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Complex national sampling design for long-term monitoring of protected dry grasslands in Switzerland

Tillé, Yves ; Ecker, Klaus

In: Environmental and Ecological Statistics, 2014, vol. 21, no. 3, p. 453-476

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    Titre
    • Complex national sampling design for long-term monitoring of protected dry grasslands in Switzerland
    Auteur
    • Tillé, Yves. Institute of Statistics, Faculty of Economics, University of Neuchâtel, Pierre à Mazel 7, 2000, Neuchâtel, Switzerland
    • Ecker, Klaus. Ecosystem Dynamics, Biodiversity and Conservation Biology, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Environmental and Ecological Statistics, 2014, vol. 21, no. 3, p. 453-476. Springer US; http://www.springer-ny.com
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:326554
    Summary
    We describe a probabilistic sampling design of circular permanent plots for the long-term monitoring of protected dry grasslands in Switzerland. The population under study is defined by the perimeter of a national inventory. The monitoring focus is on the species composition of the protected grassland vegetation and derived conservation values. Efficient trend estimations are required for the whole country and for some predefined target groups (six biogeographical regions and eleven vegetation types). The target groups are equally important regardless of their size. Consequently, intensified sampling of the less frequent groups is essential for sample efficiency. The prior information needed to draw a targeted sample is obtained from the sampling frame and external databases. The logistics and generalized delineation of the target population may pose further problems. Thus, investments in fieldwork and travel time should be well balanced by selecting a cluster sample. Second, any access problems in the field and non-target units in the sample should be compensated for by selecting reserve plots as they otherwise may considerably reduce the effective sample size. Finally, the design has to be flexible as the sampling frame may change over time and sampling intensity might have to be adjusted to redefined budgets or requirements. Likewise, the variables and biological items of interest may change. To fulfil all these constraints and to optimally use the available prior information, we propose a multi-stage self-weighted unequal probability sampling design. The design uses modern techniques such as: balanced sampling, spreading, stratified balancing, calibration, unequal probability sampling and power allocation. This sampling design meets the numerous requirements of this study and provides a very efficient estimator.