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Assessing Landscape Relationships for Habitat GeneralistsAuthor(s): Abbie Stewart, Petr E. Komers & Darren J. BenderSource: Ecoscience, 17(1):28-36. 2010.Published By: Centre d'études nordiques, Université LavalDOI: http://dx.doi.org/10.2980/17-1-3284URL: http://www.bioone.org/doi/full/10.2980/17-1-3284

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17 (1): 28-36 (2010)

Past and current land-use practices have resulted in habitat loss and fragmentation of natural landscapes. A focus of research over the last 20 y by conservation biolo-gists and landscape ecologists has been to develop con-

ceptual and methodological approaches for assessing the effects of habitat degradation, loss, and fragmentation (Wiens, 1994; 1995; McIntyre & Hobbs, 1999; Haila, 2002; McGarigal & Cushman, 2002). It is now well recognized that modifications to landscape structure can have profound impacts on the abundance and distribution of many spe-cies. This is particularly true for species with specialized habitat requirements, especially if their habitats become fragmented in the landscape and the intervening matrix is perceived as hostile territory (Lamberson et al., 1992). The

Assessing landscape relationships for habitat generalists1

Abbie STEWART2, Petr E. KOMERS & Darren J. BENDER, University of Calgary,

2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada.

Abstract: The importance of landscape heterogeneity for the abundance and distribution of wildlife is well recognized. General relationships have been developed to link landscape pattern to demographic processes, although these relations are best demonstrated for species with specialized habitat requirements and often in landscapes that can be generalized to a simple habitat-matrix structure. Habitat generalists may interact in more complex ways with a mosaic of landscape features. A novel method for quantifying the habitat relationships of generalist species using thematic vegetation maps was proposed by Brotons et al. (2005) and based on a theoretical model by Andrén, Delin, and Seiler (1997). We tested the efficacy of this approach on moose (Alces alces) distribution in the heterogeneous landscapes of the Foothills Natural Region, Alberta, Canada, using 8 broad vegetation types. Fecal pellet group data, an index of moose occurrence, was compared across pre-selected sites. Sites were selected to represent the variable amounts and combinations of the different vegetation types available in the study area. Moose habitat preference was determined using a Chi-square test and Bonferroni confidence intervals. Moose preferred shrublands and deciduous forests. Shrubland was considered primary moose habitat as it had the highest observed proportion of pellet groups of the preferred habitats. Each vegetation type was assessed regarding its role in habitat amount, habitat compensation, supplementation, complementation, and fragmentation models using general linear modelling. Habitat amount and fragmentation were related to moose pellet occurrence. However, there was no indication of supplementation, compensation, or complementation. This mosaic approach effectively revealed habitat relationships and the potential impacts of habitat change for a generalist species at the landscape scale. Keywords: compensation, complementation, fragmentation, habitat amount, landscape context, supplementation.

Résumé : L'importance de l'hétérogénéité du paysage pour l'abondance et la distribution de la faune est bien connue. Des relations générales ont été développées pour relier les patrons du paysage aux processus démographiques, mais ces relations peuvent surtout être démontrées pour des espèces ayant des exigences spécifiques d'habitat et souvent dans des paysages qui peuvent être généralisés à une matrice composée d'habitats simples. Les espèces généralistes en termes d'habitat peuvent interagir de façon plus complexe avec une mosaïque de différentes caractéristiques du paysage. Une nouvelle méthode pour évaluer quantitativement les relations entre des espèces généralistes et leur habitat a été proposée par Brotons et al. (2005), elle utilise des cartes thématiques de végétation et elle est basée sur un modèle théorique d'Andrén, Delin et Seiler (1997). Nous avons évalué l'efficacité de cette approche dans le cas de la distribution de l'orignal (Alces alces) dans les paysages hétérogènes de la région naturelle des contreforts des Rocheuses, Alberta, Canada, en utilisant 8 grands types de couvert végétal. Des données de groupes de boulettes fécales, un indice de présence d'orignal, ont été comparées entre des sites présélectionnés. Les sites ont été choisis pour représenter la variabilité des quantités et des combinaisons des différents types de couvert végétal disponibles dans l'aire d'étude. Les préférences d'habitat de l'orignal ont été déterminées en utilisant un test de chi-carré et des intervalles de confiance de Bonferroni. Les orignaux ont préféré les arbustaies et les forêts feuillues. L'arbustaie a été considérée comme étant l'habitat principal de l'orignal car elle avait la plus grande proportion observée de groupes de boulettes fécales de tous les habitats préférés. Le rôle de chaque type de couvert végétal a été évalué dans des modèles de quantité d'habitat, de compensation, de supplémentation, de complémentation et de fragmentation en utilisant un modèle linéaire général. La quantité des habitats et leur fragmentation étaient reliées à la présence de boulettes fécales d'orignal. Cependant, il n'y avait aucune indication de supplémentation, de compensation ou de complémentation d’habitat. Cette approche en mosaïque a été efficace pour révéler les relations d'habitat et les impacts potentiels de changements dans l'habitat pour une espèce généraliste à l'échelle du paysage. Mots-clés : compensation, complémentation, contexte de paysage, fragmentation, quantité d'habitats, supplémentation.

