239 ARTICLE

Resource selection in a high-altitude rangeland equid, the (Equus kiang): influence of forage abundance and quality at multiple spatial scales Antoine St-Louis and Steeve D. Côté

Abstract: Herbivores foraging in arid and seasonal environments often face choices between plant patches varying in abun- dance and nutritional quality at several spatial and temporal scales. Because of their noncompartmented digestive system, equids typically rely on abundant forage to meet their nutrient requirements. In forage-limited environments, therefore, scarcity of food resources represents a challenge for wild equids. We investigated hierarchical resource-selection patterns of (Equus kiang Moorcroft, 1841), a wild equid inhabiting the high-altitude steppes of the Tibetan Plateau, hypothesizing that vegetation abundance would be the main factor driving resource selection at a large scale and that plant quality would influence resource selection at finer scales. We investigated resource-selection patterns at three spatial levels (habitat, feeding site, and plant (vegetation groups, i.e., grasses, sedges, forbs, and shrubs)) during summer and fall. At the habitat level, kiangs selected both mesic and xeric habitats in summer and only xeric habitats (plains) during fall. At the feeding-site level, feeding sites had higher plant biomass and percentage of green foliage than random sites in the same habitats. At the plant level, grasses were selected over forbs and shrubs, and sedges were used in proportion to their availability during all seasons. Our results indicate that resource-selection patterns in kiangs vary across scales and that both forage abundance and quality play a role in resource selection. Plant quality appeared more important than hypothesized, possibly to increase daily nutrient intake in forage-limited and highly seasonal high-altitude rangelands.

Key words: arid environment, equid, Equus kiang, high-altitude steppes, mountains, resource selection, scales, Tibetan Plateau, Tibetan wild ass. Résumé : Les herbivores vivant dans les milieux arides et saisonniers doivent souvent faire des choix entre l’abondance des végétaux et leur qualité nutritionnelle. En raison de leur système digestif non compartimenté, les équidés comptent générale- ment sur une nourriture abondante pour combler leurs besoins nutritionnels. Dans les environnements où la nourriture est limitée, la rareté des ressources alimentaires constitue ainsi un défi pour les équidés. Nous avons étudié la sélection hiérarchique

For personal use only. des ressources par le kiang (Equus kiang Moorcroft, 1841), un équidé sauvage qui habite les hautes steppes du plateau tibétain, en posant l’hypothèse que la sélection des ressources est déterminée a` grande échelle par l’abondance de végétation et, a` plus fine échelle, a` la fois par l’abondance de la végétation et sa qualité nutritionnelle. Nous avons étudié les patrons de sélection des ressources a` trois niveaux: le type d’habitat, le site d’alimentation et le groupe de végétation (graminées, cypéracées, herbacées et arbustes), en été et en automne. En été, les kiangs sélectionnaient a` la fois les habitats mésiques et xériques, alors que seuls les habitats xériques (plaines) étaient sélectionnés au cours de l’automne. À l’intérieur de chaque habitat, la biomasse végétale et le pourcentage de feuillage vert étaient plus élevés dans les sites d’alimentation que dans les sites aléatoires. Au sein des sites d’alimentation, les graminées étaient sélectionnées par rapport aux herbacées et aux arbustes, et les cypéracées étaient utilisées en proportion avec leur disponibilité. Nos résultats indiquent que les patrons de sélection des ressources par le kiang varient d’une échelle a` l’autre et selon les saisons et que l’abondance et la qualité de la végétation jouent un rôle dans la sélection des ressources. À cet égard, la qualité nutritionnelle des plantes semble être plus importante que ce que nous envisagions, probable- ment afin d’augmenter leur apport nutritionnel quotidien dans un environnement fortement saisonnier où la nourriture est limitée.

Mots-clés : milieu aride, équidé, Equus kiang, hautes steppes, montagnes, sélection des ressources, échelles, plateau tibétain, âne sauvage tibétain.

Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 Introduction (McNaughton 1985; Van Soest 1994; Laca et al. 2001). Plants in For mammalian herbivores, predation risk and plant distribu- low-biomass vegetation patches—early in the growing season for tion are fundamental components of resource-selection strategies example—may thus be more digestible than plants in patches (Jarman 1974; Lima and Dill 1990; Wilmshurst et al. 1999). On with high biomass (Klein 1990; Van der Wal et al. 2000; Winnie rangelands, forage abundance and quality are often inversely et al. 2008). On the other hand, stochastic patterns of precipita- related because plant maturation increases biomass but also tions may also increase both plant biomass and greenness simul- fibre content in stems and leaves, lowering forage digestibility taneously, creating a mosaic of heterogeneous vegetation patches

Received 6 August 2013. Accepted 18 December 2013. A. St-Louis* and S.D. Côté. Département de biologie and Centre d’études nordiques, Université Laval, Québec, QC G1V 0A6, Canada. Corresponding author: Antoine St-Louis (e-mail: [email protected]). *Present address: Direction de la biodiversité et des maladies de la faune, Ministère du Développement durable, de l’Environnement, de la Faune et des Parcs, 880, chemin Ste-Foy, 2e étage, Québec, QC G1S 4X4, Canada.

Can. J. Zool. 92: 239–249 (2014) dx.doi.org/10.1139/cjz-2013-0191 Published at www.nrcresearchpress.com/cjz on 15 January 2014. 240 Can. J. Zool. Vol. 92, 2014

