) of each of the seven habitat types to calculate the total AUs supported by that habitat type. Total AUs of each habitat type can then be summed for the region (Figure 3). Total AUs were divided by the number of days in spring (30 d), early summer (30 d), late summer (60 d) and winter (180 d) to estimate the number of AUs that could be supported in each season.
Mapping AUs and modelling the impacts of habitat modifications
We used ArcMap 10.2.1 (ESRI) for mapping and modeling AUs. We attributed AU values to each habitat type to create a current AU distribution map. To demonstrate how our AU map can be used to model potential impacts of habitat modification, we used a forested 25-ha parcel composed of shrublands (9.9 ha, valued at 4.07 AUs), mixed forests (12.8 ha, valued at 0.24 AUs), and barren access roads (2.7 ha, valued at 0.00 AUs) near the central developed area of JBER and modeled the impacts of a 10-ha portion (5.6 ha shrublands, 4.4 ha mixed forest) being fenced off or developed into barrens, or of the same 10-ha portion being left to mature into all mixed forest. Original AU value of the 25-ha parcel was calculated by multiplying the total area of each habitat type by the respective AU value, and then summing for all habitats. Then we edited all habitats within the 10-ha portion under both scenarios of modification and recalculated the new number of animals that could be supported.
We estimated shrub biomass and diet diversity with the Shannon-Diversity Index (Krebs 1999). We estimated diet selection for woody browse in late summer and winter with Ivlev’s Electivity Index and Strauss’ Linear Index (Ivlev 1961; Strauss 1979). To test for differences in body condition (rump fat and shoulder muscle depth, and blood-serum chemistry values) by season, and shrub biomass by habitat type, we used analysis of variance with a Tukey test for multiple comparisons between groups (α = 0.05). We conducted statistical analyses in JMP Statistical Packages (version 9.0.02; SAS Institute Inc., Cary NC).
Nutritional condition of female moose
The narrow range of mandible length (56.7 ± 0.9 cm) and metatarsal length (47.5 ± 1.9 cm) indicated the captured moose had reached asymptotic growth. Serum chemistries of captured moose were similar to those of healthy captive moose at the Moose Research Center, Alaska (Table S4; J. Crouse and P.S. Barboza, unpublished data). Muscle depths at the shoulder were similar between November (2.62 ± 0.53 cm) and March (2.22 ± 0.60 cm; P > 0.05), suggesting an insignificant loss of lean body mass during this period. Serum transaminases that are associated with degradation of muscle, liver, and kidney were also similar between captive moose at the Moose Research Center and wild moose on JBER in November and March (alanine amino transferase 36 ± 11 IU/L; gamma glutamyl transferase 28 ± 8 IU/L; Table S4). Blood urea concentrations of moose in our study area were consistently low in both November and March (9.56 ± 3.7 mg/dL), which is consistent with low intakes of N and conservation of body protein (Parker et al. 2005) and within the normal range observed for moose (Franzmann and Schwartz 1983; table 7 in Bubenik 2007).
Maximum rump-fat depth decreased from November (2.97 cm) to March (1.09 cm; P < 0.01). The corresponding estimates of body fat declined from 11.8% (44.0 kg) to 7.9% (12.4 kg of body mass [ingesta-free basis]) over the winter (Stephenson et al. 1998; Table S5). Only 4 of the 24 measures (17%) of fat depth in November were below 1.66 cm, which is the threshold for 50% probability of pregnancy in moose from south-central Alaska (Testa and Adams 1998). In March, only 3 of the 31 (10%) fat depth measures were below the mean depth for nonpregnant moose from interior Alaska (Keech et al. 2000), with two of the three animals also having rump fat depths below pregnancy thresholds the previous November. Pregnancy rates as determined by Pregnancy Specific Protein B concentration in March and November were 85% (11/13), 94% (17/18), and 100% (14/14) in 2009, 2010, and 2011, respectively. Of the animals with rump fat depths below threshold values for pregnancy, only one of four moose in November and one of three moose in March were not pregnant as determined by Pregnancy Specific Protein B, with the same nonpregnant moose responsible for both accounts. Serum progesterone varied from 156 to 8,150 pg/mL but the distribution of values did not separate into two groups that would correspond to pregnancy status (Testa and Adams 1998). We observed twins for 0% (0/5), 6% (1/17), and 22% (2/9) of all females that we observed with calves in 2009, 2010, and 2011, respectively. The proportion of females successfully rearing at least one calf through August was 40% (2/5), 50% (8/16), and 50% (5/10) for 2009, 2010, and 2011, respectively.
