Here we present maps that result from a new indicator taxa approach to the prediction of climate change effects. The indicator taxa come from three groups that have been chosen for long-term monitoring in Switzerland, vascular plants, birds and butterflies. We analyzed and modeled the species in each group that account for 95% of the geographic variation in species richness in this monitoring data. The maps are the result of the projection of models of the climate-distribution relationships of the species in each group. For each of the chosen species we used a well-known modeling algorithm to describe current relationship between climate and the sampled distribution of the species.
Current climate came from interpolated weather station data in Switzerland while data on species distribution came from a national monitoring program. (http://www.biodiversitymonitoring.ch) Once the models were calibrated, we then projected them to several future time points, using climate estimates that were produced as part of a large research project on ongoing climate change. (http://www.cru.uea.ac.uk) We used estimates of future climates that were the results of simulations of the effects of economic development under two socio-economic scenarios as it may impact the production of so-called "greenhouse gasses". These came from the Third Assessment Report of the Nobel Prize-winning Intergovernmental Panel on Climate Change. (IPCC, http://www.ipcc.ch) The A1FI scenario is one in which no measures are taken to reduce gas emissions and free-market policies dominate, while the B2 scenario is a rather optimistic scenario in which the output of greenhouse gasses is substantially reduced due to increased concern for environmental sustainability. For each of five time slices and two scenarios, the models of each species were projected to projected climate values for the 1 km resolution Swiss national grid system.
The maps represent the total number of species in the indicator groups that are predicted by the models to be present. The general tendency is for more species to be predicted present at ever-higher elevations as time progresses. Some areas lose species while other (higher) areas gain them. This is especially true for the butterfly indicators because the models successfully capture limitations to species distributions at lower elevations, which are correlated with warmer climate. This is less so for the models of the species of birds and plants for which limiting warm temperatures were absent for many species. Please note that there are a number of caveats applicable to the interpretation of these models, and a long list of assumptions. Detailed information and discussion on these aspects are contained in the original publication, which is available for download from this web site.
For further informations, see Pearman, P.B., Guisan, A., Zimmermann, N.E., 2011. Impacts of climate change on Swiss biodiversity: An indicator taxa approach. Biological Conservation 144, 866-875. (download)
Images are produced using ImageMagick. See http://www.imagemagick.org/script/command-line-tools.php
This is my csh-script: job.csh
© thomas.dalang[at]wsl.ch (2012)
