domain: info spi

text:
Jean-Baptiste Vidal
email: jean-baptiste.vidal@univ-orleans.fr

Matthieu Lauras
email: matthieu.lauras@mines-albi.fr

Jacques Lamothe
email: jacques.lamothe@mines-albi.fr

Romain Miclo
email: romain.miclo@agilea.fr

Adaptive Sales & Operations Planning: Innovative Concept for Manufacturing Collaborative Decisions?

Keywords: Manufacturing Collaborative Decisions, Sales & Operations Planning, Adaptive Sales & Operations Planning, Comparison Framework, Supply Chain Management, Demand Driven Adaptive Enterprise

By definition, Supply Chain Management includes a huge number of collaborative decision processes. In a manufacturing environment, the Sales & Operations Planning (S&OP) process is often used to support strategic and tactical decisions such as recruitment, investment on machines or subcontracting. S&OP was invented in the eighties when collaboration issues between and within companies were radically different from what obtains today. Many rules and opportunities that are important for manufacturing collaborative decisions have changed but not in this process. Recently, the Demand Driven Institute has developed the Demand Driven Adaptive Enterprise system that includes an innovative process called Adaptive S&OP. The purpose of this paper is to develop a structured comparison between the traditional S&OP and the new Adaptive S&OP, to objectively characterize their differences, and to highlight points of vigilance regarding future Adaptive S&OP implementations. Based on this analysis, some suggestions for future research works are made.

Introduction and Research Question

Sales & Operations Planning is probably the most popular collaborative process for supporting strategic and tactical decisions in manufacturing environment. Designed more than 30 years ago, this process is regularly enriched and criticized in the Supply Chain literature [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF]. Notably, scholars point out that this process is not necessarily applied as it should be and not necessarily adapted for the current organizational networks, which have changed a lot during the last decades. Parallelly, some authors have recently suggested some new paradigms and approaches to manage material flows within and between companies. This is essentially the case of what is called Demand Driven Adaptive Enterprise system [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF]. In this system, one of the pillars is called Adaptive Sales & Operations Planning (AS&OP) and the authors affirm that this can address the limits and weaknesses of the current S&OP processes. The purpose of this research is to compare the traditional S&OP process with this new AS&OP process in order to know whether AS&OP is a really innovative contribution for collaborative decisions in a manufacturing environment. The objective of this analysis is also to highlight some points of vigilance on AS&OP that should be further studied.

The paper is divided into four complementary sections. The first one discusses some key points in the literature on this topic. The second one develops a structured framework that can be used to compare processes in terms of both objective and coordination issues. The third section develops a discussion based on these comparisons while the last section gives a rapid conclusion on this research work.

Background

Manufacturing Collaborative Decisions

Managing a manufacturing plant entails making decisions at different levels: strategic, tactical and operational. For decades, decision-makers use the Manufacturing Resource Planning (MRP II) system to support their decisions [START_REF] Wight | The executive's guide to successful MRP II[END_REF]. This system is a cascading planning system that offers a set of planning process for each decision level. Regarding the strategic and tactical levels, MRP II is executed through the Sales and Operations Planning (S&OP). This was made to support collaboration between the different stakeholders of a company (finance, sales, production, logistics, supply, etc.) regarding decisions such as recruitment, investment on machines or subcontracting. Definitively, MRP II and S&OP have brought benefits to companies during decades. But these tools seem now to become obsolete as the economic rules and environment have drastically changed [START_REF] Miclo | Challenging the « Demand Driven MRP » Promises : a Discrete Event Simulation Approach[END_REF]. Consequently, there has emerged recently from the corporate world a fundamental rethinking of the MRP II logic -a rethinking that draws on elements of developments such as Lean Systems and Theory of Constraints to improve the overall performance of manufacturing operations and collaborative mechanisms. This rethinking has resulted in a new approach -Demand Driven Adaptive Enterprise [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF].

"Sales & Operations Planning"

Richard C. Ling [START_REF] Ling | Breakthrough Sales & Operations Planning: How we developed the process[END_REF] is recognized by academics and practitioners as the father of S&OP in the late 1980s. Since his first model of the process [START_REF] Ling | Orchestrating Success: Improve Control of the Business with Sales & Operations Planning[END_REF], S&OP was popularized by practitioners through the exchanges of best practices in the supply chain community such as APICS. The APICS defines S&OP as "A process to develop tactical plans that provide management the ability to strategically direct its businesses to achieve competitive advantage on a continuous basis by integrating customer-focused marketing plans for new and existing products with the management of the supply chain. The process brings together all the plans for the business (sales, marketing, development, manufacturing, sourcing, and financial) into one integrated set of plans". This definition underlines the collaborative nature of S&OP so that all the company's functions generate an aligned business plan.

Practitioners tried to standardize this process in order to implement it in different business activities. Fig. 1. shows the five-step common approach accepted as traditional in business activities [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF]. The goals of S&OP are easy to understand, but practitioners in many different companies failed to totally implement this process or did not achieve the expected competitiveness in their markets. Fig. 1. S&OP model for practitioners [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF] Considering S&OP at the supply chain level, Fig. 2. shows a model used during APICS Annual Event 2017. Main boxes fit the Wallace model [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF], but they are now detailed and linked to the long-term business objectives. This underlines the idea of practitioners to keep original methodology. Fig. 2. S&OP Model by [START_REF]APICS Event[END_REF] According to several recent literature reviews [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF], [START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF], [START_REF] Kristensen | Context-based sales and operations planning (S&OP) research: A literature review and future agenda[END_REF], the interest of academics for S&OP has grown since 2000. These literature reviews show two important points:

• The complexity of the S&OP process is a reality. Multiple extensions of the original model were proposed and there is now needs to build a common framework for comparison. • Despite growing research activity, there is a requirement for fine granulometry studies for academics as well as for practitioners.