Nomenclature: Kays & Wilson, 2002.

Introduction

1Rec. 2009-01-21; acc. 2009-12-21. Associate Editor: Daniel Fortin.2Author for correspondence. Present address: 4603, 41 Street NW, Calgary, Alberta T3A 0N4, Canada, e-mail: abbie.stewart@mses.ca

DOI 10.2980/17-1-3284

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influence of landscape heterogeneity, and particularly habi-tat modification, has been more difficult to understand for habitat generalists that use a wider range of habitat types than specialists (Virgos, 2002; Bender & Fahrig, 2005).

Multiple habitat types within a landscape can interact and influence species distribution (Dunning, Danielson & Pulliam, 1992; Law & Dickman, 1998; Debinski & Holt, 2000). This mosaic perspective considers ecological pro-cesses operating among an assortment of different habitat types in a landscape (Law & Dickman, 1998; Estades & Temple, 1999). This approach also recognizes that vegeta-tion surrounding the habitat of a given species may not sim-ply be a matrix of unsuitable land cover but could provide some resource value, such as protective cover or dietary alternatives (Estades & Temple, 1999). Further, there may be a neighbourhood effect whereby species occurrence in a vegetation type may be positively or negatively influenced by the surrounding vegetation depending on the composi-tion of that vegetation and the wildlife species using it (Dunning, Danielson & Pulliam, 1992).

Management of wildlife populations relies strongly on understanding how the elements in a mosaic of varying habitats interrelate (Morrison, Marcot & Mannan, 1998; Wiens, Van Horne & Noon, 2002). However, most of our understanding is based on the study of specialist species, where it is a relatively simple matter to define habitat and non-habitat (Lamberson et al., 1992; McIntyre & Hobbs, 1999; Goodwin & Fahrig, 2002; Suorsa et al., 2005). There has been little consensus about how to approach a land-scape-level analysis of habitat use for generalist species that use multiple vegetation types as habitat.

A novel method for quantifying habitat relationships at the landscape scale was suggested by Andrén, Delin, and Seiler (1997) and developed by Brotons et al. (2005). In their study, Brotons et al. (2005) used a multiple-class thematic map of a steppe–agricultural mosaic occupied by 4 different passerine species to test habitat relationships. We believe this approach warrants further investigation because it is a potentially useful and general method for relating eco-logical processes operating at a landscape scale for species that use multiple habitat types. Here we review the method developed by Brotons et al. (2005) and determine its effica-cy in evaluating habitat relationships for a generalist species using moose (Alces alces) as a case study.

There are 5 possible responses to modifications of the structure and composition of a landscape, depending on an organism’s degree of habitat specialization (Andrén, Delin & Seiler, 1997): responses to habitat amount, habitat compensa-tion, supplementation, complementation, and fragmentation. These relationships represent the range of possible numerical population responses to landscape change that will impact the distribution of a species within a landscape mosaic.

FIVE POSSIBLE RESPONSES TO LANDSCAPE CHANGE

HABITAT AMOUNT

The habitat amount hypothesis states that primary habitat (e.g., a single vegetation or land-cover type impor-tant to the species) provides all required resources and that alternative land cover types have no resource value (Andrén, Delin & Seiler, 1997; Brotons et al., 2005). Support for the habitat amount hypothesis is indicated by

a positive, linear relationship between the amount of pri-mary habitat and animal occurrence in the landscape, with occurrence reaching zero where there is no primary habitat (Figure 1, Series a). This relationship reflects a change in animal occurrence in the landscape proportional to a change in primary habitat amount.

COMPENSATION

The compensation hypothesis states that an alterna-tive habitat is substitutable for the primary habitat and may have only marginally lower quality than the primary habitat (Norton, Hannon & Schmiegelow, 2000; Brotons et al., 2005). Animal occurrence in the landscape remains constant across a range of primary habitat amount or, if the compen-sating habitat is of slightly lower quality, occurrence decreas-es moderately as primary habitat is removed (Andrén, Delin & Seiler, 1997) (Figure 1, Series b1 and b2). Animal occur-rence will not reach zero through removal of primary habitat in a landscape that contains compensating habitat because all resources for occurrence are still available.

