How much effort is required for proper monitoring? Assessing the effects of different survey scenarios in a dry acidic grassland

Authors

  • Annett Baasch,

    1. Martin-Luther-University of Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, D-06108 Halle, Germany.
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  • Sabine Tischew,

    1. Anhalt University of Applied Sciences, Department for Nature Conservation and Landscape Planning, Strenzfelder Allee 28, D-06406 Bernburg, Germany.
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  • Helge Bruelheide

    1. Martin-Luther-University of Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, D-06108 Halle, Germany.
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  • Baasch, A. (corresponding author, annett.baasch@botanik.uni-halle.de) and Bruelheide, H. (helge.bruelheide@botanik.uni-halle.de): Martin-Luther-University of Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Am Kirchtor 1, D-06108 Halle, Germany.
    Tischew, S. (s.tischew@loel.hs-anhalt.de): Anhalt University of Applied Sciences, Department for Nature Conservation and Landscape Planning, Strenzfelder Allee 28, D-06406 Bernburg, Germany.

  • Co-ordinating Editor: Dr. Janos Podani.

Abstract

Questions: The quality of any inferences derived from field studies or monitoring programmes depends on expenditure of time and effort to make the underlying observations. Here, we used a long-term data set from a succession-monitoring scheme to assess the effect of different survey scenarios. We asked: (1) how well does a survey reflect successional processes if sampling effort varies (a) in space (b) in length of total observation period, (c) in observation frequency and (d) with a combination of these factors? (2) What are the practical implications for devising monitoring programmes?

Location: Lignite mining region of Central Germany, post-mining landscape of Goitzsche (Saxony-Anhalt).

Methods: Based on our full data set, we constructed subsamples. For the full data set and all subsets, we constructed Markov models and compared them based on the predictions made. We assessed effects of survey intensity on model performance using generalized linear models and multiple logistic regressions.

Results: Exploring the effects of different survey scenarios revealed significant effects of all three main features of survey intensity (sample size, length, frequency). The most important sampling feature was study length. However, we found interactive effects of sample size with study length and observation interval on model predictions. This indicates that for long-term observations with multiple recording intervals a lower sample size in space is required to reveal the same amount of information as required in a shorter study or one with fewer intervals. Conversely, a high sample size may, to some degree, compensate for relatively short study periods.

Conclusions: Monitoring activities should not be restricted to intensive sampling over only a few years. With clearly limited resources, a decrease of sampling intensity in space, and stretching these resources over a longer period would probably pay off much better than totally abandoning monitoring activities after an intensive, but short, campaign.

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