Spatial decomposition of predation risk using resource selection functions: an example in a wolf–elk predator–prey system

1. What they did

Predation risk is often treated as simply “where the predators are”. The authors argue that it is more than that. A predator first has to find, or encounter, its prey, and then has to succeed in killing it. Landscape features can affect these two stages differently. Building on Holling’s functional response and Lima and Dill’s formula for predation risk, they show how to map each stage separately with resource selection functions (RSF), statistical models that compare places that were used with places that were available, and then combine the two into one map of predation risk.

They tested the approach on gray wolves hunting elk in winter, in and next to Banff National Park, Alberta, Canada, from 1997 to 2001, using the Cascade wolf pack (about eight wolves). They asked two questions:

• Do the places where wolves occur differ from the places where wolves encounter and kill elk?
• At which stage of predation (search, encounter or kill) do landscape features have their greatest effect?

To answer them they collected four kinds of data:

• Search: weekly aerial locations of radio-collared wolves.
• Encounters: following wolf tracks in the snow and recording each place where wolf tracks crossed the tracks of an elk group.
• Kills: elk killed by wolves, found while snow tracking and through telemetry.
• Elk: weekly locations of radio-collared female elk, from the air or the ground.

For each data set they measured distance to roads and trails, slope, elevation, aspect and vegetation cover in a Geographic Information System (GIS). They built four models with the same six landscape features (elk, wolf search, encounter and kill), compared them, and turned the encounter and kill models into maps.

2. What they found

The data

• 119 wolf locations at least 24 hours apart, from six wolves.
• About 1250 km of wolf trails followed in the snow, with 77 elk groups encountered.
• 119 elk killed by wolves, more adult males (52) than any other class.
• 4890 locations from 104 collared adult female elk.
• After an encounter, elk were chased an average of 262 m (range 10 to 1700 m) before being killed.

The models

• Six features stayed in the best models for every stage: distance to roads and trails, slope, elevation, grassland, pine and open conifer.
• The elk, wolf and encounter models fitted the data well. The kill model fitted poorly, and many features did not predict where elk were killed once encountered.

Where elk and wolves were

• Elk were more likely near roads, at lower elevations and on gentler slopes, and used grasslands and open conifer more and pine forest less.
• Wolves were also more likely near roads and at lower elevations and on gentler slopes, and used grasslands and pine forest more and open conifer less.

Encounters and kills

• Wolves avoided high, steep ground more than elk did, so encounters were concentrated in valley bottoms. Topography (slope and elevation) drove encounters.
• Once an elk was encountered, habitat mattered most. Elk were more likely to be killed in pine forest, and less likely in grasslands and open conifer, than the encounter stage suggested.
• Distance to roads and slope steepness had the same effect on encounters and kills.
• If predation risk had been defined simply as where wolves were, risk would have been overestimated by about 60% in grasslands and open conifer, and underestimated by about 20% in pine forest and at higher elevations.

3. What they concluded

• Predation risk is not the same as where predators are found. Landscape features can make prey more or less vulnerable once they have been encountered, and a feature can affect the encounter and kill stages differently.
• If a feature lowers the chance of encounter but raises the chance of being killed, studies that do not separate the stages can miss it, because the two effects cancel out.
• Separating the stages lets researchers form and test ideas about why risk differs. For example, safety in grasslands may come from better predator detection, vigilance or larger elk groups; in pine forest, shorter sight lines or fallen timber may slow fleeing elk (fallen timber was not measured).
• The risk map applies only to the Cascade pack and to the season studied. Total risk also depends on predator numbers, so with several packs, each pack’s map could be weighted by pack size.
• Where encounters cannot be recorded, comparing kill sites with available habitat can approximate risk, but cannot show which stage is responsible.
• The authors suggest these risk maps could be used in spatial predator–prey models, for example to spread a known number of kills across a landscape.

4. How tracking was or was not used

Tracking was central to the study. Radio-collared wolves were located from the air, and these locations were used to start continuous snow-tracking sessions. Field teams then followed (backtracked) wolf trails in the snow for about 1250 km in total. They recorded:

• Encounters: each place where wolf tracks crossed the tracks of an elk group. The authors note this is a spatial encounter, not necessarily the exact time and place the animals met.
• Chases: the distance elk were chased after an encounter, read from the tracks.
• Kills: elk killed by wolves found along the trail.

The authors assume that wolves were always hunting while travelling. They also name snow tracking, alongside direct observation and Global Positioning System (GPS) collars, as one way to estimate encounters in other studies.

5. Tracker or observer certification

Not reported. The paper thanks “the dozens of field assistants who made this study possible” but does not say who did the snow tracking or how their skill was assessed. This matters because both the encounter and kill models rest on reading wolf and elk tracks correctly.

6. Other technologies, skills or innovations

• Radio collars on wolves and elk, with aerial and ground relocation (fixed-wing aircraft).
• Mapping in ArcGIS 8.2: a digital elevation model, a human-use atlas of roads and trails, and two merged land cover maps.
• A method for turning separate encounter and kill models into a single predation risk map.

7. Implications for conservation (from the paper)

• Knowing which stage of predation a landscape feature affects can focus conservation on the stage with the greatest effect or the most room for management.
• For endangered mountain caribou, it matters whether roads and trails make wolves more likely to find caribou, or make caribou easier to kill once found: these may need different actions.
• Studies that assume predation risk equals predator habitat use may be misleading.