(Fryxell 1991). Herbivores may thus face choices among fluctuating large scale and that plant greenness would influence resource selec- patterns of plant abundance and quality across several spatio- tion at finer scales. Accordingly, (i) habitat types with highest plant temporal scales, potentially leading to scale-dependent resource- biomass should be used in greater proportion than their relative selection patterns (Hebblewhite et al. 2008; Singh et al. 2010). availability within the study area, especially when plants are green; Strategies of resource selection also depend on the anatomy (ii) feeding sites should have higher percentage of green foliage and of the digestive system, as illustrated by the dichotomy between also higher plant biomass than random sites within habitat types; ruminant bovids–cervids and hindgut-fermenter equids (Janis (iii) plants with higher crude protein content should be consumed in 1976; Demment and Van Soest 1985; Duncan et al. 1990; Menard greater proportion than their relative availability within feeding et al. 2002). Because plants are digested poorly and have a fast sites. Moreover, as plant abundance increases but quality declines passage rate in the digestive tract, equids rely on a high forage throughout summer and fall, (iv) patterns of resource selection intake strategy to meet their nutrient requirements (Janis 1976; should be mostly driven by forage abundance early during the grow- Duncan 1992). In arid and highly seasonal environments (e.g., ing season, and shift toward plant quality as summer and fall prog- arctic and alpine ecosystems), the growing season is short, re- ress. sources are generally scarce, and vegetation distribution is hetero- geneous and highly dependent on rainfall or snowmelt patterns Materials and methods (Noy-Meir 1973; Kudo 1991; Moen et al. 2006; Pettorelli et al. 2007). Under such circumstances, forage availability and distribution is Study area likely to be a strong limiting factor for wild equids (Rubenstein We conducted this study in fall 2003 (29 August to 21 November), 1989; Duncan et al. 1990; Ward 2006; Henley et al. 2007). Rettie and summer 2004 (17 June to 25 August), and summer 2005 (26 June to 2 Messier (2000) suggested that hierarchical patterns of resource 20 August) in a 390 km area located in the Tso Kar basin, eastern selection reflect a hierarchy of limiting factors in foraging herbi- , India (32°15=N, 78°0=E). This area lies at the westernmost vores. Accordingly, we may expect arid-adapted equids to select limit of the extensive Tibetan Plateau and is similar to the Chang resources primarily to maximize their forage intake (Janis 1976, Tang region of northwest (Schaller 1998). Elevation within Duncan 1992). Since forage abundance and quality may fluctuate the Tso Kar basin ranges from 4550 to 6000 m. The climate is that heavily across seasons and scales in such an extreme environment of high altitude – cold desert ecosystems, with annual tempera- (e.g., Van der Wal et al. 2000), investigating patterns of resource tures ranging from –40 to 30 °C, and mean annual precipitation selection may bring clues regarding the relative importance of below 100 mm, which represents the most arid and extreme con- forage availability and quality in arid-adapted wild equids, which, ditions encountered by kiangs across their distribution range to our knowledge, has never been attempted. (Mani 1978; Schaller 1998). Fresh water running off from glaciers Here, we assess resource-selection patterns at several spatial was available in the southern part of the study area near mesic scales in kiangs (Equus kiang Moorcroft, 1841), a rarely studied wild habitats, but otherwise water came only from precipitations. The equid living in high-altitude rangelands of central Asia (St-Louis vegetation is typical of alpine and desert steppes (Schaller 1998), and Côté 2009). Kiang, the largest species among wild asses, mainly characterized by grasses (family Poaceae), sedges (family inhabits the steppes of the Tibetan Plateau, a highly seasonal Cyperaceae), and short dicotyledonous forbs and shrubs (Mani ecosystem that shares characteristics with both arid and arctic– 1978; Rawat and Adhikari 2005). Most common plant genera are alpine environments (Wang 1988; Schaller 1998). Harsh climatic Stipa L. (needlegrass), Carex L. (sedge), Leymus Hochst. (wildrye), conditions are prevailing on the plateau and overall plant produc- Eurotia Adans. (= Krascheninnikovia Gueldenst.; winterfat), Artemisia

For personal use only. tivity is very low (Schaller 1998). The growing season is short and L. (sagebrush), Oxytropis DC. (crazyweed), Elymus L. (wildrye), Kobresia occurs between early June and mid-September, characterized by Willd. (bog sedge), Alyssum L. (alyssum), Caragana Fabr. (peashrub), increasing biomass but also fibre content in plants (Rawat and and Potentilla L. (cinquefoil) (Rawat and Adhikari 2005). Tibetan Adikhari 2005). Moreover, seasonal precipitations and landscape wolves (Canis lupus chanco Gray, 1863) occasionally prey on kiangs, features help create a mosaic of vegetation patches varying in but they occur in small numbers in the Tso Kar basin (Pfister abundance and quality throughout the growing season (Mani 2004). An estimated 250 kiangs inhabit the area on a yearly basis 1978). Under such conditions, both forage availability and quality (Fox et al. 1991). There is no permanent human settlement in the could exert a strong influence on kiang resource-selection strate- basin, but the nomadic community occupies the area in gies at several scales (Johnson 1980; Senft et al. 1987). seasonal camps, where they herd sheep (Ovis aries L., 1758), goats Our objective was to assess the effect of vegetation abundance and (Capra hircus L., 1758), and yaks (Bos grunniens L., 1766) (Bhatnagar quality on resource-selection patterns by kiangs at three nested spa- et al. 2006; Namgail et al. 2007). From November to April, nomads’ tial scales: (1) at the large scale, we investigated whether kiangs se- winter camps are moved within the Tso Kar basin, while summer lected among five habitat classes, using a use versus availability camps are located in valleys outside the basin (Ahmed 1999). approach (Manly et al. 2002); (2) at the intermediate scale, we as- sessed selection for feeding sites using a site-attribute design Large-scale selection patterns: habitat type (Garshelis 2000) based on plant abundance and percentage of green We evaluated resource-selection patterns at large scale by con- foliage; (3) at the fine scale, we assessed selection among four vege- trasting the use and availability of five discrete habitat types: hills

Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 tation groups (i.e., grasses, sedges, forbs, and shrubs) by contrasting (xeric), plains (xeric), meadows (mesic), marshes (mesic), and bar- their use and relative availability on each feeding site. Since green ren ground (Fig. 1). The calculation of habitat-type availability was leaves usually have higher cell content than dry leaves and thus a based on a Landsat 7 ETM+ image taken in August 2002 (EROS Data higher nutrient content (Duncan 1992; Van Soest 1994), plant green- Center, Sioux Falls, South Dakota, USA). We performed a super- ness at large and intermediate scales and crude protein content at vised classification with ENVI version 4.1 (ITT Visual Information fine scale were used as surrogates for plant quality. Observations Solutions Inc. 2004) using the minimum distance parametric rule were made during summer and fall to take into account seasonal to discriminate plains, meadows, and marshes. Because it was variations in forage biomass and greenness for both large- and fine- difficult to discriminate hills and plains with this method, we used scale resource-selection patterns. In a previous study, we showed slope and elevation to differentiate these two habitat types: areas that kiangs spent more time foraging and had higher intake rates in with slopes >5° and elevations higher than 4600 m were classified habitat types with high forage biomass, and also that forage intake as hills, whereas areas with slopes <5° and elevations <4600 m was maximized when feeding on green, highly digestible plants were classified as plains The area covered by each habitat type (St-Louis and Côté 2012). We therefore hypothesized that vegetation was calculated using ArcView GIS version 3.2 (ESRI 2000) and abundance would be the main factor driving resource selection at a converted into percentage of the study area. Fresh water and