SUPPLEMENTATION

The supplementation hypothesis states that some of the required resources for a species are available in an alterna-tive habitat, but not enough to solely support a species. In the case of supplementation, animal occurrence in their primary habitat may be positively influenced by close asso-ciation with a supplementing habitat, but only while enough primary habitat is still available (Dunning, Danielson & Pulliam, 1992; Andrén, Delin & Seiler, 1997; Brotons et al., 2005). Habitat supplementation is indicated by a non-linear relationship, which could be marked by a threshold, between animal occurrence in the landscape and the percent of

FIGURE 1. Five hypotheses explaining the role of different habitat types for species inhabiting complex landscapes: a) habitat amount (the species can only persist in primary habitat), b) degrees of compensation (b1 and b2; another habitat type can substitute for primary habitat, but may be of lower quality), c) supplementation (another habitat type has some, but not all, required resources for species persistence); d) complementation (2 habitats mutually supply required resources for species persistence, with abundance peaking when both habitats optimally co-occur), e) fragmenta-tion (non-primary habitat has no resource value and negatively influences species abundance in primary habitat).

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primary habitat in the landscape (Figure 1, Series c). Animal occurrence decreases more slowly than expected from a decrease in primary habitat amount alone because the non-primary habitat provides some resources normally acquired from the primary habitat. Animal occurrence decreases to zero at no availability of primary habitat because supple-mental habitat does not have sufficient resources required for species persistence in the landscape.

COMPLEMENTATION

The complementation hypothesis states that resources necessary for survival are distributed among at least 2 habitat types. These resources are not substitutable, and all are required for species persistence. Species occur-rence in the landscape is positively influenced by the close association of the primary habitat and its complementary habitat (Dunning, Danielson & Pulliam, 1992; Choquenot & Ruscoe, 2003; Brotons et al., 2005). Habitat complemen-tation is indicated by a bell-shaped relationship between animal occurrence in the landscape and the percent of primary habitat in the landscape, where the highest animal occurrence is expected at some optimal combination of the primary and complementary habitats (Figure 1, Series d).

FRAGMENTATION

The fragmentation hypothesis states that primary habitat provides all required resources; however, unlike the habitat amount hypothesis, the fragmenting vegetation has a negative influence on species occurrence in primary habitat (Andrén, Delin & Seiler, 1997; Franklin, Noon & George, 2002; Brotons et al., 2005). Fragmentation is indi-cated by a non-linear relationship between animal occur-

rence in the landscape and the percent of primary habitat in the landscape (Figure 1, Series e). Animal occurrence decreases faster than expected from a decrease in primary habitat amount alone because the animals are sensitive to changes in the configuration of the remaining primary habi-tat (Andrén, Delin & Seiler, 1997).

Methods

STUDY AREA

The Alberta Foothills Natural Region (AFNR) covers about 25 000 km2 along the eastern edge of the Rocky Mountains in Alberta, Canada (Centre of study area: 53° 13.847' N, 116° 28.454' W). The boundaries of Alberta Natural Regions are defined according to vegetation, soils, and physiographic features, resulting in multiple regions, each with relatively consistent vegetation composition (Natural Regions Committee, 2006). Vegetation in the AFNR con-sists mainly of closed-canopy coniferous, deciduous, and mixedwood forests. Grassland and shrubland vegetation is infrequently interspersed among forest stands (Strong, 1992; Beckingham, Corns & Archibald, 1996). Commercial timber management has been ongoing for over 50 y in this region (Murphy et al., 2002). Other human activity in this region includes mining, agriculture, urbanization, and oil and gas production. Only the AFNR was sampled in order to mini-mize the influence of any gradient in vegetation distribution across the study area (Figure 2).

MAPPING METHODS

The Foothills Model Forest provided remote sensing vegetation information for this research (Foothills Model

FIGURE 2. The study area falls within the AFNR, Alberta, Canada.