Shrubs dominated the diet of moose through most of the year (>70%), except during spring when forbs accounted for an equal proportion of the indigestible particles in the feces (Table S6; Table 1). However, when the diet was adjusted for digestibility, we estimated that forbs accounted for 70% of the diet in spring (Table 1). Diets were most diverse when moose were foraging on many types of forbs in the spring (Table 1). Willows were the predominant shrub in the diet throughout the year. In summer, Barclay willow and Scouler willow were selected over other willows (Table 2) and accounted for 74% of the willow intake. The winter diet was dominated by equally high proportions of willow and birch (Table 1) and these were consumed in proportion to their availability (Table 2).
Modeling the relative value of habitat types
Moose forage biomass estimation
Mean biomass density by habitat type ranged from 3 kg/ha to 381 kg/ha in summer and 1 kg/ha to 127 kg/ha in winter (Figure 2; Table S3). Shrublands provided the greatest mean biomass density of leaves (381 ± 344 kg/ha) and stems (126 ± 126 kg/ha) among all the habitat types (P < 0.01). Shrublands were only 12% of the total study area but provided 72% of forage biomass in late summer (Figure 2). Leaf biomass in shrublands was dominated by paper birch (37%), Bebb willow (36%), and poplar (23%).
Nutritional composition of forages and diets
Dietary proportions of horsetail Equisetum spp. and grass were greater than those for willow in spring (Table 1). However, horsetail and grass were similar to newly emerged willow leaves with respect to concentrations of DDM (87%–91%), digestible neutral detergent fiber (79%–86%), and N (3.8%–4.2%; Tables S7, S8). Fern rhizomes accounted for 34% of the spring diet (Table 1). However, concentrations of fiber in the rhizomes were similar to those of emerging willow leaves (30%–39% neutral detergent fiber), but contained less N (2.1% vs. 4.0%) and digestible dry matter (69% vs. 89%) than the preferred willows (Tables S7, S8, S9). In summer, preferred willows (Barclay willow and Scouler willow) were higher in DDM (84% vs. 81%) than the most abundant species of willow (Bebb willow; Table S7). Concentrations of N decreased from early (2.6%–2.9%) to late summer (2.1%–2.2%) in both preferred species and Bebb willow (Table S8). Low summer intakes of paper birch leaves were associated with lower DDM than preferred willow species in early summer (67% vs. 86%) and late summer (64% vs. 83%; Table S7). However, N concentrations of paper birch were similar to preferred willow in early summer (2.3%–2.5%) and higher than the preferred willows in late summer (2.4% vs. 2.1%; Table S8). In winter, birch and willow accounted for similarly high proportions of the diet (Table 1). Birch stems were higher in N than were willow stems (1.2% vs. 1.1%), but had lower DDM (72% vs. 89%) during winter (Tables S7, S8).
The overall quality of the diet was shaped by phenological changes in forages (Table 3). As the N content of forages decreased from early summer to winter, the availability of that N to the moose also decreased as seasonally increasing fiber levels bound more N to indigestible diet fractions (Table 3). These higher fiber concentrations were accompanied by increases in phenols and reduced fiber and dry matter digestibility (Table 3).
Modeling demands and intakes of energy, biomass, and nitrogen
Adjusted DMIs of browses alone were 30%, 74%, 85%, and 98% of the total DMI in spring, early summer, late summer, and winter, respectively. Adjusted N demands were 45%, 87%, 86%, and 98% of the N requirements estimated with the unadjusted DMI. We did not calculate the number of moose supported in spring because of the low proportion of browse in spring diets.
