CHARACTERISTICS OF WINTER BROWSING AREAS OF MOOSE DETERMINED BY MULTIVARIATE ANALYSIS

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1 CHARACTERISTICS OF WINTER BROWSING AREAS OF MOOSE DETERMINED BY MULTIVARIATE ANALYSIS IN WESTERN QUEBEC AS ANDRE POLIQUIN, present address: Departement de biologie, C.E.G.E.P. Edouard Montpetit, 945 boul. Chambly, Longueuil, P.Q. BRUNO SCHERRER, Departement des Sciences biologiques, Universite du Quebec a Montreal, Box 888, Montreal, P.Q. H3C 3P8 ROBERT JOYAL, Departement des Sciences biologiques, Universite du Quebec a Montreal, Box 888, Hontreal, P.Q. H3C 3P8 AbJtaet: Twenty-five browsing areas were compared with randomly selected unbrowsed areas in l1ont-tremblant Provincial Park to determine if characteristics of winter browsing areas differed significantly. In other words, we tried to determine if moose (AleeJ aleej) were selective of browsing areas. A multivariate analysis of variance was used and a stepwise discriminant analysis was performed to describe the indicators of differences between the 2 groups of sampling plots. Ninety-two percent of all plots was correctly classified and highly significant differences were found between browsed and unbrowsed areas. Twelve independent indicators explained the differences. Furthermore, browsing areas were separated into open (early winter) and closed (late winter) browsed areas. Besides the works of Des Meules (1962,1963), moose winter-yard studies are very recent in Quebec. Brassard et al. (1974), Crete and Bedard (1975), Audet and Grenier (1976), and Joyal (1976) are the 1st to publish on winter habitat. Most of these works are based on vegetation and food habit studies. However, Brassard et al. (1974) showed in their review of moose distribution in Quebec that about 60 percent of the winter yards is on the southern slopes, introducing for the 1st time an abiotic characteristic. Our approach was different since we tried to determine 1st if moose select specific winter sites corresponding to their needs and secondly, if this occurs, what are the site characteristics on which this selectivity is based. To answer these questions measured variables have been processed by multivariate analysis of variance and discriminant analysis. The ultimate goal of this research was to find a way to recognize those sites which have the characteristics of a winter yard; in other words, with a 128

2 potential for winter habitat. Lumber companies would then be able to use this information in planning their logging programs. STUDY AREA This research took place in Mont-Tremblant Provincial Park, about 100 km northwest of Montreal in the L 4a section of the Great Lakes-St. Lawrence region (Rowe 1959). The topography of the park is gently sloping with altitudes from 100 to m. The vegetation is a mixed wood forest dominated by balsam fir (Abie balamea), yellow birch (Beula alleghanieni), white birch (B. papyki6eka), black spruce (Picea makiana), and trembling aspen (Populu kemuloide). Snow accumulation ranges between 75 and 100 cm. Extensive logging operations have created a mosaic of uneven-age mixed forest stands with a regeneration of feeding species for moose, such as mountain maple (Acek picaum), red maple (A. kubkum), willow (Salix spp.), and moosewood (Vibuknum alni6olium). Moose inhabit the northern section of the park almost exclusively while white-tailed deer (Odocoileu6 vikginianu6) are frequent in the southern section. The moose population density is evaluated at approximately 4/10 km 2. METHODS During a 3-year period (1971,1973,1974), many moose winter yards were located by aerial or terrestrial surveys made by Quebec Wildlife Service or by ourselves. No data were obtained in 1972 because of an airplane crash during a survey. From these yards, 25 were randomly selected where 3, 1 m by 20 m plots (Passmore and Hepburn 1955) were randomly placed in the browsing areas (75 plots). Furthermore, 162 other sampling plots were randomly selected in unbrowsed areas in the park during those years. In every plot, 41 variables were measured. Fifty variables described site characteristics and 12 described moose browsing intensities in browsed areas (Table 1). The 2 groups of sampling plots were compared by a multivariate analysis of variance to determine if moose select their browsing areas. To describe the differences between the 2 groups, a discriminant analysis was performed. This statistical technique discriminates the 2 groups by finding the angle 129

3 Table 1. List of 41 variables measured in each sampling plot. Mean Number Number of height of stems available twigs Abie. bl1 l1me.11 ABH ABN ABA Arce.It pe.nnb yli1i1,lrc"m APH APN APA Arce.It It"blt"m ARH ARN ARA Arce.It bpircl1;/:"m ASH ASN ASA 8e.;/:" e.g hl1nie.l1b i BAH BAN BAA 8 e.;/: " 11 pl1pljiti 6 e.ltl1 BPH BPN BPA COItY "b rcolt'w;/:11 CCH CCN CCA Pirce.11 ml1ltil1l1l1 PMH PMN PMA POP" llb ;/:lte.mll oide. PTH PTN PTA SI1 e.>: spp. SXH SXN SXA Vib"ltl1llm 11 l1i6o llm VAH VAN VAA Mean of arborescent stratum Mean of arbustive stratum Mean slope Soil exposition Mean cover Soil relative humidity Type of soil Year of sampling AGE YNG INC EXP COV HUM TYP ANN 130