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Fig. 1. Habitat-type classification of the study area in the Tso Kar basin, Ladakh, India. Black dots indicate the location of the three observation points used to count kiang (Equus kiang) groups. For personal use only.

brackish lakes were not included in the total area because they mated in 10% classes, and mean plant height was estimated by mea-

Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 were not considered available to kiangs. suring five randomly selected plants of each group to the nearest To estimate plant abundance in the different habitat types, we centimetre using a ruler. Three consecutive days were necessary to conducted vegetation surveys in June (i.e., early summer) and August complete a survey. In early summer 2005, surveys in marshes were (i.e., late summer) 2004 and 2005. Three permanent 1 km linear tran- affected by high water levels in the vegetation plots, which may have sects were positioned in each of the four vegetated habitats, i.e., hills, decreased the estimated available biomass emerging out of the plains, meadows, and marshes. The starting point of these transects water. was chosen randomly and transects were laid in random directions Habitat use was evaluated by mapping kiang groups observed provided that they were located entirely into the same habitat. We from three vantage points, located up to 12 km apart, along survey divided each transect in two 500 m sections, and on each we ran- routes above 4800 m that allowed us to cover the whole basin. domly placed six 1 m2 plots along the transect line, for a total of Groups ranged from 1 to approximately 40 individuals, but typi- twelve plots/transect. We estimated visually the percentage of plant cally contained less than 15 kiangs. Even if groups were small, we cover and the mean plant height (cm) of four vegetation groups: could detect all groups located within the zones associated with grasses (mainly Poaceae), sedges (Cyperaceae), forbs (all herbaceous the three observation points. Because kiangs within groups are dicots), and shrubs (woody stemmed species). Plant cover was esti- highly synchronized (St-Louis 2010), we assumed that habitat-

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selection decisions at the large scale are the same for all individ- composite samples covering the entire sampling period (i.e., Septem- uals in a single group. Therefore, we analyzed groups as single ber, October, November 2003; June, July, August 2004; June, July units irrespectively of their size. In general, three consecutive 2005). Six slides were then prepared for each composite sample using

days were necessary to complete a survey, and because movements H2O2 for bleaching and mounted with DPX mounting medium. We of kiangs among the different regions of the basin were minimal, the prepared a reference collection based on the 25 most common plant vast majority of animals were only sampled once during a survey. species occurring in the study area using the same procedure. Each The surveys were performed at three-week intervals during fall 2003 slide of feces was divided into height observation rows of a width (September to November), and summer 2004 and summer 2005 (June equal to the diameter of the visible microscope field. Five observa- to August for both years). We noted directly the number of groups in tion fields were selected randomly along every observation row, to- each habitat type. Counts were made between the hours of 0500 and talling 240 observation units (40 units × 6 slides) by monthly 1000, generally under sunny conditions. composite sample. For each unit, we counted every fragment by plant species whenever possible, otherwise they were classified as grass, Intermediate-scale selection patterns: feeding site sedge, forb, shrub, or unknown. We used the 100× magnification. To evaluate resource selection at the intermediate scale, we mea- sured the attributes of feeding sites and performed comparisons Nutritional quality of plants with random sites located within the same habitat type. A group To evaluate the nutritional quality of the different vegetation feeding site was defined as an approximately circular area groups, we analyzed the crude protein content of 24 plant species where >50% of the kiangs in a group were seen feeding together for among the 25 most common species occurring in the study area. a minimum of 30 min. Kiang observations spanned the entire day- Plant samples were collected in mid-October 2003 and during the light period. Kiangs were approached on foot and were observed at last week of July in 2004 and 2005 (2003: N = 42; 2004: N = 15; 2005: distance between 100 and 500 m. On the rare occasions where kiangs N = 25). Samples were dried in the field in paper bags, and then were disturbed, observations started after they resumed their activ- oven-dried at 60 °C for 48 h and ground through a 1 mm mesh. ities. Two observers were necessary to locate a feeding site. One ob- Analyses were performed using the Kjeldhal method (Van Soest server moved to the site where kiangs had been seen feeding 1994). Results for each plant species were then merged according immediately after an observation session, guided through radio con- to their vegetation groups (grasses = 6 species; sedges = 3 species, tact by the other observer with the spotting scope, and marked the forbs = 9 species; shrubs = 5 species). site. The presence of fresh grazing marks was required to confirm the location of the feeding site. Feeding-site attributes were estimated by Data analyses laying six 1 m2 plots, randomly placed within a 25 m radius circle We used the method of Manly et al. (2002) to evaluate the signifi- centred on the feeding location (Schaefer and Messier 1995). In every cance of the selection ratios for the five habitat types. Because we plot, plant cover and percentage of green material were estimated observed groups in marshes and on barren ground rarely, we had to visually for each vegetation groups in 10% classes. Mean plant height merge these two habitat categories with meadows and plains, re- was calculated by measuring five randomly selected plants of each spectively, so that the observed frequency of all habitat types in all seasons was above 5. Selection ratios ( ) were then calculated as group to the nearest centimetre, using a ruler. To compare feeding- wi follows: site characteristics to characteristics of random locations, we re- peated the same design twice in random directions from each (1) w ϭ o /␲ feeding site at fixed distances of 100 and 500 m. We chose these two i i i

For personal use only. distances to allow the analysis of feeding-site selection at more than

one spatial resolution, thus taking into account the potential heter- where oi is the proportion of groups observed in habitat type i and ␲ ogeneity in spatial distribution of vegetation communities within i is the relative availability of habitat type i. Confidence intervals each habitat type (Fortin et al. 1989). were calculated for each selection ratio using To convert plant cover and height into biomass, we clipped individual plants 1 cm above the ground in one randomly chosen ͑ ͒ ϭ ͙͕ Ϫ ␲2 ͖ (2) z␣/2 se wi z␣/2 oi(1 oi)/(uϩ i ) plot (out of six plots) on each feeding site. Plant samples were dried in the field in paper bags, and then oven-dried for 48 h at 60 °C and weighed using a 0.1 g precision scale. We used linear where u+ is the total number of groups observed in all habitat regressions to calculate regression coefficients for plant cover and types (Manly et al. 2002). Selection ratios where the 99% confi- height, and then estimated biomass for each plant category in all dence interval excluded 1 were considered significant. Habitat plots (for details see appendix Tables A1 and A2, as well as Hamel types with ratios >1 were regarded as “selected”, whereas habitat and Côté 2007). The feeding-site sampling design also allowed us types with selection ratios <1 were considered “avoided”. to estimate the mean percentage of green vegetation in three We compared feeding sites to paired random sites using condi- habitat types for each field season. For that purpose, we used only tional (i.e., case-controlled) logistic regressions, using the LOGISTIC the data collected on random feeding sites. procedure and the STRATA statement in SAS version 9.1 (SAS Institute Inc. 2003). We performed separate analyses for the two

Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 Fine-scale selection patterns: vegetation group spatial resolutions of the random sites, i.e., sites located at 100 and We investigated selection patterns among the four vegetation 500 m from feeding sites. Because we hypothesized that plant abun- groups (grasses, sedges, forbs, and shrubs) by contrasting their dance, quality, and composition could all influence the selection of respective use and availability within each feeding site. In each of feeding sites, we considered the following covariates to explain the the six plots laid on every feeding site, the percentage of grazed use of feeding sites relative to random sites: (i) total plant biomass; individual plants was estimated visually to the nearest 10% for (ii) percentage of green foliage; percentage of (iii) grasses, (iv) sedges, each group (Schaefer and Messier 1995). The availability of each (v) shrubs, and (vi) forbs. Since we had no a priori information on how group was estimated by their relative contribution to the total these factors would influence the selection of feeding sites in a wild plant biomass for each plot, expressed in percentage. equid, candidate models were built as to include the following: a full To gain additional information on seasonal plant use by kiangs, we model, a null model (without covariate), a model for every single collected fecal samples approximately every 2 weeks in all habitat covariate, a set of models comprising multiple combinations of classes during each field season. Fecal samples were dried in the field two covariates, and a set of models where total plant biomass and in paper bags, and then oven-dried at 60 °C for 48 h and ground with percentage of green foliage were in interactions with each of the a 1 mm mesh. Individual samples were mixed to make 8 monthly vegetation group covariates. Models were ranked based on Akaike’s

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Table 1. Percentage of green foliage in each habitat type observed in the hills than in the plains. When considering the rela- during fall and summer in the Tso Kar basin, Ladakh, tive availability of each habitat type, meadows and marshes were India, estimated from random sites. selected during both summers, but not in the fall (Fig. 2b). Plains % Green foliage were selected and hills were avoided in fall 2003 and summer 2004 Season, year N Habitat (mean ± SE) (Fig. 2b). In summer 2005, kiangs made no selection for plains or hills. Fall 2003; a 17 Hills; a 4.0±2.0 Selection of feeding sites Plains; a 1.0±0.2 The percentage of green foliage was the main variable explain- Meadows; a 2.0±0.5 ing the selection of feeding sites at the 100 m scale, since it was the Summer 2004; b 21 Hills; a 69.0±3.1 ⌬ only variable common to the models with i <2 (model-averaged Plains; a,b 73.0±7.7 estimate for “green”: 0.17 ± 0.06; Tables 2A, 3). The best model Meadows; b 89.0±4.1 (green + sedges) predicted 59.7% of the observations (McFadden’s Summer 2005; c 23 Hills; a 94.0±1.0 adjusted r2 = 0.19). Plants on feeding sites had 2.4% more green Plains; a 87.0±4.1 foliage than plants at random sites located 100 m farther (N = 67; Meadows; a 95.0±1.5 95% confidence interval (CI): 0.8–4.0). In general, plant biomass Note: Different letters indicate nonoverlapping 95% confi- was the main variable explaining the selection of feeding sites at dence intervals among years and habitat. the 500 m scale (model-averaged estimate for “biomass”: 0.07 ± 0.04; Tables 2B, 3). The best model (biomass + grasses) predicted information criterion corrected for small sample size (AIC ; c 72.2% of the observations (McFadden’s adjusted r2 = 0.27). Feeding Burnham and Anderson 2002). For each candidate model i, we calcu- sites had, on average, 10.4 g/m2 more plant biomass than random lated the difference between the AICc of model i and the AICc of the ⌬ ␻ sites located at 500 m (N = 54; 95% CI: 3.4–17.4). best model ( AICc), the Akaike weight ( i), and the evidence ratio, ␻ ␻ expressed as the ratio between the i of the best model and the i of Selection of vegetation groups model i. This ratio indicates how the first model (i.e., with the lowest Grasses and sedges were the vegetation groups mostly con- AICc value) is likely to be the best model compared with model i. For sumed by kiangs, as observed both on feeding sites and in their 2 the best model, we then calculated the McFadden’s adjusted r seasonal diet (Figs. 3a, 3c; Table 4). The grass/sedge ratio in kiang (Compton et al. 2002): feces increased throughout both summer seasons, while it de- creased throughout the fall season (Table 4). When comparing the 2 (3) McFadden’s adjusted r two summers, the grass/sedge ratio was lower in 2004 than in ϭ Ϫ ͕ Ϫ ͖ 1 (LLcovariate k)/LLwithout covariate 2005 (Table 4). Within feeding sites, grasses were always selected, whereas sedges were consumed in proportion to their relative where LL is the log-likelihood and k is the number of parameters. availability (Fig. 3b). Forbs and shrubs were avoided in all habitats. We computed the mean percentage of grazed plants by group In fall, selection among the four vegetation groups was less on each plot, divided by the relative availability of vegetation marked than during the two summer seasons (Fig. 3d). Grasses groups computed for every feeding site. Selection for vegetation were selected during both summer seasons and were consumed in groups within feeding sites was analyzed using Manly’s standard- proportion to their availability in fall. Sedges were consumed in proportion to their availability in all seasons. Forbs were con- ized ratios (Bi) calculated as

For personal use only. sumed in proportion to their availability only during fall 2003 and ϭ ͚ avoided in summer. Shrubs were avoided during all seasons. (4) Bi wi/( wi) Crude protein content

where Bi represents the estimated probability that a vegetation Overall, plants had 2.4 times less crude protein in fall 2003 than group would be used if all vegetation groups were equally avail- during summers 2004–2005 (t[1,74] = –15.31, P < 0.001; Fig. 4). Crude able in each plot (Manly et al. 2002). The 95% confidence intervals protein content did not differ between the two summers (t[1,70] = were calculated from the mean standardized ratios obtained for 0.225, P = 0.16). Forbs had about 1.5 times more crude protein than

each feeding site. The protein content of vegetation groups was grasses (t[1,70] = 3.35, P = 0.001) and sedges in summer 2004, and arcsine-transformed and data were analysed using ANOVA. Signif- 1.6 more than sedges (t[1,70] = 5.26, P < 0.001) in summer 2005. Forbs icance level was set at ␣ = 0.05 for all statistical analyses. also had 1.3 times more crude protein than shrubs in fall 2003

