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Larval presence prediction through logistic regression: an early warning system against Mytilopsis leucophaeata biofouling
Verween, A.; Hendrickx, F.; Vincx, M.; Degraer, S. (2007). Larval presence prediction through logistic regression: an early warning system against Mytilopsis leucophaeata biofouling. Biofouling (Print) 23(1): 25-35. https://dx.doi.org/10.1080/08927010601092952
In: Biofouling. Taylor & Francis: Chur; New York. ISSN 0892-7014; e-ISSN 1029-2454, meer
Is gerelateerd aan:
Verween, A.; Hendrickx, F.; Vincx, M.; Degraer, S. (2007). Larval presence prediction through logistic regression: an early warning system against Mytilopsis leucophaeata biofouling, in: Verween, A. Biologische kennis als een instrument voor een ecologische verantwoorde biofouling beheersing: een case study van de invasieve mossel Mytilopsis leucophaeata in Europa = Biological knowledge as a tool for an ecologically sound biofouling control: a case study of the invasive bivalve Mytilopsis leucophaeata in Europe. pp. 111-131, meer
Peer reviewed article  
Beschikbaar in  Auteurs 
Trefwoorden
    Control > Fouling control
    Developmental stages > Larvae
    Population dynamics
    Regressions
    Statistics
    Mytilopsis leucophaeata (Conrad, 1831) [WoRMS]
    Haven van Antwerpen [Marine Regions]
    Marien/Kust
Author keywords
    Mytilopsis leucophaeata; biofouling control; population dynamics; larvae; logistic regression; predictive statistics
Auteurs  Top 
Abstract
    Mytilopsis leucophaeata is a biofouling bivalve causing major problems in the cooling water system of BASF, Antwerp NV, Belgium, a large water-using industrial facility. This study aimed to develop a statistical model to predict the response of M. leucophaeata larvae to environmental conditions in estuarine ecosystems. Multiple logistic regression, taking into account temporal autocorrelation, was applied on a large dataset allowing the prediction of the probability of occurrence of M. leucophaeata larvae at BASF NV as a response to the environmental variables. The final model made it possible to predict larval presence in the water column solely by monitoring water temperature. The results from subsampling indicated that the model was stable. The model was tested with 2005 data, demonstrating a 98% precise prediction of the occurrence of M. leucophaeata larvae in the water column, with a sensitivity of 100% and a specificity of 97%, even though autumn 2005 was exceptionally warm, which led to an extended presence of the larvae.
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