Empirical modelling of trait selection by partitioning selection into direct selection and selection that is mediated by interspecific interactions

2021

J Vegetation Science

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The observed covariation among plant traits and the environment has been the focus of plant ecology since its early days and has been demonstrated in numerous cases (e.g. Garnier et al., 2016). Furthermore, trait selection, i.e., a change in the distribution of plant traits in a specific area, has been observed in manipulated experiments (e.g. Laliberté et al., 2012;Pellissier et al., 2014) and during successional processes (e.g. Garnier et al., 2004;Garnier et al., 2007;Douma et al., 2012). Based on the above findings, it has been suggested to focus on the analysis of changes in mean plant traits rather than species abundance in order to understand community assembly rules and the causal mechanisms underlying species coexistence patterns (e.g. Stegen and Swenson, 2009;Shipley, 2010).

Section: Discussionmentioning

confidence: 99%

Indication of a reduction in the cover of thin‐leaved plants in Danish grasslands over an eight‐year period

2021

J Vegetation Science

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“…However, if there were access to permanent plot time-series cover data, it would have been interesting to explore more sophisticated selection models, e.g. whether the observed trait selection could be partitioned into direct selection and selection that is mediated by interspecific interactions (Damgaard 2016; Pedersen et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Decline in large-seeded species in Danish grasslands over an eight-year period

2021

Preprint

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The historic land-use changes in grassland habitats may have lead to community level selection on seed mass as a response to the environmental changes. A time-series analysis of Danish grassland pin-point plant cover data from an eight-year period was carried out to investigate such a selection response. Across four grassland habitat types, community selection towards lighter seeds was observed during the eight-year period, but generally only limited trait selection was observed on seed mass. The mostly negative result may partly be due to the relatively conservative analysis, where the continuous plant trait variables are used for grouping plant species into functional types, which are then treated as dependent variables. This method was chosen in order to account for the sampling process of the vegetation data and is in contrast to most other analyses of trait selection, where the community weighted mean of the traits is used as the dependent variable. In conclusion, only limited seed mass selection was observed in semi-natural grasslands that was not subjected to successional processes.

“…For this purpose, Damgaard (2016) presented a method based on theoretical justifications and assumptions about the competitive processes using nonlinear models. These models make it possible to: 1) Decide which of the three types of fundamental selection forces described by Mather that are supported by the data.…”

Section: Introductionmentioning

confidence: 99%

A nonlinear Bayesian model of trait selection forces

Pedersen

Bonis

2019

Ecological Modelling

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We model trait selection by deriving and comparing models for direct trait selection and selection that is mediated by interspecific interactions. The purpose is to model the two selection forces simultaneously, in order to account for potential trait covariance in the estimation procedure when including multiple trait values. In addition, we identify those traits most important for selection forces, and the role of flooding duration. A Bayesian modelling approach is applied and fitted to plant species cover including stochastic variable selection, to test the importance of individual traits on selection forces. We used pinpoint data from wet grasslands of the French Atlantic Coast. Our results show that trait selection forces are driven by direct selection and, to a minor extent, by selection mediated by interspecific interactions. Of the tested traits leaf dry matter content (LDMC) and specific leaf area (SLA), seemed most important for selection forces. We find a significant effect of flooding on selection forces. Parameter covariance analysis revealed flooding to be most strongly correlated with direct selection forces. The method has less critical assumptions regarding the geographical limit of the ecological environment than a traditional filtering approach, and allows weighting traits simultaneously and accounting for their covariance. Inference is easy to obtain from the posterior parameter distributions of the Bayesian estimation. Thus, the Bayesian method presented is very objective. A program architecture is supplied in the R-programming language.