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Butterflies (Lepidoptera) have been suggested for environmental monitoring of genetically modified organisms (GMO) due to their suitability as ecological indicators, and because of the possible adverse impact of the cultivation of current transgenic crops. A critical point is the sampling effort to be invested in such a monitoring. Here, we estimated the required sample size necessary to monitor potential effects of genetically modified crops on butterflies (Lepidoptera).

We used data from two Swiss long-term butterfly monitoring surveys applying the common transect count method. The two monitoring surveys differed in several basic aspects such as geographical area covered, landscape context and sampling intensity. We carried out prospective power analyses in order to estimate the required sample size to detect effects of differing magnitude on mean species number, total individual abundance, mobility classes of butterflies and selected individual species.

The required sample size decreased substantially when effect sizes above 10% were estimated. For example, a sample size of 79 transects would be sufficient to detect changes of 30% in total individual abundance for both survey types. Detecting effects on mean species number would need much less transects. Considerably more samples would be needed to analyze the abundance of single species. Several options are presented to increase statistical power or reduce required sample size, respectively. Also, we recommend to pool species to different mobility classes, and/or analyze patch occupancy of species instead of their individual abundance.

The transect count approach is a suitable method for butterfly monitoring, both on a local as well as on a landscape scale. Consequently, both types of Swiss butterfly monitoring schemes are basically suitable for GMO monitoring. If transects are short and restricted to intensely used landscape, even non-professional field workers may yield data sufficient for effective monitoring, which might be relevant with respect to involved costs.

Lang, A., & Bühler, C. (2012). Estimation of required sampling effort for monitoring the possible effects of transgenic crops on butterflies: Lessons from long-term monitoring schemes in Switzerland. Ecological Indicators, 13(1), 29–36. https://doi.org/10.1016/j.ecolind.2011.05.004

 

Maddalena, T., Blant, B., Marchesi, P., Märki, K., von Wattenwyl, K., Torriani, D., & Zanini, M. (2012). L’Arvicola di Savi (Pitymys savii de Sélys-Longchamps, 1838) nel Cantone Ticino (Svizzera), situazione attuale e proposte per la sua conservazione. Bollettino della Società ticinese di Scienze naturali 100: 133-134. Link to PDF.

Nutrient enrichment is a threat to botanical diversity in Europe, and its assessment is part of biodiversity monitoring schemes. In Switzerland, this is done by calculating the average nutrient (N) indicator value of the vegetation based on a country-wide systematic vegetation survey. However, it is questionable whether N values indicate eutrophication and resulting species loss equally well across an entire country, which includes wide topographic gradients and distinct biogeographic regions. Here we analyze vascular plant species lists from 415 grassland plots (10 m²) between 365 and 2770 m.a.s.l. throughout Switzerland to investigate how the relationship between N value and species richness differs with altitude and among regions. The N value strongly decreased with altitude (piecewise regression: r2 = 0.77), particularly between 800 and 2000 m a.s.l., where this decrease was related to a decreasing proportion of fertilized grasslands. In the alpine belt, lower N values were associated with a greater frequency of acidic soils and a restricted species pool. Vascular plant species richness was maximal at intermediate altitude (piecewise regression: r2 = 0.33) and intermediate N value (polynomial regression: r2 = 0.46). When analyzed separately by altitudinal belt, the relationship between species richness and N value was negative in the lowlands and montane belt but unimodal in the subalpine belt. In the alpine belt, soil pH (R indicator values) explained most of the variation in species richness. Two indices of between-plot diversity (floristic dissimilarity and the contribution of individual plots to total species richness) were negatively related to N values from the lowlands to the subalpine belt but not in the alpine belt. All relationships differed little among the biogeographic regions of Switzerland, but they might be modified by changes in management and by the expansion of common lowland species into mountain grasslands.

Güsewell, S., Peter, M., & Birrer, S. (2012). Altitude modifies species richness–nutrient indicator value relationships in a country-wide survey of grassland vegetation. Ecological Indicators, 20, 134–142. https://doi.org/10.1016/j.ecolind.2012.02.011

Switzerland is one of the first countries in the world to monitor its biological diversity. The Federal Office for the environment (FOEN) has launched a programme for this purpose called Biodiversity Monitoring in Switzerland (BDM). In conjunction with the BDM programme, experts contracted by the Federal Government will regularly count animals and plants in numerous predetermined areas in the field. Whereas numerical qualitative objectives are accepted in most areas of environmental protection (emissions thresholds in air pollution control, for example), there are so far no targets for how biodiversity should change. Biodiversity monitoring helps us to define specific targets for nature conservation policy and to establish whether the measures that have been implemented are enabling us to reach these targets. Mosses are surveyed for the state indicator Z9 (species diversity in habitats). This report describes the available content in the vegetation-plot database of the Swiss Biodiversity Monitoring BDM (Z9 Mosses) (GIVD ID EU-CH-010).

Stalling, T. (2012). Swiss Biodiversity Monitoring BDM (Z9 Plants). In: Dengler, J., Oldeland, J., Jansen, F., Chytrý, M., Ewald, J., Finckh, M., Glöckler, F., Lopez-Gonzalez, G., Peet, R.K., & Schaminée, J.H.J. (2012). [Eds.]: Vegetation databases for the 21st century. Biodiversity & Ecology 4: 89 - 94.