(t[1,70] = 2.25, P = 0.03). Grasses and sedges had similar crude pro- Results tein content in 2004 (t[1,70] = 1.32, P = 0.19), but in 2005, grasses had Availability of habitat types, plant biomass, and percentage 1.2 times more crude protein than sedges (t[1,70] = 2.50, P = 0.02). of green foliage Discussion Xeric habitats covered more than 88% of the study area (hills: 239.2 km2; plains 97.1 km2), while mesic habitats occupied only 3% Integrating more than one spatial scale in ecological studies Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 (meadows: 7.7 km2; marshes: 3.3 km2)(Fig. 1). Inversely, summer allows a better understanding of patterns, processes, and limiting mean for aboveground plant biomass (2004 and 2005 combined) factors determining resource selection (Senft et al. 1987; Wiens was higher on marshes (64.5 g/m2), followed by meadows (37.8 g/m2), 1989; Rettie and Messier 2000). To our knowledge, this is the first plains (10.3 g/m2), and hills (3.0 g/m2) (for details see St-Louis study to document resource selection in a wild equid using a 2010). In fall, the mean percentage of green foliage was only 2.5%, hierarchical approach. Previous studies have shown that components of whereas in summer it was 76% in 2004 and 90% in 2005 (Table 1). resource selection in arid-adapted equids may be driven by both Mean percentage of green foliage in plants was comparable among forage availability and quality (Henley et al. 2007; Sundaresan et al. hills, plains, and meadows at all times. 2007; Kaczensky et al. 2008), but none considered a multiscale framework. Our results support our hypothesis that resource se- Selection of habitat types lection in kiang is scale-dependent and varies across seasons. At a During all seasons, kiang groups used plains and hills in greater large scale, forage abundance was selected for, but only in sum- proportion than meadows and marshes (Fig. 2a). More groups were mer, at a time when plants are greener. At an intermediate scale, observed in plains than in hills during fall 2003 and summer 2004. both forage abundance and quality influenced the selection of This pattern, however, was reversed in 2005 when more groups were feeding sites. At a fine scale, plant quality measured through

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Fig. 2. Use and selection of habitat types at large scale by kiangs (Equus kiang) in the Tso Kar basin, Ladakh, India. (a) Habitat types used by kiang groups during fall 2003, summer 2004, and summer 2005 based on the number of groups (mean ± SE) observed per habitat during each survey. (b) Manly’s selection ratios (mean ± SE) for each habitat type during the same periods. Selection ratios that differed significantly from 1 are marked with asterisks (␣ = 0.05).

Table 2. Final models for feeding-site selection of kiangs (Equus kiang) within the Tso Kar basin, Ladakh, India, in fall 2003, summer 2004, and summer 2005, where selection for feeding sites were compared with (A) random sites located in a random direction 100 m away and (B) random sites located in a random direction 500 m away. Evidence ⌬ ␻ Models* AICc AICc i ratio (A) Random direction 100 m away Green + sedges 83.78 0.00 0.27 1.00 Green + sedges + green × sedges 84.79 1.01 0.16 1.66 Green 85.62 1.85 0.11 2.52 Green + biomass 85.67 1.89 0.10 2.57 Without covariates (null) 92.88 9.11 0.00 94.89 (B) Random direction 500 m away Biomass + grasses 62.80 0.00 0.28 1.00

For personal use only. Biomass + forbs + biomass × forbs 63.06 0.27 0.25 1.14 Biomass + grasses + biomass × grasses 64.76 1.96 0.11 2.66 Biomass 64.77 1.97 0.10 2.68 Without covariates (null) 74.86 12.06 0.00 416.28 Note: Random sites were located within the same habitat type for both scales. Model components are as follows: “biomass”, plant biomass in feeding sites (g/m2); “green”, percentage of green foliage; “grass”, percentage of grasses (mainly Poaceae); “sedges”, percentage of sedges (Cyperaceae); “forbs”, percentage of herbaceous dicots; “shrubs”, percentage of woody shrubs. Candidate models

were compared based on their AICc values, calculated from the conditional logistic regression ⌬ analyses. AICc, Akaike’s information criterion corrected for small sample size; AICc, difference ␻ between the AICc of model i and the AICc of the best model; i, Akaike weight of model i. ⌬ *Only the best models (i.e., AICc ≤2) and the null model are presented. All candidate models are listed in appendix Table A2.

crude protein content did not explain resource selection in sum- 2004. Moreover, kiangs selected feeding sites primarily based on mer, but its effect was higher in fall because forbs were used in plant greenness at a fine spatial resolution (100 m scale), which similar proportions as graminoids. We suggest that patterns of suggest that they responded to local variations in plant quality resource selection by kiangs may be the outcome of a feeding within habitat types, potentially increasing their forage intake by Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 strategy aimed at increasing nutrient intake in a forage-limited selecting food patches with a high availability of green plants and highly seasonal environment. (Spalinger and Hobbs 1992; Illius 2006). Kiangs could thus make the most of high-biomass habitats and vegetation patches at times Selection of habitat types and feeding sites when plant quality is also high, which highlights a greater role of During previous surveys on Tibetan wildlife, kiangs were plant quality than hypothesized. In other equids, green foliage mostly observed in open, xeric habitats, but locally used mesic was also an important factor of resource selection at several and riparian habitats (Harris and Miller 1995; Schaller 1998; scales. Kaczensky et al. (2008) observed that arid-adapted Asiatic Bhatnagar et al. 2006). Our results support these observations and wild asses had large home ranges in xeric habitats and moved show that both small mesic habitats and widely available xeric between green vegetation patches for feeding. Seasonal move- habitats are used by kiangs. As hypothesized, forage abundance ments of feral horses were also related to the availability of green was an important factor of resource selection by kiangs at large forage at large scales (Berger 1986). Duncan (1992) further noted scale, but only in summer when plant quality was high. When horses that were feeding selectively on plants with a high contrasting the two summer seasons, kiangs made greater use of proportion of green parts in the Camargue wetlands (France) hills than plains in 2005, a year when plants were greener than in improved the quality of their diet.

