Wildlife Damage Management, Internet Center for

 

Date of this Version

2012

Citation

Western North American Naturalist 72(4), 2012, pp. 432–441

Abstract

Establishment of sampling frameworks to monitor the occurrence of ecological indicators and to identify the covariates that influence occurrence is a high-priority need for natural resource restoration and management efforts. We utilized occupancy modeling to identify patterns of beaver occurrence and factors influencing these patterns (i.e., type and amount of vegetation cover) in the Grand Canyon of the Colorado River ecosystem. We used rafts and kayaks to access a stratified random sample of sites (i.e., 100-m-long sections of riverbank) and used repeated sampling procedures to sample for beaver sign (i.e., lodges, cuttings, tracks, and beaver sightings). We quantified the type and amount of vegetation cover at each sampled section by using a GIS database of remotely sensed information on the riparian vegetation in the Grand Canyon. We first modeled occurrence of beaver sign as a function of the total amount of vegetation cover (summed across classes) and then determined the relative importance score for each of the 7 vegetation classes. Detection probability (p) was 2 times higher when observers traveled in kayaks (0.61) than when they traveled in rafts (0.29). Occurrence of beaver sign (ψ) in sampled transects was widespread throughout the Grand Canyon (ψ = 0.74, SE = 0.06) and positively associated with total vegetation. The relative importance scores for Tamarix and Pluchea vegetation classes were 1.5–2.5 times larger than those for all other vegetation classes, indicating that occurrence of beaver sign was most strongly associated with the cover of these 2 vegetation classes. Our results imply that quantifying the amount of riparian vegetation in close proximity to a river helps determine the occurrence of an important ecological indicator in riparian systems. The results also demonstrate a useful and cost-effective method for monitoring riverine species’ usage patterns by explicitly accounting for detectability.

Share

COinS