Energy demands increased from gestation (17.1 MJ/d) to peak lactation (19.6 MJ/d), requiring an increase in dry matter intake from spring (9.1 kg/d) to early summer (10.4 kg/d; Table S5) to maintain energy balance. Declines in digestibility in late summer increased the intake of energy (24 MJ/d) and dry matter (12.9 kg/d) required to restore body fat before winter. Predicted dry-matter intakes subsequently declined in winter (7.6 kg/d) because decline in energy demand due to fat catabolism (14 MJ/d) was proportionately greater than the decline in digestibility as animals shifted from mature leaves to stems (Table S5; Table 3). Our estimates for utilized proportions of forages were within 12% of the diet proportions determined by microhistology of feces collected from moose in this area.
Intakes of N followed that of forage intake and the concentration of N in the plant from spring through summer (Table 3). Dietary concentrations of N were 2% of dry matter or greater from spring through summer when fiber bound <22% of the total N (Table 3). The concentration of available N in the diet exceeded the threshold to meet N demands of the animal from spring to summer: 2.1% vs. 0.7% in spring, 2.2% vs. 1.1% in early summer, and 1.2% vs. 0.6% in late summer (Table 3). Fiber-bound N increased with decreasing N content in winter, and hence, available N in the dry mass of forage declined to 0.8%, which was near the threshold of 0.7% N required to meet N demands in winter.
Calculating animal units
Derived estimates of AUs in all habitat types were greater for N than for dry mass in late summer (Figure 4A; Table S10). Energy, and thus dry mass intake, was most limiting in late summer. Conversely, low concentrations of available N limit the number of AUs that can be supported in winter, which was the most limiting season overall (Figure 4B; Table S10).
High biomass density of shrublands (Figure 2) provided the greatest amount of dry mass and available N to support the largest number of AUs among all the habitat types in both summer and winter (Figure 4; Table S10). In units of moose-days during winter, each hectare of shrubland was equivalent to 11 ha of shrubby wetlands, 17 ha of mixed forest, 19 ha of deciduous forest, 75 ha of coniferous forest, and 81 ha of grasslands (Figure 4). Thus, the distribution of AUs across habitat types was determined by the distribution of shrublands for both dry mass and N (Figure 5). Shrublands were most prevalent in the subalpine region on the southeast boundary of JBER and within the central developed area (Figure 5). Foraging habitat types (i.e., excluding barrens) accounted for 77% of JBER, but shrublands were only 15% of the foraging habitat type. However, foraging habitat types accounted for only 31% of the central developed area, but shrublands were 21% of that foraging habitat.
The sex, age, size, and reproductive state of the modeled moose would alter the projected number of AUs that can be supported in an area. For instance, an increase in a female’s body mass by 42% from 360 to 510 kg decreased the estimates of the number of reproductive females by 24% in later summer and by 27% in winter. Similarly, reducing the energy demands of the animal by projecting a young female without reproductive demands increases the projected number of small moose (360 kg) by 45% in early summer.
Mapping AUs and modelling the impacts of habitat modifications
Fine-scale AU maps showed that AUs were concentrated in the subalpine and wherever development had disturbed the landscape, particularly in the central developed area (Figure 5). In our habitat modification scenarios, we calculated that the original 25-ha parcel could support 43.4 AUs. By manipulating our AU map, we calculated that fencing off the 10-ha parcel eliminated 23.8 AUs; whereas, allowing the shrublands to mature into a mixed forest had a similar effect on AUs, and eliminated 21.4 AUs (55% vs. 49% reduction, respectively).
Assessing the nutritional condition of female moose
Our data on body condition and reproductive output indicate that this urban population is in moderate condition (Boertje et al. 2007). Rump fat depths were above the thresholds for pregnancy defined by Testa and Adams (1998) and Keech et al. (2000) and, likewise, pregnancy rates in our study area were high and similar to most populations of moose (Ballard et al. 1991; Gasaway et al. 1992; Testa et al. 2000; Bertram and Vivion 2002). Calf survival was high compared with other studies across the state (Ballard et al. 1991; Gasaway et al. 1992; MacCracken et al. 1997; Testa et al. 2000; Bertram and Vivion 2002), but this estimate is based on a small sample size, as were our twinning rates, and all parameters of recruitment should be augmented with more observations. Continued observations could be combined with examining the reproductive tract and body fat depots of hunter harvests to monitor fecundity and health of yearlings, 2-y-olds and prime-aged females (Heard et al. 1997). Continued monitoring is also needed to assess the effect of movement of moose into JBER for sustaining the high harvest from Game Management Unit14C.