4 giving the best separation. If we take, for instance, 2 groups of sampling plots characterized by 2 intercorrelated variables, these 2 variables can be represented by 2 axis, X and y. In this co-ordinate system the 2 groups will be represented by 2 elliptic clusters (Fig. 1). The X or y axis cannot be used to discriminate the 2 groups because their frequency distributors largely overlap. It is on the V axis that the 2 frequency distributions show the minimal overlap. The V axis is called the discriminant axis and is considered the linear function of X and y. In order to retain only independent and significant habitat characteristics, a stepwise discriminant analysis was performed. Discriminant analysis is also a powerful classification technique; classification meaning the process of identifying the likely group membership of a sampling plot when the only information known is the values of the sampling plots on discriminating habitat characteristics. The computer program for such statistical processing is the Statistical Package for the Social Sciences (S.P.S.S., Nie et al. 1975). RESULTS The difference between centroids (the means) of the 2 groups was highly significant (P<.Ol). This result showed that moose choose their browsing areas. Indeed, we do not think that the animals sufficiently modified their habitat to produce this difference because the moose density was not high enough. This is shown by V linear function: V 23 ASA + 20 ASN - 6 BAN + 9 ABN + 19 PTA - 14 SXH + 23 VAA + 55 AGE - 49 YNG + B INC - 13 HUM - 5 EXP. Twelve significant and independent indicators explained the differences for Mont-Tremblant Park. These indicators were: slope and exposition, age of trees and shrubs, food availability of mountain maple, balsam, fir, moosewood, and trembling aspen, the number of stems of mountain maple and yellow birch, height of willow, and drainage. In spite of the statistical difference between browsed and unbrowsed areas, there was still an overlap in the frequency distribution of the 131

5 y (j} R.I // GR.2 I II I II I I II I II I I III I I I I I I I I I I I I I I I/ X FIG. I: ILLUSTRATION OF THE DISCRIMINANT AXIS FOR TWO GROUPS OF SAMPLING UNITS.

6 co-ordinates (discriminant score) of browsed and unbrowsed plots on discriminant axis (Fig. 2). Hence in the classification process, 10 percent of the unbrowsed sampling plot has been classified as browsed and 6.8 percent as unbrowsed. Finally, 93.2 percent of all sampling plots was correctly classified (Table 2). The study of our row data showed that 2 kinds of winter yards could be recognized. According to our field observations, we divided winter yards into 2 major groups based on the fact that yards with opened cover differed from those with closed cover. The opened yard corresponded best to early winter and the closed yards best to late winter. The criterion used for this separation was the presence or absence of trembling aspen. Then 2 discriminant analyses can be performed, 1 for uncovered areas, the other for covered areas. Referring to the F test (Fig. 3), the difference between the centroid of browsed and unbrowsed sampling plots of open areas was still highly significant, showing that moose choose their browsing area. Only 10 significant and independent variables are now retained to explain the difference between browsed and unbrowsed areas in open stands, according to the following equation: V = - 59 ASA - 37 ABN + 24 ARA + 29 SXH - 18 VAH - 16 BPH - 43 AGE + 36 YNG + 11 HUM - 17 PMH. The indicators are: age of tree and shrub strata, availability of mountain maple and red maple twigs, height of willow, white birch, moosewood, and black spruce, the number of balsam fir, and medium drainage. In open areas, 100 percent of the sampling plots was correctly classified as to group (Table 3). If we compare these results to those previous, we can see that this discriminant analysis is more accurate and that indicators are less numerous. The last discriminant analysis explains that an area will be classified as a browsing area if variables with a negative coefficient have high values within their limits of variation and if variables with positive coefficients have low values. One can now describe and represent the uncovered winter 133

7 I/)!:::: 80 Z ::::> C> Z... a... -c( I/) 40 u.. o a.: w co ::::> Z Fobs. = 35.9 F I "3 +4 FACTORIAL DISCRIMINANT AXIS FIG.2: DISCRIMINANT ANALYSIS BETWEEN BROWSED AREAS (-) AND UNBROWSED AREAS (---l.

8 V) Fobs.= 22!:: 20 F.05 = 2 Z t!> -' a.. < V) 10 u.. 0 c.:: W <1:1 Z I +1 FACTORIAL DISCRIMINANT AXIS FIG.3 : DISCRIMINANT ANALYSIS BETWEEN BROWSED AREAS (-) AND UNBROWSED AREAS(---).

9 N OF CASES ACTUAL GROUPS PREDICTED GROUPS Gil. 75 GR BROWSED AREAS (90.7% ). ( 9.3% 162 GR UNBROWSED AREAS ( 5.6 %) ( 94.4% TABLE 2 PREDICTED RESULTS OF BROWSED AND UNBROWSEDAREAS AS CLASSIFIED BY THE FACTORIAL DISCRIMINANT ANALYSIS.

