Moose Alces alces Mammals Profile ABMI

Moose are generalist browsers of woody deciduous shrubs and use a wide variety of open habitats in the summer for foraging, such as burns, harvested areas, riparian areas, and shrublands. These habitats are often in close proximity to forest edges or water to minimize heat stress. Moose also frequent wetlands and lake margins where they forage for salt-rich, submerged vegetation. In the winter, mature/old forests with good snow interception interspersed with open areas with extensive shrub growth jointly provide bedding sites, thermal cover, security cover, and foraging habitat.

Methods

Full habitat models for relatively common species

We use a set of statistical models to relate the relative abundance of each species measured at ABMI sites to three sets of variables: vegetation types, human footprint and geographic location. The analyses are done separately for two overlapping regions: North or Forested Region (including Boreal Forest, Foothills, Canadian Shield and Parkland Natural Region), and South or Prairie Region (includes Grassland and Parkland Natural Regions, and the Dry Mixedwood Natural Subregion in the Boreal Natural Region).

Relative abundance for vascular plants, bryophytes, lichens, and mites is the probability that the species will occur in a 50 m x 50 m ABMI quadrat, or a soil sample in the case of mites. For birds, relative abundance is the number of birds detected at a point count, adjusted for the species’ detection distance.
Vegetation types in the boreal and foothills regions are based on several province-wide GIS layers of vegetation variables, and include main forest stand types by broad age classes and several categories of open vegetation (grass, shrub, open wetland, open water, barren). For the vegetation figures, the broad age classes are interpolated to 20-yr age classes. In the south, ‘vegetation types’ are actually mapped soil types, such as loamy, sandy, etc.
Several categories of human footprint are distinguished in the models, including ‘successional’ (forestry, temporary linear features), ‘alienating’ (agriculture, industrial, urban area, roads), as well as linear versus non-linear features. A flexible curve is used for the footprint relationships so that the models can show different responses when there is partial footprint at a site compared to 100% footprint. This allows the models to represent edge effects. The scale of the footprint information used in the intactness models is 64 ha for birds and 1 ha for other taxa.
Flexible relationships with latitude and longitude are also included in the models to represent the geographic distribution of the species.

The analysis is conducted in a ‘model selection framework’, which means that the models for each species are only as complex as the data for that species support. For example, the models for a rarer species with few records may not be able to separate different age classes or even broad stand types. All age classes or several stand types will therefore all have the same average value in the vegetation figures. This does not mean that there are truly no differences between these types for that species. It just means that we do not yet have enough data to estimate abundances in all those individual vegetation types.

We cannot distinguish different open vegetation types in the boreal and foothills—grass, shrubs and open wetland—because these are rare types in our terrestrial sampling. We also have few samples in very young or very old forest, because these are also rare. Our results might miss species that have a strong association with one of these rare vegetation types.

This modeling requires moderate sample sizes, so we can only do the analyses for species that have at least 20 records in a natural region. ABMI has some information on many other rarer species. We can provide simpler summaries of our records for these species.

Figures show mean relative abundances, with 90% confidence intervals.

The bird modeling differed from other taxa from some aspects because the data set is more extensive (see Collaborations). The combined data set needed to be standardized due to the differences in survey protocol among the data sets. Algorithms to deal with different protocols in bird surveys and methods for estimating predictive uncertainty were developed in collaboration with the Boreal Avian Modelling Project (BAM) team (Sólymos et al. 2013). BAM also provided data management help for organizing bird point count data. The minimum number of detections required for model building for birds was 25.

The bioclimatic variables used in the modeling were calculated at a 4-km resolution using monthly climate normals of temperature and precipitation averaged over 1961-1990. The monthly climate normals are based on instrument-measured climate data that were interpolated by PRISM (Daly et al., 2002) and WorldClim (Hijmans et al., 2005). The western North American portion of these data are described by Wang et al. (2011).

Full details of habitat modeling can be found in the ABMI Species Website Manual.

Limitations

There are many limitations and caveats about the use of these habitat models. Click on each for further details:

For vascular plants, bryophytes, lichens and mites, we are only measuring the occurrence of the species at 0-4 of the 4 quadrats or soil samples per site. The number of individuals, percent cover, total volume or biomass, reproductive status, etc. of the species is not part of this measure -- a single sapling of a tree species is an occurrence as much as complete cover of large trees. There is also no adjustment for the probability that the species was present but was missed by the field technicians. That probability is higher in quadrats with complex vegetation, species rich habitats and for groups of species that are hard to distinguish in the field, all of which could affect the habitat models.

