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Thresholds of functional trait diversity driven by land use intensification

Nature Ecology & Evolution volume 9, pages 1224–1233 (2025)Cite this article

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Abstract

It is unclear how much land use intensification ecosystems can withstand before undergoing abrupt changes in their structure and dynamics. Here we assess how the functional structure, diversity and temporal stability of 150 agricultural grasslands responded to large variations in land use intensification, namely, different intensities of fertilization, grazing and mowing. Using multi-site time series (2008–2020) of plant trait distributions, we identify two thresholds where the functional structure, diversity and stability of grasslands changed dramatically. The first threshold occurred between unfertilized and fertilized grasslands, with maximization of trait evenness indicating the persistence of plant species with diverse resource-use strategies in extensively managed grasslands. The second threshold occurred when fertilization exceeded 80 kg N ha−1 yr−1 or when grazing exceeded 500 livestock units days ha−1 yr−1, beyond which the most intensively managed grasslands were functionally poor, highly unstable and vulnerable to extreme weather events. These findings allow us to quantify the level of perturbation beyond which grasslands depart from a high biodiversity state and show that highly intensive management pushes the system to a more unstable state. The identified thresholds may provide targets for sustainable management and fertilization practices.

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Fig. 1: The SKR.
Fig. 2: LUI abruptly changes the functional structure of grasslands.
Fig. 3: LUI deviates grasslands from the maximum of trait evenness.
Fig. 4: LUI hampers the stability of grasslands.

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Data availability

The datasets (LUI calculation and vegetation records) used are available at https://www.bexis.uni-jena.de/lui/LUICalculation/index and https://www.bexis.uni-jena.de/ddm/data/Showdata/27386?version=2, respectively.

Code availability

The code and the outputs generated during the current study are available in the repository https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/RHPWEF.

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Acknowledgements

We thank the managers of the three Exploratories, J. Bass, A. K. Franke and F. Marian and all former managers for their work in maintaining the plot and project infrastructure; V. Grießmeier for giving support through the central office, A. Ostrowski for managing the central database and M. Fischer, E. Linsenmair, D. Hessenmöller, D. Prati, I. Schöning, F. Buscot, E.-D. Schulze, W. W. Weisser and the late E. Kalko for their role in setting up the Biodiversity Exploratories project. We thank the administration of the Hainich National Park, the UNESCO Biosphere Reserve Swabian Alb and the UNESCO Biosphere Reserve Schorfheide-Chorin as well as all land owners for the excellent collaboration. The work has been partly funded by the Deutsche Forschungsgemeinschaft Priority Program 1374 ‘Biodiversity-Exploratories’. Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen and Brandenburg. We also thank all the members of the German Biodiversity Exploratories network for the collection of field data, for their help with data organization and management and for their comments and suggestions on early stages of the manuscript. Y.L.B.-P. and M.B. were supported by the European Research Council (BIODESERT). Y.L.B.-P. was also supported by a Marie Sklodowska-Curie Actions Individual Fellowship within the European Program Horizon 2020 (DRYFUN Project 656035). N.G. was supported by the AgreenSkills+ fellowship programme, which has received funding from the European Union’s Seventh Framework Programme under grant agreement number FP7-609398 (AgreenSkills+ contract). M.B. is also supported by a Ramón y Cajal contract (RYC2021-031797-I). This research was also supported by The French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25) and was part of the ‘Biodiversa2021-421’ project funded through the 2021 BiodivERsA BIODIVPROTECT call for research proposals, with the national funders Agence Nationale de la Recherche (France), Agencia Estatal de Investigación, Fundación Biodiversidad (Spain), Innovation Fund Denmark (Denmark), Ministry of Universities and Research (Italy), General Secretariat for Research and Innovation (Greece) and Dutch Research Council (Netherlands).

