A slow-fast trait continuum at the whole community level in relation to land-use intensification

Domain: earth_science
Published: 2024-02-10
Editorial outcome: accept

Abstract
Organismal functional strategies form a continuum from slow- to fast-growing organisms, in response to common drivers such as resource availability and disturbance. However, whether there is synchronisation of these strategies at the entire community level is unclear. Here, we combine trait data for >2800 above- and belowground taxa from 14 trophic guilds spanning a disturbance and resource availability gradient in German grasslands. The results indicate that most guilds consistently respond to these drivers through both direct and trophically mediated effects, resulting in a ‘slow-fast’ axis at the level of the entire community. Using 15 indicators of carbon and nutrient fluxes, biomass production and decomposition, we also show that fast trait communities are associated with faster rates of ecosystem functioning. These findings demonstrate that ‘slow’ and ‘fast’ strategies can be manifested at the level of whole communities, opening new avenues of ecosystem-level functional classification.

Introduction
Understanding how functional strategies respond to environmental drivers is one of the longest-standing and most fundamental questions in ecology (e.g., r/K strategist theory1, CSR (competitive/stress-resistant/ruderals) strategies2). Due to evolutionary trade-offs, species allocate resources differently to their capacity to grow, reproduce, and survive, and for several taxa, it is well established that this leads to sets of co-varying traits that represent ecological strategies3–5. At the community level, a range of positive and negative biotic interactions6 and abiotic factors constrain which individuals, bearing specific traits, persist in a community7. In any given environment this is likely to lead to the dominance of the trait set best adapted to local conditions, leading to trait similarity between co-occurring species and resulting in community-level trait covariation.

Functional strategies, and their trait proxies, have been particularly well studied and characterised in vascular plants, both at the species2,8–10 and community levels11, but have also been described for groups as diverse as fishes, arthropods12, and more recently, microorganisms13. While the hypotheses underlying such patterns are often underdeveloped compared to those for plants, similar drivers of strategy orientation have been identified, namely resource availability and disturbance; and these are consistently seen to act concurrently to shape both individual species and community-level strategy traits in terms of growth, reproduction, and survival across the tree of life14,15. Body size in particular is a fundamental trait of functional strategies that shows consistent responses to these drivers, with undisturbed environments filtering for larger (‘slow’) organisms and disturbed environments for smaller ('fast') organisms across groups14,16, a finding that is consistent with general theory17.

At the level of guilds and communities, winning strategies can be seen as manifesting as an emergent property, and represented in community-level trait measures, typically the community abundance-weighted trait mean11,18 (CWM). While there can be significant trait variation within a community, a CWM captures the average functional strategy of the community; and changes in CWM across space and time reflect both turnover in species with different trait values, and variation in their relative abundance, in response to changes to the species pool and environmental conditions. The combined response of multiple trait CWMs thus represents a change in the overall functional strategy at a community level. This means that slow-fast strategy responses – encompassing a range of traits – may emerge at the community level from a concurrent change in individual CWM traits related to ‘slow’ and ‘fast’ strategies. In particular, communities of resource-acquisitive, fast-growing organisms with numerous offspring, a fast pace of life and good dispersal abilities tend to be found in resource-rich and disturbed habitats, while resource-conservative, slow-growing organisms with longer lifespan and fewer offspring tend to be favoured in undisturbed or resource-poor habitats14,15,19–21.

Consistent with these common responses at both species and community levels, associations between traits have been reported between interacting guilds, including plants and soil microorganisms22–24, plants and arthropods25, and plants and frugivores26. These shared responses between guilds to similar environmental drivers, along with the existence of widespread strategies within guilds, suggest the possibility of synchronised responses across trophic groups and therefore the potential existence of trait syndromes at the level of entire communities. The possibility of such community-level coupling27 has been discussed previously, in particular in terms of linkages between plants, herbivores and the soil food web28. However, while there is theoretical and empirical support for a community-level slow-fast trait response, conflicting observations challenge this hypothesis. First, while the slow-fast spectrum is well defined in plants, additional axes of variation in functional and life-history strategies (e.g., reproductive strategies, and their defining traits such as the timing of reproductive onset and reproductive allocation) have been identified in both plants and other organisms29,30, and these may respond to different drivers and dominate the distribution of certain organismal groups, decoupling them from the response of others3,31. Furthermore, guilds might vary in their strength of response to the drivers of the slow-fast functional axis32 and could respond over different spatiotemporal scales33 leading to weak overall coupling.

If community-level slow-fast responses to disturbance and resources are present, then they may be driven by shared direct responses to the environment such as rapid reproduction and effective dispersal of both plants and their consumers in highly disturbed environments. In contrast, synchronous responses could also be mediated by bottom-up34 trophic interactions: at higher resource availability, we expect higher productivity and resource concentrations, and faster rates of resource transfer between trophic levels, encouraging organisms with faster growth and reproduction both above and below ground28,35. Thus, the slow-fast response of the entire community to resource and disturbance drivers might emerge from both direct effects (mainly through shared responses to disturbance) and indirect effects (e.g., a trophic cascade driven by the resource availability component).

Finally, many studies have shown that community-level trait measures of individual guilds explain variation in individual ecosystem functions, which is to be expected given the link between functional traits and rates of metabolic and tissue turnover processes23,36,37. Meanwhile, several recent studies have demonstrated that overall ecosystem functioning can be described in terms of just a few fundamental functional axes38–40. Given this, we predict that the entire community ‘slow-fast’ trait axis corresponds to an ecosystem functioning ‘slow-fast’ axis, with ‘fast’ functioning defined as fast process rates (e.g., high productivity, rapid decomposition and nutrient turnover).

While theory often considers the effects of resource availability and disturbance as orthogonal (e.g., CSR theory2), these drivers are often confounded in real ecosystems. Agricultural systems in particular, tend to be found on a continuum between low intensity (low disturbance, no nutrient input) and high intensity (high disturbance, such as mowing, and high nutrient input). We can thus expect to find a continuum between ‘slow and steady’ to ‘grow fast, die young’ strategies along land-use intensity gradients – and indeed land-use intensification tends to select for faster strategies of plants41, arthropods25,42, and microorganisms43.

Despite the evidence base presented above, until now, a lack of coordinated multitrophic abundance, functional trait, and ecosystem function data has hindered the investigation of synchronised responses at the level of entire communities, and their link to ecosystem functioning. However, a recent explosion in trait data availability44–47, combined with large-scale research platforms that survey multiple organismal groups and ecosystem functions simultaneously48–50, now allows this long-standing question to be addressed. Here, we use data from the large-scale and long-term Biodiversity Exploratories49 to test the hypotheses that there is a common, whole community-level slow-fast response to disturbance and resource availability in the form of land-use intensity, and that this community-level strategy drives a slow-fast ecosystem functioning response. The Biodiversity Exploratories is, to our knowledge, the most comprehensive data source for multiple guilds, with abundance data available from the same sites and at all trophic levels, both above- and belowground, including bacteria, fungi, protists, as well as plants, invertebrates and vertebrates33. These are collected in 150 grassland plots in three regions of Germany. We focus on the land-use intensity gradient of the Exploratories, which is a combined gradient of both resource availability (fertilisation, with high fertilisation being usually applied to the most inherently productive sites) and disturbance (mowing, which is strongly correlated with fertilisation; and grazing)51. Because existing theory on slow-fast strategies was lacking for some guilds, we first conducted expert workshops to identify traits expected to represent the slow-fast continuum for each guild (Tables S1 and S2). Based on pre-defined hypotheses from existing theories, observational studies, and expert knowledge, we selected several traits for each guild to represent the slow-fast continuum, and generated expectations of how these respond to resource availability and disturbance (Fig. 1, see Table S2 for full detail). Focusing on these pre-selected traits, we then tested three hypotheses: (H1) there is a synchronous functional response across trophic levels, shifting from ‘slow and steady’ to ‘grow fast, die young’ strategies with increasing land-use intensity. (H2) Land-use intensity drives the slow-fast response of the entire community through both direct and indirect (through a trophic cascade) pathways. (H3) The entire community ‘slow-fast’ trait axis corresponds to an ecosystem functioning ‘slow-fast’ axis, with ‘fast’ functioning defined as fast process rates (e.g., high productivity, rapid nutrient turnover). We show that the slow-fast response is coordinated among most guilds and drives an ecosystem functioning slow-fast axis, thus forming a whole ecosystem slow-fast axis.

Results
We first identified ‘slow-fast’ traits at the community level within individual trophic guilds. To do this, we classified individual species into trophic guilds of broadly comparable trophic status (e.g., aboveground primary consumers or belowground omnivores). Different taxonomic groups with the same trophic status were aggregated into a single trophic guild if all trait data was consistent, otherwise they were kept separate. For instance, we had comparable trait data for many aboveground arthropods (size, feeding generalism, dispersal ability compiled from ref. 52, but data for Lepidoptera was available for different traits (hibernation stage, voltinism, etc) and compiled from a range of other sources53–56. Therefore, this group was treated separately from other primary consumer arthropods. This led to the definition of 14 homogeneous trophic guilds (hereafter ‘guilds’), listed in Table 1. For each guild, abundance-weighted, community-level mean (CWM) trait values were calculated and corrected for environmental covariates (see Methods). Some expected trait responses of different guilds to resource availability and disturbance are shown in Fig. 1 (see Table S2 for full details).

Identification of guild-level slow-fast axes
We assumed that for each guild, the main axis of covariation between the selected slow-fast traits (Fig. 2) should represent the guild’s overall slow-fast response (‘axis’). If the selected traits were not strongly associated (weak covariation) in a principal coordinates analysis (PCA), we concluded that there was no observable slow-fast axis in the considered guild. Conversely, if most selected traits within a guild covaried, then their main axis of covariation was retained as the guild’s slow-fast axis. This covariation axis was defined through a principal component analysis with all traits within each guild, and retained if it explained more than twice of the shared variance as expected if traits were independent; and if the axis was correlated (r>0.4) with at least 60% of the traits (see specific criteria in Methods).

The existence of the hypothesised guild-level slow-fast axis was supported by the data for most guilds, and it explained on average 52% (±sd 13) of the total variation in the 4.2 (±1.8) CWM traits per guild that were included in the analyses. For three protist guilds (primary consumers, bacterivores, secondary consumers), only one trait was available, cell size, and we therefore assumed the existence of a ‘slow-fast’ axis defined by large to small cell size in subsequent analyses1,57. Of the remaining 11 trophic guilds, we found strong support for a community-level ‘slow-fast’ axis in nine guilds: plants (primary producers), aboveground arthropods (primary consumers), aboveground arthropods (secondary consumers), birds (tertiary consumers), belowground bacteria and fungi (decomposers), belowground arthropods (primary consumers), Collembola (omnivores), Oribatid mites (omnivores), and other belowground arthropods (secondary consumers). For instance, aboveground arthropods (primary consumers) displayed a clear trade-off between ‘slow’ communities dominated by large body size and slow reproduction, and ‘fast’ communities dominated by generalist-feeding species with smaller body size, higher dispersal abilities and multiple generations per year (PC1: 53.8% variance, Fig. 2). Similarly, Collembola (omnivores) displayed a trade-off between ‘slow’ communities, dominated by soil-surface-dwelling species with a large body size and sexual reproduction, and ‘fast’ communities, characterised by more organisms living deeper in the soil and capable of parthenogenesis, which leads to faster generation times (PC1: 65% variance). There was partial support for the existence of a ‘slow-fast’ axis in the remaining guilds (Fig. 2).

Slow-fast trait continuum across trophic levels
Next, we explored the degree of correspondence between the community-level ‘slow-fast’ trait axes of individual guilds. The data supported our overarching hypothesis H1: most of the guild-level ‘slow-fast’ axes were closely correlated with each other and with the land-use intensity gradient. A Principal Components Analysis (PCA) conducted on the slow-fast axes of all guilds with sufficient data availability (all except belowground arthropods (primary consumers), see Methods) revealed that 25% of the combined variation in all the guilds’ slow-fast axes was explained by the first PC axis of variation. This axis was strongly correlated with land-use intensity (Pearson correlation r=0.74, p<10−20, Fig. 3a). We also observed a significant and positive correlation between land-use intensity and the slow-fast axis score of nine (out of 14) guilds (p ranging from 1.10−14 to 0.04 and |r|>0.2), with this correlation being particularly strong at low trophic levels. In contrast to our expectations, the highest trophic levels tended to have ‘slower’ traits at high land-use intensity (birds, r=−0.2, p=1.5 10−2). We also tested for the existence of the whole community slow-fast axis by running a PCA on all considered traits from all guilds simultaneously (rather than aggregated into guild-level slow-fast axes, themselves extracted from PCAs). This analysis also supported the existence of a whole community slow-fast axis, with ‘slow’ and ‘fast’ traits clearly separated across sites (Fig. 3b).

The observed covariation in slow-fast traits across guilds demonstrates that land-use intensity drives differentiation between entire communities, with ‘slow’ and ‘fast’ communities characterised by distinct trait syndromes (Fig. 3, Table S2). This conclusion was supported by the analysis of individual traits’ responses to land-use intensity, which shows that the CWMs of 54% of individual traits responded to land-use intensification in the hypothesised direction, and only two (4%) responding in an opposing direction (Fig. 4). ‘Slow’ communities are found at low resource availability and infrequent disturbance, and are characterised by slow-growing resource-conservative plants (correlation of leaf dry matter content with land-use intensity: r=−0.28, p=7 10−4; root tissue density: r=−0.22, p=7 10−3). Their soil microbial communities are dominated by fungi rather than bacteria (fungal:bacteria ratio: r=−0.28, p=3 10−5), consistent with previous observations that low nutrient availability selects for fungal-dominated communities and slow-growing plants21,22. Bacterial communities in ‘slow’ communities also contain a higher proportion of small-celled organisms with large genomes (cell volume: r=−0.30, p<10−8; genome size: r=−0.14, p=2 10−4), an adaptation to growth in nutrient-poor (oligotrophic) conditions58 (oligotrophic:copiotrophic ratio: r=−0.14, p=0.02). In contrast to the overall trend, birds had slower strategies in high intensity than low-intensity grasslands (slower generation length, r=0.19, p=0.02 and larger body size, r=0.23, p=0.005).