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Table 3. Parameter estimates of the best models explaining selection for feeding sites by kiangs (Equus kiang) in the Tso Kar basin, Ladakh, India, at the 100 and 500 m spatial scales. 95% Wald CI Spatial scale Model Covariate Estimate SE Lower Upper 100 m 1 Green 0.14 0.05 0.04 0.25 Sedges 0.02 0.01 0.00 0.04 2 Green 0.15 0.06 0.04 0.27 Sedges 0.07 0.06 −0.04 0.18 Green × sedges 0.00 0.00 0.00 0.00 3 Green 0.14 0.06 0.03 0.25 4 Green 0.13 0.06 0.02 0.24 Biomass 0.01 0.01 −0.01 0.03 500 m 1 Biomass 0.07 0.02 0.02 0.12 Grasses 0.03 0.01 0.00 0.05 2 Biomass 0.01 0.03 −0.05 0.07 Forbs −0.04 0.03 −0.08 0.00 Biomass × forbs 0.00 0.00 0.00 0.00 3 Biomass 0.08 0.03 0.01 0.14 Grasses 0.03 0.02 0.00 0.07 Biomass × grasses 0.00 0.00 0.00 0.00 4 Biomass 0.05 0.02 0.01 0.09 Note: Variables with 95% Wald confidence intervals (CI) excluding 0 are in boldface type.

Fig. 3. Use and selection of vegetation groups by kiangs (Equus kiang) within feeding sites in the Tso Kar basin, Ladakh, India, for each habitat type (hills: N = 28 feeding sites; plains: N = 32 feeding sites; meadows: N = 13 feeding sites). (a) Percentage (mean ± SE) of grazed plants of each vegetation group within feeding sites among habitat types; (b) Manly’s standardized selection ratios (mean ± SE) of each vegetation group within feeding sites among habitat types; (c) percentage (mean ± SE) of grazed plants of each vegetation group within feeding sites among years; (d) Manly’s standardized selection ratios (mean ± SE) of each vegetation group within feeding sites among years. Selection ratios that differed significantly from 0.25 (dotted line) are marked with asterisks (␣ = 0.05). For personal use only. Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14

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Table 4. Proportion of different vegetation groups (mean ± SE) in feces of kiangs (Equus kiang) by year and month, based on microhistological analyses. Year Month N Grass Sedge Forb Shrub Unknown 2003 September 12 38.0±1.8 54.5±3.6 5.2±1.6 1.7±0.7 0.6±0.4 October 16 27.2±1.8 62.8±1.8 3.2±0.4 5.1±1.0 1.7±0.6 November 6 19.7±3.0 75.7±3.0 1.7±0.3 0.0±0.0 2.9±0.6 2004 June 6 24.4±2.4 62.4±2.9 4.6±1.2 0.0±0.0 8.6±2.2 July 11 40.6±3.1 48.6±3.8 4.9±1.9 2.5±0.6 3.5±0.7 August 14 41.8±1.6 52.8±1.3 2.2±0.9 1.3±0.7 1.9±0.7 2005 June 4 61.1±2.4 36.9±2.2 1.6±0.7 0.2±0.2 0.2±0.2 July 18 66.6±2.9 29.2±3.0 3.1±1.1 1.1±0.6 0.0±0.0 Total 90 37.0±7.4 55.0±6.2 4.0±1.0 1.3±0.7 2.8±1.2 Note: N is the number of individual fecal samples used in each composite sample. Means and SE com- puted for six slides per composite sample per month.

Fig. 4. Percentage of crude protein content (mean ± SE) of forbs and shrubs. Previous studies indicated that grasses of the vegetation types used by kiangs (Equus kiang) in fall 2003 (mid- genus Stipa constituted 90% of the summer diet of kiangs in Tibet October) and during summers 2004–2005 (last week of July). (Harris and Miller 1995; Schaller 1998). In the Tso Kar basin, the Different letters indicate statistical significance (␣ = 0.05). main sedge species is Carex melanantha C.A. May, a coarse grami- noid with acute leaves, whereas grasses are mostly represented by Stipa sp., Leymus secalinus (Georgi) Tzvelev (wildrye), and Elymus nutans Griseb., species with thin leaves (Schaller 1998; Rawat and Adhikari 2005). When facing a choice, kiangs could possibly in- crease their daily nutrient intake by choosing to feed on plants with thinner leaves that are easier to digest (Laca et al. 2001). Overall, protein contents were lower in fall than in summer, which suggests that nitrogen may be a limiting factor at that time of the year. Moreover, protein content was generally lower for grasses and sedges than forbs and shrubs, following a pattern also observed in eastern Tibet (Long et al. 1999). Considering that the minimum protein content required to maintain basic meta- bolic functions in herbivores is around 6%–7% (Van Soest 1994; Owen-Smith 2002), it seems that during fall only forbs could fulfill this requirement in our study area. The observation that kiangs expanded their diet in fall to include a higher proportion of forbs may reflect the need to increase their protein intake at that time

For personal use only. of the year. In summer, when nitrogen is less limiting, kiangs could thus afford to feed more selectively on preferred grass species.

Implications for equids in forage-limited environments The Tibetan Plateau shares common characteristics with desert, In habitat-selection studies, the assumption that the relation- alpine, and arctic ecosystems, where forage availability is limited ship between use and availability is linear under random selection and plant quality fluctuates heavily throughout the growing sea- is not always true and may generate difficulties when interpreting son (Schaller 1998). In the xeric habitats of the Tso Kar basin, resource-selection ratios (Garshelis 2000). Beyond a given avail- ability, habitats may no longer be used in higher proportions, snowmelt patterns contribute to create a patchy vegetation distri- which would lead to a decreasing relationship between use and bution, which is typical of arctic and alpine ecosystems (Kudo availability (Mysterud and Ims 1998). Our result that hills were 1991; Van der Wal et al. 2000). In these highly seasonal environ- “avoided” probably suffered from this problem. In cases where some ments, herbivores are often limited by the quality of the vegeta- habitat classes clearly predominate, both use and use versus avail- tion and resource selection is often influenced by the phenological ability results should be considered (Garshelis 2000). In our study stages of the vegetation (Klein 1990; Albon and Langvatn 1992; area, hills predominate and cover a large portion of the landscape. Mysterud et al. 2001; Singh et al. 2010). It is possible that the Although they were not selected, it would be inappropriate to digestibility of the vegetation in mesic habitats is high in early classify hills as being not important for kiangs because a high summer but declines rapidly throughout the growing season (Van Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 number of groups were observed there for long consecutive peri- Soest 1994), since most plant growth occurs early in summer fol- ods. Kiangs, however, regularly made daily movements between lowing water run-off in streams. In xeric areas, the vegetation different habitat types. These daily movements undoubtedly add a could occur in patches of varying quantity and quality throughout degree of uncertainty to observations at a large scale. But because the season, because stochastic rainfall patterns are more likely to our observations were repeated in time and over long periods, we induce a substantial variation in plant growth than in mesic en- believe that our results provide a reliable estimation of the pro- vironments. Feeding on green vegetation patches could also help portion of time that kiangs spent in the various habitat types. kiangs fulfill their water requirements, because kiangs do not appear to drink frequently from running water (Schaller 1998; Selection of vegetation groups St-Louis and Côté 2009). It is not excluded, however, that selection Equids likely do not have the capacity to detoxify plant phenolic at a much larger scale than our study (e.g., landscape scale) is compounds (Janis 1976; Duncan 1992). High amounts of these ar- partly determined by the availability of a perennial source of wa- omatic constituents in forbs and shrubs (Long et al. 1999) may thus ter, as noted for Asiatic wild asses (Henley et al. 2007). Flesch and deter kiangs to feed on them. At a fine scale, kiangs selected Steidl (2010) highlighted the importance of considering environ- grasses over sedges within feeding sites in summer and avoided mental gradients in resource-selection studies. Ours was con-