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Author notes

  1. These authors contributed equally: Yoann Le Bagousse-Pinguet, Nicolas Gross.

Authors and Affiliations

  1. Aix Marseille Univ, CNRS, Avignon Université, IRD, IMBE, Technopôle Arbois-Méditerranée Bât. Villemin - BP 80, Aix-en-Provence, France

    Yoann Le Bagousse-Pinguet

  2. Institute Botany Department, State Museum of Natural History Stuttgart, Stuttgart, Germany

    Pierre Liancourt

  3. Plant Ecology Group, University of Tübingen, Tübingen, Germany

    Pierre Liancourt

  4. Departamento de Biodiversidad, Ecología y Evolución, Facultad de Biología, Universidad Complutense de Madrid, Madrid, Spain

    Miguel Berdugo

  5. Institute of Plant Sciences, University of Bern, Bern, Switzerland

    Eric Allan & Caterina Penone

  6. Centre for Development and Environment CDE, University of Bern, Bern, Switzerland

    Eric Allan

  7. UCA, INRA, VetAgro Sup, UMR Ecosystème Prairial, Clermont-Ferrand, France

    Raphael Martin & Nicolas Gross

  8. Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Grenoble, France

    Caterina Penone

  9. Departamento de Ciencias Agrarias y Medio Natural, Escuela Politécnica Superior, Instituto Universitario de Investigación en Ciencias Ambientales de Aragón (IUCA), Universidad de Zaragoza, Huesca, Spain

    Hugo Saiz

  10. Departamento de Ecología, Universidad de Alicante, Alicante, Spain

    Santiago Soliveres

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  1. Yoann Le Bagousse-Pinguet
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  4. Eric Allan
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  7. Hugo Saiz
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  9. Nicolas Gross
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Contributions

Y.L.B.-P., P.L. and N.G. conceived the study. Y.L.B.-P., P.L. and N.G. developed the original idea of the analyses presented in the paper, with inputs from E.A., M.B., R.M., C.P., H.S. and S.S. Statistical analyses were performed by M.B., R.M. and N.G. Y.L.B.-P. and N.G. wrote the paper draft, and all authors worked on the final version.

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Correspondence to Yoann Le Bagousse-Pinguet.

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Extended data

Extended Data Fig. 1 Trait distributions are highly variable over space and time.

(a–d) Here we show the lack of clear response pattern to LUI when considering the four moments separately. The four moments of the 1950 (150 sites x 13 years) studied SLA-distributions (mean, variance, skewness and kurtosis) and their responses to LUI using null model approaches (see methods). The response variables are presented using standardised effect size (SES). The SES computes the departure from the mean of random communities divided by their standard deviations. Black and grey dots represent distributions out and within the 95% Confidence Intervals (95% CI). (e-h) Range and variability over the 2008-2020 time period of the 1950 studied SLA-distributions (grey lines; see also Extended Data Fig. 2 for temporal autocorrelations). As an example, we also show the variation of the four moments of SLA-distributions observed for an extensively managed grassland (blue line). (i) We exemplify the changes in the shape of SLA-distributions over the time period considered observed for an extensively managed grassland. Altogether, we showed here that SLA-distributions strongly differed from a Gaussian distribution, and were highly variable over space and time. Such a high variability of trait distributions highlighted the inherent stochastic dynamics of grassland systems, and the difficulty to detect the signature of LUI-driven deterministic processes from the noisiness of trait data. These results warn the need for trait-based approaches that specifically account for the dynamic nature of ecological systems if we aim at detecting ecological thresholds in real-world ecosystems19,22,32,36,37.

Extended Data Fig. 2 Temporal autocorrelation of the studied trait distributions.

(a–d) We show the time lags (t against t + i year) from one to 13 years (from black to white) using autocovariance functions for the mean, variance, skewness and kurtosis of the 150 SLA-distributions observed across 13 years (Ntot = 1950 observations). The line and the band indicate the means and the standard errors, respectively. The red dashed line indicates the 1:1 relationship. (e-f) Time lags of the autocorrelation parameters (coefficient of determination (r²) and slope) for the four moments of trait distributions. In f, error bars show the 0.95 confidence interval. We showed that the moments of trait distributions overall had weak temporal autocorrelation (except the mean).

Extended Data Fig. 3 Observed skewness-kurtosis relationships (SKRs) and deviation from null expectations at low, intermediate and high land use intensification (LUI).

