Methods

  Agriculture Footprint

Areas of annual or perennial cultivation, including crops and tame pasture, as well as confined feeding operations and other high-density livestock areas.

  Energy Footprint

Areas where vegetation or soil has been disturbed by the creation of mine sites, peat mines, pipelines, seismic lines, transmission lines, well sites, and wind-generation facilities. This footprint type is called “energy footprint” because most of this footprint type is associated with the energy industry.

  Forestry Footprint

Areas in forested landscapes where timber resource extraction has occurred for industrial purposes, including clear-cut and partial-cut logging methods.

  Effective Forestry Footprint

The area of forestry footprint pro-rated for how much it has recovered for biodiversity, based on harvest area age and stand type (deciduous or conifer). A newly harvested area is considered to be 0% recovered, while mature forest is 100% recovered.

  Human-created Waterbodies

Human-made waterbodies created for a variety of purposes, such as to extract fill (borrow pits, sumps), water livestock (dugouts), transport water (canals), meet municipal needs (water supply and sewage), and store water (reservoirs).

 
  Transportation Footprint

Railways, roadways and trails with hard surfaces such as cement, asphalt, or gravel, roads or trails without gravel or pavement, and the vegetation strips alongside transportation features.

  Urban/Industrial Footprint

Combines urban, rural, and industrial footprint types including residences, buildings, and disturbed vegetation and substrate associated with urban and rural settlements, such as housing, shopping centres, industrial areas, golf courses, and recreation areas, as well as bare ground cleared for industrial and commercial development.

  Total Human Footprint

Summary of six human footprint categories combined, including: agriculture, energy, forestry, human-created waterbodies, transportation, and urban/industrial.

  Effective Human Footprint

Same definition as Total Human Footprint but replacing Forestry Footprint with Effective Forestry Footprint.

The ABMI uses human footprint data measured annually at a 1:5,000 scale to track changes in human footprint over time. Detailed annual samples of human footprint are measured in a 3 × 7-km rectangular area centred near each of the ABMI’s 1,656 sites, which when summed across all sites amounts to about 5% of the province’s land surface. ABMI human footprint trend data are available from 1999 to 2017, except for 2000 - 2003 (inclusive). Trend data and the metadata associated with these data can be accessed here.

At the provincial scale, the ABMI merges 20 human footprint sub-layers (based on 117 feature types) into a single integrated layer by applying a specific order of precedence to create the ABMI Human Footprint Inventory (HFI), circa 2017. Some of these 20 sub-layers are created by the ABMI and Government of Alberta (Alberta Environment and Parks) as part of the Alberta Human Footprint Monitoring Program (more details of this partnership are available in Collaborators and Contributors). We use the HFI 2017 for two purposes in this report: to generate maps of human footprint in the Al-Pac FMA area and AEI, and to standardize the 3 × 7-km trend estimates before reporting. This product is updated approximately every two years; these data and the metadata associated with this product are available here.

In general, cities and cultivated fields have high human footprint, while protected and undeveloped areas have low human footprint. Human footprint is summarized for total human footprint and by the six reporting categories.

Trend in human footprint in the Al-Pac FMA area was assessed using the 3 × 7 km detailed inventory of human footprint available from 1999 to 2017, except for 2000-2003. Trend is presented for total human footprint, effective human footprint and by the seven reporting categories. For forestry footprint and total human footprint (both status and trend), we also report on the area of their respective footprints after reducing the area of older forestry footprint based on how recovered it is, referred to as Effective Forestry Footprint and Effective Human Footprint.

The maps used to visualize human footprint in this report are based on the GIS Inventory of Provincial Human Footprint, circa 2017.

 
  Seismic Line (Conventional)

Seismic lines constructed prior to the use of Low-Impact-Seismic (LIS) construction methods. Conventional seismic lines were constructed using older technology that required the lines to be between 5 to 8 meters in width to allow equipment to operate on the lines.

 
  Seismic Line (Low Impact)

Seismic lines typically 2 - 3 m in width constructed using low impact techniques such as avoidance construction methods and/or hand-cut lines to reduce both the width of the line and the line of sight, making the line more sinuous.

 
  Pipeline

A line of underground and over ground pipes, used for the delivery of petrochemicals, as well as the physical clearing around the pipeline(s).

 
  Roads Major

All roadways paved with asphalt or concrete, roads surfaced with gravel and which serve as a main access route, as well airplane runways and interchange ramps.

 
  Roads Minor

Roadways surfaced with dirt and/or low vegetation which serve as a minor access routes.

 
  Transmission Lines

Utility corridor greater than 10 m wide with poles, towers and lines for transmitting high voltage (> 69 kV) electricity.

 
  Railway

A road or track for trains including active and abandoned tracks.

Total Linear Footprint

Summary of all linear footprint categories combined.

The ABMI collects data on six taxonomic groups (birds*, winter-active mammals, soil mites, vascular plants, mosses, and lichens) and builds statistical models to identify how the occurrence and relative abundance of species varies in relation to native land cover, human footprints, and spatial/climate variables. For species with sufficient data, we determine cumulative effects of human footprint on species by comparing predictions under current landscape conditions to predictions in reference landscapes where all human footprints have been removed (backfilled). We convert the difference in relative abundance between predicted current and reference abundances to a scaled Biodiversity Intactness Index (BII).

