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Data from: Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values


Carroll, Tadhg et al. (2019), Data from: Improving estimates of environmental change using multilevel regression models of Ellenberg indicator values, Dryad, Dataset,


Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi-quantitative description of species‘ ecological requirements. Typically, point estimates of mean EIV scores are compared to infer differences in the environmental conditions structuring plant communities – particularly in resurvey studies with no historical environmental data available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots, and gives equal weighting to means calculated from sites with differing numbers of species. We present a set of multilevel models – fitted with and without group-level predictors – to improve precision and accuracy of site mean EIV scores, and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in re-visitation studies. We compare multilevel model performance to GLMM’s fitted to point estimates of site mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample sites. Hierarchical modelling led to more accurate and precise estimates of site-level differences in mean EIV scores between time-periods, particularly for datasets with incomplete records of species occurrence. They also revealed directional environmental change within ecological habitat types, which estimates from GLMM’s were inadequate to detect. Multilevel models also highlighted a prominent role of hydrological differences as a driver of community change in our case study, which traditional use of EIVs failed to reveal. We have demonstrated that multilevel modelling of EIVs allows for a nuanced estimation of environmental change underlying ecological communities from plant assemblage data, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end.

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