Document Type

Other

Department/Program

Biology

Pub Date

1-2-2017

Publisher

DIVERSITY AND DISTRIBUTIONS

Volume

23

Issue

1

Abstract

AimWe explored the extent to which occupancy of butterflies within three biogeographic regions could be explained by vegetation structure and composition, topography and other environmental attributes; whether results were consistent among regions; and whether assumptions of closure were met with assemblage-level sampling designs. LocationChesapeake Bay Lowlands (Virginia), central Great Basin (Nevada) and western Great Basin (Nevada and California) (all USA). MethodsWe applied single-season occupancy models that either assumed closure or relaxed the closure assumption to data from 2013 and 2014 for 13-15 species in each region. ResultsMaximum single-year estimates of detection probabilities ranged from 0.14 to 0.99, and single-year occupancy from 0.28 to 0.98. The assumption of closure was met for a maximum of 54% of the species in a given region and year. Detection probabilities of >90% of the species in each region increased as the categorical abundance of nectar or mud increased. Measures of the dominance or abundance of deciduous woody species and structural heterogeneity were included in the greatest number of occupancy models for the Chesapeake Bay Lowlands, which may in part reflect the intensity of browsing by white-tailed deer (Odocoileus virginianus). Elevation and precipitation were prominent covariates in occupancy models for Great Basin butterflies. Main conclusionsBecause occupancy models do not rely on captures or observations of multiple individuals in a population, they potentially can be applied to a relatively high proportion of the species in an assemblage. However, estimation of occupancy is complicated by taxonomic, temporal and spatial variation in phenology. In multiple, widely divergent ecosystems, all or some associations between covariates and detection probability or occupancy for at least one-third of the species could not be estimated, often because a given species rarely was detected at locations with relatively low or high values of a covariate. Despite their advantages, occupancy models may leave unexplained the environmental associations with the distributions of many species.

DOI

10.1111/ddi.12504

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