It is possible to understand that there are multiple reasons for failure when S&OP is implemented. Despite many improvements and the identification of diverse and numerous success factors [10], S&OP remains a difficult process to implement [START_REF] Grimson | Sales and operations planning: an exploratory study and framework[END_REF]. The gap between expected and actual benefits is big [START_REF] Jonsson | Future of supply chain planning: closing the gaps between practice and promise[END_REF]. However, practitioners still need answers to their vital problem: balancing supply capabilities with market expectations and needs at a strategic level while generating profit. In an environment where the variety and complexity of products is growing, the time to react is very low, supply chains and markets are spread around the world and customer requirements are very strong, it is more and more difficult to achieve success with the implementation of S&OP. With this new environment, S&OP was kept with its initial structure, without major modification. Academics and Practitioners continue to extend and refine the theoretical vision of S&OP with the aim of identifying the reasons for the failures in reality or the difficulty of reaching a high level of maturity in its implementation: recent research works show that it seems necessary to carry out practical and theoretical studies including contextual impacts [START_REF] Kristensen | Context-based sales and operations planning (S&OP) research: A literature review and future agenda[END_REF] [START_REF] Jonsson | Future of supply chain planning: closing the gaps between practice and promise[END_REF].

"Adaptive Sales & Operations Planning"

In 2017, the Demand Driven Institute (DDI) created a new approach called Demand Driven Adaptive Enterprise -DDAE [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF] based on three major processes:

• Demand Driven Operating Model (DDOM) at the operational level;

• Demand Driven Sales & Operations Planning (DDS&OP) at the tactical level; • Adaptive Sales and Operations Planning (AS&OP) at the strategic level.

Fig. 3. Model AS&OP by Demand Driven Institute

(the evolution of the first concept DDMRP create by DDI) proposes an integration of multiple approaches such as Lean, Theory of constraint and MRP II. [START_REF] Miclo | Challenging the « Demand Driven MRP » Promises : a Discrete Event Simulation Approach[END_REF] shows that this integrated approach works at the operational level but the demonstration at the strategic level is yet to be done. As the first and second processes have just been assimilated by first practitioners, DDI introduced in 2017 the third AS&OP process (see Fig. 3.) as a new way to formalize and use the S&OP philosophy.

Research Questions and Proposal

Research Questions and Assumptions

Today the number of DDAE practitioners is low in proportion to those practicing S&OP but the interest of the practitioner community is high. Although the concept of DDAE is recent, there are studies on operational parts of the DDAE model like DDMRP [START_REF] Miclo | Challenging the « Demand Driven MRP » Promises : a Discrete Event Simulation Approach[END_REF], but none of them is based on AS&OP. As the research on S&OP is rich [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF], as much on the mastered aspects as on those that are yet to be developed, it seems logical to try to compare S&OP and AS&OP by relying on the major features already studied on S&OP. The corpus of knowledge on DDAE is beginning to be accessible on this subject, through trainings and conferences proposed by the DDI and its global affiliates [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF]. Based on this material, we propose in this paper to study the potential of this new collaborative approach by answering two simple research questions: RQ1: Is AS&OP really a novelty compared to traditional S&OP? RQ2: Which priority aspects of AS&OP should be studied in future academic research works for supporting efficiently the first implementations?

We considered the following assumptions to conduct this qualitative comparison:

• The two other components of the DDAE system (i.e. DDOM and DDS&OP) are supposed to be well implemented on the field. • The study is limited to the public DDAE body of knowledge gathering from DDI white papers [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF] and dedicated trainings and conferences.

Comparison Framework

Create an analysis process

As underlined by [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF], S&OP is a multifaceted concept. As a result, we used a progressive approach, drawn from different academic journal papers, to design and fulfill a Goal framework and adding points of comparison, step by step, to build a Coordination framework. Fig. 4. shows this analysis process. For each construct, we selected the most convincing analysis axis to make our comparison on the basis of relevant information mainly from [START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF] and [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF]. In the same way, for each analysis axis, we have associated a criterion with 2 or 3 differentiating levels of characterization. The S&OP concept was created nearly 40 years ago. With the contributions of the practitioners and academics, a theoretical and practical (rather rich) vision of S&OP can be spread out according to several stages of maturity [START_REF] Jansson | Sales and operations planning in the process industry : A diagnostic model[END_REF]. Nevertheless, a large number of companies are struggling to go beyond a low maturity stage [START_REF] Grimson | Sales and operations planning: an exploratory study and framework[END_REF]. To compare S&OP and AS&OP, three distinct levels of S&OP have been identified as a reference: low maturity level of theoretical S&OP, high maturity level of both theoretical S&OP and practical S&OP. Practical S&OP means the concrete practices observed through the implementation of S&OP on the knowledge acquired from the feedback of a dozen S&OP implementation experts. in [START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF], [START_REF] Grimson | Sales and operations planning: an exploratory study and framework[END_REF], [START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF], [START_REF] Kristensen | Context-based sales and operations planning (S&OP) research: A literature review and future agenda[END_REF] for the theoretical S&OP and in [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF] and our knowledge acquired by interviews with dozens of S&OP implementation experts for the practical S&OP. Table 2 shows the results.

