Woodland caribou (Rangifer tarandus caribou (Gmelin)) populations are in decline throughout much of their range. With increasingly rapid industrial, recreational, residential, and agricultural development of caribou habitat, tools are required to make clear, knowledgeable, and explainable management decisions to support effective conservation of caribou and their range. We developed a series of Bayesian belief networks to evaluate conservation policy scenarios applied to caribou seasonal range recovery areas. We demonstrate the utility of the networks to articulate ecological understanding among stakeholders, to clarify and explicitly depict threats to seasonal range. We also show how simulated forecasts of spatially explicit seasonal range can be compared with landscape potential with range under assumed conditions of natural disturbance. These tools have provided opportunities to operationally define and measure conditions for recovery of caribou in north-central British Columbia.R6sum6 : Les populations de caribou des bois (Rangifer tarandus caribou (Gmelin)) sont en dQlin dans la majeure partie de leurs aires naturelles. Avec l'expansion rapide des activitts industrielles, rtcrhtives, rtsidentielles et agricoles dans I'habitat du caribou, des outils sont ntcessaires pour prendre des dtcisions d'amtnagement claires, bien documentees et explicables, et ainsi aider ti la conservation du caribou et de ses aires naturelles. Nous avons dtvelopp6 une sene de rtseaux de croyances baytsiens pour tvaluer des sdnarios de politiques de conservation applicables aux quartiers de recouvrement saisonniers du caribou. Nous dtmontrons I'utilitt de ces rheaux pour articuler la comprthension tcologique chez les dtcideurs, pour clarifier et d h i r e de faqon explicite les menaces dans les quartier~ saisonniers et pour montrer comment des prCvisions simultes de la rtpartition saisonnibre spatialement explicite peuvent €we compar k s au potentiel du paysage et B la rtpartition saisonnibre, dans des conditions prtsumtes de perturbations naturelles.Ces outils ont fourni des opportunitts de dtfinir optrationnellement et de mesurer les conditions ntcessaires au recouvrement du caribou dans le centre-nord de la ~olombie-~ritanni~ue.[Traduit par la R6dactionI

Outbreaks of mountain pine beetle are evaluated as a generic disturbance agent, and comparisons are made with other forest disturbances such as wildfire, windthrow, and logging. A useful basis for comparison is the degree of disruption to the overstorey, understorey, and forest floor layers. Clear differences are observed in the impacts of bark beetles, fire, and windthrow, but there is overlap with various harvesting systems. Insects are selective in terms of the species or size of tree that is killed; this selectivity varies with stand composition, stand structure, and outbreak stage. The mountain pine beetle functions as part of larger natural disturbance regimes in western North America, which vary with climate and forest type. Outbreaks of many different insects occur throughout western Canada, with the relative role of fire and insects differing among ecoregions and over time. Beetle-killed stands may facilitate extreme fire behaviour and may be more susceptible to future burning. Large expanses of dead or removed trees also result in altered soil water balance and stream flows, disposing some sites to mass movement or flooding. All disturbances generate heterogeneity, with much of the value to biodiversity and ecosystem recovery depending on residual structure and biological legacies. The capacity for unassisted recovery and the value of each stand to timber supply, carbon balance, and habitat needs in a landscape context are relevant when considering salvage logging or forest rehabilitation. The future role of forest pests is expected to fluctuate in response to changes in climate and the altered composition and structure of western forests.

Effective action planning for recovering endangered populations of boreal caribou (Rangifer tarandus caribou) requires an understanding of the functional interactions between: (1) responses by predators to current and prospective future habitat conditions, (2) responses of prey to population and habitat conditions influencing apparent competition, and (3) residual effects of past population bottlenecks. Monitoring recovery trajectories when human-altered habitats are restored requires consideration of many cause-effect linkages operating among multiple species and across multiple ecological scales. We developed a Bayesian Belief Network (BBN) to help frame potential functional responses of 2 predators (wolves, bears) and 2 prey (moose, caribou) to a large-scaled, silviculturallybased, habitat restoration experiment conducted within the Cold Lake caribou herd area in the Alberta oil sands. The full BBN consists of three general components: (1) those related to predator and prey movements, including use of non-restored and restored habitat features as those opposed conditions affect travel speed and search rates of predators; (2) those related to daily and seasonal use of habitat and how that may affect encounter probabilities between predators and prey; and (3) those related to the probability of kill given an encounter between predator and prey. These components structured in a BBN architecture support the application of the basic parameters in Holling's disc equation, particularly: seasonal predator search rates and probability of a kill given an encounter (i.e., the area of effective search). We used the BBN to map the functional response spatially and to assess the dynamics of the predators and prey (demographics and response to restoration treatments). Our understanding of these hypothetical responses will, in the future, help shape management actions designed to reduce predator density and prey risk. To demonstrate the management utility of this approach, we plan to set BBN prior probabilities for each model component using 4 types of data: (1) habitat conditions measured semi-annually; (2) GPS relocation data from 128 individual predators (77 wolves; 51 bears) and 34 prey (25 moose and 9 caribou); (3) kill site investigations; and (4) DNA analyses of prey species density.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.1996v1 | CC-BY 4.0 Open Access | rec

Effective action planning for recovering endangered populations of boreal caribou (Rangifer tarandus caribou) requires an understanding of the functional interactions between: (1) responses by predators to current and prospective future habitat conditions, (2) responses of prey to population and habitat conditions influencing apparent competition, and (3) residual effects of past population bottlenecks. Monitoring recovery trajectories when human-altered habitats are restored requires consideration of many cause-effect linkages operating among multiple species and across multiple ecological scales. We developed a Bayesian Belief Network (BBN) to help frame potential functional responses of 2 predators (wolves, bears) and 2 prey (moose, caribou) to a large-scaled, silviculturally-based, habitat restoration experiment conducted within the Cold Lake caribou herd area in the Alberta oil sands. The full BBN consists of three general components: (1) those related to predator and prey movements, including use of non-restored and restored habitat features as those opposed conditions affect travel speed and search rates of predators; (2) those related to daily and seasonal use of habitat and how that may affect encounter probabilities between predators and prey; and (3) those related to the probability of kill given an encounter between predator and prey. These components structured in a BBN architecture support the application of the basic parameters in Holling’s disc equation, particularly: seasonal predator search rates and probability of a kill given an encounter (i.e., the area of effective search). We used the BBN to map the functional response spatially and to assess the dynamics of the predators and prey (demographics and response to restoration treatments). Our understanding of these hypothetical responses will, in the future, help shape management actions designed to reduce predator density and prey risk. To demonstrate the management utility of this approach, we plan to set BBN prior probabilities for each model component using 4 types of data: (1) habitat conditions measured semi-annually; (2) GPS relocation data from 128 individual predators (77 wolves; 51 bears) and 34 prey (25 moose and 9 caribou); (3) kill site investigations; and (4) DNA analyses of prey species density.