# Intensification-driven changes in temperate ecosystem functions: comparison of managed grasslands and tree plantations using a cross-system index

Lucie Bon, Laurent Augusto, Frédérique Louault, Pierre Trichet, Juliette

M.G. Bloor

► To cite this version:

Lucie Bon, Laurent Augusto, Frédérique Louault, Pierre Trichet, Juliette M.G. Bloor. Intensification-driven changes in temperate ecosystem functions: comparison of managed grasslands and tree plantations using a cross-system index. Ecological Indicators, 2026, 182, pp.114489. 10.1016/j.ecolind.2025.114489. hal-05412142

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# Intensification-driven changes in temperate ecosystem functions: comparison of managed grasslands and tree plantations using a cross-system index

Lucie Bon<sup>a,b,*</sup>, Laurent Augusto<sup>b,2</sup>, Frédérique Louault<sup>a,3 ID</sup>, Pierre Trichet<sup>b,4 ID</sup>, Juliette M.G. Bloor<sup>a,5 ID</sup>

## ARTICLE INFO

### Keywords:

Intensification

Management

Cross-system index

Carbon

Nitrogen

Forests

Upland grasslands

## ABSTRACT

Modern land management is faced by the challenge of meeting rising demands for food and biomass while maintaining ecosystem functions, but the interactions between management intensity and ecosystem functioning in perennial production systems remain unclear. Here we examined how management intensity affects carbon and nitrogen storage in contrasting perennial ecosystems using a novel cross-system index that harmonizes land-use intensity (LUI) calculations and enables direct comparison of different systems across an intensification gradient. Data from two long-term experimental management trials (pine plantations and upland grasslands, seven years after trial implementation) were used to explore the linkages between management intensity, carbon and nitrogen storage in soils and aboveground biomass. We found that managed grasslands showed a broader range of LUI values, highlighting differences in the levels of management considered to be ‘high intensity’ across systems. Photosynthetic biomass and aboveground nitrogen stocks showed a positive response to LUI gradient in both tree and grassland systems, but the degree of response was stronger in tree plantations. Soil nitrogen decreased with LUI under tree plantations alone, and soil carbon showed no response to management intensity in either system. In general, there was limited evidence of trade-offs between provisioning and regulating services. Our findings demonstrate the usefulness of a cross-system LUI index to compare diverse ecosystem responses. By providing a common framework, our LUI approach offers a valuable tool to disentangle the effects of management intensity on provisioning and regulating services, and identify strategies that reconcile productivity with long-term sustainability.

## 1. Introduction

One of the biggest challenges of modern land management is to meet the growing demand for food and biomass, while maintaining other ecosystem functions and services essential for climate change mitigation and ecosystem functioning in the long term (IPCC, 2023). Widespread evidence suggests that managed perennial ecosystems have a higher potential to achieve ecosystem multifunctionality than annual crops

(Allan et al., 2015; Manning et al., 2018; Pohjanmies et al., 2021; Schils et al., 2022), supporting productivity as well as carbon (C) storage (Fontaine et al., 2024; Jian et al., 2020; Ledo et al., 2020), nutrient retention, biodiversity (Kreitzman et al., 2022; Werling et al., 2014) and water quality (Asbjornsen et al., 2014). Although perennial systems are generally considered more sustainable land-use options, they may however be subjected to varying levels of management intensity, with implications for ecosystem functions. The nature (e.g. plantation

<footnote><sup>a</sup> Université Clermont Auvergne, INRAE, VetAgro-Sup, UREP, Clermont-Ferrand, France</footnote>

<footnote><sup>b</sup> INRAE, Bordeaux Sciences Agro, ISPA, F-33140 Villenave d'Ornon, France</footnote>

<footnote>* Corresponding author.
E-mail address: lucie.bon.pro@gmail.com (L. Bon).</footnote>

<footnote n="1">1. Lucie Bon: 0000-0002-3014-8124</footnote>

<footnote n="2">2. Laurent Augusto: 0000-0002-7049-6000</footnote>

<footnote n="3">3. Frédérique Louault: 0000-0002-9430-4261</footnote>

<footnote n="4">4. Pierre Trichet: 0000-0002-6938-1353</footnote>

<footnote n="5">5. Juliette Bloor: 0000-0002-8668-1323</footnote>
density, harvesting, soil improvement practices...) and intensity of management practices both have significant direct and indirect effects on C and nitrogen (N) cycling, via changes in primary production, soil microbial biomass and activity, and decomposition processes (LeBauer and Treseder, 2008; Mäkipää et al., 2023; Rumpel et al., 2015). Improved understanding of the interactions between management intensity and ecosystem functioning in perennial production systems is critical for more accurate and reliable guidelines for sustainable management.