10 N OF CASES 22 ACTUAL GROUPS PREDICTED GROUPS GK. uk."l GR.I 22 0 BROWSED AREAS (100 %) ( o %) 48 GR UN BROWSED AREAS ( 0%) (100 %) TABLE 3 PREDICTED RESULTS OF OPENED BROWSED AND OPENED UNBROWSED AREAS AS CLASSIFIED BY THE FACTORIAL DISCRIMINANT ANALYS.

11 browsing area by a semirealistic diagram (Fig. 4). These characteristics are: great food availability principally mountain maple, balsam fir, and yellow birch, young arbustive stratum so that food might be accessible for moose, arborescent stratum composed of isolated clusters of black spruce and balsam fir older than 40 years, higher than 14 m, and a relatively dry soil. The comparison between browsed and unbrowsed areas in covered sites indicates also that moose choose their feeding area (Fig. 5, Table 4). The difference between centroids of the 2 groups of sampling plots was highly significant. Hence, here, only 7 variables were used to explain the difference between the 2 groups. V = 62 YNG + 35 VAA + 31 AGE + 16 INC + 7 EXP + 12 HUM + 21 ASA. These indicative variables are the age of both strata, availability of mountain maple and moosewood, slope exposition, and relative humidity of soil. The frequency distribution of the 2 groups on discriminant axis presented a very small overlap (Fig. 5). So more than 96 percent of browsed sampling plots was correctly classified as browsing areas and 98.2 percent of unbrowsed sampling plots was correctly classified as unbrowsed areas (Table 4). This analysis is again more accurate than the 1st 1 and classification criteria are less numerous. A covered browsing area is now characterized by a young arbustive stratum in order to offer many twigs, great availability of mountain maple and mooseberry, old and coniferous (black spruce and balsam fir) arborescent stratum, a slope over 12 degrees, exposed to east-southeast, and a soil relatively mesic. This area, which best corresponds to the late winter period when snow depth becomes an important factor, is represented by the semirealistic diagram (Fig. 6). CONCLUSIONS This analysis shows that moose select their winter browsing area and that the selectivity criteria at the beginning of winter are slightly different than at early spring. In any yard moose choose their feeding site and in any yard a cornmon indicative variable is the age of both strata. The arbustive stratum is 138

12 FIG.4 : SEMI REALISTIC DIAGRAM OF OPENED WINTER BROWSING AREA AS SHOWN BY THE SIGNIFICANT INDICATORS OF THE DISCRIMINANT FUNCTION.

13 VI Z 25 ::I C> Z :::::; D. "«VI o co: ::I Z Fobs.= F.OS = Ft\CTORIAL DISCRIMINT AXIS FIG.5 : DISCRIMINANT ANALYSIS BETWEEN CLOSED BROWSED AREAS (-) AND CLOSEDUNBRCM'SED AREAS (---).

14 FIG.6 : SEMI REALISTIC DIAGRAM OF CLOSED WINTER BROWSING AREA AS SHOWN BY THE SIGNIFICANT INDICATORS OF THE DISCRIMINANT FUNCTION.

15 N OF CASES ACTUAL GROUPS PREDICTED GROUPS GR. (ill. 54 GR BROWSED AREAS (.96.3%) ( 1.8%) 112 GR UNBROWSED AREAS ( 9.3%) (98.2%) TABLE 4 PREDICTED RESULTS OF CLOSED BROWSED AND CLOSED UNBROWSED AREAS AS CLASSIFIED BY THE FACTORIAL DISCRIMINANT ANALYSIS.

16 young enough to offer availability of twigs, and the tree layer is older than 40 years and dense enough to offer protection against wind. It appears also that in late winter, slope and exposition become important site indicators. It also appears that in addition to browsing, covered areas offer protection and coverage. This was pointed out by Des Meules (1962) who compared those sites to a kitchen and bedroom site together. This difference between closed and opened winter browsing areas can be explained by the "unwels" concept, by behavioral needs of the animal which are subject to variation even during a single season. Finally, with very few measurements, this analysis can be used as a prediction model because it can give the probability of a randomly selected site in western Quebec as a characteristic browsing area. LITERATURE CITED Audet, R. and P. Grenier Habitat hivernal de l'orignal dans la region de la Baie James, etude preliminaire. Service de la Recherche biologique. Ministere du Tourisme, de la Chasse et de la Peche. 38pp. Brassard, J.M., E. Audy, M. Crete, and P. Grenier Distribution and winter habitat of moose in Quebec. Naturaliste Can. 101(1 and 2) : Crete, M. and J. Bedard Daily browse consumption by moose in the Gaspe Peninsula, Quebec. J. Wildl. Manage. 39(2) : Des Meules, P Intensive study of an early spring habitat of moose (Aee aee ameeana) in Laurentides Park, Quebec. Proc. N.E. Wildl. Conf., Monticello, New York The influence of snow on the behavior of moose. Proc. N.E. Wildl. Conf., Handforth, Connecticut. Nie, N.H., C.H. Hull, J.G. Jenkins, K. Steinbreme, and D.H. Bent statistical package for the social sciences. Second ed. McGraw-Hill, New York. Rowe, J.S Forest regions of Canada. Can. Dept. N. Affairs, Res. Bull