For birds, the measure is based on counts of birds heard in the field by trained observers in some studies, or heard on recordings made using various technologies in other studies (including ABMI). These are mainly, but not exclusively, singing male birds. Adjustments are made for different detection distances in the different studies, habitats and for different species, but these adjustments all have statistical uncertainty. The number of vocalizing birds may also not directly reflect the total number of birds or reproductive success in different habitat types. There are also several groups of birds, such as waterfowl or raptors, where point counts are not a good survey technique. We provide only simple summary information for these species, not full habitat models.

For mammals, ABMI is switching from snow transects to camera-based indices, but results from cameras are not available yet. The current models based on snow-track transects reflect how far the species travels in different habitat or human footprint types during winter. This can be very different from the habitats that they use for foraging, or during summer. Additionally, ABMI transects were along linear features that could be travelled by snowmobile, so the results are confounded by how those features affect winter travel by the species.
The confidence intervals on individual habitat values for a species are often wide, and should be integral to any use of the habitat models. There are two main reasons for wide uncertainty in our results: 1) We estimate values for many habitat and human footprint types, particularly in the North, with many combinations of broad stand types and age classes. Ideally we would have hundreds of observations of a species to estimate so many values. Instead, we use a fairly complicated modeling procedure to extract reasonable values for many species with lower sample sizes by simplifying the complexity of habitat types. For all but the most common species, the resulting habitat models are simplified (some habitat types are assumed to be the same) and estimated with large error. 2) ABMI sites are systematically located, and the Alberta landbase often contains a fine-scale mix of several habitat types and human footprint. Individual sites, even 1-ha areas, therefore usually fall in more than one habitat and/or human footprint type. Our models are designed to separate out the effects of each individual habitat or human footprint type as best as possible, but the analysis is necessarily imperfect with limited sample sizes. A highly specialised species, for example, will almost inevitably show some predicted abundance in habitat types that it does not use, when these co-occur with preferred habitat types.
We have limited ability to separate effects of each vegetation and human footprint type, and spatial and climate variables. In addition to the challenges generating uncertainty in the individual values, ABMI is not an experimental study. We do not design and manipulate habitat and human footprint types, and simply use what is sampled in the province. As a result, habitat and human footprint types are partially confounded with geographic location and climate variables. Our modeling separate out these effects as best as possible, but without impossible province-wide experiments, we can never be sure how well we have estimated the separate habitat, human footprint and geographic/climate effects.
Our model selection approach weights different models in a model set based on the support provided by each species’ data. However, we use the same set of models for all species. We currently do not have the resources to develop separate model sets for individual species to benefit from the differences in natural history.
All taxa have potential for errors in species identification, including the mammal track surveys and the bird recording interpretation. Taxonomic issues are common with the plants, bryophytes, and lichens, where many species groups are difficult to distinguish and taxonomy changes frequently. This adds to uncertainty in models for some species.
ABMI’s vegetation map is compiled from various sources, not all of which cover the entire province. Some layers are less accurate than others. Also, even if individual polygons are accurate, their boundaries may not be precise, and many ABMI sites fall on the edges of vegetation polygons. Resulting error in the vegetation types at sites increases error in the habitat models. Some habitat types may have additional challenges. Grass and shrub sites in the North (forested region), for example, may be true permanent open habitats, but the same designation is often given to early seral stages of forested sites after fire. Grass and shrub sites in the prairies are also poorly mapped. Mixedwood stands are also poorly mapped, because they represent a gradient between deciduous and upland conifer stands, which can often change as a stand ages.
Few species show strong responses to soil type in our models, suggesting that these groups may not be well-defined or relevant to many species. We have too few sites to treat lowland sites in the South (prairie region) separately.
Human footprint is mapped more precisely than vegetation, but we use only broad groups of footprint types in the modeling. Industrial sites and urban or rural residential areas, for example, are not reliably distinguished. Values are particularly difficult to estimate reliably for linear features, because these usually occupy only a small portion of the area of a site. We also have few sites with older forestry cutblocks. Values for those habitat types combine our rough estimates with an assumed rate of convergence of aging forestry and natural stands.
Many sites in the South (prairie region) are grazed to varying degrees but this information is not available. Thus grazing effects cannot be included in our models, and if grazing differs among native habitat types the effects confound those difference. Any wide-scale human effects, like pollution away from visible human footprint or recent climate change, are also not included in our modeling. Additionally, we only include effects of footprint at the local scale—the 1-ha scale we sample at, or larger sampling areas for birds and mammals. Due to sample size constraints and confounding among variables, we do not include any additional effects that might be caused by human footprint in the larger surrounding area.