Forests and permanent grasslands represent two major perennial ecosystems, covering respectively 31 % and 25 % of the global land area (FAO, 2021). In the case of managed forests, intensive management is generally characterised by the plantation of monocultures where species are selected specifically for biomass production, increased stand density, or short rotation cycles –either separately or in combination (Duncker et al., 2012; FAO, 2020; Fox, 2000; Mason and Meredieu, 2011). These practices are also often associated with high agrochemical and fossil energy inputs from the use of machinery necessary to optimise species potential (FAO, 2020). In permanent grasslands, intensive management involves mineral and/or organic fertilizer inputs (primarily aimed at maintaining or enhancing productivity) and harvesting, either via mowing or grazing practices (Sollenberger et al., 2019). Intensification of management, and the focus on provisioning ecosystem services in both forest and grassland systems, is not always easy to reconcile with the delivery of other ecosystem services such as C storage and nutrient regulation (Pohjanmies et al., 2021; Richter et al., 2024). Trade-offs between provisioning and regulating ecosystem services have often been highlighted in the literature (Bradford and D'Amato, 2012; Fox, 2000; Pan et al., 2014), although impacts of intensification on soil C and/or nutrient pools can generate mixed results, particularly where combinations of management practices are involved. For example, high intensity management practices may either lead to a depletion of soil resources in the case of intensive harvests (Achat et al., 2015; Olsson et al., 1996), or the opposite in the case of high nutrient inputs (Apostolakis et al., 2022; Sebilo et al., 2013). Land management may also influence plant N utilisation efficiency –an indicator of the ability of a plant to use N to produce biomass– but responses vary depending on the type of intervention considered (Egan et al., 2019). Variation in responses to a given management practice may also be driven by the dominant plant growth forms in the system (i.e. woody trees vs. herbaceous plants), due to inherent differences in plant carbon allocation and nitrogen remobilisation strategies (Xia and Wan, 2008).

One ecosystem service that is known to be influenced by the management intensity is the C storage function (Sing et al., 2018; Ward et al., 2016). Indeed, recent meta-analyses exploring management effects on forests and grasslands suggest that these systems show a different magnitude of response to practices that deplete or affect system resources (Beillouin et al., 2023; Liu et al., 2024). Increased intensity of harvesting or litter removal appears to have stronger negative effects on soil C in forests compared to grasslands, whereas mineral fertilisation has stronger positive effects on soil organic C in grasslands rather than forests (Beillouin et al., 2023; Liu et al., 2024). However, in one of the rare studies to date to explicitly compare function of forests and grasslands under identical management treatments, long-term nitrogen addition was found to have no overall effect on soil C in either system (Kazanski et al., 2019). Collectively, these results suggest that the effects of intensification on ecosystem function may be difficult to generalise across long-lived perennial systems. Moreover, although there is a strong interest in understanding the effects of intensification on ecosystem services in contrasting perennial systems, cross-ecosystem comparisons remain challenging due to the lack of standardized tools for harmonizing calculations of land-use intensity (LUI) across systems. By convention, quantification of LUI in forests and grasslands involves distinct metrics (Blüthgen et al., 2012; Gossner et al., 2014; Kahl and Bauhus, 2014). To our knowledge, no previous study has investigated the effect of management intensity on ecosystem functions –such as C

and N storage– in forests and grasslands using a single cross-system index. Consequently, cross-system tools are needed to better assess and compare the impacts of intensification beyond biomass production in perennial systems.

The aim of this study was to examine impacts of management intensity on C and N storage in contrasting long-lived perennial ecosystems (forest plantations, permanent grasslands). A key originality of the present work lies in the development of a cross-system index to harmonize the quantification of LUI and enable a direct comparison between contrasting ecosystems subjected to an intensification gradient. We tested our approach using data from two long-term experimental management trials (pine plantations, upland grasslands) exploring the linkages between management intensity, C and N storage in soils and aboveground biomass. In both case studies, high management intensity reflected a specific combination of management practices intended to enhance biomass production, with cascading effects on ecosystem functioning. In particular, we hypothesized that i) grass- and tree-dominated systems vary in the magnitude of response to nutrient-driven LUI (Beillouin et al., 2023; Xia and Wan, 2008), ii) increasing nutrient availability is associated with a decrease in nutrient use efficiency across systems (Campioli et al., 2015; Peng et al., 2023), and finally that iii) variation in LUI drives trade-offs between provisioning (plant C) and regulating services (soil C and N) (Bradford and D'Amato, 2012).

## 2. Materials and methods

### 2.1. Study design and experimental treatments

Case study data was obtained from replicate experimental plots selected from within two long-term agroecosystem management trials in France (Fig. 1): XyloSylve (44° 43'47.9" N, 0° 47'05.4 W, https://www.anaee-france.fr/en/infrastructure-services/in-natura-experimentation/foret/xylosylve) and Theirx AnaEE-F-ACBB (45° 43' 23" N, 3° 1' 21" E, https://www.anaee-france.fr/service/acbb/experimentation-in-natura-acbb-theix/). These two sites represent maritime pine-dominated plantation forests and permanent grasslands respectively, and are both under a climate classified as temperate (°Cfb: fully humid and warm summer) using the Koppen-Geiger classification (Kottek et al., 2006).

