Title: Land use intensification results in abrupt transitions between contrasting grassland states

Authors: Saiz*^1,2, H; Neuenkamp¹, L; Penone¹, C; Birkhofer³, K; Bluthgen⁴, N; Boch⁵, S; Bonkowski⁶, M; Buscot^7,8, F; Felipe-Lucia^9,10, M.R; Fiore-Donno⁶, A. M.; Fischer¹, M; Freitag¹¹, M; Godoy¹², O; Goldmann⁷, K; Gossner^13,14, M; Hamer¹¹, U; Hölzel¹¹, N; Jung¹⁵, K; Kandeler¹⁶, E; Klaus¹⁷, V. H; Kleinebecker¹⁸, T; Leimer,^19,20 S; Marhan¹⁶, S; Oelman²¹, Y; Overmann^22,23, J.; Prati¹, D; Renner²⁴, S.; Rillig^25,26, M; Seibold^27,28, S; Schloter²⁹, M; Schoening³⁰, I; Sikorski²², J; Socher³¹, S; Solly³², E; Steffan-Dewenter³³, I; Stempfhuber²⁹, B; Westphal³⁴, C; Wilcke¹⁹, W; Wubet^10,35, T; Wurst³⁶, S; Allan¹, E.

¹Institute of Plant Sciences, University of Bern, Altenbergrain 21, 3013, Bern, Switzerland.

² Departamento de Ciencias Agrarias y Medio Natural, Escuela Politécnica Superior, Instituto Universitario de Investigación en Ciencias Ambientales de Aragón (IUCA), Universidad de Zaragoza; 22071 Huesca, Spain.

³Department of Ecology, Brandenburg University of Technology Cottbus-Senftenberg, 03046 Cottbus, Germany.

⁴Ecological Networks, Department of Biology, Technical University of Darmstadt, 64287 Darmstadt, Germany.

⁵Biodiversity and Conservation Biology, Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL), 8903 Birmensdorf, Switzerland.

⁶Terrestrial Ecology, Institute of Zoology, University of Cologne, 50674 Cologne, Germany.

⁷Soil Ecology Department, Helmholtz Centre for Environmental Research (UFZ), 06120 Halle (Saale), Germany.

⁸German Centre for Integrative Biodiversity Research (iDiv), Halle - Jena - Leipzig, 04103 Leipzig, Germany.

⁹Department of Ecosystem Services, Helmholtz Centre for Environmental Research (UFZ), 04103, Leipzig, Germany.

¹⁰German Centre for Integrative Biodiversity Research (iDiv), Halle - Jena - Leipzig, 04103 Leipzig, Germany.

¹¹Institute of Landscape Ecology, University of Münster, 48149 Münster, Germany.

¹²Departament of Biology, Marine Research Universitary Institute (INMAR), University of Cádiz, 11510 Puerto Real, Spain.

¹³Forest Entomology, Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL), 8903 Birmensdorf, Switzerland.

¹⁴Department of Environmental Systems Science, Institute of Terrestrial Ecosystems, ETH Zurich, 8092 Zurich, Switzerland.

¹⁵Institute of Evolutionary Ecology and Conservation Genomics, Ulm University, 89081 Ulm, Germany.

¹⁶Institute of Soil Science and Land Evaluation, Soil Biology Department, University of Hohenheim, 70593 Stuttgart, Germany.

¹⁷Institute of Agricultural Sciences, Swiss Federal Institute of Technology (ETH) Zürich, 8092 Zürich, Switzerland.

¹⁸Department of Landscape Ecology and Resources Management, Justus Liebig University Giessen, 35392 Giessen, Germany.

¹⁹Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany.

²⁰Research Data Services, University Library, University of Duisburg-Essen, 47057 Duisburg, Germany.

²¹Department of Geosciences, University of Tübingen, Rümelinstr. 19-23, 72070, Tübingen, Germany.

²²Leibniz Institute, DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, 38124 Braunschweig, Germany.

²³Faculty of Life Sciences, Technical University Braunschweig, 38106 Braunschweig, Germany.

²⁴Ornithology, Natural History Museum Vienna, 1010 Vienna, Austria.

²⁵Institut für Biologie, Ecology of Plants, Freie Universität Berlin, 14195 Berlin, Germany.

²⁶Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany.

²⁷Ecosystem Dynamics and Forest Management Group, Department of Ecology and Ecosystem Management, Technical University of Munich, 85354, Freising, Germany.

²⁸Berchtesgaden National Park, 83471 Berchtesgaden, Germany.

²⁹Research Unit for Comparative Microbiome Analysis; Helmholtz Center for Environmental Health, 85764 Neuherberg, Germany.