Discussion and Research Agenda 4.1 Comparison between High Level Maturity S&OP and AS&OP

First of all, we compare the result between the column "high maturity level" and AS&OP. We observe two discordant elements in this comparison: S&OP Process and Performance Management. The other ones are not discriminating.

The first difference refers to the non-linearity of the AS&OP process as described in [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF]. Fig. 5. shows that the processes (1.) portfolio and new activities, (2.) demand, (3.) supply and (4.) financial, are considered together to accomplish an (5.) integrated strategic reconciliation. Practitioners [START_REF] Ling | Breakthrough Sales & Operations Planning: How we developed the process[END_REF] who originally introduced S&OP recommended to have a cross-functional process with interconnections between all services. This was broken in [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF] with the introduction of the pre-meeting step (iv) and the linearization of steps i, ii and iii. AS&OP finally suggests coming back to the initial approach. The traditional linear approach of S&OP has made this concept understandable for a wide range of companies but it has also led to set-up failures such as the lack of interest of some of the strategic players concerned other than production and sales. Indeed, a information useful for the coordination of this process is established in a non-linear way, requiring more complex round trips than the present model, with coordination to be agile and permanent between the services. The nonlinear matrix vision of the AS&OP model can potentially be a best way to success in this coordination point of view, enabling the departments concerned to better find their place according to their real operating rhythm. It is also a difficulty that will have to be overcome particularly in terms of practical organization. AS&OP are mainly flow-oriented (i. working capital, ii. contribution margin, iii. customer base). DDI practitioners think that the most important thing is to use relevant information at each level. In that sense, they are inspired by Theory of Constraints and Lean Management philosophy to opt for a flow vision rather than a cost approach. Fig. 6. AS&OP Strategic KPIs [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF] The goal of S&OP is to help the coordination of a company's services to make both strategic and tactical decisions for the success of the company. The problem of choosing indicators to drive this process is an important research topic. Traditionally, the practitioners focus on the optimization of the resources until they see only the problem of the capacity versus demand. This constitutes a problem because it ends up concerning only the services of the production and the sales. Consequently, there generally is a disengagement from other departments, with ultimately a malfunction of the S&OP process. Also, the choice of AS&OP specific KPIs (see fig. 6.) seems to be a major asset to ensure the success of its implementation.

Comparison S&OP Practices vs AS&OP

Let's now compare the results between the columns practical S&OP and AS&OP. The idea is to test the ability of the AS&OP approach (as a whole DDAE) to be implemented efficiently in the field by avoiding the conventional pitfalls of traditional S&OP. About performance management, AS&OP used a flow-based metrics as highlighted by DDAE implementation experts. This is not a detail. They explain that the flow approach makes sense to people at the operational and tactical levels (DDOM and DDS&OP have actually been implemented in several companies). In the early stage of AS&OP, this can enable to obtain the adherence of top management from each department including sales and finance. A general (success of S&OP culture and leadership) disinterest is a major reason for the failure of stakeholders' integration and the lack of meetings (S&OP Organization). S&OP Process, data and IT (S&OP tools and data) are required to optimize business profit (performance management) [START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF]. Regarding the A&SOP process model, the risk of failure is as much as in S&OP. But it seems necessary to try to detail this model, for example on the link with DDS&OP. Similarly, A&SOP is based on the simulation of optimistic, pessimistic and median strategic scenarios, and this requires a dedicated IT, which does not completely exist yet. These links and specificities can make AS&OP and S&OP approaches successfully reach a high stage of maturity.

Point of Vigilance

DDI experts on AS&OP develop analogies with the concept of S&OP in a grid at different levels of maturity [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF]. On the basis of our understanding we can make some recommendations for both scholars and practitioners as a set of points of vigilance.

• For S&OP, the formalization of a global point of view is good, but to allow a successful implementation, developing a detailed approach will be a plus; • Little information is given by DDI on how to make all the steps, from 1 to 6 (see Figure 3). The implementation of the S&OP process is already difficult, the AS&OP model of flows needs to be clarified/explained; • Step 6 links AS&OP and DDS&OP. The positioning of buffer (stock, time, capacity) or control point, but also the definition of families and other information required by the DDS&OP process also needs to be clarified specifically such as to achieve a successful implementation of the vertical alignment between AS&OP and DDS&OP; • Regarding S&OP tools and data, they need to be supported with relevant data that require dedicated software. Studying and developing these software and decision support systems seem to be mandatory. Otherwise, the AS&OP may have unrealistic goals (to carry out long-term simulations of optimistic, pessimistic and median scenarios); • Concerning performance management, more studies on the benefits and limits of this choice of metrics (i. working capital, ii. contribution margin, iii. customer base) can be done in order to demonstrate their value, compared to existing flowbased metrics.

Conclusion and Further Research

In this paper, we focused on decision support processes for strategic collaborative manufacturing activities. We argued that usual approaches are based on tools that were developed a long time ago and which seem not to be relevant anymore. At the same time, new proposals have recently appeared on the market to address this issue, but no scientific study has been developed to assess their benefits and limits. Therefore, we have presented and discussed in this paper a structured comparison between the S&OP and AS&OP concepts. Our findings show that in terms of goals, AS&OP and S&OP are quite similar. But on the issue of coordination, AS&OP includes real and significant novelties compared to the traditional S&OP approach and has a better potential for success However, lack of scientific studies on DDAE, DDMRP and especially on AS&OP allow us making only partial conclusions on the real benefits of AS&OP. Given that our results can be considered as preliminary, further research is still needed.