At each site, we selected three different management treatments (combining several practices detailed below) in order to generate a gradient of management intensity impacting nutrient availability within each long-lived perennial system; data for this study was obtained from plots which had been subjected to experimental treatments for seven years. In the tree plantation (TP) trial, plots were established in 2012 on acidic, non-structured, sandy podzols (FAO and IUSS, 2007) previously used for pine stands rotations that were clear-cut at the end of the last rotation. During the study period, temperatures at this site averaged 13.3 °C annually, while cumulative rainfall over the year reached 1009 mm on average. The site preparation involved initial subsoiling for half the plots and phosphorus fertilisation (50 kg-P ha⁻¹) at planting. The three management treatments were as follows: (i) a low stand density (953 trees ha⁻¹) to alleviate inter-tree competition for nutrients, and regular mechanical controls in the furrows to reduce the competition with spontaneous vegetation (four interventions over the seven-year study period); (ii) a medium density treatment typical of the region (1250 trees ha⁻¹) with, in addition, N-fixing species (Ulex europaeus, 100 seeds m⁻²) sowed in the stand furrows, and a single mechanical intervention five years after planting to improve soil nutrient availability by crushing the N-fixers biomass; (iii) a dense plantation (2500 trees ha⁻¹) of a maritime pine seed lot that was selected to grow fast. Each treatment was repeated in 4 blocks, each containing two replicate plots of 2000 m², generating a total of 24 plots.

In the permanent grassland (PG) management trial, treatments were established in 2005 on a cambisol. The land was formerly managed for
<figure><img src="image_3.png" /><figcaption>Fig. 1. Geographical map of the experimental trials described in this study.</figcaption></figure>

mixed grazed and hay production for over 10 years. During the study period, the mean annual temperature at this site was 8.4 °C, and total annual precipitation reached 788 mm. For two years prior to the start of the management trial, the site was mown three times a year without any fertiliser input. Three fertiliser treatments were subsequently applied to generate a gradient of nutrient availability, with minor variations in the fertilisation doses over the years (see Appendix 1 for more details). The least intensive management treatment received no fertilisation during the whole duration of the trial. An intermediate fertiliser treatment received 26 kg ha⁻¹ of phosphorus and 146 kg ha⁻¹ of potassium per year (mean 2005–2011). Finally, the most fertilised treatment received 256 kg ha⁻¹ of nitrogen, 34 kg ha⁻¹ of phosphorus and 188 kg ha⁻¹ of potassium per year (mean 2005–2011). All plots were cut three times each year, in May, July and October, and fertiliser inputs (where applied) were fractioned over early spring, after the first and the second cuts. Each treatment was repeated in two blocks, each composed of two replicate plots of 400 m², generating a total of 12 plots.

## 2.2. Data collection

In the tree plantation trial, maritime pine aboveground biomass was estimated seven years after plantation using field measurements of tree height and stem circumference at breast height, and allometric models following the methodology in Bon et al. (2023), and equations from Shaiek et al. (2011) (see Appendix 2 for full details of the calculations). Pine needles were collected during the winter by sampling 10 trees per plot. For each tree, a twig directly exposed to sunlight was collected using a precision rifle. Intact current-year needles were collected and

needles from all the trees from the same plot were pooled to produce a composite sample. These foliage materials were dried (60 °C, 72 h) and weighed to obtain photosynthetic biomass (needle biomass only). Total aboveground standing biomass was obtained by summing stem, branches and needle biomass per tree.

Dried needle samples were ground, mineralised ($H_2SO_4 + H_2O_2$) and analysed (AutoAnalyser-3-High-Resolution colorimeter, SEAL) to obtain data on nutrient content. N stocks in the photosynthetic biomass were calculated as the product of needle biomass and N concentrations obtained from measurements (Appendix 2). N content in tree trunks and branches was estimated following Augusto et al. (2008) (Appendix 2). Soil characteristics were measured on five soil samples per plot collected during the seventh year after tree plantation (i.e. June 2019) for the 0–15 cm soil layer. Total carbon and nitrogen ($C_{tot}$ and $N_{tot}$, respectively) were measured with a dry combustion analyser (Thermo Finnigan Flash EA 1112 Series USA; Matejovic, 1997) on ground soil samples. Since these soils contain no carbonate (Augusto et al., 2010), soil total C was considered as a reliable estimate of soil organic C. Total Ca, K, Mg, Na and P were measured following the *aqua regia* microwave-assisted digestion method (Anton Paar Multiwave PROMicrowave Reaction System®) and analysed by ICP-OES (Agilent®). Soil bulk density was estimated based on a local pedo-transfer function (Augusto et al., 2010). C and N stocks were calculated by multiplying soil bulk density with the elemental content of the fine soil fraction, and then averaged per plot.

In the grassland management trial, the production of photosynthetic aboveground biomass was determined by cutting four quadrats (0.6 × 0.6 m) per plot at a height of 5.5 cm immediately prior to each of the
three cuts per year (i.e. in the seventh year after the start of the trial for the present study). All plant samples were dried (60 °C, 24 h) and weighed. Annual aboveground net primary production (ANPP; g m⁻² y⁻¹) was calculated as the sum of the dry mass of the three cuts. Total plant C and N were determined as a weighted mean using biochemical analysis of composite ground plant samples for each plot and cut (Dumas method, Elementar Vario Macro), and the dry mass data of each harvest. Soil sampling took place during the seventh year after trial installation at eight locations within each plot on the 0–20 cm soil layer. After air drying, the samples were sieved using a 2 mm mesh. The resulting fractions (coarse and fine soil) were weighed and their volumes measured to calculate bulk soil density, including coarse fragments. Total C and N were measured on four composite samples per plot, each obtained by mixing two soil samples (Dumas method, Elementar Vario Macro). C and N stocks were estimated as the product of bulk soil density and the elemental content of the fine fraction, and averaged per plot.