At the ‘fast’ end of the spectrum, high resource availability is associated with fast-growing plants with high nutrient content (leaf N content: r=0.46, p<10−8, leaf P content: r=0.51, p<10−8), which is often related to lower levels of physical and chemical defence59. This strategy was associated with a higher dominance of pathogenic fungi60 (proportion of pathogenic soil fungi, r=0.37, p<10−8) and plant pathogenic soil protists61 (r=0.46, p<10−8). In addition, high land-use intensity selected for smaller body size in four guilds (bacterivore protists: r=−0.29, p=3 10−4; secondary consumer protists: r=−0.39, p<10−8, aboveground arthropods (primary consumer) size: r=−0.24, p=4 10−3; belowground arthropods (primary consumers): r=−0.24, p=2 10−3) and faster reproduction19 in two guilds (voltinism of Lepidoptera (primary consumers): r=0.21, p=0.01; and other aboveground arthropods (primary consumers): r=0.56, p<10−8). This response is likely driven by disturbance, which causes higher mortality in large-bodied arthropods62,63, and favours early reproductive maturity and fast reproduction as organisms can reproduce before disturbance, and population numbers can recover quickly afterwards. In addition, dispersal ability was greater in the fast communities of high land-use intensity (dispersal ability of aboveground arthropod (secondary consumers): r=0.33, p=3 10−5, aboveground arthropods (primary consumers): r=0.37, p<10−8). This higher dispersal capacity enables faster recolonisation after disturbance15,62.

The slow-fast trait responses of some consumer guilds was potentially driven not directly by resources and disturbance, but indirectly via losses in plant diversity associated with increased fertilisation64,65. This commonly leads to the loss of associated specialist plant species that are typically found in slow ecosystems33,66,67. As such specialist plant species often have higher degrees of physical and chemical defences, they tend to be associated to specialist herbivores68. In our study, this effect was manifested by an increased dominance of generalist species with land-use intensity, both among Lepidoptera and other aboveground arthropods (primary consumers) (feeding generalism of Lepidoptera: r=0.30, p=3 10−4; and of aboveground arthropods (primary consumers): r=0.34, p<10−8).

Changes in community-level traits along environmental gradients can be due to changes in species identity (taxonomic turnover) or variation in species relative abundance. In our case, we found that both factors were responsible for the observed slow-fast responses to land-use intensity within the different guilds (average turnover across guilds: 0.95±0.03; average nestedness: 0.03±0.03, Table S3).

Land-use intensity effect on community-level slow-fast response
The observed entire community-level slow-fast continuum could be driven by a common, but independent, response of individual guilds to land-use intensity, or mediated by cascading bottom-up trophic interactions between guilds, and we hypothesised that both pathways were important (Hypothesis 2). To test this, we used Structural Equation Modelling (SEM) to assess the effect of these pathways on the ‘slow-fast’ axes of the 13 guilds. We found that both pathways were important, supporting Hypothesis 2: slow-fast strategies axes are shaped both directly via shared responses to land-use intensity and indirectly via trophic interactions. Land-use intensity showed significant direct effects on the ‘slow-fast’ axis of eight guilds out of 13 (average estimate: 0.20±0.06), and indirect, i.e., trophically-mediated, effects on six guilds (0.07±0.02). The pathways combined to make significant total effects of land-use intensity (0.27±0.07) on nine out of the 13 guilds (Fig. 5a).

Next, we obtained a general assessment of how land-use intensity affected the slow-fast axis of each trophic level, by averaging the direct, indirect and total effects for all guilds at each trophic level (producers vs. primary vs. secondary vs. tertiary consumers). This showed that the ‘slow-fast’ axes of lower trophic levels responded more strongly than higher trophic levels to land-use intensity, via both the direct and indirect paths. While there was a significant total effect of land-use intensity on all trophic levels, except belowground secondary consumers (Fig. 5b), the direct (p=2 10−3), indirect (p=0.01) and total (p=4 10−4) effects all significantly decreased in intensity with trophic levels, indicating more synchronous trait responses at lower trophic levels (Fig. 5c).

Slow-fast ecosystem functioning response
To test our third hypothesis, that the community-level slow-fast continuum was related to whole ecosystem function, we first conducted a PCA based on 15 ecosystem functions related to carbon fluxes, nitrogen fluxes, biomass production and decomposition. This showed that ecosystem functions covary, from ‘slow’ sites characterised by slow decomposition, biomass production and low enzyme activities to ‘fast’ sites with faster nutrient cycling (first axis of the PCA: 29% variance, Fig. 6a). This whole ecosystem functioning slow-fast axis was better explained by the entire community ‘slow-fast’ traits axis (r=0.4, R2=0.34, p<10−8) than by other hypothesised drivers of ecosystem functioning (single guild community traits measures (plants and microbes), land-use intensity or taxonomic diversity, R2 from 0.11 to 0.26, Table S4). The exception to this was fungal-bacterial ratio, which was correlated with the whole slow-fast axis (r=−0.84, P<10−8) and better explained the functions slow-fast axis (R2=0.47, Figs. S5 and S6). This was likely due to the prevalence of soil-related measures in our selected set of functions. Additionally, some individual functions were more strongly associated with specific guilds or traits (e.g., fast plant community traits were positively associated with biomass production (Fig. S5)). Results were similar when using the all traits slow-fast axis (PC1 in Fig. 3b) as the main community slow-fast axis (Fig. S4).

A mediation analysis found that the effect of land-use intensity on the ecosystem functioning ‘slow-fast’ axis was both direct and mediated by the entire community ‘slow-fast’ trait axis (Fig. 6b). This confirms that the ‘slow-fast’ trait axis is a key mediator of the effect of land-use intensity on ecosystem functioning, and that the entire community ‘slow-fast’ axis is a good predictor of ecosystem functioning.

Sensitivity analyses
While there were strong hypothetical reasons to expect body size to be a key trait driving fast-slow variation at the community level, it can respond to a range of drivers and drive life-history variation in the absence of other trait responses; as a result, the effect of body size is often removed in analyses that seek to identify life-history trade-offs69,70. To assess the robustness of our results to this trait, we conducted additional analyses in which we excluded all body size data (resulting in the exclusion of bacterivorous and predatory protists as other trait data were not available for these groups). Even in the absence of body size we were still able to identify a strong guild-level slow-fast axis for most guilds, except Oribatid mites. This resulted in somewhat weaker, but still consistent results regarding the synchrony of slow-fast axes across guilds and the effect of the whole community slow-fast axis on ecosystem functioning (Tables S8–S10, Figs. S7–S9).

Other sensitivity analyses included checking the impact of using raw CWM data (uncorrected for environmental covariates) and unweighted (instead of using abundance-weighted) trait values. These are discussed in the supplementary analyses (Tables S11–S18; Figs. S10–S20); they show overall weaker, but consistent results with those presented here.

Methods
Study area
The study was conducted as part of the long-term Biodiversity Exploratories project (www.biodiversity-exploratories.de). Data was collected in 150 grassland plots in three regions of Germany: the Schwäbische Alb plateau and UNESCO Biosphere Reserve in south-western Germany; the Hainich National Park and surrounding areas in central Germany (both are hilly regions with calcareous bedrock) and the UNESCO Biosphere Reserve Schorfheide-Chorin in the post-glacial lowlands of north-eastern Germany. The three regions differ in climate, geology and topography, but each is characterised by a gradient of grassland land-use intensity that is typical for large parts of temperate Europe49. In each region, 50 plots (50m × 50m) were chosen in secondary wet, mesic and dry grasslands by stratified random sampling from a total of 500 candidate plots on which initial vegetation, soil and land-use surveys were conducted. This ensured that plots covered the whole range of land-use intensities and management types, while minimising confounding factors such as spatial position or soil type.

Land-use intensity
Land-use intensity was assessed annually via questionnaires sent to land managers in which they reported the level of fertilisation (kg N ha−1 yr−1), the number of mowing events per year (from one to three cuts, starting in May and continuing up to September/October in the most intensive plots), and the number and type of livestock and their duration of grazing (number of livestock units ha−1 yr−1)49,51. Mowing and grazing intensities determine the frequency, and intensity, at which aboveground biomass is removed; and thus represent the intensity of disturbance in the plot. Fertilisation provides additional nutrients, and is typically mostly applied in naturally productive plots: it thus represents a resource availability gradient. In our study system, mowing and fertilisation intensities are positively correlated (r=0.7), while grazing and mowing intensities are negatively correlated (r=−0.6). Thus, independent effects of each land-use component, and the respective effect of resource availability and disturbance, cannot be reliably estimated. We therefore used a compound index of land-use intensity, characterising a combined resource availability and disturbance gradient. The land-use intensity index (LUI) was calculated as the square-root-transformed sum of standardised measures of global mowing, fertilisation and grazing intensities across the three regions for each year92. We calculated the mean LUI for each plot over the years 2008–2018 because this reflects the average LUI around the years when most of the data was collected. At low LUI (0.5–0.7), grasslands are typically neither fertilised nor mown, but grazed by one cow (>2 years old) per hectare for 30 days (or one sheep per hectare for the whole year). At an intermediate LUI (around 1.5), grasslands are usually fertilised with less than 30 kg N ha−1 y−1, and are either mown twice a year or grazed by one cow per hectare for most of the year (300 days). At a high LUI (3), grasslands are typically intensively fertilised (60–120 kg N ha−1 y−1), are mown 2–3 times a year or grazed by three cows per hectare for most of the year (300 days), or are managed by a combination of grazing and mowing. The study area did not cover very high intensity grasslands (e.g., cut more than three times per year and ploughed annually).

In some figures, LUI was added as a supplementary variable to e.g., PCAs, along with a ‘fertilisation’ variable and a ‘disturbance’ index. The fertilisation variable is the standardised fertilisation value. The disturbance index is calculated as the square-root of the sum of mowing and grazing intensities, both standardised by dividing by their overall average.

Acquisition of environmental covariates
To measure pH and soil texture, composite samples were taken in 2011 in all 150 plots, by mixing 14 mineral topsoil samples (0–10cm, using a split tube manual soil corer with 5cm diameter). Ten g of sieved and air-dried soil were mixed with 25ml 0.01M CaCl2 solution and shook for 2h. Afterward the pH of the soil suspension was measured using a glass electrode. The pH of each sample was measured twice. We determined soil texture by separating soil particles into sand (2–0.063mm), silt (0.063–0.002mm) and clay (<0.002mm) by sieving and sedimentation (DIN-ISO 11277). Open datasets from Biodiversity Exploratories: refs. 93–95.

The topographic wetness index (TWI) combines measures of upslope contributing area (determining the amount of water received from upslope areas) and slope (determining the loss of water from the site to downslope areas) and has been shown in previous analyses to be a better predictor than local humidity measures33. It is defined as ln(a/tanB), where a is the specific catchment area (cumulative upslope area which drains through a Digital Elevation Model (DEM, http://www.bkg.bund.de) cell, divided by per unit contour length) and tanB is the slope gradient in radians calculated over a local region surrounding the cell of interest. TWI was calculated from raster DEM data with a cell size of 25m for all plots, using GIS tools (flow direction and flow accumulation tools of the hydrology toolset and raster calculator). The TWI measure used was the average value for a 4×4 window centred on the plot, i.e., 16 DEM cells corresponding to an area of 100m × 100m. Dataset from the Biodiversity Exploratories: ref. 96.

Finally, we measured mean annual temperature as the temperature 2m above ground level in each plot, aggregated at the year level and averaged between 2008 and 2015. Open dataset from Biodiversity Exploratories: ref. 97.

Sampling protocols by trophic guild
In each plot, we measured the relative abundance of multiple guilds using standard methodology. Most data was extracted from an aggregated diversity dataset from the Biodiversity Exploratories98,99. Original datasets are listed below.

Vascular plants. We sampled vascular plants in an area of 4m × 4m in all 150 plots, and estimated the percentage cover of each occurring species every year from 2008 to 2019. Open dataset from Biodiversity Exploratories: ref. 100.

Soil fungi and bacteria. Composite soil samples were taken in 2011, 2014 and 2017 in all plots, by mixing 14 mineral topsoil samples (0–10cm, using a split tube manual soil corer with 5cm diameter). For bacteria the analysis only included data from 2011 (148 plots) and 2014 (150 plots). 10g of the homogenised soil was put immediately on liquid nitrogen and stored until RNA extraction. RNA was extracted using a custom protocol (Lueders protocol). Total RNA was isolated from soils and reverse transcribed into cDNA. Amplicons of the V3 region of the 16S rRNA gene were sequenced on an Illumina Hiseq platform using universal bacterial primers.

For fungi, total microbial DNA was isolated from the bulk soil sample using a MoBio PowerSoil DNA Isolation Kit. A PCR approach was used to amplify fungal ITS-rDNA by using the primer pair ITS4/fITS7, containing the Illumina adapter sequences. PCR products were then purified, cleaned and sequenced using Illumina MiSeq. Data was available for 150 plots in 2011, 2014 and 2017.

Open datasets from Biodiversity Exploratories: refs. 101–105. Other dataset from the Exploratories: ref. 106.

