Abstract
Survey data improve population management, yet those data often have associated bias. We quantified one source of bias in moose survey data (observer detection probability, p), by using repeated ground-observations of calves-at-heel of radio-collared moose in Colorado, USA. Detection probabilities, which varied both spatially and temporally, were estimated using an occupancy-modelling framework. We provide an efficient offset for modelled calf-at-heel occupancy (ψ) estimates that accommodates summer calf mortality. Detection probabilities were most efficiently modelled with seasonal variation, with the lowest probability of detecting calves-at-heel occurring during parturition (i.e. May) and later autumn periods (after August). Our most efficiently modelled detection probability estimate for summer was 0.80 (SE = 0.05). During the four years of this study, ψ estimates ranged from 0.54–0.84 (SE = 0.08–0.11). Accounting for 91.7% monthly calf survival corrected ψ estimates downward (ψ = 0.42–0.65). Our results suggest that repeated ground-based observations of individual cow moose, during summer months, can be can a cost-effective strategy for estimating a productivity parameter for moose. Ground survey results can be further improved by accounting for calf mortality.
Highlights
BioOne Complete is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses
Observations made of moose while they were moving tended to be short in duration, but effective for observing calves
Nonresponse error occurred during the collection of our moose calf-at-heel data
Summary
BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses. We quantified one source of bias in moose survey data (observer detection probability, p), by using repeated ground-observations of calves-at-heel of radiocollared moose in Colorado, USA. Harvest management of many large ungulate species often requires biologists to inform decisionmakers about how many hunting licenses a herd can support Under these scenarios, population monitoring data are pivotal. Even when available for observation, a proportion of animals remain undetected This form of nonresponse error, referred to as a detection probability, introduces a consistent negative influence on observation data (Thompson et al 1998, White 2005). The license permits use, distribution and reproduction in any medium, provided the original work is properly cited
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