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Spatial predictions of Baltic phytobenthic communities: Measuring robustness of generalized additive models based on transect data
Stockholm University, Faculty of Science, Department of Systems Ecology.
AquaBiota Water Research, Stockholm.
Institute of Coastal Research, Swedish Board of Fisheries, Öregrund.
Stockholm University, Faculty of Science, Department of Systems Ecology.
2008 (English)In: Journal of Marine Systems, ISSN 0924-7963, Vol. 74, no Supplement 1, S86-S96 p.Article in journal (Refereed) Published
Abstract [en]

The spatial distributions of benthic surface sediments and phytobenthic plant species were modelled at a high spatial resolution using generalized additive models together with field data from diving transects. The efficiency of different modelling options was validated using independent datasets, and model fit versus predictive power was analysed. For rock/boulder, sand and mud/clay increasing complexity of the model resulted in higher Reciever Operating Characteristics (ROC) values for the model fit, but lower ROC values for the independent validation. The same pattern was found for hard substrate algae species, whereas it was not true for the rooted plant species. As high model ROC values were often found to be connected to low predictive power of the models, this implies that internal model validation results should be treated cautiously. In general, the models should be kept simple, as the performance of the explanation model increases with increasing complexity, while the predictive power of the model generally decreases. Only by using external validation datasets, the true predictive capacity of an explanation model can be reliably measured, as internal validation schemes tend to over-estimate model performance. Our results also indicate that the Akaike Information Criterion is a more reliable model selection method than Cross-selection when there are few predictor variables.

Place, publisher, year, edition, pages
2008. Vol. 74, no Supplement 1, S86-S96 p.
Keyword [en]
Spatial distribution, GAM, AIC, Cross-selection, Phytobenthic species, Line transects
National Category
URN: urn:nbn:se:su:diva-16101DOI: 10.1016/j.jmarsys.2008.03.028ISI: 000262091600010OAI: diva2:182621
Available from: 2008-12-13 Created: 2008-12-13 Last updated: 2011-01-31Bibliographically approved
In thesis
1. Modelling spatial and temporal species distribution in the Baltic Sea phytobenthic zone
Open this publication in new window or tab >>Modelling spatial and temporal species distribution in the Baltic Sea phytobenthic zone
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Statistical modelling is often used to relate the presence or abundance of species to environmental predictors, thereby providing a basis for predictive mapping of species or biodiversity. The variables included must thus be relevant and reflect actual changes in the environment. Therefore, the quantification of species–environment relationships is an important aspect of predictive modelling.

This thesis examines how phytobenthic species or communities in the Baltic Sea relate to environmental gradients, and if different aspects of phytobenthic species distribution in the Baltic Sea could be explained by spatial or temporal variation in environmental factors. Predictive distribution modelling usually focuses on how environmental variables control the distribution of species or communities. Thus the relative weight of the predictor variables on different scales is of importance. In this thesis, I show that the relative importance of environmental variables depends both on geographic scale and location, and that it also differs between species or species groups.

There are no simple explanations to the temporal variability in species occurrence. I here show that the temporal changes in species distribution within the phytobentic zone varies in a spatial context. I also try to find temporal and spatio-temporal patterns in species distribution that could be related to changes in climate or anthropogenic disturbance. However, the findings in this thesis suggest that single factor explanations are insufficient for explaining large-scale changes in species distribution. A greater understanding of the relationship between species and their environment will lead to the development of more sensitive models of species distributions. The predictions can be used to visualise spatial changes in the distribution of plant and animal communities over time.

Place, publisher, year, edition, pages
Stockholm: Department of Systems Ecology, Stockholm University, 2011. 38 p.
species distribution modelling, niche, gradient, prediction, environmental factors, phytobenthos, scale
National Category
Research subject
Marine Ecology
urn:nbn:se:su:diva-54269 (URN)978-91-7447-230-1 (ISBN)
Public defence
2011-03-04, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 09:30 (English)
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Submitted. Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript.Available from: 2011-02-10 Created: 2011-01-27 Last updated: 2011-01-31Bibliographically approved

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Sandman, AntoniaKautsky, Hans
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