Soil protists. Soil DNA was extracted in 2011 and in 2017, from 400mg of soil (not sieved), 3- to 6-times, using the DNeasy PowerSoil Kit (Qiagen GmbH, Hilden, Germany) following the manufacturer’s protocol. Data for one plot in 2011 was missing, data for all 150 plots was available in 2017. Specific primers for Cercozoa and Endomyxa were used to amplify, by two semi-nested PCRs, the V4 region of the 18S rRNA gene. Libraries were prepared using TruSeq DNA PCR-Free (Illumina, San Diego, CA, US). Sequencing was performed with a MiSeq v3 Reagent kit of 300 cycles (on a MiSeq Desktop Sequencer, Illumina). The bioinformatics pipeline was conducted with Mothur v.39.5. After assembling and quality filtering, sequences were clustered at 97% similarity, and rare clusters (<0.01% of the reads) were removed. OTUs were identified in the PR2 database using BLASTn with an e-value of 1e-50 and keeping only the best hit. Chimeric OTUs were identified using UCHIME and removed61.

Open datasets from Biodiversity Exploratories: refs. 107, 108.

Collembola and Oribatid mites. In 2019, we sampled four soil cores of 4.5cm × 10cm per plot. Collembola and Oribatid mites were then extracted following a Kempson extraction and identified. Collembola data was available for 140 plots. For Oribatid mites, it was available in 149 plots but some plots had to be excluded due to low trait data coverage (see below), resulting in 136 used plots in total. Datasets from the Biodiversity Exploratories: refs. 109, 110.

Lepidoptera. Lepidoptera were recorded using sweep netting along transects of 300m during 30 min, done three times per plot in 2008. Data was available for 137 plots. Open dataset from the Exploratories: ref. 19.

Other arthropods. All arthropods of the herb layer were sampled twice per year between 2008 and 2017 in June and August to represent different phenological windows within the peak season of adult arthropod activity. Total number of plots sampled each year varied from 143 to 150, all plots were sampled at least eight years (average: 9.8 years). Based on monthly samplings at the beginning of the study, we identified these two months to represent the best trade-off between reducing sampling effort and covering most species. Arthropods were sampled by sweep netting along a 150-m long transect comprising three of the virtual borders of a site by conducting 60 double sweeps per site. Sweep netting was only conducted on days without rain, low wind speed and after morning dew had dried. All samples were sorted to order level in the laboratory. For taxonomic groups that occurred in larger numbers and for which expert taxonomists were available, adult specimens were identified at species level: Araneae, Coleoptera, Hemiptera (Heteroptera and Auchenorrhyncha), Orthoptera. Species that spend a significant part of their lifecycle in the soil or litter were classified as belowground species. Open data from the Exploratories: ref. 111.

Birds. Birds were recorded using audio-visual point counts during breeding times (March-June), at the centre of each respective grassland plot (50m × 50m). This was done every year between 2008 and 2012 and again in 2018. Records were available for 72–128 plots per year, with four years of records available per plot on average. Open datasets from Biodiversity Exploratories: refs. 112–117.

Bats. Acoustic recordings were conducted along the edges of each plot, using a combination of point-stop and transect monitoring in 2009 and 2010. Point-stops were located at each corner of the plot. Transects were walked slowly and in a direct line between the point stops. Survey time at point-stops and during transect walks were 3min each. This resulted in a total survey time of 24min along a 200m transect per plot. Recordings were made using a Pettersson D1000× ultrasound detector (Pettersson Electronic AG, Uppsala, Sweden). Echolocation calls were identified to species level or to Sonotype using the software Avisoft SASLab Pro, Version 5.0.24 and onward (Raimund Specht, Avisoft Bioacoustics, Berlin Germany). Records were available for 148 plots in total, including 134 in 2009 and 130 in 2010. Open datasets from Biodiversity Exploratories: refs. 118, 119.

Aggregation to functional guilds
These taxa were compiled into functional guilds in which trophic status was broadly comparable (e.g., aboveground primary consumers or belowground omnivores). Within each functional guild, organisms of different taxa were aggregated if trait data was comparable, and if not, the functional guild was classified at the taxonomic level (Table 1). Aboveground guilds included vascular plants (primary producers); Lepidoptera (primary consumers – incl. butterflies and day-flying moths); other aboveground primary consumers arthropods (incl. primary consumers of Orthoptera, Coleoptera, and Hemiptera); aboveground secondary consumers arthropods (including omnivorous Hemiptera, carnivorous Coleoptera, Araneae, Orthoptera and Hemiptera); bats (tertiary consumers); and birds (tertiary consumers, i.e., excluding herbivorous and granivorous birds). Belowground guilds included: bacterial and fungal communities (symbionts, decomposers and parasites); plant parasite protists (primary consumers); bacterivorous protists; secondary consumers protists (incl. predators and omnivores); Collembola (omnivores); Oribatid mites (omnivores); other herbivorous, detritivorous and fungivorous belowground arthropods (primary consumers, incl. Coleoptera and Hemiptera); and predatory and omnivorous, soil-dwelling arthropods (incl. Araneae, Coleoptera, and Hemiptera).

Functional trait data acquisition and treatment
Although it is general in overall concept, and has been discussed in a wide range of contexts, the spectrum of slow-fast strategies and their associated trade-offs has a well developed, trait-specific theory for only a few specific taxa, such as plants. Such theories are also emerging in the literature for other taxa13,120 but in many cases are not fully developed yet. Selecting appropriate traits thus required us to combine hypotheses from existing group-specific theories8, more general ecological theories (e.g., r/K1,4), empirical observations, and expert knowledge. We conducted multiple expert consultation discussions within subteams of the authors to identify traits potentially related to a resource availability and disturbance, and thus potentially falling on a slow-fast axis, i.e., size, dispersal abilities, feeding niche, etc. Detailed hypotheses guiding the choice of traits for each guild can be found in Table S2.

Trait data was obtained from multiple sources. Abundance data and trait databases were matched using the GBIF taxonomy (packages taxize, trait data form121) when necessary. Guild-specific details on trait data acquisition and treatment are described below.

Plants. Aboveground plant traits were compiled from the TRY database5,23,36,45,122–220. TRY data was first cleaned to remove duplicates, non-mature, non-healthy plants and plants which were grown in non-standard exposition (e.g. shade) as well as non-standard trait measurements. The trait data was then subsetted by data from central Europe to avoid geographic bias. Finally, resulting trait values were averaged by contributing sources, with outlier sources being excluded; and were then averaged for each species221. Belowground trait data was obtained from pot experiments122. Where appropriate, data from synonyms was used. When individuals were identified at the genus level, average trait values for all other species from the considered genus found during the survey was used. Community-weighted means were calculated by excluding tree saplings which are not part of the stable grassland communities and do not have the (adult) traits usually reported in databases.

Birds. Bird trait data for body length and incubation time was obtained from the literature and own datasets47,222–226. Data on functional groups and trophic levels was extracted from ref. 225 and the Avonet dataset47. Identification of ground-nesting species followed ref. 223. Maximum longevity and generation length were extracted from ref. 222. While trait data was also available for herbivorous species, the abundance data was not sufficient to include this group as 46 plots did not have any primary consumers (primarily herbivorous, frugivorous, and granivorous species). Thus, only secondary consumers (including carnivores, invertivores, and omnivores whose diet includes vertebrates or insects) were included in the study.

Bats. Bat traits data was obtained from ref. 227 (morphological traits) and ref. 228 (life-history traits). Both datasets were then restricted to species occurring in Germany based on ref. 229. Abundance data was based on acoustic recording, which did not always allow for species-level identification: some individuals were classified as genera (Myotis sp., Plecotus sp.) or similarly acoustic groups (Nyctaloids). In that case, traits were attributed based on the average values of species from the same genus present in Germany, or on all species in the Nyctaloid group (Nyctalus noctula, Vespertilio murinus, Eptesicus serotinus, Nyctalus leisleri, Eptesicus nilssonii), respectively. The interpolation was usually relatively conservative (trait mean > 2× trait sd).

Arthropods: Araneae, Coleoptera, Hemiptera and Orthoptera. Body size (in mm), feeding guild, stratum use and dispersal ability of Araneae, Coleoptera, Hemiptera and Orthoptera were assembled from various literature sources and validated in correspondence with taxonomic experts for the respective groups. These were augmented by published trait data on feeding generalism from ref. 52 and voltinism from multiple sources3,230–232. Species were classified as secondary consumers if one of their life stages was mostly carnivorous, and as primary consumers otherwise (i.e., all life stages primarily herbivorous, fungivorous or detritivorous). Species were classified as belowground if at least one of their life stages was primarily soil- or ground-dwelling, and aboveground otherwise. Voltinism was coded as a numeric variable (0.5: semivoltine, 1: univoltine; 1.5: uni-orbivoltine; 2: bivoltine; 3: multivoltine). Dispersal was coded as a numerical variable (in five gradations (from low to high: 0, 0.25, 0.5, 0.75, 1) based on flight ability and wing dimorphism for insects and ballooning for spiders). Body size was log-transformed to avoid non-normal distribution. When sampled individuals were only identified at genus level in the abundance dataset, they were attributed the average traits of the other species in the genus only when the trait data was homogeneous (i.e., dominant category if it is representative of 90% of the species in the genus among the sample species, and if the mean is > 2× standard deviation for quantitative variables); and was considered as a missing value otherwise. Open dataset from the Biodiversity Exploratories: ref. 233.

Arthropods: Lepidoptera. Traits for Lepidoptera were compiled from multiple databases. We used as primary sources ref. 55 for moth traits and ref. 19 for butterfly traits. In addition, we completed this data with traits from ref. 54, and the European and Maghreb Butterfly Trait database56. When datasets disagreed, we primarily kept the data from ref. 55 which was specifically curated for German moths. Voltinism was coded as a numerical variable: 0.5 for semivoltine species, 1 for strictly univoltine species, 1.25 for univoltine species with partial generation, 1.5 for uni- or multivoltine species, and 2 for multivoltine species. Feeding generalism was coded as a numerical variable: 1 for monophagous species, 2 for oligophagous species (within one plant genus), 3 for oligophagous species (within one plant family), and 4 for polyphagous species. Body size was provided as different metrics for the different databases: estimated dry mass, forewing minimum and maximum length in ref. 54, forewing average length in ref. 56, and body mass and wing length in ref. 55. The most complete was the forewing maximum54; we used data imputation (function mice, using default parameters) to impute the missing values in this variable, using all other size-related variables as predictors. The distribution of the data was similar before and after imputation, indicating reliable imputation. Body size was then log-transformed. Overwintering stage was coded as a numeric variable, from 1 (overwintering as egg), 2 (larvae), 3 (pupa) and 4 (adult). When datasets disagreed, we retained the latest stage of development as overwintering stage. Flight period was measured as the maximum number of flight months for adults. Again, ref. 54 was the most complete database. We used this data when available, and imputed missing values (function mice, default parameters) based on additional data from ref. 56. Flight period was then log-transformed. We completed the resulting data for three additional species (Lythria purpuraria, Zygaena carniolica, Aphantopus hyperantus) based on https://lepiforum.org/ and http://www.pyrgus.de/. Open datasets from Biodiversity Exploratories: refs. 53, 234, 235.

Arthropods: Collembola and Oribatid mites. Data on adult body mass and habitat specificity was collected on specimens collected during sampling (see above). Habitat specificity was coded numerically from 1 (non-specific habitat, most generalist), 2 (soil-dwelling), 3 (surface-dwelling) and 4 (litter-dwelling, which are the most specific species). Feeding specialisation was coded numerically from 1 (omnivorous, most generalists), 2 (herbifungivorous), 3 (herbivorous) and 4 (fungivorous, most specialist). (V. Wolters and A. Zaytsev, pers. comm). Time to maturity (in days) and reproduction type (sexual or parthogenetic) were provided by experts and completed from the literature and coded as binary variables. Seven plots had low trait data coverage (<50%); they were given NA values for all CWM values and not used in the analysis. Dataset from the Biodiversity Exploratories236.

Bacteria and fungi. We used a published dataset46 to characterise bacterial communities. For bacteria, trait data was used at genus level when available. If no genus-level data was available, it was extrapolated to the order level if the values of all genera within the order were consistent (mean > standard deviation for quantitative traits, and >60% of all genera sharing one trait value for qualitative traits). If the data was not consistent within the order, it was kept as missing values. Then, cell volume was estimated as (cell radius)^2 * 3.14 * cell length and log-transformed before further analysis. Other traits such as doubling time were considered but had very low coverage (e.g., for doubling time median of OTUs with available data over all plots <20%) and were excluded from the analysis. Community-weighted means were calculated using OTUs numbers as an approximation for relative abundances. We completed these traits with community levels characteristics. For bacteria, we quantified the relative abundance (approximated as OTU number) of groups commonly classified as oligotrophs (Acidobacteria, Verrucomicrobia, Planctomycetes) to copiotrophs (Actinobacteria, Betaproteobacteria, Gammaproteobacteria, Proteobacteria, Bacteroidetes).

For fungi, we calculated the relative abundance of pathotroph fungi among all fungi. Finally, we also measured the PLFA-based fungal-bacteria ratio from 2011 and 2014 (150 plots each year); data that were first published by ref 22. This was done by sampling two g of field moist soil (from the same composite samples as described above) for lipid extraction and fractionation following the alkaline methylation method described in ref. 237. Samples were measured by gas chromatography (AutoSystem XL. PerkinElmer Inc., Massachusetts, USA) using a flame ionization detector, an HP-5 capillary column and helium as the carrier gas. Fatty acid nomenclature used was described by ref. 238. Total bacterial PLFAs were calculated as the sum of Gram-positive (a15:0, i15:0, i16:0 and i17:0) and Gram-negative (cy17:0 and cy19:0)239 plus the FAME 16:1ω7 which is widespread in bacteria in general. Fungal biomass was represented by the PLFA 18:2ω6,9239. Open datasets from the Biodiversity Exploratories: refs. 104, 105.

