Anthropogenic landscape change is a leading driver of biodiversity loss. Preceding dramatic changes such as wildlife population declines and range shifts, more subtle responses may signal impending larger-scale change. For example, disturbance-induced shifts to species’ activity patterns may disrupt temporal niche partitioning along the 24-h time axis, compromising community structure via altered competitive interactions. We investigated the impacts of human landscape disturbance on species’ activity patterns and temporal niche partitioning in the Canadian Rocky Mountain carnivore guild using camera trap images collected across two regions encompassing a wide gradient of human footprint. Applying kernel density estimation techniques, we tested for carnivore species’ activity shifts 1) between a low versus high disturbance landscape, and 2) in relation to site-scale disturbance. To test our hypothesis that human disturbance impacts species’ temporal niche partitioning, we modelled activity overlap between co-occurring carnivore species in relation to natural and anthropogenic landscape features, as well as carnivore community composition. Multiple carnivore species altered activity patterns between the low versus high disturbance landscapes and camera sites, but these shifts varied considerably among species. While wolves appeared to increase nocturnal activity in relation to disturbance, coyote activity consistently trended towards cathemerality and marten increased diurnal activity. Detecting effects of landscape disturbance on activity overlap between co-occurring species was highly sensitive to site-level detection sample sizes, and our results suggest altered temporal niche partitioning between marten and wolverine in relation to forest cover. This study indicates that mesocarnivores may respond differently and perhaps indirectly to anthropogenic disturbance compared to apex predators. Apex predator shifts to nocturnality may facilitate a ‘behavioural release’ in mesocarnivores. This may be a likely component of mesocarnivore population release, with important management implications for ecological communities on disturbed landscapes.

Taken from "Methods" in article:

We used photographic data collected in remote camera arrays deployed using identical sampling designs and sampling methods (Heim et al. 2017) explicitly designed to be networked (sensu Steenweg et al. 2017) to examine predator distribution across gradients of disturbance the Canadian Rocky Mountains. Species were surveyed in a systematic grid-based sampling design, with individual 12 × 12 km2 grid cells as our sampling unit, the size of a wolverine’s home range which is the most wide-ranging species we sought to sample. Within grid cells cameras were placed on landscape features to maximize probability of detection. We deployed baited camera sites in both the WW (n = 66) and KR (n = 157) in October–December, monitoring monthly until March. Our sampling period is therefore representative of species’ activity over the prolonged winter season in the Canadian Rockies. Sites were accessed by ground (ski, snowmobile, all-terrain vehicles) or helicopter. While sampling years and duration differed between the two study areas (WW = 2006–2008; KR = 2011–2014), we have no evidence to indicate this affects any observed differences in species’ behaviour. In both areas Reconyx infrared-triggered digital cameras (models RM30, PM30 and PC900) were deployed paired with a frozen beaver carcass nailed to the tree facing the camera ca1 6-m away. Cameras were placed ca 1.5 m off the ground oriented north to prevent false triggers and set to high sensitivity with no trigger delay. Data were collected and bait replenished monthly (see Fisher and Bradbury 2014 for further details).

We quantified human landscape disturbance using digital map inventories (Alberta Biodiversity Monitoring Institute, Human Footprint Map 2012). Linear features were the predominant landscape development feature present in the WW and the most widespread anthropogenic feature in the KR, so we used these as proxies of overall landscape disturbance. In the WW, seismic lines from petroleum exploration were the only linear feature (Fisher and Burton 2018) whereas in the more disturbed KR, this included a variety of anthropogenic linear disturbance features such as roads, recreational trails and industrial cutlines (e.g. seismic and power lines). Based on the available human footprint data, we calculated the percent cover (% area) of linear features at a 5000-m buffer around each camera site (Fisher et al. 2011) by merging all anthropogenic linear features into a single ‘linear disturbance’ variable.

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Details and description of variables used to generate species' activity curves across camera sites with differing levels of human disturbance.

Willmore Camera Data:

1. Camera_Site: Name of the camera site

2. Date: Date that the image was captured

3. Time: Time that the image was captured

4. Minutes: Minutes passed since midnight (used to calculate Radians)

5. Radians: Radians, calculated as Minutes * π/720  

6. Species: Name of species observed in image

7. Total: Total number of individuals observed in image

Willmore Linear Data:

1. Camera_Site: Name of the camera site.

2. Percent_Area.Seismic_Lines: Percent cover (% area) of seismic lines at a 5000-m buffer around each camera site

Kananaskis Camera Data:

1. Camera_Site: Name of the camera site

2. DateImage: Date that the image was captured

3.TimeImage: Time that the image was captured.

4. Minutes: Minutes passed since midnight (used to calculate Radians)

5. Radians: Radians, calculated as Minutes * π/720

6. Species: Name of species observed in the image

7. Total: Total number of individuals observed in the image

Kananaskis Linear Footprint Data:

1. Camera_Site: Name of the camera site

2. Percent_Area.All_Linear: Percent cover (% area) of all combined linear features at a 5000-m buffer around each camera site.

Linear features included canals, pipelines, rail (hard surface and vegetated verge), roads (hard surface and vegetated vegre), vegetated road trails, seismic lines, and transmission lines.