An extension of this research might consist in improving the framework of the analysis, which allowed only a comparison with a large mesh as well as including additional criteria as analysis axes. [START_REF] Kristensen | Context-based sales and operations planning (S&OP) research: A literature review and future agenda[END_REF] for instance showed the relevance of including finer study parameters to better understand the concept of S&OP and consequently of

Fig. 4 .

 4 Fig. 4. The process of analysis and synthesis proposed in this paperGoal-framework:[START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF] records 5 goals for S&OP: (i) Produces integrated and functional tactical planning; (ii) Unifies all business plans into one; (iii) Develops a vision from 3 months to 18 months; (iv) Allows alignment between operations and strategy; (v) Generates values and drivers of the company's performance. We will use them to compare S&OP and AS&OP. Coordination-framework: four elements will be used to build it to make a comparison between S&OP and AS&OP: (i) literature search synthesis framework[START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF] (ii) Coordination of S&OP framework[START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF] (iii) the classical pitfalls of implementing S&OP in the real world[START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF],[START_REF]CPIM -Certified in Production and Inventory Management[END_REF] (iv) feedback from a dozen S&OP implementation experts. The coordination-framework described by[START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF] defines six principal coordination mechanisms. They are selected to constitute our constructs defined as point of views that are traditionally used by academics and practitioners to describe S&OP concepts: (i) S&OP organization; (ii) S&OP Process; (iii) S&OP Tools and Data; (iv) Performance management; (v) Strategic alignment; (vi) S&OP culture and leadership. For each construct, we selected the most convincing analysis axis to make our comparison on the basis of relevant information mainly from[START_REF] Tuomikangas | A coordination framework for sales and operations planning (S&OP): Synthesis from the literature[END_REF] and[START_REF] Tavares Thomé | Sales and operations planning: A research synthesis[END_REF]. In the same way, for each analysis axis, we have associated a criterion with 2 or 3 differentiating levels of characterization.

Fig. 5 .

 5 Fig. 5. Evolution of S&OP by Dick Ling (APICS Event 2016)

Table . 1

 . . Criteria on S&OP Coordination

			Cost-based
		Metrics	Flow-based
			Production-based
	management		Used to perform internal performance only
		KPI's	Used Internal and for Firm Performance, effectiveness of
			the S&OP Process
	Strategic alignment	Vertical alignment/tactical reconciliation	Disconnected Effective reconciliation
		Rewarding and	Functional incentives with no integration
		incentives	Collaborative S&OP enhances integration
	S&OP culture and leadership	Top management ownership	Low participation of executives High participation of executives
		Change	Top-down approach
		management	Bottom-up approach
	Coord. Constructs	Analysis axis	Criteria
			All internal service except finance
	S&OP organization	Stakeholders	All internal service included finance All internal service included finance and external suppliers
		Ritual meeting	Sporadic and informal meeting Regular meeting or upon request according to event
		Formalization	High Level (Macro vision) Detailed Level (Micro)
		Links between	Linear
	S&OP Process	activities	Nonlinear interconnected
			Plan production only
		Outcome	Plan sales, production, investment not integrated
			Plan sales, production, investment integrated
	S&OP Tools and	IT	Manual/Spreadsheet Dedicated IT system
	Data	S&OP data	Numbers/No Consolidation/No integration
		requirement	Numbers consolidated and integrated

Table . 2

 . Table. 2. shows the complete characterization framework . Coordination-framework gap analysis

			Low Maturity	High Maturity		High Maturity
	Construct	Analysis axis	Level S&OP	Level S&OP	S&OP practice	Level AS&OP
			theoretical	theoretical		theoretical
	S&OP organization	Stakeholders Ritual meetings	All internal services except finance Sporadic and informal meetings	All internal services, included finance and external suppliers Regular meeting or upon request according to event	All internal services except finance	All internal services, included finance and external suppliers Regular meetings or upon request according to event
		Formalization	High Level (Macro vision)	High Level (Macro vision)	High Level (Macro vision)	High Level (Macro vision)
	S&OP Process	Cycle	Linear Plan sales,	Linear Plan sales,	Linear	Non-Linear Interconnected Plan sales,
		Outcome	production, investment not	production, investment	Plan production only	production, investment
			integrated	integrated		integrated
	S&OP Tools and Data	IT S&OP data requirement	Manual/Spreadshe et Numbers/No Consolidation/No integration	Dedicated IT system Numbers consolidated and integrated	Manual/Spreads heet Numbers/No Consolidation/N o integration	Dedicated IT system Numbers consolidated and integrated

AS&OP. Another perspective could be on an objective assessment of AS&OP implementation (the first ones are ongoing and not yet finished) results compared to those of S&OP. We could, in view of the craze of practitioners for DDAE, take advantage of the future experiences to study in more depth the specifics of AS&OP. Lastly, future research works should consist in developing a kind of AS&OP maturity model that end-users can use to improve their performance.