Protists. Genus-level protist trait data was obtained from ref. 240 and completed for size class data by K. Dumack. Size classes were coded numerically: 1 (<10 microns), 2 (11-30 microns), 3 (31–50 microns), 4 (>51 microns). Trophic levels were classified as plant parasites, bacterivores, or secondary consumers (non-plant parasites, eukaryvores, omnivores). Primary consumers (plant parasites) had only one trait combination (size class 1) so we characterised these communities only by the relative abundance of plant parasites among all protists.

Measurement of ecosystem functions
We selected 15 ecosystem function indicators from five bundles of related functions: decomposition (dung, litter and root decomposition), nitrogen cycling in soils (potential nitrification, activity of urease and denitrification enzyme, gene abundances of ammonia oxidising bacteria and archaea (amoA AOA and amoA AOB), abundances of nitrogen fixation (nifH) and nitrite reductase genes (nxrA)), functions related to the carbon cycle in soils (activity of ß-glucosidase, N-acetyl-ß-glucosaminidase, xylosidase), aboveground biomass production and total soil respiration. Most ecosystem functions data was extracted from a synthesis dataset of the Biodiversity Exploratories: ref. 241.

Soil enzymes. Soil samples were taken from each plot in the beginning of May 2011 and 2014 as a mixed sample from 14 soil cores of the top horizon (0–10cm). Denitrification enzyme activity (DEA) was measured in 2011 only according to refs. 242, 243. Urease activity was determined in 2011 only by incubating 1g of fresh soil with 1.5ml of 0.08M substrate (urea) solution at 37°C for 2h244. Released ammonium was extracted with 12ml of a 1M potassium chloride/0.01M hydrochloric solution and determined by a modified Berthelot reaction. The abundance of ammonia oxidation gene of archaea, bacteria and nitrogen fixation gene in soil bacteria different functional genes (nifH, amoA) is quantified via real-time qPCR analysis in both 2011 and 2014 and averaged across years. The abundance of ammonia nitrite-oxidizing Nitrobacter bacteria and archaea was estimated in 2014 using respective amoA gene, while NS-like and NB-like NOBs were targeted by primer sets for 16S rRNA genes for NS and nxrA primers genes specific for NB245. The abundance of nitrite-oxidizing Nitrospira was estimated using 16S rRNA gene primer specific for Nitrospira245. For potential nitrification, ammonium and nitrate concentrations were measured after CaCl2 extraction, potential nitrification was determined according to ref. 246 and averaged across years. Activities of the soil enzymes soil enzyme ß-glucosidase, N-acetyl-ß-glucosaminidase, and ß-xylosidase were determined according to ref. 247 as described in detail in ref. 248, using fluorescent 4-methylumbelliferone substrates (4-MUF; Sigma-Aldrich, St. Louis, USA) and a buffered solution (pH 6.1). Open datasets from Biodiversity Exploratories: refs. 249–252.

Decomposition rates. To measure dung decomposition rates, five dung samples were placed on each site in 2014. We used dung with a fresh weight of approx. 220.7 (±19.9) g of cow, 34.4 (±3.8) g of horse, 50.5 (±3.6) g of sheep, 32.6 (±1.6) g of deer, 14.5 (±1.4) g of fox and 47.6 (±2.4) g of wild boar. All dung samples were placed on cellulose paper. After 48h dung samples of removal experiments were collected, transferred into small paper bags, labelled (date, site-ID, dung type) and stored in a freezer at −20°C. Removal samples were transferred into drying ovens and kept there at 60°C for at least five days. Afterward the dry weight for each dung sample was weighed (Mettler Toledo “EL2001” (±0.01g), Columbus, Ohio) and noted for further calculations. Open dataset from Biodiversity Exploratories: ref. 253.

We measured the decomposition of fine roots (<2mm) within the upper 10cm of the mineral soil in 2012 with standardised herbaceous roots. Root material was left to decompose in litter bags with mesh size of 100µm during six months (until October 2012), and mass loss was determined. Open dataset from Biodiversity Exploratories: ref. 254.

To measure litter decomposition, around 1.5g of dry plant material collected in each plot in spring 2012 were put back in their original location in January 2013. The aboveground vegetation was removed, and bags were fixed to the ground with nails or wood sticks. There were ten replicate bags in each plot, five of them were collected two months later, and the five last ones two more months later. The dry biomass remaining in each bag was measured to calculate daily decomposition rates. Open dataset from Biodiversity Exploratories: ref. 255.

Soil respiration. Soil respiration was measured from late June–July in 2018 and 2019, using the soda-lime adsorption absorption method with an open and static chamber to determine soil CO2 efflux. Soda-lime, i.e., mainly Ca(OH)2 and NaOH, was used as the adsorption absorption material. We installed four chambers, forming a square of 10m side length (around the weather station), and one bottom-sealed trap served as control. The mass of soda-lime for each measurement was ~12g/d and the exposure time was three days. Aboveground vegetation was carefully clipped and removed from the installation area and PVC rings was were plugged down to 1cm soil depth. Soda-lime and lid straps were installed five to six days after vegetation clipping. Just before installation, we re-wetted the soda-lime to compensate for the initial moisture content of about 18%, since CO2 needs to be hydrated before reacting with soda-lime. The CO2 flux is calculated from the dry soda-lime mass differences considering i) the exposure time, ii) the area of the chamber, iii) the coefficient of 1.69 to account for the water lost during the reaction and iv) after correcting with controls (bottom-sealed) traps. The soil efflux is calculated by the equation 256,257: Rs=[(WGsample-WGblank)/CA]×[24/T]×[12/44]×1.69 where Rs is the soil respiration in [g CO2-C m-2 d-1], WG is the weight gain [g], CA is the chamber basal area in [m-2], T is the implementation time in [h], 12/44 is the ratio of carbon atomic mass over carbon dioxide molecular mass and 1.69 compensates for the H2O formed during CO2 sorption and lost during drying. Open dataset from Biodiversity Exploratories: ref. 258.

Plant biomass production. On two subplots of 1.5m × 2m size (varying position every year between 2009 and 2017), aboveground biomass was harvested between mid-May and mid-June by clipping the vegetation at a height of 5cm. Samples were dried at 80°C for 48h and weighted. An arithmetic mean of biomass per m2 for each plot plot was calculated. Data was then averaged across years 2009–2017. Open datasets from Biodiversity Exploratories: refs. 259–267.

Data analysis
All the data analyses were conducted using the R software v. 4.0.3268. Initially, in a confirmatory analysis, species-level fast-slow axes were identified from PCA that included all species within most guilds (Fig. 2).

As we hypothesised changes in community level properties across environmental gradients (rather than changes in individual species), we focused on changes in average trait values across gradients. We thus did not consider the diversity of functional strategies in a community, or the potential for differing responses of individual species, in this analysis. Average trait values can represent both changes in species identity (taxonomic turnover) and variation in their relative abundance along the gradient. Thus, all further analyses were conducted at the level of community abundance-weighted trait mean18 (CWM). CWM traits captures the average functional strategy of the community both in terms of response to environmental conditions (response traits) and how it reciprocally affects the functioning or other biotic components of the system (effect traits269). In practice, the same traits can influence both response and effect and thus can correlate strongly with both environmental drivers and ecosystem functioning270. CWM were calculated using relative abundance data depending on the considered guild (e.g., cover for plants, number of individuals for arthropods, number of Amplicon Sequence Variants (ASV) (fungi) or Operational Taxonomic Units (OTU) (bacteria, protists), use of the habitat and activity for birds and bats). As a result of the Biodiversity Exploratories design, data from different guilds was sometimes collected in an uncoordinated way (e.g., every year, every three years, or on fewer consecutive years). To allow comparison across guilds, when data was available for more than one year, we first calculated CWM values independently for each year, then aggregated the CWM across years. Data collected in different years, but from the same plot, were considered comparable because both land-use intensity and multivariate CWM for all guilds that were sampled more than once differed more across plots than across time. This was shown by conducting variance partitioning analyses (using the varpart function, R package vegan) with either the land-use intensity or the trait CWM value for each Plot Year combination as the response matrix, and the plot and year (as factors) as explanatory matrices. The variance explained by the plot term was on average >15 higher by that explained by the year term (Table S5). Biologically this is because high intensity field tend to be managed at high intensity year after year, and vice-versa.

Each guild was represented by two to six traits (4.2 on average), except protists for which only one was available. Taxa with missing trait data were excluded from the corresponding CWM calculation (except in a few specific cases where data was imputed, see Methods section on functional traits); the resulting coverage (% of abundance, in term of cover or individuals with available trait data) was always above 80% except for bacterial and Oribatid mites traits (Table S2). The CWM approach was favoured over joint species distribution modelling of trait-environment relationships271, as it was the trait values of entire guilds and communities, not species, that was the appropriate unit of replication in our study. It was therefore essential to summarise variables at the whole guild, community, and ecosystem levels prior to analysis.

To gain a more reliable estimate of how CWM trait data was related to the hypothesised drivers, it was necessary to correct for environmental covariates before analysis. As such correction has been shown to produce biased parameter estimates in the presence of correlation between the environmental covariates272, we identified highly correlated variables from our list of potential covariates (mean annual temperatures and precipitation, topographic wetness index, soil clay and sand content, pH, depth and region). We excluded soil depth and sand content which were both highly anticorrelated to soil clay content (|r|>0.72); and precipitation which was highly anticorrelated with temperature (r=−0.77). We thus retained mean annual temperature, topographic wetness index, soil clay content and pH as well as the region as covariates. To investigate the importance of these factors relative to land-use intensity, our focus variable, we fitted linear models with each trait CWM as a response and all covariates and land-use intensity as explanatory variables. This showed that on average the region explained as much variance as land-use intensity (around 8%), followed by the topographic wetness index (2%), but that there was high variability across guilds. For instance, bats and Collembola were primarily driven by the region, secondary consumer protists by soil pH, and plants or aboveground primary consumer arthropods by land-use intensity (Fig. S1). After that, we fitted linear models with each trait CWM as a response, and the covariates, excluding land-use intensity, as explanatory variables. The residuals of these linear models were used for all further analyses. Results of sensitivity analyses with uncorrected values can be found in Tables S15–S18 and Figs. S15–S20.

In cases where abundance data was not available for all 150 plots, guild-level analyses were run on all available plots (never <110 plots). To test for the response of each individual CWM trait to land-use intensity, we calculated the slope of the regression of the CWM trait against land-use intensity (excluding missing values) between each trait and land-use intensity (p-values corrected for false detection rates using the p.adjust function, n=47, shown in Fig. 4). For each guild, we then sought to identify a slow-fast axis based on pre-established hypotheses based on either environmental filtering through resource availability and disturbance, or indirect, trophically-mediated mechanisms. Indeed, because traits represent consistent functional strategies, ‘slow’ and ‘fast’ traits are expected to covary. This guild-level aggregation from individual traits CWM to guild-level slow-fast axes was necessary to make the guilds comparable in the subsequent analyses. Also, this approach was chosen because the main objective of the study was not to establish the existence of such guild-level slow-fast axes, but rather examine their association at the whole community level. We ran a Principal Component Analysis (PCA) on all CWM traits of each considered guild separately. For these PCA, we used the R package factoextra, retained three principal components axes (or two if only two traits were available) and identified the PCA axis that best represented a slow-fast continuum. This was axis 1 for all guilds except the aboveground predatory arthropods (Fig. 2). Notably, the fast-slow axis was also always that which most strongly correlated with LUI, except for belowground primary consumer arthropods and birds where axes 1 and 3 (respectively) had a similarly strong correlation (Fig. S2). Guild-level PCA axes were considered to represent a slow-fast continuum (and called hereafter ‘guild slow-fast axes’) if they fulfilled all following conditions: (i) the considered axis explained at least twice the variance expected at random (i.e., 2/(number of traits), with 1/(number of traits) the average variance per axis expected if traits are independent); and ii) the considered axis was significantly correlated with all traits (p-value<0.05, corrected for false detection rates, n=34), with at least 60% correlations ≥0.4. The ‘slow-fast’ axis was considered only partially represented if it (i) explained at least twice the variance expected at random; (ii) was correlated (r>0.25) with at least 60% of the traits, or (iii) had correlations in the unexpected direction with one trait.

For analyses across guilds, we excluded one guild (belowground primary consumer arthropods) as more than 20% of plot data were missing due to absence of this functional guild in the samples associated to some plots.

To test whether the slow-fast responses were synchronous across guilds, we ran a PCA on the previously identified slow-fast axes of all guilds, and projected the land-use intensity index (LUI) as a supplementary variable on this PCA. This approach of combining multiple PCAs into a “main” PCA follows the same logic as Multiple Factor Analysis (MFA273,274) and allowed us to simultaneously analyse the response of guilds with different traits and individual responses. Missing values for the slow-fast axis at the plot level (shown at the individual trait level in Table S2) were given the average of the considered guild across all plots to allow comparison across guilds (imputed values: 1.8% of all values, 0–12% range within guilds). This was done to allow comparison across guilds, and because PCA cannot be used with incomplete data. Additionally, we also tested for the existence of the whole community slow-fast axis by running a PCA on all considered traits (rather than guild-level slow-fast axes, themselves extracted from PCAs, Fig. 3b).