³⁰Department for Biogeochemical Processes, Max Planck-Institute for Biogeochemistry, 07745 Jena, Germany.

³¹Department of Biosciences, Plant Ecology, Botanical Garden, University of Salzburg, 5020 Salzburg, Austria.

³²Sustainable Agroecosystems Group, Department of Environmental Systems Science, ETH Zürich, 8092 Zurich, Switzerland

³³Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.

³⁴Department of Crop Sciences, Functional Agrobiodiversity, Georg-August-Universität Göttingen, 37077 Göttingen, Germany.

³⁵Community Ecology Department, Helmholtz Centre for Environmental Research (UFZ), 06120 Halle (Saale), Germany.

³⁶Institut für Biologie, Funktionelle Biodiversität,Freie Universität, 14195 Berlin, Germany.

Running title: abrupt regime shifts associated to land use

Keywords: Above- and Below-ground Diversity, Ecosystem functions, Grasslands, Hysteresis, Land use intensification, Regime shifts, Thresholds

Type of article: Letter

Length: Abstract = 149 words; Main text = 4998; References = 74 citations

Figures: 4 figures

*Contact details:hsaiz@posta.unizar.es; Telephone: +34 974 29 26 57; Fax: +34 974 23 93 02

Statement of authorship

HS, LN, CP and EA designed the project. KB, NB, SB, MB, FB, AMFD, MF, KG, MG, UH, NH, KJ, EK, VHK, TK, SL, SM, YO, JO, DP, SR, SS, MS, IS, JS, SS, ES, ISD, BS, CW, WW, TW and SW collected the data. HS and CP assembled the dataset. HS analysed the data, HS, CP and EA interpreted the results and wrote the first draft of the manuscript. All authors discussed and commented on the final version of the manuscript.

Data accessibility statement

All data is currently available inhttps://www.bexis.uni-jena.de/, within the dataset with id 27087 and 31207. Should the manuscript be accepted, the data supporting the results will be archived in an appropriate public repository (Dryad, Figshare or Hal).

Abstract

Understanding whether land use intensification causes regime shifts is of key importance for management, particularly if these shifts are associated with thresholds separating different ecosystem states and with hysteretic dynamics. Here we use a unique, long-term grassland database to identify thresholds in the response of 16 ecosystem functions and the diversities of 21 taxa to land use intensity. We show that aboveground diversity (5 of 10 taxa), shoot biomass and soil N retention showed threshold responses to land use intensity, i.e., abrupt changes between extensively and intensively managed grasslands. Time-series analysis revealed that ecosystem functions showed hysteresis around the threshold, while diversity did not. Shifting back to the functioning seen in extensively managed grasslands may therefore require larger reductions in land use intensity than shifting to the high intensity state. Identifying such thresholds along land use gradients is critical to prevent ecosystem degradation and conserve biodiversity and ecosystem functions.

Introduction

Land use intensification is a major cause of biodiversity loss across trophic levels (Allan et al. 2015; Rounsevell et al. 2018) and leads to a homogenization of community composition (Gámez-Viruéset al. 2015; Gossner et al. 2016). In addition, increasing land use intensity decreases ecosystem multifunctionality by increasing yield at the expense of other ecosystem functions (e.g. soil C storage and nutrient retention) (Soussana & Lemaire 2014) and services (e.g. cultural and aesthetic value, pollination and pest control) (Allan et al. 2015; Dainese et al. 2019). Land use effects on ecosystems are frequently non-linear, which translates into abrupt changes in grassland biodiversity and functioning as land use intensity increases (Kleijn et al. 2009; Allan et al. 2014). However, few studies have tested whether these non-linear changes result in regime shifts, i.e. persistent, large changes in system-state variables, such as biodiversity or ecosystem functions (Scheffer & Carpenter 2003). Identifying regime shifts is therefore central for determining the sustainability of different forms of ecosystem management, particularly when they affect human well-being (Crépinet al. 2012). Thus, in order to prevent losses of services and to avoid ecosystem degradation it is critical to know whether land use intensification leads to regime shifts in grasslands.

