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E&S Home > Vol. 6, Iss. 2 > Art. 11 > Abstract Open Access Publishing 
Cost-effective Sampling Design Applied to Large-scale Monitoring of Boreal Birds

Matthew Carlson, University of Alberta
Fiona Schmiegelow, University of Alberta

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Abstract

Despite their important roles in biodiversity conservation, large-scale ecological monitoring programs are scarce, in large part due to the difficulty of achieving an effective design under fiscal constraints. Using long-term avian monitoring in the boreal forest of Alberta, Canada as an example, we present a methodology that uses power analysis, statistical modeling, and partial derivatives to identify cost-effective sampling strategies for ecological monitoring programs. Empirical parameter estimates were used in simulations that estimated the power of sampling designs to detect trend in a variety of species’ populations and community metrics. The ability to detect trend with increased sample effort depended on the monitoring target’s variability and how effort was allocated to sampling parameters. Power estimates were used to develop nonlinear models of the relationship between sample effort and power. A cost model was also developed, and partial derivatives of the power and cost models were evaluated to identify two cost-effective avian sampling strategies. For decreasing sample error, sampling multiple plots at a site is preferable to multiple within-year visits to the site, and many sites should be sampled relatively infrequently rather than sampling few sites frequently, although the importance of frequent sampling increases for variable targets. We end by stressing the need for long-term, spatially extensive data for additional taxa, and by introducing optimal design as an alternative to power analysis for the evaluation of ecological monitoring program designs.

Key words

allocation of sample effort, boreal birds, community metrics, cost-effective sample design, forest bird populations, long-term monitoring, partial derivatives, power analysis, sample error, temporal and spatial variation, trend detection
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Ecology and Society. ISSN: 1708-3087