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Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package
Populations and Evolution · Submitted 9 Dec 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity.
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The abstract, sentence by sentence
BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | BEF studies aim to understand how ecosystems respond to a gradient of species diversity. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. | background 0.88 | 0.06 | 0.05 | asserted 0.91 |
| s2 | These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. | background 0.72 | 0.06 | 0.05 | asserted 1.00 |
| s3 | The number of species in the community (richness) is also implicitly modelled through this approach. | background 0.58 | 0.15 | 0.04 | asserted 1.00 |
| s4 | It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. | gap 0.50 | 0.06 | 0.08 | hedged possibility 0.79 |
| s5 | In this paper, we introduce the DImodels R package for implementing DI models. | contribution 0.97 | 0.92 | 0.05 | asserted 0.99 |
| s6 | We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. | contribution 0.85 | 0.87 | 0.07 | asserted 0.83 |
| s7 | We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. | result 0.99 | 0.87 | 0.63 | asserted 0.99 |
| s8 | Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). | method 0.37 | 0.39 | 0.08 | asserted 0.94 |
| s9 | Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. | background 0.85 | 0.08 | 0.04 | asserted 0.54 |
| s10 | However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. | background 0.53 | 0.07 | 0.14 | hedged possibility 0.82 |
| s11 | DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. | contribution 0.50 | 0.17 | 0.15 | asserted 0.99 |
| s12 | It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference. | interpretation 0.95 | 0.17 | 0.17 | hedged possibility 0.74 |
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The paper
Original abstract
BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference.
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