## 2.3. Development of a cross-system land-use intensity index

Here we present a generic land-use intensity index (LUI) which accounts for different kinds of management practices associated with multiple land uses (e.g. forestry, managed grasslands) using a compound, additive approach (following Blüthgen et al., 2012). In order to develop this index, we first reviewed the literature on existing LUI indices in grasslands and forests (e.g. Blüthgen et al., 2012; Gossner et al., 2014; Kahl and Bauhus, 2014) to define the main types of interventions which impact ecosystem productivity and contribute to LUI in these managed systems. Five generic categories were identified (Table 1): mechanical soil preparation (M), density of soil occupation by the culture (D), inputs to the system (I), biomass removal without recycling (BR; i.e. complete removal from the site), biomass management (BM; i.e. biomass removal with high potential of nutrient return, e. g. grazing or understory crushing with residues left on site). When constructing the cross-system land-use intensity (LUI) index, we assign equal weights to the five intervention types. For each experimental plot i, the land-use intensity LUIi is defined as:

$$LUI_i = \frac{M_i}{M_s} + \frac{D_i}{D_s} + \frac{I_i}{I_s} + \frac{BR_i}{BR_s} + \frac{BM_i}{BM_s} \quad (1)$$

Each individual LUI component is standardized relative to its corresponding site mean (s), generating a dimensionless LUI index which avoids components with values of inherently-different orders of magnitude. In order to account for potential temporal variation in land-use type and intensity that may have occurred over time, land-use

<table><caption>**Table 1**
Management interventions affecting land use intensity.</caption><thead><tr><th>Intervention type</th><th>Examples from tree plantations and grasslands</th><th>Rationale</th></tr></thead><tbody><tr><td>Mechanical soil preparation (M)</td><td>Ploughing, harrowing, subsoiling tillage</td><td>Interventions causing a mechanical disturbance of the soil matrix.</td></tr><tr><td>Density of soil occupation by the culture (D)</td><td>Tree plantation density, seed sowing</td><td>Interventions altering the density of soil occupation by the culture (i.e. species of interest).</td></tr><tr><td>Inputs to the system (I)</td><td>Fertilisation, irrigation, herbicides, pesticides</td><td>Interventions increasing system inputs (nutrients, water, or chemicals).</td></tr><tr><td>Biomass removal without recycling (BR)</td><td>Wood harvests, tree thinning, mowing</td><td>Interventions exporting the totality harvested biomass out of the system.</td></tr><tr><td>Biomass management (BM)</td><td>Burning, understory management, grazing</td><td>Interventions redistributing part or all of the removed biomass within the system (e.g. burning generates atmospheric emissions and ashes).</td></tr></tbody></table>

intensity data can be summarised for several years at both the plot and site level (sensu Blüthgen et al., 2012). From an ecological mechanism perspective, the degree of impact of different intervention types on ecosystem carbon and nitrogen cycles may vary but there is a certain weighting that occurs naturally in the index because the frequency and intensity of the intervention are both considered. It is important to note that any *a priori* weighting may involve subjectivity where weights are driven by outcomes/ desired objectives, similarly to the debates on ecosystem multifunctionality indices (Manning et al., 2018).

## 2.4. Quantification of LUI in the case studies

Management interventions for all plots were summarised for the seven-year period after establishment of each management trial. For tree plantation plots, the interventions included: subsoiling in half the plots and phosphorus fertilisation at planting, stand density, legume sowing in the furrows and understory crushing with a bladed roller. Legume sowing and understory crushing were combined as “biomass management”. For grasslands plots, the interventions that took place in the first seven years of the trial included mineral fertiliser inputs and biomass exportation. Given that multiple nutrients were added in some treatments each year, fertilisation was calculated as a weighted average of the fertilisation doses for N, P and K, considering the number of interventions required to apply each element (three for N, two for P and K). Plot level data on management interventions was normalised according to yearly site means to minimize the risk of a disproportionate influence of discrete and irregular interventions (e.g. understory crushing for the tree plantations). The final LUI equations were:

$$LUI_{TP2012-2018} = \frac{1}{7} \left( \frac{M_{I2012}}{M_{S2012}} + \frac{M_{I2013}}{M_{S2013}} + \dots + \frac{M_{I2018}}{M_{S2018}} \right) \\ + \frac{1}{7} \left( \frac{D_{I2012}}{D_{S2012}} + \frac{D_{I2013}}{D_{S2013}} + \dots + \frac{D_{I2018}}{D_{S2018}} \right) \\ + \frac{1}{7} \left( \frac{I_{I2012}}{I_{S2012}} + \frac{I_{I2013}}{I_{S2013}} + \dots + \frac{I_{I2018}}{I_{S2018}} \right) \\ + \frac{1}{7} \left( \frac{BM_{I2012}}{BM_{S2012}} + \frac{BM_{I2013}}{BM_{S2013}} + \dots + \frac{BM_{I2018}}{BM_{S2018}} \right) \quad (2)$$

$$LUI_{PG2005-2011} = \frac{1}{7} \left( \frac{I_{I2005}}{I_{S2005}} + \frac{I_{I2006}}{I_{S2006}} + \dots + \frac{I_{I2011}}{I_{S2011}} \right) \\ + \frac{1}{7} \left( \frac{BR_{I2005}}{BR_{S2005}} + \frac{BR_{I2006}}{BR_{S2006}} + \dots + \frac{BR_{I2011}}{BR_{S2011}} \right) \quad (3)$$

