Landscape – how to deal with it in science

Introduction
Some definitions
Metrics…
…and their calculation
Conclusion
Further Reading
References

Conclusion

There are several points I would like to evaluate after carrying out all of the above.
First of all we can say that professional and production work can be carried out with open source software today. This has some limitations if modules or other parts of the software experience changes such as updates. The user is in this case also the tester and has to get used to filing tickets for bugs and has to accept that sometimes it takes a little longer until a bug has been fixed. QGIS has the advantage that it comprises the functionality of several other (open source) GIS-programs in one program and gets the best possible synergies out of this, unfortunately it was not possible for me to develop a reliable work-flow that included QGIS and R so that I eventually decided to us R exclusively. Alternative workflows include swaping data and output from one program to the other and a rather huge workload for those processes that include maintaining the work-flow. Carrying out the work in R is probably not advisable for everyone, since it has a rather steep learning-curve.

I love working with datasets such as the CORINE or Global Forest Change dataset. We have a tool here which can help us answer so many questions on a landscape scale and it is awesome that those tools are available to everyone! Unfortunately the CORINE 2012 Update has not been published yet but is expected very soon (2014).  This would of course make sense to take into account, especially for the newer works that took part after 2006. I wrote them a mail, we will see what the answer will be quite soon. The GFC dataset shows us quite impressively that many areas in Europe undergo a constant forest change. Especially when looking at the layers we created above per year, we can see that there are plenty of loss-events which take place, on forest edges as well as in closed forests. When looking at the data, loss-events seem to be quite a lot more frequent than gain-events. This depends of course on the region and I am looking forward to a closer evaluation of those patterns.

For the recent meta-analysis, those data are perfect and for future works which want to look at patterns on a true landscape-scale across Europe. Landscape metrics have always been a major restrain in comparability on larger scales. Often spatial data were used from national networks and were not really comparable amongst each other. With those two datasets we can now approach this and gain an insight into the heterogeneity which lays in the different methods and landscape under investigation.

With the meta-dataset we can derive even more information. We could fit (non-meta-analysis) models and compare them with the meta-analysis. But we could of course also include other animal groups and calculate additional landscape metrics relevant for those groups (let me know what you think about this. I will probably not have time for this within the next months, but maybe someone of you has?).

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Further Reading

Monica G. Turner gives an overview of landscape ecology in Northern America (Turner, 2005 a) and in general (Turner, 2005 b) and discusses the current state and where we should go from there. Robert V. O’Neill asks the Question “Is it time to bury the Ecosystem Concept?” (O’Neill, 2001) and I think those works can serve really well to read about the theoretical background of landscape (ecology).

References

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One thought on “Landscape – how to deal with it in science

  1. Pingback: Landscape – how to deal with it in science | steffen ehrmann, ecologist

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