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ducted in the westernmost and driest part of kiang’s distribution Fox, J.L., Nurbu, C., and Chundawat, R.S. 1991. The Mountain ungulates of range (Schaller 1998). In these extreme conditions, the very low Ladakh, India. Biol. Conserv. 58(2): 167–190. doi:10.1016/0006-3207(91)90118-S. Fryxell, J.M. 1991. Forage quality and aggregation by large herbivores. Am. Nat. forage availability and seasonal precipitations may have prompted ki- 138(2): 478–498. doi:10.1086/285227. angs to be more selective toward plant quality. Garshelis, D.L. 2000. Delusions in habitat evaluation: measuring use, selection, Our results supported the hypothesis that resource selection in and importance. In Research techniques in animal ecology: controversies and kiang is influenced by both forage abundance and quality, but consequences. Edited by L. Boitani and T.K. Fuller. Columbia University Press, New York. pp. 111–164. highlighted an important influence of plant quality on resource Hamel, S., and Côté, S.D. 2007. Habitat use patterns in relation to escape terrain: selection at multiple spatial scales. Selecting high-quality vegeta- are alpine ungulate females trading off better foraging sites for safety? Can. tion patches in habitats with less available forage has been reported J. Zool. 85(9): 933–943. doi:10.1139/Z07-080. Harris, R.B., and Miller, D.J. 1995. Overlap in summer habitats and diets of in other ruminant ungulates living in seasonal environments (e.g., TibetanPlateauungulates.Mammalia,59(2):197–212.doi:10.1515/mamm.1995. Hebblewhite et al. 2008; Winnie et al. 2008; Singh et al. 2010). In this 59.2.197. regard, resource selection by kiangs appears similar to other un- Hebblewhite, M., Merrill, E., and McDermid, G. 2008. A multi-scale test of the gulate species living in arid or alpine ecosystems. Considering the forage maturation hypothesis in a partially migratory ungulate population. Ecol. Monogr. 78(2): 141–166. doi:10.1890/06-1708.1. extremely seasonal conditions in Tso Kar, selecting for green foli- Henley, S.R., Ward, D., and Schmidt, I. 2007. Habitat selection by two desert- age and easily digestible plants could be a mechanism for kiangs adapted ungulates. J. Arid Environ. 70(1): 39–48. doi:10.1016/j.jaridenv.2006. to maximize their nutrient intake. 12.007. Illius, A.W. 2006. Linking functional responses and foraging behaviour to pop- Acknowledgements ulation dynamics. In Large herbivore ecology, ecosystem dynamics and con- servation. Edited by K. Danell, R. Bergström, P. Duncan, and J. Pastor. Our research was funded by the Wildlife Conservation Society, Cambridge University Press, New York. pp. 71–96. the Natural Sciences and Engineering Research Council of Can- ITT Visual Information Solutions Inc. 2004. ENVI version 4.1. ITT Visual Informa- ada, and the Bureau International de l’Université Laval, Canada. tion Solutions Inc., Boulder, Colo. Janis, C. 1976. The evolutionary strategy of the Equidae and the origins of rumen We thank K. Gailson, S. Tsering, C. Namgyal, J. Namgyal, and the and caecal digestion. Evolution, 30(4): 757–774. doi:10.2307/2407816. Rupshu nomadic community for their invaluable help in the field. Jarman, P.J. 1974. The social organization of antelope in relation to their ecology. We also thank J.L. Fox (University of Tromsø); S. Sathyakumar Behaviour, 48(3–4): 216–267. (Wildlife Institute of India); S. Ul-Haaq, J. Thakpa (Department of Johnson, D.H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology, 61(1): 65–71. doi:10.2307/1937156. Wildlife Protection of Jammu and Kashmir); and the Norwegian Kaczensky, P., Ganbaatar, O., Von Wehrden, H., and Walzer, C. 2008. Resource Agency for Development Cooperation for their logistical support. selection by sympatric wild equids in the Mongolian Gobi. J. Appl. Ecol. 45(6): We express our gratitude to A. Uphadyay (Wildlife Institute of 1762–1769. doi:10.1111/j.1365-2664.2008.01565.x. India) for helping with laboratory work and dietary analyses. Klein, D.R. 1990. Variation in quality of caribou and reindeer forage plants associated with season, plant part, and phenology. Rangifer, 3(Special Issue): We are indebted to D.I. Rubenstein, D. Fortin, C. Dussault, and 123–130. doi:10.7557/2.10.3.841. N. Owen-Smith for insightful comments on previous versions of Kudo, G. 1991. Effects of snow-free period on the phenology of alpine plants the manuscript. We also thank G. Daigle for statistical assistance, inhabiting snow patches. Arct. Alp. Res. 23(4): 436–443. doi:10.2307/1551685. and D. Réale, V. Radeloff, and M. Dubinin for their generosity Laca, E.A., Shipley, L.A., and Reid, E.D. 2001. Structural anti-quality characteris- tics of range and pasture plants. J. Range Manage. 54(4): 413–419. doi:10.2307/ throughout the project. 4003112. Lima, S.L., and Dill, L.M. 1990. Behavioral decisions made under the risk of References predation: a review and prospectus. Can. J. Zool. 68(4): 619–640. doi:10.1139/

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Appendix A

Table A1. Linear regression equations relating forage cover and height to biomass (g/m2) for grasses, sedges, forbs, and shrubs, in the Tso Kar basin, Ladakh, India. Vegetation group Regression equation NP r2 For personal use only.