Species-habitat Associations in the Forested Region

Forested Region - Species Habitat Association Graph: Predicted species relative abundance (bars) as a function of vegetation and human footprint type in the forested region. Dots are added to forest types where harvesting occurs and show the predicted species abundance in harvested stands of various ages. Vertical lines represent 90% confidence intervals.

Moose relative abundance is highest in the grass and shrub vegetation types, followed by marsh, graminoid fen, and treed swamp vegetation types.
Moose relative abundance is also high in young mixedwood and mature deciduous stands in the forested region.
In general, Moose relative abundance is higher in young harvested stands compared to naturally disturbed stands of similar age in the forested region.

Methods

Full habitat models for relatively common species

Relative abundance for vascular plants, bryophytes, lichens, and mites is the probability that the species will occur in a 50 m x 50 m ABMI quadrat, or a soil sample in the case of mites. For birds, relative abundance is the number of birds detected at a point count, adjusted for the species’ detection distance.
Vegetation types in the boreal and foothills regions are based on several province-wide GIS layers of vegetation variables, and include main forest stand types by broad age classes and several categories of open vegetation (grass, shrub, open wetland, open water, barren). For the vegetation figures, the broad age classes are interpolated to 20-yr age classes. In the south, ‘vegetation types’ are actually mapped soil types, such as loamy, sandy, etc.
Several categories of human footprint are distinguished in the models, including ‘successional’ (forestry, temporary linear features), ‘alienating’ (agriculture, industrial, urban area, roads), as well as linear versus non-linear features. A flexible curve is used for the footprint relationships so that the models can show different responses when there is partial footprint at a site compared to 100% footprint. This allows the models to represent edge effects. The scale of the footprint information used in the intactness models is 64 ha for birds and 1 ha for other taxa.
Flexible relationships with latitude and longitude are also included in the models to represent the geographic distribution of the species.

Figures show mean relative abundances, with 90% confidence intervals.

Full details of habitat modeling can be found in the ABMI Species Website Manual.

Limitations

There are many limitations and caveats about the use of these habitat models. Click on each for further details:

For vascular plants, bryophytes, lichens and mites, we are only measuring the occurrence of the species at 0-4 of the 4 quadrats or soil samples per site. The number of individuals, percent cover, total volume or biomass, reproductive status, etc. of the species is not part of this measure – a single sapling of a tree species is an occurrence as much as complete cover of large trees. There is also no adjustment for the probability that the species was present but was missed by the field technicians. That probability is higher in quadrats with complex vegetation, species rich habitats and for groups of species that are hard to distinguish in the field, all of which could affect the habitat models.

For birds, the measure is based on counts of birds heard in the field by trained observers in some studies, or heard on recordings made using various technologies in other studies (including ABMI). These are mainly, but not exclusively, singing male birds. Adjustments are made for different detection distances in the different studies, habitats and for different species, but these adjustments all have statistical uncertainty. The number of vocalizing birds may also not directly reflect the total number of birds or reproductive success in different habitat types. There are also several groups of birds, such as waterfowl or raptors, where point counts are not a good survey technique. We provide only simple summary information for these species, not full habitat models.

