title: Assessment of monitoring power for highly mobile vertebrates.
abstract: Monitoring of population trends is a critical component of conservation management, and development of practical methods remains a priority, particularly for species that challenge more standard approaches. We used field-parameterized simulation models to examine the effects of different errors on monitoring power and compared alternative methods used with two species of threatened pteropodids (flying-foxes), Pteropus conspicillatus and P. poliocephalus, whose mobility violates assumptions of closure on short and long timescales. The influence of three errors on time to 80% statistical power was assessed using a Monte Carlo approach. The errors were: (1) failure to count all animals at a roost, (2) errors associated with enumeration, and (3) variability in the proportion of the population counted due to the movement of individuals between roosts. Even with perfect accuracy and precision for these errors only marginal improvements in power accrued (-1%), with one exception. Improving certainty in the proportion of the population being counted reduced time to detection of a decline by over 6 yr (43%) for fly-out counts and almost 10 yr (71%) for walk-through counts. This error derives from the movement of animals between known and unknown roost sites, violating assumptions of population closure, and because it applies to the entire population, it dominates all other sources of error. Similar errors will accrue in monitoring of a wide variety of highly mobile species and will also result from population redistribution under climate change. The greatest improvements in monitoring performance of highly mobile species accrue through an improved understanding of the proportion of the population being counted, and consequently monitoring of such species must be done at the scale of the species or population range, not at the local level.
authors.affiliation: CSIRO Ecosystem Sciences, P.O. Box 780, Atherton, Queensland 4883, Australia. david.westcott@csiro.au
authors.forename: David A
authors.initials: DA
authors.lastname: Westcott
authors.forename: Cameron S
authors.initials: CS
authors.lastname: Fletcher
authors.forename: Adam
authors.initials: A
authors.lastname: McKeown
authors.forename: Helen T
authors.initials: HT
authors.lastname: Murphy
mesh_terms: D000823:Animal Identification Systems; D000818:Animals; D001315:Australia; D002685:Chiroptera / Q000502:physiology*; D003710:Demography; D004784:Environmental Monitoring; D005426:Flight, Animal; D009010:Monte Carlo Method; D011157:Population Dynamics; D013997:Time Factors