## 2.5. Statistical analyses

All data used in this study were collected seven years (n + 7) after the implantation of the management trials (year 2019 for the forest plantation, and 2012 for the permanent grassland), with the exception of photosynthetic biomass N content for which three years were considered in order to better account for N utilisation efficiency (NUtE) in young tree development. NUtE of photosynthetic biomass was calculated as the ratio between the aboveground biomass (for grass) or needle biomass (for trees) of the plant stand and the N stored in the same tissues (Congreves et al., 2021; Moll et al., 1982). We use the term ‘NUtE’ following Congreves et al. (2021), although similar calculations are often referred to as ‘nitrogen use efficiency’ (NUE) in the literature. To facilitate comparisons across variables, responses to LUI were harmonized by applying the following formula to each variable:

$$SLR(unitless) = \log\left(\frac{Variable_i}{Variable_m}\right)$$

SLR is a “standardized log ratio”, where i refers to the plot and m the general mean of the variable across LUI treatments (Malyjurek et al., 2019).

Regression analysis was used within each system to investigate the
levels of response of C or N stocks in aboveground and belowground compartments to the gradient of management intensity created by the treatments. Residual diagnostics (normality and homoscedasticity) were visually assessed to ensure the validity of linear model assumptions, and model significance was assessed using the p-value associated with the slope coefficient. Slopes of linear regressions fitted separately for each system along the LUI gradient were compared using a slope contrast analysis to assess differences in the magnitude of response across systems for each variable. To investigate inter-relationships between variables and their degree of response (SLR) within each ecosystem, we performed an ANCOVA. Interaction terms (covariate × treatment) were tested and retained only when they significantly improved model fit. Statistical analyses were performed using R version 4.2.2. The dataset is available on the public repository Dataverse INRAe (https://doi.org/10.57745/SVHKRN).

# 3. Results

## 3.1. Land-use intensity (LUI) index

In the tree plantations (TP), LUI<sub>TP</sub> ranged from 1.51 to 2.02, with a 1.3-fold increase between the least and most intensive treatments. Ranking between management treatments did not follow ranking in terms of stand density alone; TP<sub>low</sub> had intermediate density (1250 trees ha<sup>-1</sup>) but low mechanical intervention and addition of N-fixers whereas TP<sub>mid</sub> had low density (953 trees ha<sup>-1</sup>) and higher frequency of mechanical interventions. Differences in LUI index were similar between treatment combinations, with an increase of 16 % between TP<sub>low</sub> and TP<sub>mid</sub>, and 15 % between TP<sub>mid</sub> and TP<sub>high</sub>. In the permanent grasslands (PG), the gradient of management intensity was broader than in TP, LUI<sub>PG</sub> ranging from 1.74 to 4.57 with a 2.6-fold difference between the two endpoints of the gradient. In this system, ranking of management treatments was driven by fertiliser inputs; there was an increase of 55 % between the LUI values of PG<sub>low</sub> and PG<sub>mid</sub>, and PG<sub>high</sub> represented a 70 % increase compared to PG<sub>mid</sub>.

## 3.2. Effect of LUI on plant variables

Four out of six aboveground plant variables investigated in the tree plantations were significantly affected by the land-use intensity (LUI) gradient. Overall photosynthetic biomass of the trees, i.e. needle biomass, showed a positive increase with increasing management intensity (Fig. 2a), with an increment of 32 % between the least and most intensive LUI treatments. Needle nitrogen stocks were also positively impacted by LUI<sub>TP</sub> (Fig. 2b), but needle NUtE remained unaffected (Fig. 2c). Impacts of LUI on needle biomass were similar to those on needle nitrogen stocks (+32 % versus + 28 % between the endpoints of the gradient, respectively). LUI<sub>TP</sub> had similar effects on pine standing biomass and nitrogen stocks in the standing biomass (Appendix 3). Tree nitrogen utilisation efficiency (NUtE; Appendix 3c) did not vary with LUI<sub>TP</sub>.

In permanent grasslands (PG), management intensity (LUI<sub>PG</sub>) had significant effects on aboveground annual production and its associated nitrogen stocks. Annual biomass production was lowest in the PG<sub>low</sub> treatment, with 4.37 ± 0.53 Mg ha<sup>-1</sup> and was positively related to the LUI<sub>PG</sub> gradient (Fig. 2d), with an increase of 113 % in PG<sub>high</sub>. The associated nitrogen stocks followed a similar pattern, with a higher proportional increase as there was a difference of 136 % between the least and most intensive treatments (Fig. 2e). In contrast, aboveground biomass NUtE significantly decreased by 12 % along the most contrasted treatments of the LUI<sub>PG</sub> gradient (Fig. 2f).