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Forest, 2007). Vegetation data were based on remote sens-ing imagery from 2 satellite sensor systems, the LandSat Thematic Mapper (TM) and the Moderate Resolution Imaging Spectrometer (MODIS). The remote sensing infor-mation, collected in 2003, provided a representation of land cover, crown closure, species composition attributes, and normalized difference vegetation index (NDVI) phenology. Remote sensing information, in combination with ground and helicopter surveys of 30- × 30-m areas, resulted in a land cover classification with 81% accuracy (Nielsen, Munro & Boyce, 2006). Vegetation data were in raster for-mat with 30-m pixel resolution. A legend included with the data classified pixels into 15 categories: open conifer, mod-erate conifer, dense conifer, broadleaf, mixedwood, shrubs, upland herbaceous, regenerating, treed wetland, open wet-land, water, barren land, snow, cloud, and shadow. These 15 original categories were reduced to 8 broad vegetation types for this study in order to maximize sample size per vegeta-tion type (Table I).

We updated the vegetation data with aerial photographs and ground observations using ArcView 3.1 geographical information system (GIS) software (Environmental Systems Research Institute, 1992). Recent logging activity gener-ated new cut-blocks that were marked along their perimeter using handheld GPS units (Garmin 72/76, Olathe, Kansas, USA). GPS coordinates in combination with air photos were used to add new cut-blocks to the vegetation map.

During f ield surveys, vegetation information was recorded and used to confirm or correct vegetation types in the map. Treed stands (≥ 20% tree cover) were classified according to tree species dominance. Forests with softwood tree species making up ≥ 80% of the total tree composition were classified as coniferous. Forests with both softwood and hardwood tree species, each making up < 80% of total tree composition, were classified as mixedwood. Forests with hardwood tree species making up ≥80% of total tree composition were classified as deciduous. Patches domi-nated by shrub species and with < 20% tree overstory were classified as shrubland, as were grassy areas with ≥ 30% shrub cover. Patches dominated by grasses and containing < 30% shrub species cover were classified as grassland. Cut

blocks have no vegetation, sparse vegetation, or new forb vegetation and evidence of recent clearing. Non-vegetated areas consist of paved or gravel road systems and any other disturbances resulting in replacement of original vegetation with artificial structures. The open water category includes bodies of water wider than 30 m. Small tributaries and ponds were not distinguished from the surrounding vegeta-tion; however, the vegetation attributes associated with these smaller waterbodies were included within the broad vegeta-tion types. By including microhabitat patches such as these in the broader vegetation types, this study did not investi-gate the fine scales of microhabitat use.

SAMPLE SITE SELECTION

Updated vegetation data were used concurrently with spatial (vector) data of Alberta’s base features to select sites for sampling. Alberta Base Mapping Data (2001–2003) for facilities, roads, towns, reserves, and recreational areas were supplied through AltaLIS Ltd. (AltaLIS, 2007). Sites were 16 km2 (4 × 4 km), approximately the average moose fall and winter home range size (Mytton & Keith, 1981; Van Dyke, Probert & Van Beek, 1995; Laurian et al., 2000).

Sites were selected to represent the variable amounts and combinations of the different vegetation types available in the study area. A GIS data layer containing 1759 ran-domly placed, overlapping, 4- × 4-km squares was created and placed over the study area vegetation layer, excluding artificial surfaces. Parks, reserves, and recreational areas were also excluded due to differences in land-use activi-ties in these areas. The area of each vegetation type falling within a 4- × 4-km square was extracted from the vegetation map for sample site selection. First, sites were randomly selected from those available. Second, additional sites were selected such that the range of cover for each vegetation type was represented as completely as possible (i.e., gaps in the proportion of the 16-km2 area that each vegetation type made up were filled). Third, if any 2 sites overlapped, one of the sites was randomly removed. Configuration was not controlled for during site selection. The following ranges of coverage were obtained within the 65 sites sampled: conif-erous (1-93%), mixedwood (0-75%), deciduous (0-64%), shrubland (1-32%), grassland (0-12%), cutblock (0-30%), non-vegetated (1-15%), and water (0-13%). This allowed for an assessment of moose habitat use within the range of vegetation available in the AFNR (Beckingham, Corns & Archibald, 1996).

PELLET GROUP SURVEY

We gathered winter habitat use data on moose using fecal pellet group surveys in the spring of 2005 and 2006 (sites not re-sampled). The surveys were conducted prior to leaf-out (late April to early June) in both years to provide an index of moose distribution in winter. Our method ensured that new pellets (those lying above the previous years’ leaf litter) were easily observed, while the older pellets were concealed by leaf litter (Neff, 1968; Augustine & Frelich, 1998). Spring fecal pellet group surveys provided an index of moose occurrence representing the cumulative deposi-tions over the entire preceding winter period (Neff, 1968; Augustine & Frelich, 1998; Weckerly & Ricca, 2000).

