Optimizing Allocation of Monitoring Effort Under Economic and Observational Constraints

Abstract

Efforts to design monitoring regimes capable of detecting population trends can be thwarted by observational and economic constraints inherent to most biological surveys. Ensuring that limited resources are allocated efficiently requires evaluation of statistical power for alternative survey designs. We simulated the process of data collection on a landscape, where we initiated declines over 3 sample periods in species of varying prevalence and detectability. Changing occupancy levels were estimated using a technique that accounted for effects of false-negative errors on survey data. Declines were identified within a frequentist statistical framework, but the significance level was set at an optimal level rather than adhering to an arbitrary conventional threshold. By varying the number of sites sampled and repeat visits made, we show how managers can design an optimal monitoring regime that maximizes statistical power within fixed budget constraints. Results show that 2 to 3 visits/site are generally sufficient unless occupancy is very high or detectability is low. In both cases, the number of required visits increase. In an example of woodland bird monitoring in the Mt. Lofty Ranges, South Australia, we show that, although the budget required to monitor a relatively rare species of low detectability may be higher than that for a common, easily detectable species, survey design requirements for common species may be more stringent. We discuss implications for multi-species monitoring programs and application of our methods to more complex monitoring problems. JOURNAL OF WILDLIFE MANAGEMENT 69(2):473-482; 2005

Developing robust field survey protocols in landscape ecology: a case study on birds, plants and butterflies

“…For example, Stohlgren et al (1997) tested reducing a larger set of plant sample replicates in different vegetation communities in the Rocky Mountains and found that already ten quadrats of one square meter per sampling unit provided sufficient information in order to detect fine-scale patterns of plant diversity. Similarly, other studies showed that in Australia and California, most animal species that were surveyed could be detected even if survey effort within a given sampling protocol was reduced to three repeat surveys (Pellet 2008;Field et al 2005). Based on an assessment of birds, amphibians and invertebrates in Australia, Tyre et al (2003) further suggested that with current survey methods, sampling from 100 sites and pooling data over three repeats yielded accurate results.…”

“…Research projects investigating biodiversity distribution patterns are usually constrained by limited resources including money, personnel and time (Field et al 2005;Baasch et al 2010). These constraints pose limits on the affordable sampling effort, both with respect to the number of sites surveyed and the amount of effort per site.…”

“…One tool to investigate tolerable information loss when survey effort is reduced is to evaluate the statistical power of the different survey designs (Field et al 2005;Legg and Nagy 2006;Bailey et al 2007;Vellend et al 2008;Guillera-Arroita and Lahoz-Monfort 2012;Sewell et al 2012). Power analysis calculates the size of an effect that is detectable with a certain level of confidence and significance for a given design.…”

Brown hyaenas on roads: Estimating carnivore occupancy and abundance using spatially auto-correlated sign survey replicates

“…Furthermore, adding replicates is likely to be relatively efficient in a cluster-sampling context because once observers have reached a site, the cost of performing additional sign survey replicates is low (Mackenzie and Royle, 2005). In addition, other authors have suggested that completing more replicates rather than increasing sample size is particularly efficient for species with high occupancy (Field et al, 2005;Mackenzie and Royle, 2005;Mattfeldt et al, 2009). That being said, power increases with sample size.…”

A monitoring program for Patagonian foxes based on power analysis

“…A high proportion of zeros in the population index may also arise if monitoring effort is insufficient or the monitoring protocol is unsuitable for the target population. In this case, power partly depends upon variables that can be managed by researchers when designing a monitoring program (Field et al 2005).…”