Regionalizing Indicator Values for Soil Reaction in the Bavarian Alps – from Averages to Multivariate Spectra

Häring, Tim; Reger, Birgit; Ewald, Jörg; Hothorn, Torsten; Schröder, Boris

doi:10.1007/s12224-013-9157-1

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…; Häring et al . ). By including population dynamics, dynamic SDMs allow for the temporal aspects of a species' distribution to be investigated, including future abundance trends and species persistence.…”

Section: Discussionmentioning

confidence: 97%

Improving species distribution models: the value of data on abundance

et al. 2014

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1 Species distribution models (SDMs) are important tools for forecasting the potential impacts of future environmental changes but debate remains over the most robust modelling approaches for making projections. 2 Suggested improvements in SDMs vary from algorithmic development through to more mechanistic modelling approaches. Here, we focus on the improvements that can be gained by conditioning SDMs on more detailed data. Specifically, we use breeding bird data from across Europe to compare the relative performances of SDMs trained on presence-absence data and those trained on abundance data. 3 Species distribution models trained on presence-absence data, with a poor to slight fit according to Cohen's kappa, show an average improvement in model performance of 0Á32 (SE AE 0Á12) when trained on abundance data. Even those species for which models trained on presence-absence data are classified as good to excellent show a mean improvement in Cohen's kappa score of 0Á05 (SE AE 0Á01) when corresponding SDMs are trained on abundance data. This improved explanatory power is most pronounced for species of high prevalence. 4 Our results illustrate that even using coarse scale abundance data, large improvements in our ability to predict species distributions can be achieved. Furthermore, predictions from abundance models provide a greater depth of information with regard to population dynamics than their presence-absence model counterparts. Currently, despite the existence of a wide variety of abundance data sets, species distribution modellers continue to rely almost exclusively on presence-absence data to train and test SDMs. Given our findings, we advocate that, where available, abundance data rather than presence-absence data can be used to more accurately predict the ecological consequences of environmental change. Additionally, our findings highlight the importance of informative baseline data sets. We therefore recommend the move towards increased collection of abundance data, even if only coarse numerical scales of recording are possible.

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Section: Discussionmentioning

confidence: 97%

Improving species distribution models: the value of data on abundance

et al. 2014

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“…However, contemporaneous environmental data alongside historical data on species records are often lacking, which can hamper attempts to identify drivers of community change. As one solution, Ellenberg Indicator Values (EIVs) are widely used to infer environmental change over time where no data are available for abiotic conditions (Häring, Reger, Ewald, Hothorn, & Schröder, 2014; Krause et al., 2015; McGovern, Evans, Dennis, Walmsley, & McDonald, 2011; Newton et al., 2012; Prach, 1993; Wesche, Krause, Culmsee, & Leuschner, 2012). EIVs score plant species on an ordinal scale based on estimated optimal environmental conditions for moisture, light, soil nutrient levels, reaction (pH), and salt tolerance (F, L, N, R, and S respectively) (Ellenberg, 1988; Hill, Preston, & Roy, 2004).…”

Section: Introductionmentioning

confidence: 99%

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

Carroll

Gillingham

Stafford

et al. 2018

Ecology and Evolution

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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 over space or time to infer differences in the environmental conditions structuring plant communities—particularly in resurvey studies where no historical environmental data are 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 plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available.We present a set of multilevel (hierarchical) models—fitted with and without group‐level predictors (e.g., habitat type)—to improve precision and accuracy of plot mean EIV scores and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMMs fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modeling led to more accurate and precise estimates of plot‐level differences in mean EIV scores between time‐periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMMs of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R 2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modeling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, 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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“…Some examples include spatial regression or environmental correlation (McKenzie and Austin, 1993;Moore et al, 1993), regression trees (Adhikari et al, 2014;Lacoste et al, 2014;Miller et al, 2015a), random forests (Vasques et al, 2010;Häring et al, 2012;Schmidt et al, 2014), boosting algorithms (Häring et al, 2014) and artificial neural networks (Tamari et al, 1996;Behrens et al, 2005). In contrast to spatial autocorrelation techniques' characteristic of prediction error increasing with distance from samples, spatial association techniques' error depends on the model's ability to fit equations to the full feature space using available covariates.…”

Section: Introductionmentioning

confidence: 99%