TABLE I. Reduction of original categories provided by the Foothills Model Forest into 8 broad vegetation types.

Original categories New vegetation types

Open conifer Coniferous forest Moderate conifer Coniferous forest Dense conifer Coniferous forest Broadleaf Deciduous forest Mixedwood Mixedwood forest Shrubs Shrubland Upland herbaceous Grassland Regenerating Cutblock* Treed wetland Water* Open wetland Water Water Water Barren land Non-vegetated Snow No data* Cloud No data* Shadow No data*

*Required ground confirmation for re-classification.

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Pellet groups were counted within a 5.65-m radius cir-cular plot (100 m2) (Neff, 1968). Using a stratified random sampling procedure, we attempted to proportionally repre-sent all vegetation types within each site using pellet group plots, excluding water and non-vegetated types. Plots were distributed a minimum of 200 m from each other and roads, preferably within a separate vegetation patch and separated by natural features such as rivers. A total of 959 plots were sampled once each across 65 sites, resulting in a range of 13 to 16 plots per site.

Pellet group surveys were conducted by 6 different observers working independently. We trained all observers and allowed observers to independently sample plots once we obtained consistency between our pellet group counts. To maintain consistency, observers sampled at least 1 plot together per day to compare counts and species identifica-tion. Multiple observers were distributed within each site.

Within a plot, moose pellet groups were identified. A moose pellet group was defined as a minimum of 5 pellets within 1 pellet’s length of one another; more than half of the pellets in the group had to be within the sample plot to be counted (Strong & Freddy, 1979; Harkonen & Heikkila, 1999). Pellet groups occurring beneath fallen leaves or showing signs of decomposition (distorted shape and/or mold growth) were recorded as “old” and not included in analyses (Franzman et al., 1976; Cairns & Telfer, 1980).

STATISTICAL ANALYSIS

Moose pellet occurrence was a continuous variable, calculated as the average number of fecal pellet groups in the 16-km2 site (average pellet group density × 16 km2). We assumed that moose occurrence was reflected in moose pellet occurrence. Moose pellet occurrence was square root transformed to satisfy assumptions of normality and homoscedasticity. Moran’s I-test for spatial autocorrela-tion in residuals was used to test for spatial dependence in moose occurrence in primary habitat (this was done for Step 1 described below) among sites using ArcGIS v9.0 (Environmental Systems Research Institute, 2004). Differences in moose pellet occurrence between years was tested with a Student’s t-test using SAS v8.02 soft-ware (SAS Institute, 2001) to determine whether data from the 2 years were significantly heterogeneous, precluding data pooling. In addition, moose pellet occurrence within each vegetation type was compared between years with a Mann–Whitney U-test (data could not be made normal) to determine whether there were any significant differences. The potential influence of a difference in winter severity between years was also tested using a paired t-test. For this analysis, winter severity was represented by mean tem-perature and total snow cover for each of the winter months (October–April), as gathered from 4 weather stations within different regions of the study area, corresponding with the 2 field seasons.

The proportion of each vegetation type available within all 65 sites (expected) was compared with the proportion of moose pellet groups counted within each vegetation type (observed; pi) using a chi-square test. This tests the hypothesis that moose utilize each vegetation type in pro-portion to its availability. If the chi-square was significant,

indicating that observed moose pellet groups were not distributed proportionately amongst vegetation types, then Bonferroni confidence intervals were calculated to deter-mine which vegetation types generated the statistical sig-nificance (Neter et al., 1996). This method of habitat use assessment accounted for vegetation availability biases. The vegetation type that was used significantly more than would be expected from vegetation availability alone and that had the highest observed proportion of pellet groups was con-sidered to be primary habitat (Neu, Byers & Peek, 1974; Arthur et al., 1996).

Two vegetation types were found to have pellet occur-rence that was higher than expected based on habitat avail-ability: shrubland and deciduous. Shrubland had the highest observed proportion of pellet groups. Therefore, the area of shrubland habitat was identified as the primary habitat for the 3-step analysis (Brotons et al., 2005).