In follow-up analyses, we used Structural Equation Models to assess whether the ecosystem-level slow-fast continuum was driven by a common, but independent, response of individual guilds to land-use intensity, or mediated by trophic interactions between them leading to a cascade of trait matching. Because plants can affect the traits of other organisms not only through direct consumption but also by e.g., structuring the habitat275, we also included paths between all guilds and plants. We also allowed for some correlation paths between guilds when it made biological sense and significantly improved model fit (e.g., between birds and bats, which might be jointly affected by landscape-scale variables). Models were fitted using the lavaan R package276, separately for below and aboveground guilds; plants were included in both. The hypothesised model structures are shown in Fig. S3 and model statistics in Tables S6 and S7.

The SEM were fitted using the maximum likelihood estimator using all available data (i.e., excluding NAs/imputed data points but using all existing data for each estimate). Estimates were bootstrapped 200 times. To assess how path strength varied across trophic levels, we extracted the coefficients and corresponding confidence intervals for all direct, indirect and total effects of land-use intensity on each trophic level. We then fitted linear models with the estimated direct, indirect and total effects of LUI as the response variables and trophic level as the explanatory variable. Effect estimates were weighted by the inverse of the standard error to account for variable uncertainties across guilds. To evaluate the overall ecosystem response to LUI, we averaged the direct, indirect and total effects at each trophic level, independently above- and belowground. This was done by defining custom parameters within the SEM as the average of all corresponding parameters (direct/indirect/total for all guilds within each trophic level). This allowed the same bootstrap procedure to also estimate average effects and their confidence intervals.

Finally, we investigated the possible linkage between the entire community slow-fast axis and a potential ecosystem functioning slow-fast axis. We selected 15 ecosystem function indicators from five bundles of related functions (see above). All functions were corrected for environmental variables (as described above for traits) before analysis. All selected functions were hypothesised to represent ‘fast’ ecosystem functioning (e.g., fast nutrient cycling) at high values.

We first investigated the correlation between individual traits and functions; or guild slow-fast axes and function bundles (Figs. S5 and S6). We then conducted a PCA on all functions, weighted so that each functional bundle would have the same final weight (e.g., functions related to N fluxes were weighted 1/8, decomposition functions were weighted 1/3, etc.). This was done to prevent bundles with more functions (e.g. N fluxes) from having a disproportionate impact upon the PCA. The first axis (29% of total variance) represented a continuum from slow to fast nutrient cycling with high potential nitrification, and high activities of most enzymes. The plot-level PC scores of this ecosystem functions axis were then regressed against the ‘slow-fast’ axes of individual guilds (microbes and plants, expected to be the main driver of soil functioning), the overall ecosystem functional trait slow-fast axis, land-use intensity and multidiversity, a measure of the overall species diversity of all considered groups32. This was calculated as the average richness of all taxonomic groups, scaled by the total species richness of each group32, and also corrected for environmental variables, as described above (Table S4). Finally, to test the direct and indirect effect of land-use intensity on the ecosystem slow-fast axis, we fitted a SEM (using the lavaan package) testing the indirect effect via the entire community slow-fast axis (Fig. 6b).

