Designing a benthic monitoring programme with multiple conflicting objectives

Abstract

Summary1. Sound conservation and management advice usually requires spatial data on animal and plant abundances. The expense of programmes to determine species distributions and estimates of population sizes often limits sample size. To maximise effectiveness at minimal costs, optimisations of such monitoring efforts are critical. A monitoring programme can have multiple objectives with demands on the optimal sampling design that are often in conflict. Here, we develop an optimal sampling design for monitoring programmes with conflicting objectives, building on an existing intertidal benthic monitoring programme in the Dutch Wadden Sea and simulation models bounded in their parameter spaces by these data. 2. We distinguish three possible objectives: (1) estimation of temporal changes and spatial differences in abundance and (2) mapping, that is, prediction of abundances at unsampled locations. Mapping abundances requires model-based analyses using autocorrelation models. Such analyses are as good as the model fits the data; therefore, the final objective was (3) accurately estimating model autocorrelation parameters. To compare sampling designs, we used the following criteria: (1) minimum detectable difference in mean between two time periods or two areas, (2) mean prediction error and (3) estimation bias of autocorrelation parameters. 3. Using Monte Carlo simulations, we compared five sampling designs with respect to these criteria (i.e. simple random, grid, two types of transects, and grid with random replacements) at four levels of naturally occurring spatial autocorrelation. 4. The ideal sampling design for objectives (1) and (2) was grid sampling and for objective (3) random sampling. The sampling design that catered best for all three objectives combined was grid sampling with a number of random samples placed on gridlines. 5. Grid sampling with a number of random samples is considered an accurate and powerful tool with the highest effectiveness. This sampling design is widely applicable and allows for accurate estimates of population sizes, monitoring of population trends, comparisons of populations ⁄ trends between years or areas, modelling autocorrelation, mapping species distributions and a mechanistic understanding of species distribution processes.

Grid-spacing and the quality of abundance maps for species that show spatial autocorrelation and zero-inflation

“…Using real datasets only, as Fortin et al (1989) Bijleveld et al (2012) and Foster et al (2014). An updated approach is to first simulate a spatial field resembling reality as much as possible, a peseudo-reality, which is then subsequently sampled using different sampling designs.…”

“…Following this approach, Bijleveld et al (2012) used the results of an existing intertidal benthic monitoring programme to construct various spatial models with an exponential spatial autocorrelation function. With these models they simulated virtual populations with a Normal distribution and sampled these populations using different sampling designs.…”

Shifts in nursery habitat utilization by 0-group plaice in the western Dutch Wadden Sea

“…In 2009, the vessel's conductivity-temperature-depth (CTD) probe was used for temperature measurements in subtidal and channel stations, but, due to technical problems these data could not fully be recovered. Therefore, to evaluate consisting of a regularly (500 m) spaced grid over the entire intertidal Dutch Wadden Sea, from July to early October (for detailed methods see Bijleveld et al, 2012 andCompton et al, 2013). From the entire grid, all points within a maximum of 500 m from stations trawled for plaice were selected.…”

Site-specific distribution of the bivalve Scrobicularia plana along the European coast

“…The distance between points varied between locations according to the total area of the sampling sites. In addition to the regular grid, ~10% (Wadden Sea) to 25% (remaining 3 locations) extra cores were collected at random positions to account for autocorrelations at smaller distances (for the rationale of this approach, see Bijleveld et al 2012). Description of the dimensions and number of points sampled in each grid can be found in Table 1.…”

“…1). In order to estimate temporal changes and spatial differences in abundance, while accurately estimating model autocorrelation parameters, the most effective sampling design was a grid with additional random samples placed on the gridlines (Bijleveld et al 2012). The distance between points varied between locations according to the total area of the sampling sites.…”