|Predictability of marine nematode biodiversity|
Merckx, B.; Goethals, P.; Steyaert, M.; Vanreusel, A.; Vincx, M.; Vanaverbeke, J. (2011). Predictability of marine nematode biodiversity, in: Merckx, B. Habitat suitability and community modelling of marine benthos = Modeleren van habitatgeschiktheid en gemeenschapsstructuren van marien benthos. pp. 45-64
In: Merckx, B. (2011). Habitat suitability and community modelling of marine benthos = Modeleren van habitatgeschiktheid en gemeenschapsstructuren van marien benthos. PhD Thesis. Ghent University: Gent. ISBN 978-90-77713-87-7. 309 pp.
Analysis > Mathematical analysis > Statistical analysis > Correlation analysis > Autocorrelation
Artificial neural networks
|Auteurs|| || Top |
- Merckx, B.
- Goethals, P.
- Steyaert, M.
- Vanreusel, A.
- Vincx, M.
- Vanaverbeke, J.
In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.