Additionally, we tested for the direct and indirect effects of land-use intensity on the ecosystem slow-fast axis, but on the community slow-fast axis that was derived from the ‘all traits’ PCA (rather than guild-level slow-fast axes extracted from individual PCAs, Fig. S4).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_3": "I have previously reviewed this manuscript (as reviewer 3), which tests hypothesis regarding how communities shift along a slow-fast continuum in response to land use intensification. As I mentioned in the previous review this is an interesting and important question and an ambitious attempt to address it using extensive data and a series interwoven models. I must strongly commend the authors for address many of the previous comments with an extensive re-writing of the manuscript and the inclusion of additional analysis. In particular, they have addressed my main concern regarding the role of body mass in their overall analysis. While the results may be weaker when mass is removed this is certainly something to be expected but shows that the main results are not just the result of some form of allometric scaling but are also associated with classic slow-fast continuum trade-offs. I also think the authors have made the hypothesis and statistical approaches clearer in this manuscript. I do agree with other reviewers that the stacked modelling approach used can feel a little uneasy in terms of how variation is lost across each step and the relatively low tractability of the approach. However, I do also think that such adventurous models are required in order to work at the scale of the question and are a useful addition to the field.", "reviewer_4": "Comments to the authors:\nI was not the original reviewer of this manuscript, but have been asked to comment specifically on several points raised in the previous review. One of those points was regarding the use of CWMs, which I will address here first. The original reviewer suggesting that “Careful revision of the intentions of the study are needed to make a stronger case for the use of CWM as a metric here.” My main interpretation of this comment was that ‘it does not capture any trade-offs between functions’. This is true.\nThe author’s reply does not specifically address this later point (i.e. trade-offs) in the justification for using CWM trait values. Rather the authors cite an article by Muscarella and Uriarte (2016) suggesting that CWMs reflect the optimal trait value of a site. To be honest, I was unfamiliar with the cited article and only briefly examined it, but I would strongly caution the authors on this justification/rationale within their introduction. I also not think this is necessary. The authors state (citing M&U 2016): “species with traits closer to the CWM value at any given site should be those with highest fitness and thus highest abundances”. This is circular. CWMs are calculated from the proportional species abundances at the locality. So that the species with the higher weighting (i.e. higher prop abundance) drive the CWM value found at the local site. How could there be any other relationship? – a CWM by definition is the trait mean weighted by proportional abundance. Perhaps this is not what the authors of the original cited article (M&U 2016) show, but regardless, I am not convinced that CWMs confer any type of ‘optimality’ of the organisms. I would agree that strong selective pressures can shape or alter the CWM value of a trait (e.g. drought tolerance trait under a drought treatment), but this is a response to a perturbation and not an ‘optimal’ life history strategy for an environment per se.\nBack to the main point however, a justification to examining CWMs, I do not see the need to invoke a hypothesis of ‘optimality’ associated with changing CWM trait values, and it does not address ideas of trade-offs or alternative life history strategies. I suggest removing paragraph lines 114-135 in the introduction, and move this to a brief rationale for CWM in the methods section, and the rest in the discussion to address reviewer’s original comment. More so, I think what is important to consider and state explicitly is that the CWM is a derived and calculated variable, AND that the trait itself may be a response or an effect trait. Differentiating this is what’s important.\nThe second part of the reply to the original reviewer comment on CWMs is in the discussion. I also agree with the points raised here by the authors, but again I think perhaps the original reviewer point is missed, i.e. an analysis of CWMs does not capture potential trade-offs in life history strategies or species response to perturbation, but the discussion provided by the authors comes closer.\nI also have a few independent comments on this manuscript.\nFirst, I commend the authors for approaching this topic. The fast-slow spectrum is well recognized, but rarely synthesised across both aboveground and belowground components of ecosystems. That said, I was disappointed by the lack of specific details on belowground aspects of this. Further to this, Table S14 suggests that the response of almost all belowground organisms with respect to fast-slow traits were inconclusive.\nI was also surprised that despite the results that body size was smaller for many groups under perturbation (Fig 3) the authors fail to invoke the concept of ‘community downsizing’ that has been well established under perturbation, and specifically climate change.\nHowever, I am always sceptical when the supplementary information is longer than the manuscript and includes extensive methods and results that were used to perform subsequent analyses. Figure 2 is an example of that manifestation. It is unclear in the main text how Figure 2 is created. The Caption does not provide much information nor does it reflect what the intention of the study was or how this figure supports the overall outcome of the study. The caption states “The variables included in the PCA are the slow-fast axes of each guild”, but what does this mean? As I understand this from the methods (at the end of the manuscript – not my favourite format for just this reason), this is the PCA axis scores for each group that the authors deemed associated with fast-slow traits (line 695). To understand this, one has to go to the 95 pages (not including references) of the supplementary material. This is where I am not sure where to look for the results that support these results (Table S2 perhaps, or Table S8, or Table S13?). But to my understanding, Fig 2 is showing the response in fast-slow traits across all groups, but this is artefactual. These vectors were already selected because they show what the authors are looking for (i.e. a fast-slow spectrum), thus Figure 2 is not surprising as it is a product of the authors finding the best vector to reflect fast-slow. Again, seems a circular argument here; selecting the fast-slow axis of each group to show there is a fast-slow axis.\nUltimately, while I support the endeavour of this manuscript, I felt real mechanism was missing linking fast-slow traits to fast-slow ecosystem system processes, and the discussion was too general in nature to be useful.", "reviewer_5": "This paper investigates the effect of land use intensity on community-level mean values of traits that reflect a continuum of functional strategies from slow- to fast growing organisms. The authors analyzed an impressive occurrence and trait database and identified the main slow-fast functional trait axes in 14 trophic guilds. They show direct and indirect effects of land use intensity on the slow-fast trait axes. They also showed that the slow-fast trait axes were associated with the rates of whole ecosystem functioning. The authors conclude by discussing the potential of the observed ‘slow-fast’ continuum for whole ecosystem-level studies.\nI very much enjoyed reading this manuscript which has a clear structure and is well written. The topic of the study fits within the scope of the journal. The main questions are relevant and timely. Overall, the work and methods meet the expected scientific standards. The conclusions are supported by the results. The figures and tables are clear and of good quality. The literature cited is appropriate.\nThe study summarizes complex processes in a simple message that can help improve future ecosystem conservation and management efforts. I think the study will be of interest to a broad range of researchers and practitioners in the field of ecology and beyond. I have not found any major weaknesses or errors in this study. However, I make several suggestions for possible minor improvements.\nNote that, as requested, my review mostly focuses on the data analysis part of the manuscript. See my comments below for further details.\nMain comments:\n1. Trait selection\nAlthough the authors did considerable efforts to explain this process, I think that the interpretation of PCA axes as slow-fast and their selection for further analyses can be seen as a weak point. For example, I agree with R3 that important traits in bats and birds are missing and would be easy to add given the high amount and quality of trait information for these two taxa. Furthermore, if traits were originally selected to represent the slow-fast continuum, why select just one PCA axis? The non-selected axes might represent other important ecological strategies. Traits related to reproduction might have different drivers than those related to growth. Some aspects of the slow-fast trait continuum might not be driven by land use intensity. To me, this selection process, especially, the selection of PCA axes, casts some doubts on the validity and generality of the conclusions.\nHowever, the authors did provide a complementary analysis based on all traits (Fig.S3) where the slow-fast axis emerges in a more direct and organic way. This additional analysis alongside Fig.3 did put my initial concerns at ease as the results show that the slow-fast axis is present even without selecting PCA axes. I encourage the authors to put more emphasis on these two analyses in the text. For example, the authors could mention Fig.S3 directly in the caption of Fig.2. See also my comments about fourth corner/RLQ below.\n2. Controlling for the effect of environmental covariates\nControlling for the effect of environmental covariates is indeed a necessary step and I agree with the approach used by the authors to address this issue. However, I miss summary information about this step. How much variation is explained in each taxon? Do we have large differences among taxa? Which variables explain the most variation? After my first read, I was wondering whether this step can cause some biases. However, Table S14 shows correlations between LUI and CWM where CWMs were not corrected for environmental covariates and the results are consistent with the other analyses. This suggests that the results are robust.\nI think that RLQ would be interesting to characterize the links between traits and environmental variables. This analysis would provide important information about the strength of the effect of LUI on traits as compared to that of other covariates. It would also provide a more general picture of the ecology of the different taxonomic groups. Whether or not the authors decide to use the RLQ, I think that more information is needed about the relative importance of the influence of LUI on traits as compared to that of other environmental variables.\nDray, S., Choler, P., Dolédec, S., Peres-Neto, P. R., Thuiller, W., Pavoine, S., & ter Braak, C. J. (2014). Combining the fourth‐corner and the RLQ methods for assessing trait responses to environmental variation. Ecology, 95(1), 14-21. DOI 10.1890/13-0196.1\n3. Stacking of models\nI agree with the main logic behind the multiple PCAs. Basically, it is the same idea as in a multiple factor analysis, MFA (see refs below). It is, to me, a valid and interesting way to conduct a multi-taxa analysis. However, I also agree with R2 that the analysis procedure is relatively complex which makes it difficult to keep track of the potential biases and information loss at each step. All in all, I see no major problem with the stacking of models and encourage the authors to cite the MFA papers as a reference framework for their approach.\nEscofier, B. and Pages, J. (1994) Multiple Factor Analysis (AFMULT package). Computational Statistics and Data Analysis, 18, 121-140.\nBecue-Bertaut, M. and Pages, J. (2008) Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data. Computational Statistice and Data Analysis, 52, 3255-3268.\n4. Trait-function relationship\nAlthough not essential to the story, I miss the direct relationship between traits and EF. Here, the fourth-corner/RLQ analysis could also be used to test the associations between individual traits and EF variables. I think that such analyses would constitute a complementary and more direct approach to highlight the slow-fast trait continuum. I expect that this analysis will reveal significant correlations that will further validate the other analyses and increase the confidence in the results.\nMinor comments:\nThe use of CWM: Whether CWM reflects optimal strategies has been subject to much discussion. I personally think that this is not always the case. For instance, a bimodal distribution of trait values within a community can indicate the coexistence of two successful strategies. In such a case, the mean value would reflect a sub-optimal strategy. Having said that, I do not think that such bimodal trait distributions are highly frequent and this should not be an issue for the validity of the results. I, nevertheless, encourage the authors to add a few words about this.\nIt was difficult for me to understand how was Fig.3 done. I encourage the authors to provide a few additional explanations in the text and directly in the caption.\nFigure 4: Having two slightly different color scales for model estimates is confusing. Would it be possible to use one single scale for both panels?\nTable S6: What is the shared variation?\nOverall, this is a very nice work and I hope that my comments will help to improve it further."}, "author_response": "dear Reviewers,\nThank you for the opportunity to revise this manuscript and for your careful reviews. In response to your feedback we have made numerous changes to the manuscript. The most important of these are:\n- To provide more transparent and interpretable information on the guild-level slow-fast axes, we now provide a new extended data figure (and reference in the text) of the guild-level PCA from which the slow-fast axes originate. These were previously in a table in the supplementary material and are now more prominent in the main text. We now also highlight the PCA conducted on all traits (rather than guild slow-fast axes) by promoting it to extended data figure 2. This helps support our interpretation of a whole community-level slow-fast axis.\n- We moved the methods tables from the supplementary material to the extended data tables to improve clarity (if this is possible for the journal format).\n- We ran additional analyses regarding the role of environmental covariates across the different guilds, and the relationship between individual traits and functions; we reference them in the text and provide them as supplementary material.\n- We added additional discussion and clarification to the text of the main paper and methods relating to several points raised by the reviewers\nWe have done our best to accommodate suggestions from this round of review while still retaining revisions made in response to earlier reviews, as the requests of reviewers sometimes diverged slightly (e.g. on whether to include the introductory paragraph on CWM; and whether to include extensive sensitivity analyses in the supplementary material). We also opted to keep the addition of new material to a minimum given the complexity of the paper and already large supplementary materials (as pointed out by reviewer 4). We therefore highlighted existing materials, or expanded these slightly to address reviewer concerns rather than adding additional complex analyses.\nThe lines indicated in this response letter correspond to the line numbers in the document with change visible.\nWe feel that these changes have significantly improved the manuscript and hope you will find them satisfactory.\nMargot Neyret, on behalf of all authors\nREVIEWER COMMENTS\nReviewer #3 (Remarks to the Author):\n3.1. I have previously reviewed this manuscript (as reviewer 3), which tests hypothesis regarding how communities shift along a slow-fast continuum in response to land use intensification. As I mentioned in the previous review this is an interesting and important question and an ambitious attempt to address it using extensive data and a series of interwoven models. I must strongly commend the authors for address many of the previous comments with an extensive re-writing of the manuscript and the inclusion of additional analysis. In particular, they have addressed my main concern regarding the role of body mass in their overall analysis. While the results may be weaker when mass is removed this is certainly something to be expected but shows that the main results are not just the result of some form of allometric scaling but are also associated with classic slow-fast continuum trade-offs. I also think the authors have made the hypothesis and statistical approaches clearer in this manuscript. I do agree with other reviewers that the stacked modelling approach used can feel a little uneasy in terms of how variation is lost across each step and the relatively low tractability of the approach. However, I do also think that such adventurous models are required in order to work at the scale of the question and are a useful addition to the field.\nR3.1 Thanks again for your previous constructive suggestions, and for your positive feedback on our revision.\nReviewer #4 (Remarks to the Author):\nComments to the authors:\n4.1 I was not the original reviewer of this manuscript, but have been asked to comment specifically on several points raised in the previous review. One of those points was regarding the use of CWMs, which I will address here first. The original reviewer suggesting that “Careful revision of the intentions of the study are needed to make a stronger case for the use of CWM as a metric here.” My main interpretation of this comment was that ‘it does not capture any trade-offs between functions’. This is true.\nThe author’s reply does not specifically address this later point (i.e. trade-offs) in the justification for using CWM trait values. Rather the authors cite an article by Muscarella and Uriarte (2016) suggesting that CWMs reflect the optimal trait value of a site. To be honest, I was unfamiliar with the cited article and only briefly examined it, but I would strongly caution the authors on this justification/rationale within their introduction. I also not think this is necessary. The authors state (citing M&U 2016): “species with traits closer to the CWM value at any given site should be those with highest fitness and thus highest abundances”. This is circular. CWMs are calculated from the proportional species abundances at the locality. So that the species with the higher weighting (i.e. higher prop abundance) drive the CWM value found at the local site. How could there be any other relationship? – a CWM by definition is the trait mean weighted by proportional abundance. Perhaps this is not what the authors of the original cited article (M&U 2016) show, but regardless, I am not convinced that CWMs confer any type of ‘optimality’ of the organisms. I would agree that strong selective pressures can shape or alter the CWM value of a trait (e.g. drought tolerance trait under a drought treatment), but this is a response to a perturbation and not an ‘optimal’ life history strategy for an environment per se.\nBack to the main point however, a justification to examining CWMs, I do not see the need to invoke a hypothesis of ‘optimality’ associated with changing CWM trait values, and it does not address ideas of trade-offs or alternative life history strategies. I suggest removing paragraph lines 114-135 in the introduction, and move this to a brief rationale for CWM in the methods section, and the rest in the discussion to address reviewer’s original comment. More so, I think what is important to consider and state explicitly is that the CWM is a derived and calculated variable, AND that the trait itself may be a response or an effect trait. Differentiating this is what’s important.\nThe second part of the reply to the original reviewer comment on CWMs is in the discussion. I also agree with the points raised here by the authors, but again I think perhaps the original reviewer point is missed, i.e. an analysis of CWMs does not capture potential trade-offs in life history strategies or species response to perturbation, but the discussion provided by the authors comes closer.\nR4.1. Thank you for your feedback. We retained a paragraph on CWM in the introduction to remain consistent with previous reviewers’ requests, and because it is a key concept in the paper, but have revised it to remove the initial point on optimality. The full paragraph in the intro now reads:\nl. 115. “At the level of guilds and communities, winning strategies can be seen as manifesting as an emergent property, and represented in community-level trait measures, typically the community abundance weighted trait mean (Bruelheide et al., 2018; Garnier et al., 2004) (CWM). While there can be significant trait variation within a community, a CWM captures the average functional strategy of the community; and changes in CWM across space and time reflect both turnover in species with different trait values, and variation in their relative abundance, in response to changes to the species pool and environmental conditions. The combined response of multiple trait CWMs thus represents a change in the overall functional strategy at a community level. This means that slow-fast strategy responses – encompassing a range of traits – may emerge at the community level from a concurrent change in individual CWM traits related to ‘slow’ and ‘fast’ strategies. In particular, communities of resource-acquisitive, fast-growing organisms with numerous offspring, a fast pace of life and good dispersal abilities tend to be found in resource-rich and disturbed habitats, while resource-conservative, slow-growing organisms with longer life span and fewer offspring tend to be favoured in undisturbed or resource-poor habitats (Börschig et al., 2013; Daou et al., 2021; de Vries et al., 2006; Pedley and Dolman, 2014; Simons et al., 2016). »\nWe also moved/expanded some of the justification and limits in the methods in relation to response/effect traits.