Slope contrast analyses revealed that the slope of the relationship between forest photosynthetic biomass -i.e. needles- and LUI<sub>TP</sub> was marginally steeper than that of grassland annual production and LUI<sub>PG</sub> (P = 0.097). This response pattern was further reinforced when comparing the degree of response of forest standing biomass versus LUI to that of grassland annual production versus LUI (estimate = 7.810; SE = 2.660; P = 0.006). In contrast, the degree of response of N stocks in photosynthetic biomass to LUI did not differ across systems (non-significant slope contrasts).

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Photosynthetic biomass responses to LUI gradient in tree plantations (top) and permanent grasslands (bottom). Significant relationships are shown by a solid line.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Soil variables responses to LUI gradient in tree plantations (top) and permanent grasslands (bottom). Relationship significance: solid = significant; dashed = marginally significant.</figcaption></figure>

### 3.3. Effect of LUI on soil variables

Across treatments, soil C stocks in the tree plantations averaged $46.23 \pm 2.00 \text{ Mg ha}^{-1}$ in the 0–15 cm layer of the forest site, and soil N averaged $1.51 \pm 0.08 \text{ Mg ha}^{-1}$. Management intensity had no effect on soil carbon stocks in the tree plantations (Fig. 3a), and only marginally decreased soil nitrogen stocks (Fig. 3b). Soil C/N ratio, on the other hand, increased significantly with management intensity in the tree plantations (Fig. 3c). In the permanent grasslands, neither soil C, soil N nor soil C:N were affected by land-use intensity (Fig. 3c–f). Soil C stocks in the grass plantations averaged $69.07 \pm 1.15 \text{ Mg ha}^{-1}$ in the 0–20 cm layer of the grassland site across treatments, whereas soil N averaged $6.28 \pm 0.11 \text{ Mg ha}^{-1}$.

### 3.4. Interrelationships between plant and soil responses to LUI

In the tree plantations (TP), photosynthetic biomass was unrelated to soil C or N stocks across management treatments, but it was positively related to soil N stocks in the TP<sub>low</sub> treatment (Appendix 4, Fig. 4). The degree of response (sensitivity) to LUI in needle biomass was also positively related to the sensitivity of soil N stocks to LUI in the TP<sub>low</sub> treatment alone. In contrast, we did not find any relationship between annual biomass production and soil variables in the permanent grasslands. Sensitivity to LUI of annual biomass production was also unrelated to sensitivity to LUI of soil variables across grassland management treatments. Interestingly, the degree of responsiveness of aboveground production to LUI was systematically more important than that of soil C and N stocks in the PG<sub>high</sub> treatment (Appendix 4).

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Interrelationships (a) between needle biomass and soil N stocks and (b) their sensitivity (standardized log ratio; SLR) to LUI in tree plantation. Light green points represent the low treatment level, medium green points the medium level, and dark green points the high treatment level. Significant relationships within management levels were tested and are indicated by a solid line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
# 4. Discussion

Understanding the interactions between management intensity and ecosystem functioning in perennial systems is crucial for the development of sustainable management guidelines (IPCC, 2023). This is all the more important to identify where trade-offs in management choices are necessary to balance multiple functions (Duncker et al., 2012). In the present work, we applied a novel, generic cross-system LUI index to compare responses of two contrasting long-lived, perennial ecosystems subjected in the medium term (seven years) to a LUI gradient. Our study resulted in three key findings: (i) patterns of aboveground plant responses (photosynthetic biomass, nitrogen stocks) to LUI are consistent in the contrasting long-lived perennial systems, but the magnitude of response varies depending on the system studied, (ii) soils characteristics are more sensitive to LUI under tree plantations than grasslands, and (iii) there is limited evidence of trade-offs between provisioning and regulating services in either study system. In addition, our approach provides a road map for implementing this index in studies and adapting it to local site management interventions.

## 4.1. Expected patterns and observed results

Although moderate increases in LUI are generally associated with increases in productivity, the magnitude of response may vary depending on plant growth form and ontogenetic stage (Xia and Wan, 2008). In line with our first hypothesis, we found that photosynthetic biomass responded more to LUI in pine plantations compared with grasslands, i.e. the positive effect of LUI on the photosynthetic biomass in TP was stronger than in PG after seven years of management treatments. The stronger biomass response to LUI in tree plantations is somewhat surprising since previous, shorter-term studies have found stronger biomass responses to nitrogen addition in herbaceous species compared to trees (Xia and Wan, 2008). However, unlike in grasslands, pine photosynthetic biomass —i.e. needles— is known to be highly dependent on lignified biomass (Bartelink, 1996), which accumulates over time. Strong photosynthetic biomass responses to LUI in pine plantations may therefore reflect the long study period (seven years), and a cumulative influence of LUI on photosynthetic biomass through tree size effects (Stephenson et al., 2014).