THE 3 MODELLING STEPS

We modelled moose pellet occurrence (our index of winter moose distribution) as a function of vegetation types with a general linear model using SAS v8.02 software (SAS Institute, 2001). Predictions were tested following methods proposed by Brotons et al. (2005). The analysis was run in 3 sequential steps, using a hierarchical regression whereby several predictor variables were added at each step accord-ing to theory-based hypotheses (Wampold & Freund, 1987). We extended the method of Brotons et al. (2005) by includ-ing 2 covariates in each step to account for their potential influence on moose pellet occurrence in the landscape. The covariates, length of linear features (km) per site and total hunter-days, were included to reduce redundancy in the independent variables. Moose hunting effort per Wildlife Management Unit (WMU) was provided by the Alberta Sustainable Resource Development, Fish and Wildlife Division (ASRD, 2007). These covariates are believed to be representative of a multitude of covariates that could poten-tially influence moose pellet occurrence at the landscape scale (i.e., town & road proximity, level of human distur-bance). In addition, a ridge regression was run when a high Variance Inflation Factor (VIF > 10) was noted for any of the independent variables in the models (Neter et al., 1996). Ridge regression has been found to increase the precision of coefficient estimates, using a technique of biased estimation (Hoerl & Kennard, 1970a,b; Marquardt & Snee, 1975). It should also be noted that data centring and scaling, removal of variables, and a combination of these 2 methods for deal-ing with multicollinearity were also assessed.

In step 1, moose pellet occurrence was regressed against the amount of primary (shrubland) habitat area (independent variables: primary habitat, total hunter-days, and length of linear features). If primary habitat was the only significant predictor of moose pellet occurrence after all 3 steps were executed, then the habitat amount hypoth-esis was supported (Table II).

In step 2, the area of primary habitat (if significant in step 1) and all individual remaining vegetation types were included in the model in order to test the prediction of habi-tat compensation (independent variables: primary habitat, coniferous, mixedwood, grassland, cutblock, non-vegetated,

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water, total hunter-days, and length of linear features). Non-primary habitat significant in step 2 needed to remain individually significant in step 3 for the compensation hypothesis to be supported (Table II).

In step 3, any individual habitat type showing sig-nificance in previous steps was included, as well as the interactions between primary habitat and each vegetation type (independent variables: primary habitat, coniferous × primary habitat, mixedwood × primary habitat, grass-land × primary habitat, cutblock × primary habitat, non-vegetated × primary habitat, water × primary habitat, total hunter-days, and length of linear features). This simultane-ously tested the concepts of habitat supplementation, land-scape complementation, and fragmentation. Support for habitat supplementation or complementation was indicated by a positive, significant interaction between primary habi-tat and non-primary habitat.

Supplementation was supported when primary habitat was a positive main effect in the final model and there was a positive interaction between primary habitat and another habitat type (Table II). Complementation was distinguished from supplementation by a significant interaction between primary habitat and non-primary habitat but no positive main effect in the final model (Table II). Fragmentation was indicated by a negative interaction between primary habitat and another vegetation type. This type of interaction indi-cates that the non-primary vegetation has a negative effect on moose pellet occurrence in the primary habitat (Table II).

Results

Residuals from the simple linear model (moose occur-rence in primary habitat among sites) were not significantly spatially autocorrelated among sites (Moran’s I: –0.03, P > 0.05, n = 63), and there was no significant difference in moose pellet occurrence between years (t-test, P > 0.05, t = 0.91, df = 63). There was no significant difference in moose pellet occurrence between years for any of the veg-etation types (Mann–Whitney U, P > 0.134 for all tests). There was no significant difference in mean temperature

(paired t-test, P > 0.05, t = –0.69, df = 6) or total monthly snow cover (paired t-test, P > 0.05, t = 1.63, df = 6) between years. Therefore, data from 2005 and 2006 and from all sites were pooled.

Moose pellet groups were not distributed proportion-ately amongst the available vegetation types (Chi-square, P ≤ 0.05, χ2 = 31.95, df = 5). Moose used shrubland and deciduous habitat significantly more often than expected, according to Bonferroni confidence intervals (Table III). Coniferous vegetation was used by moose significantly less than expected (Table III).

In step 1, the amount of primary (shrubland) habitat available within the 16-km2 sites was a positive significant predictor of moose pellet occurrence (Table IV). In step 2, alternative vegetation types did not have any significant influence on moose pellet occurrence, indicating that there was no compensation or supplementation. In step 3, a sig-nificant, negative interaction between primary (shrubland) habitat and grassland indicated fragmentation effects for moose in primary (shrubland) habitat (Table IV). In addi-tion, total hunter-days was a positive, significant predictor of moose pellet occurrence in all 3 steps.

Discussion

The method developed by Brotons et al. (2005) helped us to uncover habitat relationships for a generalist spe-cies based on existing vegetation maps. Using moose as a case study, we provided evidence of a positive relationship between the extent of habitat cover and moose pellet occur-rence at the landscape scale (within a site). Specifically, we found evidence of both habitat amount and fragmentation effects at the landscape scale. This was analogous to other studies regarding the relationship between habitat amount and species distribution (Andrén, Delin & Seiler, 1997; Brotons et al., 2005).