For mammals, ABMI is switching from snow transects to camera-based indices, but results from cameras are not available yet. The current models based on snow-track transects reflect how far the species travels in different habitat or human footprint types during winter. This can be very different from the habitats that they use for foraging, or during summer. Additionally, ABMI transects were along linear features that could be travelled by snowmobile, so the results are confounded by how those features affect winter travel by the species.
The confidence intervals on individual habitat values for a species are often wide, and should be integral to any use of the habitat models. There are two main reasons for wide uncertainty: 1) We estimate values for many habitat and human footprint types, particularly in the North, with many combinations of broad stand types and age classes. Ideally we would have hundreds of observations of a species to estimate so many values. Instead, we use a complicated modeling procedure to extract reasonable values for many species with lower sample sizes by simplifying the complexity of habitat types. For all but the most common species, the resulting habitat models are simplified (some habitat types are assumed to be the same) and estimated with large error. 2) ABMI sites are systematically located, and the Alberta landbase often contains a fine-scale mix of several habitat types and human footprint at a site. Most 1-ha and 150m radius areas fall in more than one habitat and/or human footprint type. Our models are designed to separate out the effects of each individual habitat or human footprint type, but the analysis is imperfect with limited sample sizes. A highly specialised species, for example, will almost inevitably show some predicted abundance in habitat types that it does not use, when these co-occur with preferred habitat types.
We have limited ability to separate effects of each vegetation and human footprint type, and spatial and climate variables. In addition to the challenges generating uncertainty in the individual values, ABMI is not an experimental study. We do not design and manipulate habitat and human footprint types, and simply use what is sampled in the province. As a result, habitat and human footprint types are partially confounded with geographic location and climate variables. Our modeling separate out these effects as best as possible, but without impossible province-wide experiments, we can never be sure how well we have estimated the separate habitat, human footprint and geographic/climate effects.
Our model selection approach weights different models in a model set based on the support provided by each species’ data. However, we use the same set of models for all species. We currently do not have the resources to develop separate model sets for individual species to benefit from the differences in natural history.
All taxa have potential for errors in species identification, including the mammal track surveys and the bird recording interpretation. Taxonomic issues are common with the plants, bryophytes, and lichens, where many species groups are difficult to distinguish and taxonomy changes frequently. This adds to uncertainty in models for some species.
ABMI’s vegetation map is compiled from various sources, not all of which cover the entire province. Some layers are less accurate than others. Also, even if individual polygons are accurate, their boundaries may not be precise, and many ABMI sites fall on the edges of vegetation polygons. Resulting error in the vegetation types at sites increases error in the habitat models. Some habitat types may have additional challenges. Grass and shrub sites in the North, for example, may be true permanent open habitats, but the same designation is often given to early seral stages of forested sites after fire. Grass and shrub sites in the prairies are also poorly mapped. Mixedwood stands are also poorly mapped, because they represent a gradient between deciduous and upland conifer stands, which can often change as a stand ages.
Few species show strong responses to soil type in our models, suggesting that these groups may not be well-defined or relevant to many species. We have too few sites to treat lowland sites in the South (prairie region) separately.
Human footprint is mapped more precisely than vegetation, but we use only broad groups of footprint types in the modeling. Industrial sites and urban or rural residential areas, for example, are not reliably distinguished. Values are particularly difficult to estimate reliably for linear features, because these usually occupy only a small portion of the area of a site. We also have few sites with older forestry cutblocks. Values for those habitat types combine our rough estimates with an assumed rate of convergence of aging forestry and natural stands.
Many sites in the South (prairie region) are grazed to varying degrees but this information is not available. Thus grazing effects cannot be included in our models, and if grazing differs among native habitat types the effects confound those difference. Any wide-scale human effects, like pollution away from visible human footprint or recent climate change, are also not included in our modeling. Additionally, we only include effects of footprint at the local scale—the 1-ha scale we sample at, or larger sampling areas for birds and mammals. Due to sample size constraints and confounding among variables, we do not include any additional effects that might be caused by human footprint in the larger surrounding area.

Species-habitat Associations in the Prairie Region

Non-Treed Sites in the Prairie Region

Treed Sites in the Prairie Region

Prairie Region - Species Habitat Association Graph: Predicted species relative abundance (bars) in each soil type and human footprint type in the prairie region. Vertical lines indicate 90% confidence intervals. The presence/absence of trees greatly affects the presence and abundance of many species; therefore, separate figures are presented for treed and non-treed sites in the prairie region.

Moose relative abundance is the same at treed and non-treed sites in the prairie region.

Moose relative abundance is highest in thin break and rapid drain soil types in the prairie region.

Relationship to Linear Footprint

Relationship to Linear Footprint in the Forested Region

Linear Footprint Graph: Species relative abundance predicted for habitat with no human footprint compared to habitat in which 10% of the area is converted to either soft or hard linear footprint.

Moose relative abundance is predicted to have a slight negative relationship with soft linear footprint and hard linear footprint in the forested region.

