Human impact on ecosystems has been repeatedly recognized as crucial cause of rapid species and habitat loss. While several strategies, such as protected areas, improving connectivity or the landscape matrix have been developed to counteract this loss of biodiversity, it remains totally unclear how effective these strategies are in terms of climate change. This is on the one hand because of big uncertainties in future climate change projections and on the other hand because of greater uncertainties in how biodiversity with complex interacting systems will respond. Reducing greenhouse gas emissions and habitat-based conservation strategies would help to mitigate climate impacts on biodiversity but might not fully compensate.
Usually the modelling starts with observing species occurrences and with finding environmental variables that are thought to influence habitat suitability. In any case, conceptual models help to find the main biotic and abiotic factors that control species distribution.
Species occurrence data can be obtained by complex field surveys at the one hand but can also be derived from existing database records. Environmental data is normally derived from digital maps, such as digital elevation models (DEM) or climate surfaces. These data often come from remote sensing or are also the result of a spatial model.
It also needs a model that links the species occurrences to the environmental variables. These models can be statistical, descriptive, logical, or rule based.
We also need tools to process and store the input and output data. There are several tools available. Most of the preprocessing and data management could be done in a common GIS system like QGIS or ArcGIS. Another interesting software environment, primary for statistical analysis and graphics, is R. This software can be used for building the models but also for repetitive GIS processing.
Last but not least we need data and criteria to evaluate the predictions.
This was only a brief overview and I will get more in depth for each of the above mentioned points.
Primula clusiana is an endemic vascular plant species of the north eastern calcareous Alps and could be found from the montain to the alpine zone. The species could rarely be found in the central Alps. The species is common in moist and rocky grassland and snow bed communities. Continue reading
To investigate changes at different spatial scales environmental scientists often use statistical models to extrapolate environmental data in space. Species distribution models (SDM), also called ecological niche models or habitat suitability models, utilize relationships between environmental variables and species observations to find environmental conditions where these species of interest could potentially occur. In other words SDMs extrapolates species distribution data in space and time, usually based on a statistical model.
Download und Extrahieren der Daten:
Wenn man Sentinel 2A Daten über Science Hub herunterlädt, erhält man einen Zip-Ordner, mit einem extrem langen Dateinamen. Das kommt daher, da im Dateinamen Metainformationen, wie etwa Start und Stop der Sensing Vorganges darin enthalten sind. Der lange Dateiname kann bei der Extraktion der Dateien Probleme machen. Mit „Bandizip“ funktioniert die Extraktion jedoch problemlos möglich.