To study S&OP practices we used classical pitfalls reported by practitioners about S&OP [START_REF] Wallace | Sales and Operations Planning: The How-To Handbook[END_REF], [START_REF]CPIM -Certified in Production and Inventory Management[END_REF] that have been classified in our selected points of view:

• S&OP organization: The strategic players of the company think that only the supply chain and the production are concerned by this process; the meeting monthly loses its meaning in the eyes of the main actors: no decisions made, short-term management only, too much details, search for the culprit; no integration of suppliers' or customers' vision; • S&OP Process: lack of formal processes about S&OP meeting, no formal approach to link tactical and strategic decisions; • S&OP Tools and Data: product life stages are not considered, lack of data accuracy or gathering; • Performance management: the actors focus on the short-term objectives and do not consider the possibility of taking important decisions regarding the future of the business. They focus on only one goal: "establish a demand forecast instead of other key issues concerning S&OP"; • Strategic alignment: Strategy alignment is dysfunctional as it is not shared between company functions; • S&OP culture and leadership: The S&OP team does not try to avoid conflict neither to monitor the efficiency of the S&OP process and to improve the company.

Gap Analysis

Goal-framework:

Through the test-one, AS&OP principles [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF] seem to cover all the different points that are usually identified in a traditional S&OP. We conclude that an AS&OP can be assimilated to a type of S&OP as academics and practitioners usually describe it.

Coordination-framework:

On this dimension, the comparison was done based on data available in [START_REF]The Demand Driven Institute -World Leader in Demand Driven Education[END_REF] for the


Mathieu Santonja
email: mathieu.santonja@gmail.com

Héctor Rodríguez-Pérez

Nathalie Le Bris

Christophe Piscart

Leaf nutrients and macroinvertebrates control litter mixing effects on decomposition in temperate streams RUNNING HEAD: Litter mixture decomposition in temperate streams

Keywords: Biodiversity-ecosystem functioning, community weighted means, functional trait dissimilarity, litter traits, litter decomposition, litter nutrients, temperate stream HIGHLIGHTS

Plant litter decomposition is an essential ecosystem function in temperate streams. Both riparian vegetation and decomposer communities are major determinants of the decomposition efficiency and the interactions occurring within litter mixtures. However, the extent to which such litter mixture interactions are affected by combined shifts in litter traits and decomposer community is not well understood. We used leaf litter from 10 European tree species in order to study litter decomposition and litter mixture effects occurring in two-species litter mixtures in a temperate forested stream of northwestern France. The study distinguished between (i) decomposition involving microorganisms alone or together with invertebrates, and (ii) decomposition involving litter mixtures of similar or dissimilar nutrient content. Increasing mean litter nutrient concentration favored both microbial activity and litter decomposition rate.

Surprisingly, the highest litter mixture effects occurred in mixtures containing two nutrient-rich litters and occurred mainly in macroinvertebrate presence. Both the "mass-ratio hypothesis", expressed as the community weighted mean traits (TraitCWM), and the "niche complementarity hypothesis", expressed as the functional dissimilarity of litter traits (TraitFD), contributed to explain litter mixture effects. However, TraitCWM was found to be a better predictor than TraitFD.

Finally, when evaluating the individual contributions of litter nutrients, calcium and magnesium appeared as important drivers of litter mixture effects. Our findings suggest that the mass-ratio hypothesis overrules the niche complementarity hypothesis as a driver of litter diversity effects.

Our study highlights the key importance of macroinvertebrates and of leaf nutrients, such as Ca and Mg, which are often neglected in decomposition studies in streams.

INTRODUCTION

Litter decomposition is an essential ecosystem function controlling the carbon (C) and nutrient cycles in both terrestrial (Swift and others 1979;[START_REF] Cadish | Driven by Nature: Plant Litter Quality and Decomposition[END_REF] and aquatic (Cummins 1974;Wallace and others 1997) ecosystems. Litter decomposition rate is jointly affected by the litter traits (e.g. Cornwell and others 2008;Garcia-Palacios and others 2016), the environmental conditions, such as the temperature (e.g. Fierer and others 2005;Follstad Shah and others 2017), and the decomposer communities (e.g. Hättenschwiler and others 2015;Gessner and others 2010;Jabiol and others 2013;Santonja and others 2018a). Rates of litter decomposition are essentially controlled by litter chemistry (Cornwell and others 2008;Garcia-Palacios and others 2016). In addition to secondary compounds (e.g. tannins) and fiber components (e.g. lignin), concentrations of nutrients such as nitrogen (N) and phosphorus (P) are useful litter traits for the prediction of the decomposition rates of single-species litter [START_REF] Gessner | Importance of stream microfungi in controlling breakdown rates of leaf litter[END_REF]Cornwell and others 2008;[START_REF] Schindler | Functional leaf traits and biodiversity effects on litter decomposition in a stream[END_REF].

Nevertheless, as mainly reported in terrestrial ecosystems, other nutrients such as calcium (Ca), magnesium (Mg), potassium (K), sodium (Na), and sulfur (S), can also affect the decomposition efficiency (Kaspari and others 2008;Makkonen and others 2012;Garcia-Palacios and others 2016). However, the potential importance of these nutrients is often neglected in decomposition studies in streams compared to N and P.

In the majority of natural ecosystems, litter material from different plant species decomposes together. Given that forested stream food webs are dependent upon allochthonous leaf litter (Vannote and others 1980;Wallace and others 1997;Gessner and others 1999), understanding the relationship between riparian tree species composition (and its associated litter traits) and the decomposition rates in streams draining forested watersheds is of considerable ecological importance [START_REF] Swan | Leaf diversity alters litter breakdown in a piedmont stream[END_REF]Kominoski and others 2007).