Relationship to Linear Footprint in the Prairie Region

Linear Footprint Graph: Species relative abundance predicted for habitat with no human footprint compared to habitat in which 10% of the area is converted to either soft or hard linear footprint.

Moose relative abundance is predicted to have a slight negative relationship with soft linear footprint and hard linear footprint in the prairie region.

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Impacts of Human Footprint

Moose preference for early successional habitat for foraging and their use of forest edges means that they are positively impacted by development activities that create these habitats, such as forest harvesting.

Methods

ABMI's sector effects analysis is used to assess the impact of different types of human development activities (herein called a sector) on a species’ relative abundance. We calculate the effects of different industrial sectors by predicting the abundance of each species in the current landbase versus a landbase in which the footprint associated with a particular industrial sector has been back-filled. “Back-filling” means replacing that sector’s footprint with the vegetation or soil type that would have been present before the footprint was made. We report on two aspects of these “sector effects”:

1. Under-footprint effect. Values indicate how much the abundance of a species changes in the exact areas where a sector’s footprint occurs. It is the difference between the abundance of the species in the native habitat prior to the footprint and the abundance in the footprint (Figure: Under-footprint Effects below).

If the species does not live in the sector’s footprint at all, the within-footprint effect is -100%.
If the species is a generalist that does not change in abundance in footprint, the effect is 0%.
If the species prefers footprint, the effect is a positive percentage.

2. Regional population effect. A sector’s effect on the regional population of a species shows how much the total population in the region is predicted to have changed due to that sector’s footprint (Figure: Regional Sector Effects below). It is the difference between the species’ total abundance in the current landscape, and the species’ abundance if a sector’s footprint was removed and replaced with the original native habitat (but everything else on the landbase stayed the same). Three factors determine a sector’s effect on the regional population of a species:

How much area that industry’s footprint occupies. All else being equal, the more area a sector’s footprint occupies, the more effect it will have on species.
The under-footprint effect of that sector on the species (described above). Some species may be reduced in one sector’s footprint, tolerate another’s, and increase in other footprint.
The habitat types that the industry operates in. A sector’s effect on the regional population of a species will be greater if the industry operates more in good habitat for the species. For example, two forest species might both avoid agricultural areas, but one lives in aspen stands and the other in black spruce. Agriculture will have a much greater effect on the regional population of the aspen species, because agricultural clearings are often in aspen stands and rarely in black spruce. The species most negatively affected by an industrial sector will therefore be species that are least tolerant of that sector’s footprint, and that prefer to live in habitat types where more of the sector’s footprint occurs. People interested in knowing which species are most affected by an industrial sector – positively or negatively – should look at the regional population effects.

ABMI uses the following steps to calculate sector effects:

Step 1. We develop habitat models for species based on ABMI field data.

Step 2. We define 5 industrial sectors:

Agriculture: cultivated crops or tame pastures.
Forestry: age and stand type of harvest areas are tracked to account for recovery.
Energy: mines, well sites, pipelines, transmission lines, seismic lines and associated facilities.
Rural/Urban: residential, industrial or recreational sites in rural or urban areas.
Transportation: all roads and rail lines. We cannot distinguish roads made specifically for other sectors, such as roads to access harvest areas or well sites.

Step 3. We use ABMI’s map of current native vegetation and human footprint, and a back-filled map in which human footprint has been replaced by the native vegetation most likely to have been there prior to the footprint. From these, we create “partially back-filled” maps for each sector, where just that sector’s footprint is removed, while other industries’ footprints are retained.

Step 4. We apply the habitat models to predict a species’ abundance in the current map and in the partially back-filled map for a sector. We use the differences in abundance in just the areas with the sector’s footprint to calculate the within-footprint effect. We use the differences in total abundance over the whole region to calculate the sector’s effect on the regional population of the species.

Limitations

Human Footprint Effects in the Forested Region

Local Scale Effects

Figure: Local Scale Effects. Predicted changes to Moose relative abundance inside areas that have been disturbed by each sector (human footprint type) compared to the habitat it replaced (modelled reference condition with no human footprint). Sector effect values less than 0% indicate habitat suitability is reduced (predicted related abundance is lower) compared to reference conditions, and values more than 0% indicate habitat suitability is improved (predicted relative abundance is higher) compared to reference conditions.

Moose relative abundance is predicted to be less abundant than expected in all human footprint types, except forestry, compared to the habitat each footprint replaces in the forested region.