Grasses log10BIOMASS = 0.6883 × log10COVER + 0.5304 × (log10COVER × log10HEIGHT) − 0.7131 224 >0.0001 0.76

Sedges log10BIOMASS = 1.1151 × (log10COVER × log10HEIGHT) 53 >0.0001 0.79

Forbs log10BIOMASS = 1.6493 × log10COVER + 1.0082 × log10HEIGHT − 1.0341 × (log10COVER × log10HEIGHT) 60 >0.0001 0.83

Shrubs log10BIOMASS = 0.5074 × log10COVER + 0.7982 × log10HEIGHT 100 >0.0001 0.67 Note: Pearson’s correlation coefficient (r2) is between observed and predicted values.

Table A2. Candidate models for feeding-site selection of kiangs (Equus kiang) within the Tso Kar basin, Ladakh, India, in fall 2003, summer 2004, and summer 2005, where selection for feeding sites were compared with (A) random sites located in a random direction 100 m away and (B) random sites located in a random direction 500 m away. Evidence ⌬ ␻ Model AICc AICc i ratio (A) Random direction 100 m away Green + sedges 83.78 0.00 0.27 1.00 Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14 Green + sedges + green × sedges 84.79 1.01 0.16 1.66 Green 85.62 1.85 0.11 2.52 Biomass + green 85.67 1.89 0.10 2.57 Green + shrubs 85.98 2.20 0.09 3.01 Green + biomass + green × biomass 86.96 3.18 0.05 4.91 Green + forbs 87.62 3.85 0.04 6.84 Green + grasses 87.65 3.87 0.04 6.93 Green + shrubs + green × shrubs 88.06 4.29 0.03 8.53 Green + grass + green × grasses 88.70 4.93 0.02 11.74 Green + forbs + green × forbs 88.88 5.11 0.02 12.86 Biomass + grasses + biomass × grasses 90.17 6.40 0.01 24.50 Biomass + green + grasses + sedges + forbs + shrubs 90.34 6.57 0.01 26.65 Biomass + shrubs 91.53 7.75 0.01 48.23 Sedges 91.70 7.92 0.01 52.50

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Table A2 (concluded). Evidence ⌬ ␻ Model AICc AICc i ratio Biomass 92.01 8.23 0.00 61.24 Biomass + sedges 92.82 9.04 0.00 92.07 Null (without covariates) 92.88 9.11 0.00 94.89 Sedges + shrubs 92.93 9.15 0.00 97.08 Shrubs 93.01 9.23 0.00 101.12 Biomass + shrubs + biomass × shrubs 93.13 9.35 0.00 107.18 Sedges + forbs 93.23 9.46 0.00 113.18 Grasses + sedges 93.61 9.83 0.00 136.46 Biomass + grasses 93.76 9.98 0.00 147.16 Biomass + forbs + biomass × forbs 93.92 10.14 0.00 159.17 Biomass + forbs 93.98 10.20 0.00 164.10 Grasses + shrubs 94.71 10.93 0.00 236.75 Biomass + sedges + biomass × sedges 94.83 11.05 0.00 250.76 Forbs 94.86 11.09 0.00 255.40 Grasses 94.86 11.09 0.00 255.53 Forbs + shrubs 94.98 11.20 0.00 270.29 Grasses + forbs 96.90 13.12 0.00 707.69 (B) Random direction 500 m away Biomass + grasses 62.80 0.00 0.28 1.00 Biomass + forbs + biomass × forbs 63.06 0.27 0.25 1.14 Biomass + grasses + biomass × grasses 64.76 1.96 0.11 2.66 Biomass 64.77 1.97 0.10 2.68 Biomass + forbs 65.73 2.94 0.06 4.34 Biomass + sedges 66.21 3.41 0.05 5.50 Biomass + green 66.71 3.91 0.04 7.07 Biomass + shrubs 66.81 4.01 0.04 7.43 Biomass + sedges + biomass × sedges 67.95 5.15 0.02 13.12 Biomass + shrubs + biomass × shrubs 68.51 5.72 0.02 17.42 Biomass × green 68.82 6.03 0.01 20.35 Biomass + green + grasses + sedges + forbs + shrubs 69.30 6.50 0.01 25.80 Green + forbs + green × forbs 74.22 11.42 0.00 301.79 Green 74.45 11.65 0.00 339.08 Green + grasses + green × grasses 74.67 11.87 0.00 378.13 Null (without covariates) 74.86 12.06 0.00 416.28 Sedges 75.43 12.63 0.00 552.37 Green +sedges 75.63 12.84 0.00 613.08 Green + forbs 75.80 13.01 0.00 667.47 Green + sedges + green × sedges 76.06 13.27 0.00 760.32 Green + shrubs 76.14 13.34 0.00 787.61 For personal use only. Green + grasses 76.30 13.50 0.00 854.06 Forbs 76.51 13.72 0.00 951.68 Grasses 76.75 13.95 0.00 1070.33 Shrubs 76.78 13.98 0.00 1086.51 Grasses + sedges 76.92 14.13 0.00 1168.53 Sedges + forbs 77.28 14.49 0.00 1398.98 Sedges + shrubs 77.50 14.71 0.00 1561.65 Forbs + shrubs 78.06 15.27 0.00 2066.27 Green + shrubs + green × shrubs 78.14 15.34 0.00 2143.60 Grass + forbs 78.53 15.73 0.00 2601.91 Grass + shrubs 78.77 15.98 0.00 2946.87 Note: Random sites were located within the same habitat type for both scales. Model components are as follows: “biomass”, plant biomass in feeding sites (g/m2); “green”, percentage of green foliage; “grass”, percentage of grasses (mainly Poaceae); “sedges”, percentage of sedges (Cyperaceae); “forbs”, percentage of herbaceous dicots; “shrubs”, percentage of

woody shrubs. Candidate models were compared based on their AICc values, calculated from the conditional logistic ⌬ regression analyses. AICc, Akaike’s information criterion corrected for small sample size; AICc, difference between the ␻ AICc of model i and the AICc of the best model; i, Akaike weight of model i. Can. J. Zool. Downloaded from www.nrcresearchpress.com by Université Laval Bibliotheque on 05/16/14

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