Changing plant species composition can modify litter trait control over decomposition in two ways. First, according to the "mass-ratio hypothesis" [START_REF] Grime | Benefits of plant diversity to ecosystems: immediate, filter and founder effects[END_REF]Garnier and others 2004), litter trait control over decomposition changes along community-weighted mean (CWM) trait values (Fig. 1a, Quested and others 2007;Mokany and others 2008;[START_REF] Laughlin | Nitrification is linked to dominant leaf traits rather than functional diversity[END_REF]. Second, according to the "niche complementarity hypothesis" [START_REF] Petchey | Functional diversity: back to basics and looking forward[END_REF]Diaz and others 2007), a change in the functional dissimilarity (FD) of litter trait-values affects the degree to which resource-use complementary occurs within the decomposer community, and its capacity to induce non-additive litter mixing effects on decomposition (i.e. litter mixtures that decompose at different rates than predicted by the mass-ratio hypothesis) (Fig. 1b, Wardle and others 1997;Vos and others 2013;Handa and others 2014). These two mechanisms can operate simultaneously by affecting both decomposer community and decomposition efficiency as a result of plant (litter) composition change (e.g. Garcia-Palacios and others 2017; Santonja and others 2018b). However, our knowledge about the relative importance of these two mechanisms in controlling the decomposer communities and the efficiency of the decomposition process in streams is very limited.

Mechanisms involved in positive litter mixing effects likely include nutrient transfer, such as N or P, from nutrient-rich to nutrient-poor litter species. For example, nutrients may be transported through fungal hyphae connecting two different leaf litter species [START_REF] Schimel | Nitrogen transfer between decomposing leaves of different N status[END_REF]Gessner and others 2010;Lummer and others 2012), in which case an acceleration of decomposition in species mixtures is expected since the nutrients are used more efficiently overall. According to the literature (Wardle and others 1997;Sanpera-Calbet et al. and others 2009;Santonja andothers 2015a, 2015b;Santschi and others 2018), positive mixing effects are most likely expected in the mixtures including one poor-quality litter species and a high-quality one (Fig 1b; but see Frainer and others (2015) for contrasting results). However, whether, and to what extent, mixing litter affects decomposition rates in streams remains much debated, since litter mixture effects appear to be idiosyncratic [START_REF] Schindler | Functional leaf traits and biodiversity effects on litter decomposition in a stream[END_REF]Gessner and others 2010;Cardinale and others 2011;Lecerf and others 2011). Previous studies highlighting contrasting results have focused on the effects of N and P in explaining such litter mixture effects (e.g. Frainer and others 2015;Santschi and others 2018). However, other key nutrients, such as Mg or Ca, could potentially affect decomposition rates in litter mixtures.

Additionally, it is also not clear to what extent macroinvertebrates contribute to decomposition in litter mixtures [START_REF] Hättenschwiler | Soil animals alter plant litter diversity effects on decomposition[END_REF]Swan and Palmer 2006b;[START_REF] Swan | Consumer presence and resource diversity independently induce stability of ecosystem function in a Piedmont stream[END_REF]. Shredder macroinvertebrates can play a prominent role in the decomposition process in stream ecosystems [START_REF] Anderson | Detritus processing by macroinvertebrates in stream ecosystems[END_REF][START_REF] Cummins | Feeding ecology of stream invertebrates[END_REF]Handa and others 2014;Garcia-Palacios and others 2016), by consuming and fragmenting the litter material [START_REF] Allan | Stream Ecology[END_REF][START_REF] Graça | The role of invertebrates on leaf litter decomposition in streams-a review[END_REF], by stimulating microbial decomposition [START_REF] Wetzel | Death, detritus, and energy flow in aquatic ecosystems[END_REF]Villanueva and others 2012), and by mediating plant diversity effects on litter decomposition (Lecerf and others 2005;Kominoski and others 2007;Sanpera-Calbet and others 2009).

According to Cummins and others (1989), such a role played by macroinvertebrates would be higher in litter mixtures of distinct degradability than in more homogeneous litter mixtures.

Indeed, this type of litter mixture could simultaneously create a suitable microhabitat (more refractory litter) and a food resource (more labile litter). Additionally, litter mixtures could promote an increase in both growth rate (Swan and Palmer 2006a) In order to address these gaps, we used leaf litter from 10 common European tree species to evaluate how litter diversity effects are affected by litter nutrients (Ca, K, Mg, N, Na, P, and S) in a temperate stream. Moreover, in order to elucidate which organisms may be responsible for mediating such litter diversity effects, we distinguished between decomposition involving microorganisms alone (with decomposition in fine-mesh litterbags) and both microorganisms and macroinvertebrates (with decomposition in coarse-mesh litterbags) [START_REF] Boulton | A review of methodology used to measure leaf litter decomposition in lotic environments: time to turn over an old leaf?[END_REF]. Five of the plant species showed nutrient-rich litter, while the other five showed nutrientpoor litter (Fig. 2). Mixtures of two litter species were created in all possible pairwise combinations, in order to distinguish between litter mixtures of similar nutrient content (i.e. in mixing two nutrient-poor or two nutrient-rich litters) and litter mixtures of dissimilar nutrient content (i.e. in mixing one nutrient-poor litter and one nutrient-rich litter). Despite the fact that both litter traits, litter mass loss and microbial activity are continuous variables, such artificial distinction into two discrete nutrient categories was necessary to better understand where and why litter diversity effects occur. We evaluated (i) the litter decomposition rate and the microbial activity (i.e. CO2 production) in the two-species litter mixtures, and (ii) the litter diversity effects occurring on the decomposition rate and on the microbial activity in these litter mixtures after 30 days of immersion in a temperate forested stream in northwestern France (Piscart and others 2009).