Regional Population Effects

Human Footprint Effects in the Prairie Region

Figure: Regional Population Effects. Predicted change in the total regional population by industrial sector for Moose. This incorporates the area of the footprint, the native habitats where the footprint occurs, and the species response to a particular footprint. Regional population effect values less than 0% indicate a predicted decrease in the regional population due to a particular sector’s footprint, and values greater than 0% indicate a predicted increase.

The regional population of Moose is predicted to be less abundant than expected compared to reference conditions in the forested region as a result of agriculture footprint.
Changes in the regional population of Moose are predicted to be small for the other industrial sectors in the forested region.

Human Footprint Effects in the Prairie Region

Local Scale Effects

Moose relative abundance is predicted to be less abundant than expected in all human footprint types, except forestry, compared to the habitat each footprint replaces in the prairie region.

Regional Population Effects

The regional population of Moose is predicted to be less abundant than expected compared to reference conditions in the prairie region mostly as a result of agriculture footprint, and to a lesser extent transportation and urban/industrial footprints.
Changes in the regional population of Moose are predicted to be small due to energy and forestry footprints in the prairie region.

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Predicted Relative Abundance

Moose are found throughout Alberta but are most common in the Boreal Forest, Foothills, and Parkland Natural Regions.

Methods

Province-wide maps are generated by applying the models of a species’ relationship with vegetation or soil types, human footprint, geographic location and climate to a set of 1-km² pixels covering the province. Within each pixel, the proportion of each vegetation or soil and human footprint type in the 1-km² area is determined, along with the latitude, longitude and climate values at the centre of the pixel. The ‘current’ maps summarize information from the current landbase, including current human footprint (2014 Human Footprint Inventory [HFI] is being used presently, except for birds which is based on the 2012 HFI).

The ‘reference’ maps summarize information from a back-filled map, in which footprint has been removed and replaced with the vegetation type that was expected prior to the footprint being created. Back-filling is based on adjacent vegetation types and rules about what vegetation types different footprint types can occur in (e.g., forestry occurs only in harvestable-age upland stands). The ‘difference’ maps show the areas where the species is predicted to be higher or lower in the current map compared to the reference map.

Separate predictions are made using the North (forested region) and South (prairie region) models. In the overlap area where both of these predictions are available, a weighted average prediction is used. The weighting is based on the mapped probability that the site is or was treed, with the weight for the North prediction being that probability and the weight for the South prediction 1 minus that probability. In other words, the South predictions apply to the untreed proportion of the raster, while the North applies to the treed part.

Province-wide maps are only produced for species that we can analyze from the systematic sites. The models generated for species in the wetland sites cannot be applied province-wide for two reasons:

We do not have maps of where all the wetlands of the type we sample are in the province;
The wetland models include physical and chemistry variables that need to be sampled in the field for each wetland, and therefore are not available for unsampled wetlands.

Limitations

There are several limitations to ABMI species maps:

The maps are based on predicted habitat quality. They show the average expected abundance of a species given the habitat types, footprint, geographic location and climate of a quarter-section. We do not know the actual abundances of each species at each point in the province. There are many reasons beyond habitat quality why a species may be absent or rare at a particular place where we predict it to be common, and vice versa.
There is uncertainty in the habitat models for each species. Uncertainty of habitat models is high for rarer species and for rarer habitat types. Uncertainty is also higher in the corners of the province, particularly the northwest and southwest, where we have limited sampling and are extrapolating from our models. Accompanying uncertainty maps show the absolute prediction uncertainty across the province.
The map predictions are most accurate at broad regional scales. At smaller scales, the true abundance of the species can vary from the prediction, because many factors beyond habitat quality affect a species. At small scales – individual pixels through townships – the maps show the habitat quality for the species, but should not be interpreted as showing the actual abundance of the species in that small area.
The underlying vegetation maps used to make the predictions are imperfect. There are classification errors in some places with greater errors for some particular types (e.g., grass and shrub, mixedwood), and areas missing some of the input layers particularly outside of commercial forestry areas. We have to make assumptions about stand ages in about 1/3 of the forest area that is missing that information. Human footprint is more accurate, but may be 2 or 3 years out of date.
Reference maps only show the effects of statistically removing the effects of visible human footprint. They do not show how the species would have been distributed under ‘pristine’, ‘natural’ or ‘pre-historic’ conditions.
Current maps do not show effects of other human disturbances, like pollution, cattle grazing, climate change or changes on wintering grounds for migratory species.