\nl. 728: “As we hypothesised changes in community level properties across environmental gradients (rather than changes in individual species), we focused on changes in average trait values. We thus did not consider the diversity of functional strategies in a community, or the potential for differing responses of individual species, in this analysis. Average trait values can represent both changes in species identity (taxonomic turnover) and variation in their relative abundance along the gradient. Thus, all further analyses were conducted at the level of community abundance-weighted trait mean (Garnier et al., 2004) (CWM). CWM traits capture the average functional strategy of the community, both in terms of response to environmental conditions (response traits) and how it affects the functioning or other biotic components of the system (effect traits, Lavorel and Garnier, 2002). In practice, the same traits can influence both response and effect and thus can correlate strongly with both environmental drivers and ecosystem functioning (Suding et al., 2008). CWM were calculated using relative abundance data […]”\nWe did not understand the original point on trade-offs as we are looking at multiple traits and so we would detect trade-offs between these if they exist at the community level, our focal level of study.\n4.2. I also have a few independent comments on this manuscript.\nFirst, I commend the authors for approaching this topic. The fast-slow spectrum is well recognized, but rarely synthesized across both aboveground and belowground components of ecosystems. That said, I was disappointed by the lack of specific details on belowground aspects of this. Further to this, Table S14 suggests that the response of almost all belowground organisms with respect to fast-slow traits were inconclusive.\nR4.2. Thank you. Indeed, in our system the below-ground groups appear to show a weaker response than above-ground groups. We think this can be explained by two main factors: i, our main driver (land use intensity) affects resource availability in the soil through fertilization; but the disturbance aspects of this intensification, which we expect to be an important driver of many traits, including body size, mostly occur above-ground through mowing and grazing, with a more limited impact on below-ground organisms; ii, less available trait data and taxonomic information for below- than above-ground groups led to higher-level data aggregation and this might have weakened our capacity to detect a response. We now discuss it more directly in the discussion:\nl. 524: “Below-ground organisms also tended to show weaker responses than above-ground ones, likely due lower trait data availability weakening our capacity to detect a response and lower disturbance of the soil environment than the aboveground environment by the intensification studied here- mowing and grazing (Le Provost et al., 2021).”\n4.3. I was also surprised that despite the results that body size was smaller for many groups under perturbation (Fig 3) the authors fail to invoke the concept of ‘community downsizing’ that has been well established under perturbation, and specifically climate change.\nR4.3. Thank you for mentioning community downsizing. From what we found in the literature, this concept seems to be mostly used to describe changes in response to climate change or defaunation of large animals, without a clear link to the resource availability and disturbance gradients we focus on, so we did not want to make it too central a point in our manuscript. However, we feel this could be an important avenue for future integration of our study to broader ecological theories (e.g. to link between slow-fast continuum and size in different climate change contexts) and we now mention it in the discussion.\nl. 592: “Such synchrony may also correspond to recently described global trends in the covariance of multiple ecosystem functions (Gounand et al., 2020; Migliavacca et al., 2021). It could also be related to other widely reported patterns of ecological change, such as the global “community downsizing” of animal communities that occurs in response to both climate change and anthropogenic disturbance (Lindo, 2015, Martins et al. 2023) and its impact of ecosystem functioning (Donoso et al., 2020), though further theoretical integration would also be required.”\n4.4. However, I am always skeptical when the supplementary information is longer than the manuscript and includes extensive methods and results that were used to perform subsequent analyses. Figure 2 is an example of that manifestation. It is unclear in the main text how Figure 2 is created. The Caption does not provide much information nor does it reflect what the intention of the study was or how this figure supports the overall outcome of the study. The caption states “The variables included in the PCA are the slow-fast axes of each guild”, but what does this mean? As I understand this from the methods (at the end of the manuscript – not my favourite format for just this reason), this is the PCA axis scores for each group that the authors deemed associated with fast-slow traits (line 695). To understand this, one has to go to the 95 pages (not including references) of the supplementary material. This is where I am not sure where to look for the results that support these results (Table S2 perhaps, or Table S8, or Table S13?). But to my understanding, Fig 2 is showing the response in fast-slow traits across all groups, but this is artefactual. These vectors were already selected because they show what the authors are looking for (i.e. a fast-slow spectrum), thus Figure 2 is not surprising as it is a product of the authors finding the best vector to reflect fast-slow. Again, seems a circular argument here; selecting the fast-slow axis of each group to show there is a fast-slow axis.\nR4.4. We fully agree that our supplementary material is a bit long, partly due to the addition of sensitivity analyses requested by previous reviewers. To emphasize the most important information within these appendixes, we would like to present some methods tables as extended data objects. If this is only possible for figures, we would place Table 1 in the main paper and keep Tables 2-5 in the SI as we feel their size will obscure the main sections and messages of the paper.\nWe also moved key intermediate analyses (e.g. group-level PCAs) to the main text as a new Extended Data Figure 1. The caption of Figure 2 was also updated to refer more clearly to the original PCAs shown in Extended Data Figure 1:\nFigure 2. The slow-fast trait axis of individual guilds is strongly related to land-use intensity. The variables included in the PCA are the slow-fast axes of each guild, which were estimated as the PC axis that best represents the hypothesized slow-fast axis (PC1 in 90% of cases) in a PCA of the selected CWM traits expected to be related to slow-fast strategies for each guild individually. These guild-level PCA can be found in Extended data Figure 1. Land-use intensity (which is projected on the PC axes, shown in red) is strongly associated with Axis 1. Belowground guilds are shown in brown, aboveground guilds in blue, and plants in green. Sample size: 150 (sample sizes for each individual group are shown in Fig. 3, missing values were imputed to run the PCA, see methods). An equivalent analysis, but conducted on all traits without guild-level aggregation is shown as Extended data Figure 2.\nRegarding the choice of slow-fast traits leading “circularly” to group-level fast-slow axes, we would like to note that our objective was not really to demonstrate the existence of fast-slow axes for individual groups (in which case we agree the argument would be circular) but to assess whether these fast-slow axes were strongly associated at the community level – in which case we believe that our analysis is appropriate. The aggregation steps from individual traits CWM to group-level slow-fast axes was necessary to make the groups comparable in the SEM. This is now better highlighted in the introduction and methods. To support this argument, and to make our rationale clearer we now moved a supplementary figure to the main paper as Extended data figure 2 and reference it prominently in the main text (caption of Figure 2, sensitivity analyses section). This shows a PCA conducted not on group slow-fast axes but on individual traits (ie without the aggregation step to guild slow-fast axes), thus supporting the existence of a whole-community slow-fast axis.\nl. 807: “For each guild, we then sought to identify a slow-fast axis based on pre-established hypotheses based on either environmental filtering through resource availability and disturbance, or indirect, trophically-mediated mechanisms (Extended data Figure 1). Indeed, because traits represent consistent functional strategies, ‘slow’ and ‘fast’ traits are expected to covary. This guild-level aggregation from individual traits CWM to group-level slow-fast axes was necessary to make the groups comparable in the following analyses. Also, this approach was chosen because the main objective of the study was not to establish the existence of such guild-level slow-fast axes, but rather examine their association at the whole community level.”\nSee also our response R5.1a below.\n4.6. Ultimately, while I support the endeavour of this manuscript, I felt real mechanism was missing linking fast-slow traits to fast-slow ecosystem system processes, and the discussion was too general in nature to be useful.\nR4.6 We conducted some additional analyses to evaluate whether individual traits or groups were more specifically associated to individual functions (see response R5.4 below). We have also expanded the text on how fast-slow traits translate into fast-slow ecosystem functions in both the introduction and discussion:\nl. 175: “Finally, many studies have shown that community-level trait measures of individual guilds explain variation in individual ecosystem functions, which is to be expected given the link between functional traits and rates of metabolic and tissue turnover processes (Buzzard et al., 2019; Cadotte, 2017; Evans et al., 2017)”\nl. 527. “Finally, we provide evidence that this whole-community strategy variation mediates the effect of land use intensity on overall ecosystem functioning. More specifically, communities dominated by faster, non-conservative strategies (e.g. faster reproduction, shorter lifespan) have a faster metabolic rates per unit biomass, a higher digestibility and/or tissue resource concentrations and rapid turnover of tissues. All these factors lead to a faster transfer of resources from organic to inorganic pools and between trophic levels (Wardle et al., 2004, Chapin et al. 2011, Yvon-Durocher et al. 2012). In terms of ecosystem functioning this equates to greater productivity and gas fluxes, higher soil enzyme activities, and faster decomposition and rates of organic matter mineralization. This extends previous findings that demonstrate the linkages between traits of individual guilds and ecosystem functions (Buzzard et al., 2019; Cadotte, 2017; Evans et al., 2017).”\nThank you for your constructive feedback.\nReviewer #5 (Remarks to the Author):\nThis paper investigates the effect of land use intensity on community-level mean values of traits that reflect a continuum of functional strategies from slow- to fast growing organisms. The authors analyzed an impressive occurrence and trait database and identified the main slow-fast functional trait axes in 14 trophic guilds. They show direct and indirect effects of land use intensity on the slow-fast trait axes. They also showed that the slow-fast trait axes were associated with the rates of whole ecosystem functioning. The authors conclude by discussing the potential of the observed ‘slow-fast’ continuum for whole ecosystem-level studies.\nI very much enjoyed reading this manuscript which has a clear structure and is well written. The topic of the study fits within the scope of the journal. The main questions are relevant and timely. Overall, the work and methods meet the expected scientific standards. The conclusions are supported by the results. The figures and tables are clear and of good quality. The literature cited is appropriate.\nThe study summarizes complex processes in a simple message that can help improve future ecosystem conservation and management efforts. I think the study will be of interest to a broad range of researchers and practitioners in the field of ecology and beyond. I have not found any major weaknesses or errors in this study. However, I make several suggestions for possible minor improvements.\nThank you for your supportive and constructive feedback!\nNote that, as requested, my review mostly focuses on the data analysis part of the manuscript. See my comments below for further details.\nMain comments:\n5.1. Trait selection\nAlthough the authors did considerable efforts to explain this process, I think that the interpretation of PCA axes as slow-fast and their selection for further analyses can be seen as a weak point. For example, I agree with R3 that important traits in bats and birds are missing and would be easy to add given the high amount and quality of trait information for these two taxa. Furthermore, if traits were originally selected to represent the slow-fast continuum, why select just one PCA axis? The non-selected axes might represent other important ecological strategies. Traits related to reproduction might have different drivers than those related to growth. Some aspects of the slow-fast trait continuum might not be driven by land use intensity. To me, this selection process, especially, the selection of PCA axes, casts some doubts on the validity and generality of the conclusions.\nHowever, the authors did provide a complementary analysis based on all traits (Fig.S3) where the slow-fast axis emerges in a more direct and organic way. This additional analysis alongside Fig.3 did put my initial concerns at ease as the results show that the slow-fast axis is present even without selecting PCA axes. I encourage the authors to put more emphasis on these two analyses in the text. For example, the authors could mention Fig.S3 directly in the caption of Fig.2. See also my comments about fourth corner/RLQ below.\nR5.1a. Thank you for your suggestion. We agree that the process of defining group-level slow-fast axes is a bit circular. However, we would like to stress that the main objective of the paper was not to demonstrate the existence of slow-fast axes within individual groups, but to show that these axes were associated at the level of the whole community. For each group, we selected the PCA axis which best corresponded to our understanding of the fast-slow (even if was second, though it usually was the first). This made their inclusion in the next, comparative, steps of analysis possible, especially for the structural equation modelling (SEM).\nFollowing your feedback, we considered replacing Fig. 2 with the previous Fig S3 as it shows the whole community slow-fast axis in a more intuitive way. We eventually decided against this as the group-level slow-fast axes are used in the SEM, and we expect that introducing them early on in the paper (Fig 2) will help readers to understand the analyses that follow. However, we now put more emphasis on the ‘all traits’ analysis in the text and by presenting the PCA with all traits as an extended data figure, making it much more accessible to readers than it was in the supplementary material.\nl. 324: “This conclusion was supported by the analysis of individual traits’ responses to land use intensity, which shows that the CWMs of 54% of individual traits responded to land use intensification in the hypothesised direction, and only two (4%) responding in an opposing direction (Figure 3).”\nFigure 2. The slow-fast trait axis of individual guilds is strongly related to land-use intensity. The variables included in the PCA are the slow-fast axes of each guild, which were estimated as the PC axis that best represents a slow-fast axis (PC1 in 90% of cases) in a in a PCA of the selected CWM traits expected to be related to slow-fast strategies for each guild individually. These guild-level PCA can be found in Extended data Figure 1. Land-use intensity (which is projected on the PC axes, shown in red) is strongly associated with Axis 1. Belowground guilds are shown in brown, aboveground guilds in blue, and plants in green. Sample size: 150 (sample sizes for each individual group are shown in Fig. 3, missing values were imputed to run the PCA, see methods). The corresponding analysis conducted on all traits, without guild-level aggregation, is shown as Extended data Figure 2.\nR5.1b Regarding trait selection, we think that some traits suggested by Reviewer 3 (iteroparity, survivorship type) not clearly linked to slow-fast functional strategies and thus were not retained in our original screening of suitable traits. Reviewer 3 didn’t raise this issue again so we hope our decision not to include them was appropriate.\n5.2. Controlling for the effect of environmental covariates\nControlling for the effect of environmental covariates is indeed a necessary step and I agree with the approach used by the authors to address this issue. However, I miss summary information about this step. How much variation is explained in each taxon? Do we have large differences among taxa? Which variables explain the most variation? After my first read, I was wondering whether this step can cause some biases. However, Table S14 shows correlations between LUI and CWM where CWMs were not corrected for environmental covariates and the results are consistent with the other analyses. This suggests that the results are robust.\nI think that RLQ would be interesting to characterize the links between traits and environmental variables. This analysis would provide important information about the strength of the effect of LUI on traits as compared to that of other covariates. It would also provide a more general picture of the ecology of the different taxonomic groups. Whether or not the authors decide to use the RLQ, I think that more information is needed about the relative importance of the influence of LUI on traits as compared to that of other environmental variables.\nDray, S., Choler, P., Dolédec, S., Peres-Neto, P. R., Thuiller, W., Pavoine, S., & ter Braak, C. J. (2014). Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation. Ecology, 95(1), 14-21. DOI 10.1890/13-0196.1\nR5.2 We agree that more information on the response of individual groups and traits to the environmental variables considered would add transparency to the paper.\nAfter careful consideration decided against using RLQ for a range of reasons. First, RLQ links traits of individual species to environmental covariates through the abundance of individual species. Our whole analysis is based on the expectation that community-level metrics are the appropriate scale to address our research question. Second, some of our traits are calculated directly at the level of the community (e.g. fungal-bacterial ratio, % pathogen protists) and thus cannot be integrated into an RLQ analysis. To keep the paper understandable did not want to present different analyses for some traits compared than others, as this would further add to the complexity of the manuscript.\nInstead, we decided to show the results as simple variance partitioning analyses (figure shown as Figure S1) and now mention in the methods for which groups the impact of environmental covariates was the strongest.\nThe methods text now reads:\nl. 767: “To gain a more reliable estimate of how CWM trait data was related to the hypothesised drivers, it was necessary to correct for environmental covariates before analysis. As such correction has been shown to produce biased parameter estimates in the presence of correlation between the environmental covariates (Freckleton, 2002), we identified highly correlated variables from our list of potential covariates (mean annual temperatures and precipitation, topographic wetness index, soil clay and sand content, pH, depth and region). We excluded soil depth and sand content which were both highly anticorrelated to soil clay content (|r| > 0.72); and precipitation which were highly anticorrelated with temperature (r = -0.77). We thus retained mean annual temperature, topographic wetness index, soil clay content and pH as well as the region as covariates. To investigate the importance of these factors relative to land-use intensity, our focus variable, we fitted linear models with each trait CWM as a response and all covariates and land use intensity as explanatory variables. This showed that on average the region explained as much variance as land use intensity (around 8%), followed by the Topographic Wetness Index (2%), but that there was high variability across groups. For instance, Bats and Collembola were primarily driven by the region, secondary consumer protists by soil pH, and plants or above-ground primary consumer arthropods by land-use intensity (Figure S1). After that, we fitted linear models with each trait CWM as a response, and the covariates, excluding land use intensity, as explanatory variables. The residuals of these linear models were used for all further analyses.”\n5.3. Stacking of models\nI agree with the main logic behind the multiple PCAs. Basically, it is the same idea as in a multiple factor analysis, MFA (see refs below). It is, to me, a valid and interesting way to conduct a multi-taxa analysis. However, I also agree with R2 that the analysis procedure is relatively complex which makes it difficult to keep track of the potential biases and information loss at each step. All in all, I see no major problem with the stacking of models and encourage the authors to cite the MFA papers as a reference framework for their approach.