Our patterns of metabolism and DMI were similar to other studies of moose (Schwartz et al. 1984; Renecker and Hudson 1985, 1989; Moen and Moen 1998). Our model estimated that female moose selected a diet that removed a moderate proportion (31%) of the winter biomass. Direct evaluations of winter browse removal are required to confirm this estimate because twinning rates have been shown to decline with increasing browse-removal rates in interior Alaska moose populations (Seaton et al. 2011).
Our diets were dominated by just a few species of preferred forages through most of the year. Species quality shifted seasonally, resulting in the greatest diversity of the diet in the spring. Early and late-summer diet diversity was slightly lower than that of the winter, contrary to other studies of moose (Risenhoover 1989; Hjeljord et al. 1990; Renecker and Hudson 1992; Wam and Hjeljord 2010; Wam et al. 2010) but similar to that of moose in south-central Alaska (MacCracken et al. 1997).
Changes in plant quality likely influenced diet selection. Selection of Barclay willow and Scouler willow in early summer and late summer were positively associated with digestibility, whereas selection of paper birch in winter was positively associated with available N. Changes in diet selection support our model predictions of energy limitation in summer and N limitation in winter. Absolute values for the concentration of N and fiber from plants in this study were similar to those of other studies for the same species (Oldemeyer et al. 1977; Renecker and Hudson 1988; Schwartz et al. 1988b; Weixelman et al. 1998; Spalinger et al. 2010). Preferred species of willows leaves were higher in total phenols than paper birch and yet willows were still preferred. The subsequent selection of paper birch stems in winter suggests that moose may respond differently to a wide variety of plant secondary metabolites in both deciduous and coniferous trees (Stolter et al. 2009). Morphological attributes such as stem diameter may also contribute to diet selection by affecting foraging dynamics such as bite size, and thus may affect intake rate (Spalinger and Hobbs 1992; Searle and Shipley 2008). The preference for Scouler willow may therefore reflect longer, less branched stems with larger leaves than those of the more abundant Bebb willow.
Fiber content of the whole diet was similar in spring and summer even though forage fiber concentrations were lowest in spring. Forbs that emerge early in the spring may be very important for moose until emerging willow leaves increase in abundance. Fern rhizomes and newly emergent horsetails and grasses make up the majority of the diet in spring but are only minor components of the diet in summer. Intakes of indigestible dry matter from our spring diets, which contained high proportions of fern rhizomes and highly digestible forbs, is projected at 3.2 kg/d. This intake of indigestible dry matter is intermediate to those for early summer (2.3 kg/d) and later summer (4.4 kg/d) when animals consumed predominantly willow leaves. Therefore, ingesting fern rhizomes instead of stems in spring appears to be a strategy for increasing N and digestible dry matter intakes while maintaining gut fill for optimal gut function until willow leaves become available (Spalinger and Hobbs 1992; Barboza et al. 2009). These rhizomes grow in the topsoil, are large, bite-sized balls of starch and fiber, and are easily accessible to moose soon after the snow has melted. Moose may be able to maximize intakes of rhizomes because they grow in easily accessible patches when better quality foods are at low density (Spalinger and Hobbs 1992; Shipley et al. 1998). The diversity of foraging areas may be important in late winter and spring when females seek birth sites and foraging areas that will minimize predation risk and ameliorate mass loss before the onset of lactation (Bowyer et al. 1998; Poole et al. 2007).
Modeling the relative value of habitat types
This model currently provides estimates of the relative value of habitat types for an adult reproductive female moose. This model can be extended to estimate the demand of a local population with multiple sex, age, and reproductive classes (Miquelle et al. 1992). Our model projections supported the hypothesis that shrublands would provide the greatest amount of energy and N for moose and thus support the largest number of AUs. Because of the high proportion of shrublands, we project that the central developed area of JBER could support a greater density of AUs than could be supported by the outlying undeveloped areas. The importance of shrublands as foraging areas for moose is well-documented for areas adjacent to human developments and for those accompanying natural events such as fires, earthquakes, and glacial progression (MacCracken et al. 1997; Stephenson et al. 2006; Thompson and Stewart 2007; White et al. 2012). Small areas of shrubs on the perimeter of developments and roads can mitigate some of the loss of forage from development, especially when low-forage habitat types such as conifer forests or grasslands are replaced with shrublands. However, shrub perimeters can attract moose to roads and urban development, which can result in vehicle collisions and property damage (Danks and Porter 2010) and make the central developed area a high-risk area.
