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; Buscot7,8, F;
Felipe-Lucia9,10, M.R; Fiore-Donno6,
A. M.; Fischer1, M; Freitag11, M;
Godoy12, O; Goldmann7, K;
Gossner13,14, M; Hamer11, U;
Hölzel11, N; Jung15, K;
Kandeler16, E; Klaus17, V. H;
Kleinebecker18, T; Leimer,19,20 S;
Marhan16, S; Oelman21, Y;
Overmann22,23, J.; Prati1, D;
Renner24, S.; Rillig25,26, M;
Seibold27,28, S; Schloter29, M;
Schoening30, I; Sikorski22, J;
Socher31, S; Solly32, E;
Steffan-Dewenter33, I;
Stempfhuber29, B; Westphal34, C;
Wilcke19, W; Wubet10,35, 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).