Our analysis revealed that deciduous and shrubland patches were used more than expected based on availability, which also was observed in other studies of moose habi-tat use (Telfer, 1970; Cairns & Telfer, 1980; Telfer, 1984;

TABLE II. Summary of 5 possible responses to landscape change (between moose intensity of occurrence in the landscape and habitat area variables) given a gradient in habitat composition as shown in Figure 1. The existence of any of the responses is tested by a 3-step hierarchical regression.

STEP 1 STEP 2 STEP 3 Simple linear Multiple linear Multiple linear with interactions

Coefficient sign P-value Coefficient sign P-value Coefficient sign P-value

Variable: Primary habitat Alternative habitats Interactions (primary × alternative habitat)

Responses to landscape change (Figure 1 series):

Habitat amount (a) Positive < 0.05 n/a ns n/a nsCompensation (b1, b2) n/a n/a Positive < 0.05 Positive* < 0.05Supplementation (c) n/a n/a Positive < 0.05 Positive < 0.05Complementation (d) n/a n/a Neutral ns Positive < 0.05Fragmentation (e) n/a n/a Neutral or Negative ns or < 0.05 Negative < 0.05

*Not an interaction. Alternative habitat must be significant on its own. n/a: not applicable. Only primary habitat is included in Step 1. ns: not significant.

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Peek, 1997). In assessing the 5 possible responses to land-scape change, we found evidence that focussing on a single habitat type alone was not sufficient to explain associations between moose and various vegetation types. It was neces-sary also to determine whether differing habitats provide complementary, supplementary, or compensatory resources for a species or whether certain vegetation types serve to fragment primary habitat.

After the 3-step analysis was completed, we found that primary habitat amount was not the only significant predic-tor of moose pellet occurrence. In the third step, we found a significant negative interaction, indicating fragmentation effects caused by grassland in the landscape. Grassland was associated with a fragmentary effect on moose distribution in the landscape, as illustrated by negative interaction terms. While grassland is rarely considered as moose habitat, the degree to which it influences moose pellet occurrence and distribution in the landscape may have been overlooked in the past. Grassland vegetation may be avoided by moose because it does not afford protective cover, leaving them

vulnerable to winter weather or predation. This illustrates the complexity of habitat relationships in a mosaic land-scape and the usefulness of an analysis designed to uncover these relationships.

There was no support for compensation, supplementa-tion, or complementation of moose primary habitat. Overall, this suggests that winter resources for moose are gener-ally contained within the primary habitat (i.e., shrubland). The absence of compensatory habitat is surprising given that deciduous habitat, like shrubland habitat, was used by moose significantly more than expected. This suggests that food resources and cover are largely contained in shrubland and, potentially to a lesser degree, in deciduous forests, as found in other studies (Telfer, 1970; Cairns & Telfer, 1980; Telfer, 1984; Peek, 1997). Compensation by deciduous forest may exist, but it may have been weaker than the effects of habitat amount and fragmentation in our study. Additionally, the compensatory effect may not have been significant due to limitations in the natural availabil-ity of the potentially compensating habitat. Also, one could argue that some supplementary or complementary resources could be expected in either mixedwood or coniferous for-est. However, if such resources exist in our study area, our results suggest that their effect would be slight. Although theoretically it could be argued that a larger sample size might have detected such relationships, our results clearly indicate that such effects, if existing, would be much weaker than effects of habitat amount and fragmentation for which statistically significant relationships were detected.

While this case study provided information on habitat relationships for moose at the landscape scale, there are a few potential issues with the approach that should be highlighted. One key concern is the issue of multicollinear-ity, with which it becomes difficult to estimate the separate effects of highly correlated independent variables on the

TABLE III. Bonferroni confidence intervals for moose habitat use in the study area.

Observed Expected Bonferroni proportion proportion confidence intervalsVegetation Number of pellet of pellet on proportiontype of plots groups (pi) groups of observed

Conifer 365 0.31 * 0.42 0.25 ≤ pi ≤ 0.36Mixedwood 249 0.26 0.30 0.21 ≤ pi ≤ 0.32Deciduous 136 0.18 * 0.11 0.13 ≤ pi ≤ 0.24Shrubland 125 0.23 * 0.09 0.17 ≤ pi ≤ 0.28Grassland 38 0.02 0.02 –0.04 ≤ pi ≤ 0.08Cutblock 46 0 0.02 n/a†

*Observed significantly different than expected.†Confidence cannot be calculated with 0 observations.