When regime shifts occur abruptly, they can be associated with threshold responses where a small change in an external driver, e.g. land use; leads to sudden and large changes in system-state variables, which are further accelerated by internal feedbacks (Suding et al.2004; Briske et al. 2006; Ratajczak et al. 2018). Theories postulate two types of dynamics associated with thresholds (Figure 1) (Suding & Hobbs 2009): in the first case, the system can transition back to its previous state when the driver returns to its previous level, i.e. the regime shift is reversible. However, in the second case the system may not easily transition back to the previous state, that is hysteresis occurs and there is a critical transition among different stable states. With hysteresis, the transition between states cannot be simply reversed by restoring the driver to its previous level, it requires larger changes (Scheffer & Carpenter 2003; Dakoset al. 2019). Previous studies identified thresholds for grassland vegetation responses to grazing (Briske et al. 2005; Sasaki et al. 2008), but none have tested for thresholds in the response of multiple aspects of diversity and ecosystem functioning to several elements of land use, or whether thresholds are associated with different ecosystem states and hysteresis.

Although threshold responses have sometimes been observed (Sasakiet al. 2015), several studies have suggested that there is no quantitative evidence for them and that thresholds are rarely detectable (Hillebrand et al. 2020). Several factors may explain these mixed findings: firstly, different ecosystem functions and the diversities of different taxa, are likely to vary in their response to external drivers (Allan et al. 2015; Newbold et al. 2015; Solivereset al. 2016), and the absence of a threshold for one function or diversity does not preclude the existence of thresholds for others. However, most studies looking for thresholds focus on only a small number of variables. In general, studies on thresholds have focused on diversity measures and have rarely considered ecosystem functions (Sasaki et al. 2015) (but see (Evans et al. 2017)). Secondly, consistently detecting thresholds in response to external drivers can be challenging when ecosystem responses are highly variable (Hillebrand et al. 2020), meaning that spatially extensive datasets are needed to robustly identify thresholds. Finally, extensive sampling needs to be combined with long-term data to identify critical transitions in ecosystem states, i.e. using early warning signals (indicators of a system approaching a critical transition (Schefferet al. 2009)), or to evaluate the dynamics associated with thresholds, i.e. to test for hysteresis (Sasaki et al.2015), and such data are rarely available. Thus, testing for thresholds in ecosystem responses requires studies with continuous measures of external drivers and ecosystem responses across many sites and multiple years.

In this study, we use a large database from 150 temperate grasslands to identify thresholds in the response of the diversity of 21 taxonomic groups (including plants, arthropods, birds, bats and soil microbes), and 16 ecosystem functions (including productivity, measures of nitrogen, carbon and phosphorus cycling and herbivory and pathogen attack), to a gradient of land use intensity. We considered effects of land use components, grazing, mowing and fertilization, both individually and combined in a composite land-use index (Blüthgenet al. 2012). We asked the following questions: (i) do diversities of multiple taxa (across multiple trophic levels) and ecosystem functions show a threshold response to land use? and if so, (ii) are these thresholds indicative of critical transitions and hysteresis? To answer these questions, we used a three-step analysis. First, we identified which variables showed a non-linear response to land use intensity and tested whether those responses were associated with specific thresholds. Second, we looked for early warning signals of critical transitions, by testing for increased spatial variation along the land use gradient. Finally, we used 12 years of land use data to identify hysteretic dynamics, based on whether observed thresholds changed depending on the land use history (i.e. if land use intensity had recently decreased or increased on a grassland).

Methods

Study area

The studied grasslands are part of the Biodiversity Exploratories project (www.biodiversity-exploratories.de) (Fischer et al. 2010) and are located in three different regions of Germany: the Schwäbische Alb plateau, as a part of the UNESCO Biosphere Reserve Schwäbische Alb (south-west), Hainich-Dün region (central) and the UNESCO Biosphere Reserve Schorfheide-Chorin (north-east). The regions differ in geology, topography, climate and soils (Fischer et al. 2010). Detailed information about the studied regions can be found in Supplementary Table S1. In 2007, 50 permanent grassland plots of 50 m x 50 m were established in each region (150 in total). Plots had been grasslands for at least 20 years before the start of the project.

Land use intensity

Plots in all three regions were selected to cover land use gradients typical for central Europe (Blüthgen et al. 2012). To assess land use intensity (LUI), annual questionnaires were sent to landowners asking them about the intensities of grazing, mowing and fertilization on each plot (Blüthgen et al. 2012; Vogt et al. 2019). For grazing intensity (G), farmers reported the type (cattle, horse and sheep), stocking density and the duration of grazing periods (standardized to livestock units · grazing days · ha^-1· year^-1). For mowing frequency (M), farmers reported the number of annual cuts (number of cuts · year^-1), and for fertilization (F), they reported the total fertilizer addition from which we calculated the amount of nitrogen added (kg N · ha^-1 · year^-1). The individual components (G, M, F) were then standardized to their means across regions, and a continuous compound index of LUI was calculated by summing the standardized components (LUI = G + M + F) (Blüthgen et al. 2012).