Monitoring habitat types by the mixed multinomial logit model using panel data |
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| Institution: | 1. Alterra, Wageningen University and Research Centre, PO Box 47, 6700 AA Wageningen, The Netherlands;2. Mathematical and Statistical Methods Group, Wageningen University and Research Centre, PO Box 16, 6700 AC Wageningen, The Netherlands;1. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, West Donggang Road 320, Lanzhou 730000, China;2. Yellow River Institute of Hydraulic Research, Yellow River Conservancy Commission, Shunhe Road 45, Zhengzhou 450003, China;1. Safety and Security Science Section, Faculty of Technology, Policy and Management, Delft University of Technology, 2628BX Delft, Netherlands;2. Queensland University of Technology (QUT), Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Kelvin Grove, Queensland 4059, Australia;3. Strategic Senior Research Fellow, Queensland University of Technology (QUT), Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Kelvin Grove, Queensland 4059, Australia;1. TRL (Transport Research Laboratory), UK;2. University of Würzburg, Germany;3. Nottingham University, UK |
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| Abstract: | Habitats in the Wadden Sea, a world heritage area, are affected by land subsidence resulting from natural gas extraction and by sea level rise. Here we describe a method to monitor changes in habitat types by producing sequential maps based on point information followed by mapping using a multinomial logit regression model with abiotic variables of which maps are available as predictors.In a 70 ha study area a total of 904 vegetation samples has been collected in seven sampling rounds with an interval of 2–3 years. Half of the vegetation plots was permanent, violating the assumption of independent data in multinomial logistic regression. This paper shows how this dependency can be accounted for by adding a random effect to the multinomial logit (MLN) model, thus becoming a mixed multinomial logit (MMNL) model. In principle all regression coefficients can be taken as random, but in this study only the intercepts are treated as location-specific random variables (random intercepts model). With six habitat types we have five intercepts, so that the number of extra model parameters becomes 15, 5 variances and 10 covariances.The likelihood ratio test showed that the MMNL model fitted significantly better than the MNL model with the same fixed effects. McFadden-R2 for the MMNL model was 0.467, versus 0.395 for the MNL model. The estimated coefficients of the MMNL and MNL model were comparable; those of altitude, the most important predictor, differed most. The MMNL model accounts for pseudo-replication at the permanent plots, which explains the larger standard errors of the MMNL coefficients. The habitat type at a given location-year combination was predicted by the habitat type with the largest predicted probability. The series of maps shows local trends in habitat types most likely driven by sea-level rise, soil subsidence, and a restoration project.We conclude that in environmental modeling of categorical variables using panel data, dependency of repeated observations at permanent plots should be accounted for. This will affect the estimated probabilities of the categories, and even stronger the standard errors of the regression coefficients. |
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| Keywords: | Habitats Directive Mapping Sampling design Random coefficient model Multinomial logistic regression |
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