The BII is scaled between 0 and 100, with 100 representing no difference in expected abundance between current and reference conditions, and 0 representing current species abundance as far from reference conditions as possible. Intactness thus reveals deviations in species' abundance from intact conditions. Both downwards and upwards differences from reference conditions are viewed as deviations from intact conditions. For example, an intactness value of 50% means that the current species abundance is either half or twice as abundant as that predicted under reference conditions. The index is calculated as:

• current / reference × 100%, when current < reference (these are "decreaser" species whose relative abundance is lower in human footprint compared to reference conditions), or
• reference / current × 100%, when reference < current (these are "increaser" species whose relative abundance is higher in human footprint compared to reference conditions).

Overall intactness for a region is calculated by averaging intactness of species within each taxonomic group, then averaging the six taxonomic groups. Because increasers and decreasers both reduce intactness, a predicted increase in one species does not cancel out a predicted decrease in another species. Instead, both reduce intactness. We estimate intactness for the years’ 2010 and 2016, based on native land cover and human footprint for those two years. Any change in intactness values between 2010 and 2016 is attributed to changes in land cover (i.e. human footprint area) between the two time periods; it is not an estimate of trend.

Of the full suite of species assessed by the ABMI in the Al-Pac FMA area, we profile species associated with old deciduous (> 80 years) and old mixedwood (>100 years) forests within birds, soil mites, vascular plants, mosses and lichens. We present results for all mammal species. Because the habitat associations of many species are poorly known, the association with old deciduous or mixedwood forest is based on our monitoring results—these are species that are more abundant in deciduous and/or mixedwood than other vegetation or HF types, and that are more abundant in old stands than mid-seral or young/cut ones These habitat associations may change for some species as more data are collected.

* For birds, ABMI data are combined with data from the Boreal Avian Modelling Project (BAM). Within Alberta, the BAM database is a compilation of data from the Breeding Bird Survey (BBS), Breeding Bird Atlases, Environment and Climate Change Canada (ECCC), the Bioacoustic Unit at the University of Alberta, other monitoring projects, and short-term research projects. More details are available in Collaborators and Contributors.

Intactness results are averages for the entire Al-Pac FMA area and larger AEI. Specific locations within this region are nearly 0% intact (e.g., active industrial sites), and other sites are 100% intact (e.g., undeveloped forest and wetland habitat).

Any interpretations of biodiversity intactness must take the following into account:

• Intactness is based on species' models that will change as analyses are refined and as more data are gathered.
• Intactness only shows the predicted effects of visible human footprint. Species' populations change for many other reasons. A "decreaser" species—one with lower abundance in human footprint—may in fact have an increasing population trend, if other aspects of its environment are improving. And vice versa for an "increaser" species. Measuring the actual trends of species is a long-term goal of ABMI monitoring.

• Intactness maps show the predicted average effect of the types of footprint in each area. We do not have information on the exact effects of footprint in each specific area, and we do not monitor actual changes in species in every location.
• Our models only reflect the immediate local effects of human footprint. There may be additional large-scale effects that we do not account for. For example an extensive network of linear features and other human footprint may allow a species that prefers disturbed areas to spread through a region.

As part of their environmental stewardship, industries want to know how their activities change the abundances of species. Identifying the species most affected by an industrial sector can help in land use planning and, if necessary, in developing mitigation strategies. However, with several industries operating on the same landbase, it can be difficult to tease out what effect each industry is having on different species. ABMI combines our well-developed habitat models for species with landbase information to infer the effects of different industrial sectors on many species^[4,5].

The ABMI reports on two aspects of these “sector effects”: local-footprint effects and regional population effects.

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 a species in the native habitat prior to the footprint and its abundance in the sector’s footprint. Results are interpreted as follows:

• If a species does not live in the sector’s footprint at all, the local-footprint effect is -100%.
• If the species is a generalist that does not change in abundance between native habitat and the footprint, the effect is 0%.
• If a species prefers a sector's footprint, the effect is a positive percentage.

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 in the region. It is the difference between the species’ total abundance in the reference landscape with all human footprint removed, and the reference landscape with just that sector’s footprint added back in. 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 local-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 the most affected by an industrial sector—positively or negatively—should look at the regional population effects.

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

Step 2. We define five industrial sectors:

• Agriculture: cultivated crops or 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.
• Transport: 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 reference map of current native vegetation with human footprint removed, and create another map with a sector’s footprint added back in.  

Step 4. We apply the habitat models to predict a species’ abundance in the reference map and the reference map plus the sector’s footprint. We use the differences in abundance in just the areas with the sector’s footprint to calculate the local 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.

* For birds, ABMI data are combined with data from the Boreal Avian Modelling Project (BAM). Within Alberta, the BAM database is a compilation of data from the Breeding Bird Survey (BBS), Breeding Bird Atlases, Environment and Climate Change Canada (ECCC), the Bioacoustic Unit at the University of Alberta, other monitoring projects, and short-term research projects. More details are available in Collaborators and Contributors.

There are two important caveats for the sector effects:

• The results are based on applying our habitat models to landbase information. The models have statistical uncertainty, and there may be errors in the landbase information. We do not yet have direct field measurements on actual trends in species’ abundances related to different industrial sectors.

• This is a fairly simple approach that does not account for non-footprint effects (e.g., pollution), or complexities like interactions between sectors (e.g. weedy species entering one sector’s footprint after they were introduced by another sector). We also have to use rules to account for areas where different sectors overlap (e.g., a well site in a cultivated field, forest harvest prior to an industrial development).