Mapping AUs and modelling the impacts of habitat modifications
Our AU map allows managers to visualize the current distribution of AUs and understand which areas are vital for supporting this moose population. The projected AU values for habitat types and the AU map can also be used by managers on JBER to quantitatively calculate potential impacts of habitat modification on the number of moose JBER can support. Our modeling exercise could be repeated for any number of development projects, such as housing expansions, new offices, new roads, or beneficial activities such as moose habitat-improvement projects or inadvertent shrub edge-habitat creation. On a broader scale, managers could use it to target areas to create or remove AUs in an effort to mitigate moose–human conflicts or increase recreational opportunity. Our demonstration indicates that the loss of shrublands through natural succession could reduce the number of AUs an area can support as much as development. In south-central Alaska, the natural succession from willow to spruce forest has decreased the numbers of moose over 50 y (Stephenson et al. 2006). Active management of shrublands, such as hydro-axing, may be required to maintain the existing forage base away from roads and to offset continued JBER development. Ultimately, the results of this study will facilitate better, more informed decisions regarding development, habitat modification, or succession.
Please note: The Journal of Fish and Wildlife Management is not responsible for the content or functionality of any supplemental material. Queries should be directed to the corresponding author for the article.
Table S1. Ecotype classifications from an Ecological Land Survey of Joint Base Elmendorf-Richardson, Alaska (Jorgenson et al. 2003) used to establish new classifications of moose Alces alces habitat types, 2009–2011. Fifty-two original ecotypes were grouped into seven discrete habitat types: barrens, conifer forest, deciduous forest, grassland, mixed forest, shrubland, and shrubby wetland.
Found at DOI: 10.3996/062014-JFWM-045.S1 (17 KB DOCX).
Table S2. Relationships between stem basal diameter (mm) and the dry biomass (g) of leaves and stems for deciduous browse available to moose Alces alces near Anchorage, Alaska, in 2009 and 2010.
Found at DOI: 10.3996/062014-JFWM-045.S2 (15 KB DOCX).
Table S3. Leaf and stem biomass densities (kg dry matter/ha) of common browse species for moose Alces alces by habitat type in August 2009 and 2010 near Anchorage, Alaska. Forage biomass diversity for each habitat type was calculated with the Shannon Diversity Index where higher numbers represent a higher diversity of forage biomass.
Found at DOI: 10.3996/062014-JFWM-045.S3 (17 KB DOCX).
Table S4. Blood values for moose Alces alces captured for this study in March 2009–2011 and November 2009–2010 near Anchorage, Alaska, and captive moose from the Moose Research Center (MRC), Alaska, during February 2011. Standard deviations are in parenthesis.
Found at DOI: 10.3996/062014-JFWM-045.S4 (15 KB DOCX).
Table S5. Selected model parameters, units, and values for calculating nutritional demands of moose Alces alces during winter, spring, early summer, and late summer 2009–2011 near Anchorage, Alaska.
Found at DOI: 10.3996/062014-JFWM-045.S5 (17 KB DOCX).
Table S6. Proportions of plant fragments (%) in pooled fecal samples (n = 5 except for winter where n = 10) of moose Alces alces during spring, early summer, late summer, and winter near Anchorage, Alaska, 2009–2010. Proportions were determined by microhistology (200 views/sample) at the Wildlife Habitat and Nutrition Laboratory at Washington State University, Pullman, Washington.
Found at DOI: 10.3996/062014-JFWM-045.S6 (19 KB DOCX).
Table S7. In sacco digestible dry matter (DDM, g/g) and digestible neutral detergent fiber (DNDF g/g) for major forage items for moose Alces alces near Anchorage, Alaska, 2009–2011. Samples (0.5–1.0 g) were incubated in the rumens of two fistulated moose housed at the Alaska Experimental Farm in Palmer, Alaska. Samples were incubated 30 h for spring, early summer, and late summer forages, and 45 h for winter forages.
Found at DOI: 10.3996/062014-JFWM-045.S7 (21 KB DOCX).