(a–c) The SKRS are presented at low (LUI < 1.25; N = 665 observations), mid- (1.25 < LUI < 1.85; N = 695 observations;), and high LUI (LUI > 1.85; N = 590 observations), respectively. We indicate the equation of the SKRs. The SKRs are further compared to 1000 random SKRs (dashed line and grey dots). We provide the conditional pseudo-P values from the null model (following Gross et al. 2017) for the slope β, P(β│α), the Y-intercept α, P(α│β) and the whole model, P(β ∩ α). (d-f) We show the observed SKR-parameters expressed in a 2D space (Y-intercept [α] vs. slope [β]), and show the comparison to null models (small grey dots and convex hull). Significant patterns occur when the observed SKR-parameters are out of the convex hull envelop. (g-j) We show the coefficient of determination (r²), the Root-Mean-Square-Error (RMSE), the slope β and Y-intercept α at low (LUI < 1.25; N = 665 observations; blue dots), intermediate (1.25 < LUI < 1.85; N = 695 observations; white dots), and high LUI (LUI > 1.85; N = 590 observations; red dots). We compared the observed SKR parameters to those of 1000 random SKRs. Random SKR-parameters are represented as grey violins where the middle line is the median, the lower and upper hinges correspond to the first and third quartiles, the upper and lower lines show the 0.95 confidence intervals. The observed SKRs significantly differed from null expectations when they were either below the 0.05 or above the 0.095 percentile of the distribution of random SKRs. ** and *** indicate p values < 0.01 and < 0.001, respectively. Ns indicate non-significant differences from null expectations.

Extended Data Fig. 4 Temporal variability of the Skewness-Kurtosis Relationships (SKRs) over the thirteen years of observation.

(a) We show the changes in the SKR-parameters expressed in a 2D space (Y-intercept [α] against slope [β]; Ntot = 1950 observations), and their comparison to null models (small dots). We pinpoint the centroids observed at low (blue dots), intermediate (white dots) and high LUI (red dots), calculated as the averaged parameters observed across the thirteen years. Our analysis showed that the distributions at high LUI behaved as an outlier in 2018, a year known for an extreme summer drought in Central Europe40,41 (Schuldt et al. 2020, Moravec et al. 2021). (b) We show the variability (coefficient of variation, CV) of the SKR-parameters expressed in a 2D space (Y-intercept [α] vs. slope [β]) at low (LUI < 1.25; N = 665 observations; blue dots), intermediate (1.25 < LUI < 1.85; N = 695 observations; white dots), and high LUI (LUI > 1.85; N = 590 observations; red dots), and their comparison against the CVs of 1000 random SKRs (grey violin). The CVs of random SKRs are represented as grey violins where the middle line is the median, the lower and upper hinges correspond to the first and third quartiles, the upper and lower lines show the 0.95 confidence intervals. The observed CVs significantly differed from null expectations when they were either below the 0.05 or above the 0.095 percentile of the distribution of random CVs. *** indicate p values < 0.001 when comparing with random distributions. Ns indicate non-significant differences from null expectations. The variability was calculated as the CV of Euclidean distances among SKR-parameters in the standardized 2-dimensional space (Y-intercept α / slope β) to their mean value observed over the years such as: \(SKRs\,temporal\,{variability}=CV\sqrt{{(\,\underline{\beta }-{\beta }_{i})}^{2}+{(\underline{\alpha }-{\alpha }_{i})}^{2}}\) (Eq. 1).

Extended Data Fig. 5 Influence of the sliding window analysis on the thresholds driven by land use intensification (LUI).

We show the two LUI-thresholds and associated changes in the parameters, that is (a) the Root Mean Square Error (RMSE), (b) the coefficient of determination (r²), (c) the Y-intercept [α] and the slope [β] of the Skewness-Kurtosis relationship (SKR) along the LUI gradient. We used a sliding window analysis to evaluate changes in parameters along the LUI gradient (see Method). For each parameter, we tested the influence of the size of the window considered. Briefly, we ordered all grassland communities across all years according to their LUI value. We selected the first 300 grassland communities with the lowest LUI. We fitted Median-Based Linear Models (light grey) and computed the RMSE, the r², the Y-intercept estimator α and the slope estimator β of the relationship. We removed the community with the lowest value of LUI among the 300 observations, and added a community scoring the next higher value to repeat the same calculations. We repeated this loop as many times as communities remained to explore how SKR patterns changed with increasing LUI. We finally repeated this procedure using a set of 300-600 grassland communities (from light to dark grey) and found that the identified LUI-thresholds were robust to the size of the window used.

Extended Data Table 1 Evaluating the robustness of the observed threshold
Full size table

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Le Bagousse-Pinguet, Y., Liancourt, P., Berdugo, M. et al. Thresholds of functional trait diversity driven by land use intensification. Nat Ecol Evol 9, 1224–1233 (2025). https://doi.org/10.1038/s41559-025-02729-0

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