\nEscofier, B. and Pages, J. (1994) Multiple Factor Analysis (AFMULT package). Computational Statistics and Data Analysis, 18, 121-140.\nBecue-Bertaut, M. and Pages, J. (2008) Multiple factor analysis and clustering of a mixture of quantitative, categorical and frequency data. Computational Statistice and Data Analysis, 52, 3255-3268.\nThank you for this suggestion, we now cite these papers as an additional justification in the methods section.\nl. 834: “To test whether the slow-fast responses were synchronous across guilds, we ran a PCA on the previously identified slow-fast axes of all guilds, and projected the land-use intensity index (LUI) as a supplementary variable on this PCA. This approach of combining multiple PCAs into a “main” PCA follows the same logic as Multiple Factor Analysis (MFA (Bécue-Bertaut and Pagès, 2008; Escofier and Pagès, 1994)) and allowed us to simultaneously analyse the response of groups with different traits and individual responses. For this analysis only, missing values for the slow-fast axis at the plot level (shown at the individual trait level in Table S1) were given the average of the considered guild across all plots to allow comparison across guilds (imputed values: 1.8% of all values, 0-12% range within groups).”\n5.4. Trait-function relationship\nAlthough not essential to the story, I miss the direct relationship between traits and EF. Here, the fourth-corner/RLQ analysis could also be used to test the associations between individual traits and EF variables. I think that such analyses would constitute a complementary and more direct approach to highlight the slow-fast trait continuum. I expect that this analysis will reveal significant correlations that will further validate the other analyses and increase the confidence in the results.\nR5.4 See our response R5.2 regarding why we think the use of RLQ analyses might not be appropriate in this context – our hypotheses relate to the whole system trait measures and ecosystem functions, rather than individual trait-ecosystem function relationships for which there is a large existing body of literature (some of which is cited) Further, RLQ cannot be used on all our trait measures. We also wanted to keep additional analyses simple, because as noted by Reviewer 4 the long supplementary materials already make the paper complex.\nInstead, and so readers can assess which traits may be driving the higher-level trend seen in whole ecosystem properties, we opted to show two correlation matrices, of all traits x all functions and of individual groups slow-fast axes x functions bundles (the bundles were used previously to downweigh highly related functions, see methods). For space reasons, only the trait-functions correlations (not traits x traits or functions x functions) are shown.\nWe would like to thank Reviewer 5 for suggesting this new analysis which revealed an important pattern in our study. The analysis highlighted the importance of fungal:bacterial ratio, specifically, in driving the ecosystem functioning slow-fast axis. We think this is an important point which we now mention in both the results and discussion.\nl. 438: “This whole ecosystem functioning slow-fast axis was better explained by the entire community ‘slow-fast’ traits axis (r = 0.4, R2 = 0.33, p < 10-6) than by other hypothesised drivers of ecosystem functioning (single guild community traits measures (plants and microbes), land-use intensity or taxonomic diversity, R2- 0.11 to 0.26, Table S5). The exception to this was fungal-bacterial ratio, which was correlated with the whole slow-fast axis (r = -0.52, P < 10-6) and better explained the functions slow-fast axis (R2 = 0.47, Figure S5, S6). This was likely due to the prevalence of soil-related measures in our selected set of functions. Additionally, some individual functions were more strongly associated with specific groups or traits (e.g. fast plant community traits were positively associated with biomass production (Figure S5).”\nL. 587: “The close relationship between our ecosystem slow-fast axis and fungal-bacterial ratio also suggest that this axis is compatible with earlier literature pointing to a slow-fast functioning gradient from fungal to bacteria-dominated communities (Bardgett et al., 1996; de Vries et al., 2006; Wardle et al 2004) and that fungal to bacterial ratios could act as an indicator variable for the whole ecosystem slow-fast continuum.”\nMinor comments:\n5.5 The use of CWM: Whether CWM reflects optimal strategies has been subject to much discussion. I personally think that this is not always the case. For instance, a bimodal distribution of trait values within a community can indicate the coexistence of two successful strategies. In such a case, the mean value would reflect a sub-optimal strategy. Having said that, I do not think that such bimodal trait distributions are highly frequent and this should not be an issue for the validity of the results. I, nevertheless, encourage the authors to add a few words about this.\nR5.5 We have now revised this paragraph in response to Reviewer 4 and do not mention optimal strategies in the new version.\nl. 115: “At the level of guilds and communities, winning strategies can be seen as manifesting as an emergent property, and represented in community-level trait measures, typically the community abundance weighted trait mean (Bruelheide et al., 2018; Garnier et al., 2004) (CWM). While there can be significant trait variation within a community, a CWM captures the average functional strategy of the community; and changes in CWM across space and time reflect both turnover in species with different trait values, and variation in their relative abundance, in response to changes to the species pool and environmental conditions. The combined response of multiple trait CWMs thus represents a change in the overall functional strategy at a community level, and this joint response is expected to be stronger than that of univariate traits (Muscarella and Uriarte, 2016). This means that slow-fast strategy responses – encompassing a range of traits – may emerge at the community level from a concurrent change in individual CWM traits related to ‘slow’ and ‘fast’ strategies. In particular, communities of resource-acquisitive, fast-growing organisms with numerous offspring, a fast pace of life and good dispersal abilities tend to be found in resource-rich and disturbed habitats, while resource-conservative, slow-growing organisms with longer life span and fewer offspring tend to be favoured in undisturbed or resource-poor habitats (Börschig et al., 2013; Daou et al., 2021; de Vries et al., 2006; Pedley and Dolman, 2014; Simons et al., 2016) »\n5.6 It was difficult for me to understand how was Fig.3 done. I encourage the authors to provide a few additional explanations in the text and directly in the caption.\nWe detailed the caption, and the results and methods now point more explicitly to Figure 3.\nFigure 3. Observed responses of individual traits at the guild level (CWM) to land-use intensity, shown as estimated parameter +/- 95% confidence intervals. The responses shown were extracted for each trait individually as the slope +/- confidence interval of a linear model with the CWM trait as a response to land use intensity after correction for other environmental covariates (see Fig. S1) P-values were adjusted for multiple testing. […]\nl. 322: “The observed covariation in slow-fast traits across guilds demonstrates that land-use intensity drives differentiation between entire communities, with ‘slow’ and ‘fast’ communities characterised by distinct trait syndromes (Figure 2, Table S1). This conclusion was supported by the analysis of individual traits’ responses to land use intensity, which shows that the CWMs of 54% of individual traits responded to land use intensification in the hypothesised direction, and only two (4%) responding in an opposing direction (Figure 3)”\nl. 803: “To test for the response of each individual CWM trait to land-use intensity, we calculated the slope of the regression of the CWM trait against land use intensity (excluding NAs) between each trait and land-use intensity (p-values corrected for false detection rates using the p.adjust function, n = 47, shown in Figure 3).”\n5.7 Figure 4: Having two slightly different color scales for model estimates is confusing. Would it be possible to use one single scale for both panels?\nDone, thanks for the good suggestion.\n5.7 Table S6: What is the shared variation?\nR5.7 We have now added the shared variance to Table S6 as requested:\nTable S 5. Variance partitioning of land use intensity and multivariate trait community weighted mean across sampling plot and years. The partitioning was done using the varpart function (package vegan). Only groups with more than one sampling year are included. For bacteria and fungi, year 2017 was excluded because only one trait was available (% pathogen fungi) which did not allow us to properly partition the variance. Negative shared variance (a common artifact when using varpart) are shown as 0* (or <0.01* if between 0 and -0.01).\nOverall, this is a very nice work and I hope that my comments will help to improve it further.\nThank you for your constructive review and useful suggestions.\nReviewers' Comments:\nReviewer #4:\nRemarks to the Author:\nThe fast-slow spectrum is well recognized, and rarely synthesised across both aboveground and belowground components of ecosystems. However, inspecting the data (as far as I can tell from supplementary information) there is very little sampling and utility of the belowground aspects of the data analysis. It appears there is a single sampling event with low replication (4 samples per plot - unclear how many plots and what the treatments were). It is also unclear if all the data are from a single experiment or represent a larger dataset of multiple experiments. I think the original data and treatment of that data needs to be more transparent.\nThe authors state that ‘Full code to replicate the analyses is stored on GitHub and will be made openly available before publication’. I would argue this is necessary for a reviewer to follow along on what the authors have done.\nI really struggle to understand what the different figures and results actual represent because there are so many data manipulations performed.\nAt the same time, in the reply to reviewer comments letter, the authors have done a good and considerate job addressing comments and discussing key points. Specifically, the authors have removed the comments about ‘optimality’, clarified the CWM discussion, and changed the caption for Figure 2.\nHowever, my concern for this whole manuscript is data transparency - I’m not sure someone could recreate this based on the multitude of data manipulation and transformations the authors have performed, and the information on guild traits, and other methods used to generate these results. There is still (as another reviewer also suggested) something seeming highly circular in this entire endeavour.\nPlease also check and confirm PCA vs PCoA.\nCheck appropriateness of references (e.g. M&W 1967 in regard to protist cell size).\nGamasideae is not an oribatid mite family (or any mite family)\n267-291 - clarify belowground arthropod guilds. Are they omnivores, primary consumers, secondary consumers?\nReviewer #5:\nRemarks to the Author:\nI have read through and carefully considered the revised manuscript, with special attention toward the concerns previously raised by myself and the other reviewers.\nI am satisfied with the authors' responses and the revisions to the manuscript. In particular, I appreciate the major effort made to improve the statistical analyses. I also appreciate the addition of Figure S2, which confirms and strengthens the previous results. I think these changes have strengthened the study.\nMain comments:\n1. Trait selection\nOK, but pity to let the figure in the SupMat… To me, this figure really justifies the scale of your approach and sends a strong and important message to the readers. Fig.2 does a similar job but in a less intuitive way. All in all, I´m fine with the chosen approach and appreciate that Extended data figure 2 is now mentioned in the caption of Figure 2.\n2. Controlling for the effect of environmental covariates\nFig.S1: Nice and very useful! Consider adding a label to the x-axis.\n3. Stacking of models\nOK\n4. Trait-function relationship\nI actually think that a 4th corner analysis would be complementary in that it could show the role of individual trait-environment relationship for the whole system which would provide useful information about the role and relative importance of processes occurring at different scales. Nevertheless, I´m happy with the addition of the two correlations matrices and generally agree with the argument of keeping the paper simple.\nThe correlation between the fungal:bacterial ratio and the ecosystem functioning slow-fast axis makes a strong link to previous work in the field. I´m happy that my comment has been useful.\nOK\nOK, but I miss the R2 of the different models. How much variation in CWM is explained by LUI? Consider adding a statement along the lines of “LUI explained between XX and YY % of the total variation in the different CWM (see figure S1)” in the caption.\nDear reviewers,\nThank you for your feedback on the previous version of our manuscript. We have now integrated these modifications: in particular, following the requests of the Editor and Reviewer 4, we have now moved all the data collection details to the methods’ main text and clarified when needed the number of plots and sampling years for all groups. We have answered the other specific editorial guidelines in the attached Checklist.\nOn the behalf of all authors,\nMargot Neyret\nREVIEWERS' COMMENTS\nReviewer #4 (Remarks to the Author):\nThe fast-slow spectrum is well recognized, and rarely synthesised across both aboveground and belowground components of ecosystems. However, inspecting the data (as far as I can tell from supplementary information) there is very little sampling and utility of the belowground aspects of the data analysis. It appears there is a single sampling event with low replication (4 samples per plot - unclear how many plots and what the treatments were). It is also unclear if all the data are from a single experiment or represent a larger dataset of multiple experiments. I think the original data and treatment of that data needs to be more transparent.\nWe have now moved the methods tables, including information on sampling methodology, to the main text and clearly state the number of plots sampled for each group. There was no specific treatment/experiment; 150 plots were initially chosen along a land use intensity gradient and sampling was conducted in the same plots over several years. We state:\nl. 535: We sampled vascular plants in an area of 4 m × 4 m in all 150 plots, and estimated the percentage cover of each occurring species every year from 2008 to 2019\nl. 548: For bacteria the analysis only included data from 2011 (148 plots) and 2014 (150 plots)\nl. 558: Data was available for 150 plots in 2011, 2014 and 2017.\nL. 566: Data for one plot in 2011 was missing, data for all 150 plots was available in 2017\nl. 580: Collembola data was available for 140 plots. For Oribatid mites, it was available in 149 plots but some plots had to be excluded due to low trait data coverage (see below), resulting in 136 used plots in total\nl. 585: Data was available for 137 plots\nl. 590: All arthropods of the herb layer were sampled twice per year between 2008 and 2017 in June and August to represent different phenological windows within the peak season of adult arthropod activity. Total number of plots sampled each year varied from 143 to 150, all plots were sampled at least 8 years (average: 9.8 years).\nThe authors state that ‘Full code to replicate the analyses is stored on GitHub and will be made openly available before publication’. I would argue this is necessary for a reviewer to follow along on what the authors have done.\nThank you. We now cite the code doi in the code availability statement.\nl. 1035: Full code to replicate the analyses is stored on GitHub at https://github.com/mneyret/trait_synchronies under the DOI 10.5281/zenodo.10286643.\nI really struggle to understand what the different figures and results actual represent because there are so many data manipulations performed.\nWe realize the paper is complex but have worked hard to make the data analysis performed as clear as possible, and the results intuitive and accessible.\nAt the same time, in the reply to reviewer comments letter, the authors have done a good and considerate job addressing comments and discussing key points. Specifically, the authors have removed the comments about ‘optimality’, clarified the CWM discussion, and changed the caption for Figure 2.\nHowever, my concern for this whole manuscript is data transparency - I’m not sure someone could recreate this based on the multitude of data manipulation and transformations the authors have performed, and the information on guild traits, and other methods used to generate these results. There is still (as another reviewer also suggested) something seeming highly circular in this entire endeavour.\nThe code provided gives full transparency and allows for repeatability. The code includes several scripts which cover all the steps of the analysis, including the matching of trait and abundance data (with the links to individual datasets and a statement on whether they are currently available or not). We also provide the main script of the analysis, which starts from the CWM and environmental dataset to the production of all results and figures. All datasets are cited in the relevant paragraph in the methods, and this is also detailed in the data availability statement:\nL. 1018: The Community Weighted Mean data generated in this study have been deposited under accession code 31516 https://www.bexis.uni-jena.de/ddm/data/Showdata/31516. The raw trait and abundance, and ecosystem functions datasets are detailed below, many of which are publicly available. To give data owners and collectors time to perform their analysis the Biodiversity Exploratories' data and publication policy includes by default an embargo period of three years from the end of data collection/data assembly. Both the CWM and remaining raw data datasets are thus available under restricted access, access can be obtained by contacting data owners (listed on Bexis). At the end of the embargo period these datasets will be made publicly available via the same data repository.\nFull list of used datasets (both from the Biodiversity Exploratories and external, previously published datasets): 3,19,45,47,53–56,93–97,100–117,219–233,237,245–250,254–263,273–275.\nPlease also check and confirm PCA vs PCoA.\nPCA was used at all steps.\nCheck appropriateness of references (e.g. M&W 1967 in regard to protist cell size).\nM&W 1967 is cited in Table S1 as a general reference of why protist cell size is a relevant trait for this study (because body size in general is relevant to fast-slow strategies), not to justify the data used for cell size.\nGamasideae is not an oribatid mite family (or any mite family)\nThanks for the correction; there was a small mistake in the dataset which we now corrected. This had no impact on the results (except in terms of trait coverage) as this group were previously considered as missing trait data.\n267-291 - clarify belowground arthropod guilds. Are they omnivores, primary consumers, secondary consumers?\nBoth primary consumers and secondary consumer arthropods are included. The trophic group considered is indicated in parenthesis after, e.g. belowground arthropods (secondary consumers)\nReviewer #5 (Remarks to the Author):\nI have read through and carefully considered the revised manuscript, with special attention toward the concerns previously raised by myself and the other reviewers.\nI am satisfied with the authors' responses and the revisions to the manuscript. In particular, I appreciate the major effort made to improve the statistical analyses. I also appreciate the addition of Figure S2, which confirms and strengthens the previous results. I think these changes have strengthened the study.\nThank you for your feedback and positive assessment.\nMain comments:\n1. Trait selection\nOK, but pity to let the figure in the SupMat… To me, this figure really justifies the scale of your approach and sends a strong and important message to the readers. Fig.2 does a similar job but in a less intuitive way. All in all, I´m fine with the chosen approach and appreciate that Extended data figure 2 is now mentioned in the caption of Figure 2.\nThank you. Putting this figure as extended data, rather than in the supplementary material, will make it visible to most readers of the article as it will link directly from the article main page.\n2. Controlling for the effect of environmental covariates\nFig.S1: Nice and very useful! Consider adding a label to the x-axis.\nThank you, we now added a label for each functional group of the figure:\n3. Stacking of models\nOK\n4. Trait-function relationship\nI actually think that a 4th corner analysis would be complementary in that it could show the role of individual trait-environment relationship for the whole system which would provide useful information about the role and relative importance of processes occurring at different scales. Nevertheless, I´m happy with the addition of the two correlations matrices and generally agree with the argument of keeping the paper simple.\nIt was- thank you for your contribution!\nOK\nOK, but I miss the R2 of the different models. How much variation in CWM is explained by LUI? Consider adding a statement along the lines of “LUI explained between XX and YY % of the total variation in the different CWM (see figure S1)” in the caption.\nThank you for your suggestion. We now added the following sentence to the legend of Figure 3. Note that we reference Table S2 rather than Figure S1 as Fig S1 shows non-environmentally corrected CWM which might lead to different R2.\nFigure 3: […] LUI explained between 0 (non-significant traits) and 40% (Lepidoptera voltinism) of the total variation in individual trait CWM (Table S2). […]"}]

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