Seasonal dynamic pattern analysis in service of climate change research. A methodical case-study - monitoring and simulation based on an aquatic insect community


Autoria(s): Hufnagel, Levente
Data(s)

2005

Resumo

Our aim was to approach an important and well-investigable phenomenon – connected to a relatively simple but real field situation – in such a way, that the results of field observations could be directly comparable with the predictions of a simulation model-system which uses a simple mathematical apparatus and to simultaneously gain such a hypothesis-system, which creates the theoretical opportunity for a later experimental series of studies. As a phenomenon of the study, we chose the seasonal coenological changes of aquatic and semiaquatic Heteroptera community. Based on the observed data, we developed such an ecological model-system, which is suitable for generating realistic patterns highly resembling to the observed temporal patterns, and by the help of which predictions can be given to alternative situations of climatic circumstances not experienced before (e.g. climate changes), and furthermore; which can simulate experimental circumstances. The stable coenological state-plane, which was constructed based on the principle of indirect ordination is suitable for unified handling of data series of monitoring and simulation, and also fits for their comparison. On the state-plane, such deviations of empirical and model-generated data can be observed and analysed, which could otherwise remain hidden.

Formato

application/pdf

Identificador

http://unipub.lib.uni-corvinus.hu/1367/1/0301_079132rg.pdf

Hufnagel, Levente (2005) Seasonal dynamic pattern analysis in service of climate change research. A methodical case-study - monitoring and simulation based on an aquatic insect community. Applied Ecology and Environmental Research, 3 (1). pp. 79-132. ISSN 1589-1623

Publicador

Penkala Bt.

Relação

http://www.ecology.kee.hu/

http://unipub.lib.uni-corvinus.hu/1367/

Palavras-Chave #Ecology
Tipo

Article

PeerReviewed