TABLE IV. Outcome of three-step hierarchical regression analysis testing the 5 hypotheses of habitat function for moose pellet occur-rence (n = 63). Moose primary habitat is shrubland habitat.

Step R2 P Variable Coefficient (β) Confidence intervals

1 0.175 0.020 +Primary habitat 0.233 0.038 ≤ β ≤ 0.427 0.138 –Length of linear features –0.001 –0.002 ≤ β ≤ 0.0003 0.002 +Total hunter-days 0.071 –0.026 ≤ β ≤ 0.115

2 0.473 0.564 –Primary habitat –0.201 –9.809 ≤ β ≤ 5.408 0.540 –Deciduous –2.356 –10.027 ≤ β ≤ 5.314 0.534 –Coniferous –2.390 –10.045 ≤ β ≤ 5.265 0.526 –Mixedwood –2.446 –10.127 ≤ β ≤ 5.236 0.367 –Grassland –3.649 –11.695 ≤ β ≤ 4.398 0.474 –Cutblock –2.725 –10.309 ≤ β ≤ 4.860 0.473 –Water –2.732 –10.318 ≤ β ≤ 4.854 0.554 –Non-vegetated –2.277 –9.944 ≤ β ≤ 5.389 0.096 –Length of linear features –0.001 –0.002 ≤ β ≤ 0.0002 0.028 +Total-hunter-days 0.056 –0.006 ≤ β ≤ 0.106

3 0.400 0.784 +Primary Habitat –0.229 –1.902 ≤ β ≤ 1.442 0.225 +Primary Habitat × deciduous 0.001 –0.001 ≤ β ≤ 0.003 0.445 +Primary Habitat × coniferous 0.001 –0.0009 ≤ β ≤ 0.002 0.626 +Primary Habitat × mixedwood 0.000 –0.001 ≤ β ≤ 0.002 0.022 –Primary habitat × grassland –0.005 –0.010 ≤ β ≤ –0.0008 0.204 –Primary habitat × cutblock –0.003 –0.007 ≤ β ≤ 0.002 0.119 –Primary habitat × water –0.006 –0.013 ≤ β ≤ 0.002 0.292 –Primary habitat × non-vegetated 0.002 –0.002 ≤ β ≤ 0.007 0.089 –Length of linear features –0.001 –0.002 ≤ β ≤ 0.0001 0.001 +Total-hunter-days 0.077 0.032 ≤ β ≤ 0.122

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dependent variable. Several options are available to manage this, all with advantages and disadvantages, including centring the data (standardizing), removing redundant independent variables, principal components analysis, increasing sample size, and ridge regression (Kidwell & Brown, 1982; Neter et al., 1996; Quinn & Keough, 2002). We assessed some of these methods. Ridge regression was appropriate for this analysis because understanding the complex habitat relationships relative to moose requires that all independent habitat variables be considered. All presented results are from the standard general linear model output. The results are consistent among all approaches used to address mul-ticollinearity in terms of which variables are significant predictors of relative moose abundance and effect direction. Another issue may be that the method used to determine primary habitat could influence the final outcome of the analysis. In our case study we have used Bonferroni confi-dence intervals, which some researchers may consider to be too conservative. Alternative methods, such as the Holm test (Holm, 1979; Glantz, 2005), could be explored. In this way, habitat relationships could be assessed from multiple per-spectives to reduce the possibility of important relationships being overlooked.

Despite these potential challenges, we feel that the approach we used was effective at determining habitat relationships for a generalist species in a mosaic landscape, which is something that has been largely lacking in the literature. Our particular case study used empirical, system-atically collected data for a wide-ranging, generalist species, at a broad spatial scale with a large number of observa-tion units (i.e., landscapes). This work complements many previous landscape studies that have asked similar ques-tions using computer simulations (With, Gardner & Turner, 1997; Fahrig, 2001) or meta-analyses (Bender, Contreras & Fahrig, 1998). The quantitative approach used here illus-trated the influence of landscape resource heterogeneity on the distribution of a generalist species.

Acknowledgements

We thank M. Reid, E. Dickson, and S. Alexander for pro-viding advice and thoughtful reviews of this manuscript. We also thank M. Dubois and K. Overmoe for their assistance in data collection. This research was funded by AMEC Earth and Environmental Ltd., MSES Inc., Alberta Sport, Recreation, Parks & Wildlife Foundation, and the University of Calgary. Vegetation maps were provided by Foothills Model Forest.

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