Interpreting the Maps

Predicted relative abundance maps represent the species relative abundance that is predicted in 1 km² spatial units in Alberta under reference and current conditions. We present the following figures:

Normalized Predicted Relative Abundance: Reference. Habitat association models, plus models describing how species varied spatially and with climate gradients were used to predict species abundance in 1 km2 spatial units under reference conditions. Predictions of relative abundance of species in each 1 km2 unit were made after all human footprint in the 1 km2 unit had been ‘backfilled’ based on native vegetation in the surrounding area.
Normalized Predicted Relative Abundance: Current. Habitat association models, plus models describing how species varied spatially and with climate gradients were used to predict species abundance in 1 km2 spatial units under current conditions. Predictions of relative species abundance in each 1 km2 unit were made based on the vegetation and human footprint present in the 1 km2 unit under current conditions (in year 2014).

In the predicted relative abundance maps, red pixels are predicted to have the highest abundance for the species, grading through light tan to dark blue where the species is predicted to be less abundant or absent. Predicted relative abundance estimates are normalized before displaying to range from 0% to 100%.

In addition, we also present the difference in relative abundance between current and reference conditions:

Difference between Current and Reference relative abundance. The difference map shows where the species is predicted to be more abundant or less abundant than expected in each 1 km² unit when comparing current conditions to references conditions.

In the difference map, green indicates the species was predicted to have higher abundance under present conditions than under reference conditions, with the opposite true for 1 km² units depicted in pink. The intensity of green and pink represent the relative magnitude of increase or decrease for the species between current and reference conditions.

These maps have uncertainty due to uncertainty in the models and in the underlying vegetation map.

Predicted Relative Abundance - Current

The current condition is the predicted relative abundance of Moose taking current human footprint (circa 2018) into account.

use slider to compare maps

Predicted Relative
Abundance (%)

Predicted Relative Abundance - Reference

The reference condition shows the predicted relative abundance of Moose after all human footprint had been backfilled based on native vegetation in the surrounding area.

Predicted Relative Abundance - Difference

The difference map shows areas where the current relative abundance of Moose is predicted to be higher or lower compared to reference conditions. In other words, where habitat suitability is predicted to increase or decrease as a result of human footprint.

Predicted Change
in Relative
Abundance (%)

Moose relative abundance is predicted to be lower under current conditions compared to reference conditions throughout the Parkland and Grassland Natural Regions and portions of the Boreal Forest Natural Region.
Moose relative abundance is predicted to be higher under current conditions compared to reference conditions in scattered areas of the Foothills Natural Region.

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References & Credits

Alberta Environment and Parks. 2016. Moose http://aep.alberta.ca/fish-wildlife/human-wildlife-conflict/moose.aspx Accessed September 17, 2016.

Belovsky, G. E. 1981. Food plant selection by a generalist herbivore: the moose. Ecology 62(4):1020-1030.

Neumann, W., G. Ericsson, H. Dettki, and V. C. Radeloff. 2013. Behavioural response to infrastructure of wildlife adapted to natural disturbances. Landscape and Urban Planning 114:9-27.

Pattie, D. and C. Fisher. 1999. Mammals of Alberta. Lone Pine Publishing, Edmonton, AB.

Data Sources

Data collected by ABMI.

Photo Credits

Photos: TBD

Recommended Citation

Alberta Biodiversity Monitoring Institute. 2020. Moose (Alces alces). ABMI Website: abmi.ca/home/data-analytics/biobrowser-home/species-profile?tsn=180703.

Additional ABMI Resources

Alberta Biodiversity Monitoring Institute. 2016. ABMI Species Website Manual, Version: 2016-12-02. Alberta Biodiversity Monitoring Institute, Alberta, Canada. Report available at: abmi.ca.

Alberta Biodiversity Monitoring Institute. 2014. Manual for Species Modeling and Intactness, Version 2014-09-25. Alberta Biodiversity Monitoring Institute, Alberta, Canada. Report available at: abmi.ca.

Alberta Biodiversity Monitoring Institute. 2014. Terrestrial field data collection protocols (abridged version) 2016-05-18. Alberta Biodiversity Monitoring Institute, Alberta, Canada. Report available at: abmi.ca.