First, according to the "mass-ratio hypothesis" [START_REF] Grime | Benefits of plant diversity to ecosystems: immediate, filter and founder effects[END_REF]Garnier and others 2004), the increase in both litter decomposition rate and microbial activity would be positively correlated to the increase in CWM scores in nutrient concentration within the litter mixture (Cornwell and others 2008;Foucreau and others 2013). Therefore, in our first hypothesis, we predicted higher litter decomposition rate and microbial activity in the mixture of two nutrientrich litters (Fig. 1a). Second, according to the "niche complementarity hypothesis" [START_REF] Petchey | Functional diversity: back to basics and looking forward[END_REF]Diaz and others 2007), mixtures of very dissimilar litter species yield the highest FD scores and are related to higher litter diversity effects (Wardle and others 1997;Lecerf and others 2011;Santschi and others 2018). Thus, in our second hypothesis, we predicted the highest litter diversity effects in the mixtures of nutrient-poor and nutrient-rich litters (Fig. 1b), which would be shown by an increase in litter decomposition rate and microbial activity due to synergistic effects between the two litters in mixtures in the present study.

Finally, macroinvertebrates may contribute up to 6 times more influence than microorganisms on litter decomposition in temperate unaltered forested streams (Piscart and other 2009), favor microbial community development [START_REF] Wetzel | Death, detritus, and energy flow in aquatic ecosystems[END_REF]Villanueva and others 2012), and mediate litter diversity effects (Lecerf and others 2005), we hypothesized that litter decomposition rate, microbial activity, and litter diversity effects would be enhanced by macroinvertebrate presence (Figs. 1a and1b).

MATERIALS AND METHODS

Study site and material collection

The experiment was conducted in the Hermitage stream, located in the Villecartier Forest in northwestern France (48°28' N, 1°33' W). The stream bed was dominated by sand and leaf litter (site H1 in Piscart and others 2009). The stream water was circumneutral, welloxygenated, and possessed moderate nutrient concentrations (Piscart and others 2009): 11 ± 1 mg l -1 dissolved oxygen, 49 ± 18 µg N l -1 ammonium, 510 ± 19 µg N l -1 nitrate, 10 ± 8 µg N l - 1 nitrite, 19 ± 8 µg P l -1 phosphate. The macroinvertebrate community of the Hermitage stream was composed of species belonging to the Amphipoda, Isopoda and Trichoptera orders, with crustaceans representing 74% of this macroinvertebrate community (Supplementary Table S1).

Fagus sylvatica was the dominant tree species in the forested watershed, but the riparian vegetation was composed of diverse deciduous tree species, including those employed in the experiment.

The leaf litter of 10 common European tree species was collected: Acer platanoides, Alnus glutinosa, Betula pendula, Castanea sativa, Corylus avellana, Carpinus betulus, Fagus sylvatica, Quercus robur, Salix atrocinerea and Tilia cordata, hereafter referred to by their genus name. These 10 species were selected using the existing literature (e.g. Lecerf and others 2007;[START_REF] Schindler | Functional leaf traits and biodiversity effects on litter decomposition in a stream[END_REF]Santonja and others 2018a), based on the nutrient concentrations of their leaves, to represent five species with nutrient-rich litter (NRL), and five with nutrient-poor litter (NPL) (Fig. 2; a cluster analysis well discriminates these two groups).

Freshly abscised leaves were collected over the entire period of maximum litter fall, from October to November 2015. They were dried at room temperature, and stored until the beginning of the experiment.

Litter decomposition experiment

Leaf litter decomposition was studied for a period of 30 days, using the litterbag method [START_REF] Boulton | A review of methodology used to measure leaf litter decomposition in lotic environments: time to turn over an old leaf?[END_REF]. Coarse-and fine-mesh litterbags (5 mm and 0.5 mm mesh size, respectively) were used and filled with 2 g of dry leaves. The leaf litter enclosed in fine-mesh litterbags was only accessible to microorganisms, whereas the coarse-mesh bags also allowed access to macroinvertebrates. The litterbags contained either a single species (10 treatments) or a mixture of two species in all possible pairwise combinations (45 treatments). The mixedspecies litterbags received equal amounts of both species (i.e. 1 g). A total of 440 litterbags ([10 single-species + 45 two-species mixtures] × 2 mesh sizes × 4 replicates) were used for the experiment.

In December 2016, the litterbags were immersed for 30 days in the Hermitage stream.

After removal, the litterbags were immediately sealed in plastic bags to prevent the loss of litter material and were transported to the laboratory. The leaves were separated by species, which was possible even with small fragments of litter (owing to marked morphological differences among the species) and were carefully cleaned under water to remove macroinvertebrates and attached sediment particles.

Avoiding the central veins, six leaf discs (10 mm-diameter) for the single-species litters, or three disks for each species in the two-species litter mixtures, were cut for the purpose of microbial activity measurement. The remaining leaf material was frozen at -20 °C.