Table S8. Nitrogen concentration in dry mass (%N) of major forage items for moose Alces alces in spring, early summer, late summer, and winter near Anchorage, Alaska, 2009–2011. Percent Nitrogen (%N) is the nitrogen content of dry matter (%DM). Acid detergent fiber nitrogen (ADFN) is the nitrogen content of the acid detergent fiber (%ADF).
Found at DOI: 10.3996/062014-JFWM-045.S8 (21 KB DOCX).
Table S9. Concentrations of neutral detergent fiber (NDF, percent dry matter, %DM), acid detergent fiber (ADF, percent dry matter, %DM), and total phenols (mg/g dry matter) in dry mass of major forages for moose Alces alces in spring, early summer, late summer, and winter near Anchorage, Alaska, 2009–2011.
Found at DOI: 10.3996/062014-JFWM-045.S9 (30 KB DOCX).
Table S10. Derivation of Animal Units (AUs; moose-days/ha) by habitat type for female moose Alces alces near Anchorage, Alaska, 2009–2011. Utilizable fraction of biomass is the total biomass of each species of shrub in the habitat type (Appendix 1) multiplied by the proportion of that species in the diet (based on Table 1). Utilizable fraction of nitrogen was calculated from the nitrogen concentrations in biomass minus the nitrogen bound in the ADF of each species of shrub (Tables S8, S9) and multiplied by the proportion of that species in the diet. Requirements for biomass or nitrogen were calculated from Table S2. Animal units (AUs; days/ha) were calculated as utilizable biomass (kg/ha) or nitrogen (g/ha) divided by the corresponding daily requirement for one moose (biomass kg/d or g N·d−1).
Found at DOI: 10.3996/062014-JFWM-045.S10 (16 KB DOCX)
Text S1. Welch JH, Barboza PS, Farley SD, Spalinger DE. 2015 (unpublished). Methodology for modeling energy and nitrogen demands of moose near Anchorage, Alaska, 2009–2011.
Found at DOI: 10.3996/062014-JFWM-045.S11 (16 KB DOCX).
Reference S1. Sinnott R. 2008. Unit 14C moose management report. Pages 189–1206 in Harper P, editor. Moose management report of survey and inventory activities 1 July 2005–30 June 2007. Juneau: Alaska Department of Fish and Game. Project 1.0.
Found at DOI: 10.3996/062014-JFWM-045.S12; also available at http://www.adfg.alaska.gov/static/home/library/pdfs/wildlife/mgt_rpts/08_moose.pdf (5599 KB PDF).
Reference S2. White KS, Gregovich DP, Barten NL, Scott R. 2012. Moose population ecology and habitat use along the Juneau access road corridor, Alaska. In Department of Wildlife Conservation, editor. Juneau: Alaska Department of Fish and Game.
Found at DOI: 10.3996/062014-JFWM-045.S13; also available at http://www.adfg.alaska.gov/static/home/library/pdfs/wildlife/research_pdfs/wrr-2012-03_moose.pdf (3222 KB PDF).
We thank C. Garner, H. Griese, and C. McKee for their critical roles in funding and implementation of the project. D. Battle assisted with captures, data management, field, and GIS work. L. Solomon provided much-appreciated assistance with database management and GIS work. We appreciate all the help with captures provided by R. Sinnott, J. Coltrane, R. Graham, and JBER biological technicians. W. Collins graciously conducted forage-digestion trials used in this study. K. Hundertmark provided valuable comments and assistance with this study, and J. Crouse graciously provided unpublished data used in study. We also thank the Journal’s Associate Editor, and the anonymous reviewers that provided insightful review and comments on previous versions of this manuscript.
Funding was provided by the U.S. Army and the U.S. Air Force through the Army Corps of Engineers to the Alaska Department of Fish and Game and the University of Alaska Fairbanks.
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Citation: Welch JH, Barboza PS, Farley SD, Spalinger DE. 2015. Nutritional value of habitat for moose on urban and military lands. Journal of Fish and Wildlife Management 6(1):158–175; e1944-687X. doi: 10.3996/062014-JFWM-045
The findings and conclusions in this article are those of the author(s) and do not necessarily represent the views of the U.S. Fish and Wildlife Service.