Download ABMI Species and Habitat Data.

View ABMI Collaborations.

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Niki Wilson edited NL for copy and tone Jan-11-2017

Niki Wilson / John Wilmshurst Submitted April-30-2015

Niki Wilson / John Wilmshurst Published on April-30-2015

Niki Wilson / John Wilmshurst Unassigned on May-25-2020

SK updated results writing March-17-2021

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The Moose, the largest member of the deer family, is a wetland-loving resident of northern forests as well as treed river valleys in the mountains and prairies. Moose can be found in every natural region in Alberta.

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Moose relative abundance is predicted to be less abundant than expected in all human footprint types, except forestry, compared to the habitat each footprint replaces in the forested region.

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The regional population of Moose is predicted to be less abundant than expected compared to reference conditions in the prairie region mostly as a result of agriculture footprint, and to a lesser extent transportation and urban/industrial footprints.
Changes in the regional population of Moose are predicted to be small due to energy and forestry footprints in the prairie region.

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Moose relative abundance is the same at treed and non-treed sites in the prairie region.

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Moose relative abundance is predicted to be lower under current conditions compared to reference conditions throughout the Parkland and Grassland Natural Regions and portions of the Boreal Forest Natural Region.
Moose relative abundance is predicted to be higher under current conditions compared to reference conditions in scattered areas of the Foothills Natural Region.

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The regional population of Moose is predicted to be less abundant than expected compared to reference conditions in the forested region as a result of agriculture footprint.
Changes in the regional population of Moose are predicted to be small for the other industrial sectors in the forested region.

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Moose relative abundance is highest in thin break and rapid drain soil types in the prairie region.

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The rural / urban and agriculture human footprints have the largest negative unit effects on Moose in the forested region, but because these footprints cover small areas, this results in small predicted declines in Moose relative abundance compared to reference conditions.

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Due to its large area, the agriculture human footprint has a large predicted negative impact on Moose relative abundance compared to reference conditions in the prairie region.

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Moose relative abundance is predicted to have a slight negative relationship with soft linear footprint and hard linear footprint in the forested region.

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Moose relative abundance is predicted to have a slight negative relationship with soft linear footprint and hard linear footprint in the prairie region.

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Moose relative abundance is predicted to be less abundant than expected in all human footprint types, except forestry, compared to the habitat each footprint replaces in the prairie region.

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Moose relative abundance is highest in the grass and shrub vegetation types, followed by marsh, graminoid fen, and treed swamp vegetation types.
Moose relative abundance is also high in young mixedwood and mature deciduous stands in the forested region.
In general, Moose relative abundance is higher in young harvested stands compared to naturally disturbed stands of similar age in the forested region.

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Moose are found throughout Alberta but are most common in the Boreal Forest, Foothills, and Parkland Natural Regions.

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Alberta Environment and Parks. 2016. Moose http://aep.alberta.ca/fish-wildlife/human-wildlife-conflict/moose.aspx Accessed September 17, 2016.

Belovsky, G. E. 1981. Food plant selection by a generalist herbivore: the moose. Ecology 62(4):1020-1030.

Neumann, W., G. Ericsson, H. Dettki, and V. C. Radeloff. 2013. Behavioural response to infrastructure of wildlife adapted to natural disturbances. Landscape and Urban Planning 114:9-27.

Pattie, D. and C. Fisher. 1999. Mammals of Alberta. Lone Pine Publishing, Edmonton, AB.

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Intro content carried over, interpretations updated.

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Moose

Alces alces

Legend

Introduction

Habitat & Human Footprint Associations

Species-habitat Associations in the Forested Region

Species-habitat Associations in the Prairie Region

Non-Treed Sites in the Prairie Region

Treed Sites in the Prairie Region

Relationship to Linear Footprint

Relationship to Linear Footprint in the Forested Region

Relationship to Linear Footprint in the Prairie Region

Impacts of Human Footprint

Human Footprint Effects in the Forested Region

Local Scale Effects

Regional Population Effects

Human Footprint Effects in the Prairie Region

Local Scale Effects

Regional Population Effects

Predicted Relative Abundance

Predicted Relative Abundance - Current

Predicted Relative Abundance - Reference

Predicted Relative Abundance - Difference

References & Credits

References & Credits

Data Sources

Photo Credits

Recommended Citation

Additional ABMI Resources