|Designing a benthic monitoring programme with multiple conflicting objectives|Bijleveld, A.I.; van Gils, J.A.; van der Meer, J.; Dekinga, A.; Kraan, C.; van der Veer, H.W.; Piersma, T. (2012). Designing a benthic monitoring programme with multiple conflicting objectives. Methods Ecol. Evol. 3(3): 526-536. dx.doi.org/10.1111/j.2041-210X.2012.00192.x
In: Methods in Ecology and Evolution. Wiley: Bognor Regis. ISSN 2041-2096, meer
generalised least squares; intertidal; landscape ecology; macrobenthicinvertebrates; model-based inference; power analysis; spatialautocorrelation; spatial autocorrelation function
|Auteurs|| || Top |
- Bijleveld, A.I., meer
- van Gils, J.A., meer
- van der Meer, J., meer
- Dekinga, A., meer
- Kraan, C.
- van der Veer, H.W., meer
- Piersma, T., meer
1. 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.