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Complex Sampling Design for a Long-Term Monitoring Program of the Agricultural Species and Habitats in Switzerland (ALL-EMA)
Auteur(s)
Maison d'édition
Zurich Swiss Federal Institute for Forest, Snow and Landscape Research WSL
Date de parution
2019
Nombre de page
31
Résumé
We describe a complex probability sampling design for a long-term
monitoring program of agricultural species and habitats in Switzerland. The
program aims to monitor farmland biodiversity in predefined regions and to assess the effectiveness of funded management in promoting it. Such monitoring requires the costly collection of {\it in situ} information on species, habitat types and structures at the plot and landscape level. Sample efficiency is challenging since the majority of habitats and species is typically rare, spatially structured and previously unknown in the sampling frame. Efficient sampling aims to minimize the collection of redundant information from the big regions and the dominant habitat types. The sample should be spatially spread and balanced across environmental gradients. Decisions should be made to allocate the sampling effort within and across sample sites. Finally, the survey organization has to be simple to implement in the field.
In Switzerland zoological data are already collected on a regular grid of 1 km$^2$. We propose an additional three-stage sampling scheme for the static survey of habitats and plant species on the total agrarian landscape. An extra sample scheme is defined to monitor areas with funded biodiversity management. Both sampling designs use modern sampling techniques, such as unequal probability sampling, balancing, spatial spreading and self-weighting to ensure sample efficiency at all sampling stages. The efficiency of balancing, spreading and sample size allocation is demonstrated in simulation studies. A power analysis suggests that changes of $5-10\%$ can be statistically detected for a majority of the target habitats.
monitoring program of agricultural species and habitats in Switzerland. The
program aims to monitor farmland biodiversity in predefined regions and to assess the effectiveness of funded management in promoting it. Such monitoring requires the costly collection of {\it in situ} information on species, habitat types and structures at the plot and landscape level. Sample efficiency is challenging since the majority of habitats and species is typically rare, spatially structured and previously unknown in the sampling frame. Efficient sampling aims to minimize the collection of redundant information from the big regions and the dominant habitat types. The sample should be spatially spread and balanced across environmental gradients. Decisions should be made to allocate the sampling effort within and across sample sites. Finally, the survey organization has to be simple to implement in the field.
In Switzerland zoological data are already collected on a regular grid of 1 km$^2$. We propose an additional three-stage sampling scheme for the static survey of habitats and plant species on the total agrarian landscape. An extra sample scheme is defined to monitor areas with funded biodiversity management. Both sampling designs use modern sampling techniques, such as unequal probability sampling, balancing, spatial spreading and self-weighting to ensure sample efficiency at all sampling stages. The efficiency of balancing, spreading and sample size allocation is demonstrated in simulation studies. A power analysis suggests that changes of $5-10\%$ can be statistically detected for a majority of the target habitats.
Identifiants
Type de publication
working paper