In general, we found that management intensity had more limited effects on soil variables than on photosynthetic biomass, consistent with the idea that the total soil carbon pool is large and may respond to changes in nutrient inputs relatively slowly (Bláško et al., 2022). As with photosynthetic biomass, soil N and C:N ratio were more sensitive to LUI under pine plantations compared with grasslands, suggesting a faster onset of LUI effects on soil fertility in our tree versus managed grassland systems (despite a LUI gradient half that of in the grassland system). In the TP system, increases in the LUI gradient were associated with concurrent increases in soil C:N ratio, with implications for microbial activity and organic matter decomposition dynamics (Getino-Álvarez et al., 2023). Observed increases in soil C:N were likely driven by decreasing soil N along the LUI gradient linked to changes in legume abundance (i.e. decreasing abundance of legumes from low to high LUI; as shown in Augusto et al., (2025)), as well as by the accumulation of mined soil N in unharvested tree biomass over time. Under these conditions, progressive nitrogen limitation might therefore be expected to arise in the TP<sub>high</sub> treatment during later stand development (Vesterdal et al., 1995). In the permanent grassland, soil variables did not vary along the LUI gradient despite expectations that nutrient inputs might lead to an accumulation of resources over time (Apostolakis et al., 2022; Sebilo et al., 2013). Absence of LUI effects on soil could at least partly reflect compensation of nutrient exports and losses by high fertiliser inputs (PG<sub>high</sub>); indeed, the annual N addition through mineral fertilisation in high LUI plots corresponded to 96 % of the N exported in biomass in this treatment at n+7 years after implementation. Stability of soil N pools could be maintained by soil microbial nitrogen

mineralization and/or presence of N-fixing species in the PG<sub>low</sub> and PG<sub>mid</sub> treatments (unpublished data). The observed divergence in aboveground biomass and soil responses in the grassland system supports the idea that intensive management drives plant-soil asynchrony in nutrient cycles (Fontaine et al., 2024).

Nutrient utilisation efficiency (NUtE) is an important contributor to plant growth control, and plasticity in plant NUtE has been shown to promote stable levels of plant biomass production even under N-limited conditions (Fontaine et al., 2024). Our second hypothesis was that NUtE would decrease with increasing N availability, in line with data from both grasslands and tree systems (Palmqvist et al., 2020; Peng et al., 2023). This hypothesis was clearly supported in the grassland system by the negative relationship observed between LUI and NUtE. In the pine plantations, increasing LUI was driven by tree density (and hence increased plant competition for N) rather than increasing N inputs; assuming greater N limitation at high LUI, a positive relationship between LUI and NUtE might be expected in this system. Instead, NUtE values in the pine plantations were consistently high, and unrelated to LUI. This suggests that NUtE was buffered from LUI-driven changes in soil nutrient availability in the pine plantation system, possibly due to greater internal storage and seasonal remobilisation in trees compared with herbaceous plants (Li and Coleman, 2019), or because no thinning had yet been implemented at this early stage of the rotation (Vesterdal et al., 1995).

As intensive management practices are usually conceived to optimise biomass production regardless of other ecosystem services (Foley et al., 2005), we expected to find trade-offs between provisioning and regulating services along the LUI gradient. However, no trade-off was observed between aboveground biomass and soil C and N stocks in either the grassland or pine plantation system, implying that these processes are not functionally incompatible in the mid-term under the studied management regimes. The absence of such trade-offs along the LUI gradient may be due to compensatory effects linked to the choice of management practices. For example, in our forest trial, soil carbon stock losses induced by understory crushing may have been compensated by increased organic inputs into the soil, resulting from increased biomass, and in turn, litterfall and rhizodeposition (Bon et al., 2023). Interestingly, we did find a positive relationship between aboveground biomass and soil N stocks in the least intensive treatment in this site, indicating a synergy between provisioning and regulating services under low intensity management. In our grassland trial, the absence of trade-offs may be the result of possible compensations between carbon and nutrient losses and increased fertiliser inputs and litter. Of course, the relationship between different ecosystem services may also be non-linear, with greater values at intermediate land-use intensity levels (Felipe-Lucia et al., 2020). General trends across ecosystems could be further investigated through broader LUI ranges and longer-term experiments. Measurement of a broader range of ecosystem functions would also provide a more complete view of the relationships between provisioning and other ecosystem services (Himes et al., 2020).

