Abstract
Equivalence estimated with linear quantile regression was used to evaluate compliance with habitat management objectives at Arapaho National Wildlife Refuge based on monitoring data collected in upland (5,781 ha; n = 511 transects) and riparian and meadow (2,856 ha, n = 389 transects) habitats from 2005 to 2008. Quantiles were used because the management objectives specified proportions of the habitat area that needed to comply with vegetation criteria. The linear model was used to obtain estimates that were averaged across 4 y. The equivalence testing framework allowed us to interpret confidence intervals for estimated proportions with respect to intervals of vegetative criteria (equivalence regions) in either a liberal, benefit-of-doubt or conservative, fail-safe approach associated with minimizing alternative risks. Simple Boolean conditional arguments were used to combine the quantile equivalence results for individual vegetation components into a joint statement for the multivariable management objectives. For example, management objective 2A required at least 809 ha of upland habitat with a shrub composition ≥0.70 sagebrush (Artemisia spp.), 20–30% canopy cover of sagebrush ≥25 cm in height, ≥20% canopy cover of grasses, and ≥10% canopy cover of forbs on average over 4 y. Shrub composition and canopy cover of grass each were readily met on >3,000 ha under either conservative or liberal interpretations of sampling variability. However, there were only 809–1,214 ha (conservative to liberal) with ≥10% forb canopy cover and 405–1,098 ha with 20–30% canopy cover of sagebrush ≥25 cm in height. Only 91–180 ha of uplands simultaneously met criteria for all four components, primarily because canopy cover of sagebrush and forbs was inversely related when considered at the spatial scale (30 m) of a sample transect. We demonstrate how the quantile equivalence analyses also can help refine the numerical specification of habitat objectives and explore specification of spatial scales for objectives with respect to sampling scales used to evaluate those objectives.
Positional‐based cloning ‘fail‐safe’ approach is overpowered by wheat chromosome structural variation