Select the input values and start the execution with the “Run the workflow” button.
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The case study was proposed by the LifeWatch CT Mediterranean in order to explore the susceptibility of EUNIS habitat to AS invasions. Originally presented for freshwater and marine habitat (Boggero et al., 2014; Corriero et al, 2016), the case study was generalized to work with any biodiversity dataset available in the LW data portal. For a full comprehension of the rationale of the case study and the statistical approach used we strongly encourage the users to read carefully Boggero et al. (2014) and Corriero et al. (2016). The knowledge of the statistical tools used in this workflow (Generalized Linear Mixed Model) is a prerequisite. This is particularly relevant for two aspects: understanding if your data are suitable for this kind of analysis (i-e fit the assumption of GLMMs) and for the interpretation of the output.
This calculation allows to reshape the available datasets of Lifewatch Eric Metadata Catalogue. You can also download the dataset from Lifewatch Italy Data Portal (as csv file). We want to investigate the site vulnerability, distributed across different Eunis habitat, to different taxonomic groups. So we need to aggregate the data at site level by taxon name and Eunis Habitat name.
The calculation performs 3 main activities:
1) Reshape the dataset in order to obtain alien species and native specie richness for each family at the habitat and site level. If more that 1 EUNIS habitat is present in a site the richness will be calculate for the two (or more that 2) habitat In the site;
2) Best model fitting model selection. This subworkflow calls a set of R functions from the packages lme4 and MuMIn. Initially a full GLMM model is calculated including both richness and level-1 EUNIS habitat as fixed factor. Subsequently reduced models are calculated and compared with the full model using the Akaike Information Criteria (AIC). The model showing the best AIC is used to create the output (tables and graph);
3) Finally, a sub-workflow takes information from the reshaped dataset and plots rarefaction curves.
Select the input values and start the execution with the “Run the workflow” button.
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