## 4.2. Implications for monitoring of management practices

The extent to which patterns of ecosystem function in response to land use intensity can be generalized across distinct ecological systems remains open to debate (Foley et al., 2005; Qiu et al., 2021). One key difficulty is the lack of a generic LUI index for ranking different managed systems along a gradient of management intensity (Bloor et al., 2021). Building on previous conceptual frameworks (Blüthgen et al., 2012; Erb et al., 2013; Kahl and Bauhus, 2014), we propose that the complexities of management practices can be separated into a few measurable components which are susceptible to cause changes in resource availability, either directly or indirectly via biological mechanisms. Standardization of variables, both within sites and among variables, presents the advantage of improving comparisons across ecosystems and identification of threshold responses by minimizing mathematical artefacts, and
providing insights into multi-system dynamics via techniques such as sensitivity analysis. The LUI index developed in this article is flexible and able to integrate a wide variety of management practices, quantifying levels of LUI to better describe differences in management treatments at the site or cross-site levels. Indeed, despite limited levels per LUI gradient per site, our LUI index revealed that treatment contrasts along the experimental LUI gradients were consistent in TP, whereas the medium-intensity treatment was closer to the low treatment in the PG trial. This response pattern could reflect nutrient colimitation effects, with limited effects of P addition on biomass in the absence of N (Harpole et al., 2011). Of course, increasing the number of points along the LUI gradient would improve the power to detect responses, especially in the case of non-linear responses to LUI. The LUI range was also wider in PG than in TP, highlighting differences in the levels of management considered to be 'high intensity' across systems. In cases where several LUI components vary to a comparable extent or for complex LUI combinations, a weight-sensitivity analysis would be useful to test the robustness of LUI-based conclusions. Gradient-based analyses are a relevant method to support both the design and analysis of studies on management intensity effects since categorical comparisons of unquantified management intensity treatments may give rise to erroneous biological interpretation of results (Schweiger et al., 2016; Sommerfield et al., 2002).

In line with previous ecological studies on the impacts of LUI intensity on ecosystem structure and function (e.g. Neff et al., 2021; Seibold et al., 2019), we included inputs as one of the components which drives LUI. This is consistent with agronomic approaches where farm input intensity is characterised by total inputs (pesticides, herbicides, fertilizers), estimating the volume of inputs used per hectare based on information on input expenditures and the input price index for the year and country in question (Eurostat and AgriData, n.d.). Alternative agronomic indices based solely on inputs have also been used to compare effects of management intensity across agricultural systems. One example developed by the FAO (FAO, n.d.), calculates fertiliser intensity as the ratio between mineral fertiliser inputs and total biomass production. We argue that such agronomic indices are less appropriate for comprehensive understanding of ecosystem function, particularly in perennial managed systems where LUI is not driven primarily by inputs. However, these different existing approaches to management intensification underline the need for harmonization of integrative metrics that fully account for the complexity of management practices across both annual and perennial managed ecosystems.

The use of a generic LUI index requires that management practices be clearly documented, with full details for each intervention, such as the frequency of the management practices throughout the year. This is consistent with broader guidelines on open science and the need to go beyond studies of management intensity based on single drivers and overly-simplistic management scenarios (Richter et al., 2024). It is important to note that in our study, all LUI components have equal weighting in the LUI index, as it is difficult to attribute a weight to different management practice without a priori knowledge on the relative contribution of each component to the ecosystem response. However, it may be reasonable to assume that biomass removal with recycling (e.g. grazing or vegetation crushing) may differ in terms of impact compared to biomass removal without recycling (e.g. harvesting). Further work is required to apply this index to large-scale datasets and explore the relative importance of LUI components on ecosystem processes across contrasting perennial systems over time. This framework could be particularly valuable for meta-analyses and cross-system comparisons, as it is broadly applicable across systems subjected to anthropogenic interventions.

Due to its cross-system nature, our LUI index may be useful for better informing management policies and territorial planning, facilitating cross-sectoral recommendations for sustainable management at multiple

spatial scales. The holistic approach to incorporating management components (plant density, inputs, biomass removal with and without recycling, mechanisation) in both vegetation and soils enables the exploration of trade-offs between several components of LUI as well as between different ecosystem services across diverse managed systems.

### CRediT authorship contribution statement

**Lucie Bon:** Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Laurent Augusto:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Data curation, Conceptualization. **Frédérique Louault:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **Pierre Trichet:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Data curation, Conceptualization. **Juliette M.G. Bloor:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The XyloSylve project was initiated and designed with important contributions from D. Loustau, P. Pastuszka and J-M. Carnus. We thank J-P. Chambon, J-L. Denou, B. Dokhelar, J-B. Ornon, A. Raffin and particularly L. Severin for their help during the setting-up of the forest stands. We are particularly grateful to F. Bernier and L. Severin from INRAE-Forest Experimental Unit of Pierroton (UEFP 0570) for their crucial contribution of the XyloSylve functioning. We thank C. Meredieu and A. Nys for their help during the redaction of the present article. We thank the staff of INRAE-UE1414 Herbipole and I. Bosio, O. Darsonville, V. Guillot, N. Vassal from INRAE-UR0874 as well as S. Sauvat from VetAgro Sup and P. Note from UE1414 for technical assistance and data collection at the Theix field site.

The installation of the tree experimental platform was made possible by the financial support of the Nouvelle-Aquitaine regional council and the French National Agency for Research (“Agence Nationale de la Recherche” [ANR]; project PIA-1 Xyloforest). The XyloSylve platform functioning was supported by the French ANR, AnaEE-France (www.anaee-france.fr/en), and GIS-PMF (www.gisgpmf.fr). The Theix experiment is part of the SOERE-ACBB project co-financed by the French National Infrastructure AnaEE-F through ANR-11- INBS-0001. Finally, this work was supported by the ECODIV department at the “Institut national de la recherche en agriculture, alimentation, et environnement” (INRAE).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ecolind.2025.114489.

### Data availability

The dataset used in this study has been deposited in a public repository and is available for consultation and download (see Methods, Section 2.5).
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