Moose calf detection probabilities: Quantification and evaluation of a ground-based survey technique

Eric J. Bergman, Forest P. Hayes, Paul M. Lukacs, Chad J. Bishop

Research output: Contribution to journalArticlepeer-review

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 radiocollared 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.

Original languageEnglish
Article number00599
JournalWildlife Biology
Volume2020
Issue number2
DOIs
StatePublished - Dec 1 2020

Keywords

  • Alces alces
  • Colorado
  • Detection probability
  • Ground-surveys
  • Moose
  • Occupancy models

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