Microbial activity measurement

Microbial CO2 production measurements, as a measure of overall heterotrophic microbial activity, were performed in order to evaluate the activity of the microbial communities that colonized the leaves after 30 days of decomposition. We adapted the protocol proposed by [START_REF] Anderson | Quantification of bacterial and fungal contributions to soil respiration[END_REF]. Briefly, the six leaf disks taken in each litterbag were placed in 125 ml glass bottles with 25 ml of filtered water (GF/F glass microfiber filter, Whatman TM ) from the Hermitage stream and then preincubated for 12 h at 20 °C in the dark allowing the microbial respiration to saturate the water with CO2. In the matter of fact, at constant temperature, it is only possible to increase the CO2 concentration in the air phase of the respiration chamber if the aqueous phase is oversaturated in CO2. After a night of remaining open to the atmosphere, the respiration chambers were hermetically sealed and the first sample of air was taken, the second air sample was sampled after a 4 h incubation at 20 °C in the dark. CO2 production in a given time (i.e. 4 h) was then calculated as the difference between the final CO2 concentration and the initial CO2 concentration. Preliminary studies with our experimental conditions demonstrated that the CO2 production was linear and, since all the process of incubation occurred in the dark, there was no photosynthesis and thus no alteration of the dissolved CO2 partial pressure. In total, 440 respiration chambers, corresponding to the 440 litterbags, were prepared. At the beginning and at the end of the incubation period, 1 ml of air, taken with a syringe, was injected into a gas chromatograph (μGC SRA A 3000) in order to estimate the microbial activity (i.e. CO2 air content). The leaf discs were then dried at 65 °C for 72 h and the CO2 production was calculated as µg C-CO2 per h and per g of litter dry mass (± 0.1 mg).

In parallel to the measurement of the microbial activity, the leaves remaining in the 440 litterbags were dried at 65 °C for 72 h, and then weighed to the nearest 0.1 mg. After weighing the leaf disks and remaining leaf litter, all the litter material from a given litterbag was combined and ground to a fine powder using a ball mill, before measuring the litter ash content. We obtained ash-free dry mass by burning the combined sample at 550 °C for 4 h. Additional samples were also used to estimate ash-free dry mass of the initial litter material. The percentage of ash-free dry mass data was used to correct both the initial and the remaining leaf material before the computation of (i) the percentage of litter mass loss after 30 days of field decomposition and (ii) the microbial CO2 production per h and per g of litter.

Litter trait measurement

The initial litter traits were determined from four samples of each of the 10 litter species (Supplementary Table S2). Prior to the chemical analysis, each litter sample was ground into powder using a ball mill. The carbon (C), nitrogen (N), and sulfur (S) concentrations were determined by thermal combustion, using a Vario Pyro cube CNS analyzer (Elementar France SARL, Lyon, France). The phosphorus (P) concentration was measured colorimetrically using the molybdenum blue method (Grimshaw and others 1989). To 80 mg of ground litter sample 8 ml of HNO3 and 2 ml of H2O2 were added and the mixture was heated at 175 °C for 40 min using microwaves (Ethos One, Milestone SRL, Italy). After this mineralization step, the sample was diluted to a total of 50 ml. A hundred µl of sample, 100 µl of NaOH, 50 µl of mixed reagent (emetic tartar and ammonium molybdate solution), and 50 µl of ascorbic acid were mixed directly in a 96 well microplate. After 30 min at 40 °C, the reaction was completed, and the P concentration was measured at 720 nm using a microplate reader (Victor, PerkinElmer, Singapore). Following the mineralization step (i.e. the same as for P analysis), calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na) concentrations were measured using an atomic absorption spectrometer (AAS, iCE 3000 series, ThermoScientific, China).

To assess the "mass-ratio hypothesis", we calculated the community-weighted mean (CWM) trait values of litter mixtures as the average trait values of litter mixtures following Garnier and others (2004)

as: TraitCWM = ∑ 𝑝 𝑖 × 𝑡𝑟𝑎𝑖𝑡 𝑖 𝑛 𝑖=1
, where pi is the relative abundance for species i in the litter mixture and traiti is the trait value for species i. These calculations were performed for each of the 8 litter traits. The highest scores of CWM were reached for litter mixtures containing two litter species with the highest nutrient concentrations (Fig. 1a). Since we predicted that both microbial activity and litter decomposition rate respond to the "massratio hypothesis", we expected the increase in both decomposition rate and microbial activity to be positively correlated to the increase in CWM values.

To assess the "niche complementarity hypothesis", we calculated the functional , where pi and pj are the relative abundance for species i and j in the litter mixture, and dij the Euclidian distance between species i and j for the trait considered. These calculations were performed for each of the 8 litter traits.

The highest scores of FD were reached for litter mixtures containing two species with very dissimilar nutrient concentrations (Fig. 1b). Since we predicted that the litter diversity effects respond to the "niche complementarity hypothesis", we expected the increase in relative litter mixture effects to be positively correlated to the increase in FD values.

Statistical analyses

All of the statistical analyses were conducted using R software (R Core Team 2013), with significance levels indicated as * for P < 0.05, ** for P < 0.01, and *** for P < 0.001.

A principal component analysis (PCA) was conducted using the values of the eight measured litter traits of the 10 tree species (Supplementary Table S2) in order to discriminate the five tree species with nutrient-rich litter (NRL) from the other five tree species with nutrient-poor litter (NPL). The differences in the initial litter traits were assessed using one-way ANOVAs, followed by Tukey's tests to carry out post-hoc pairwise comparisons.

For the single-species litter, three-way ANOVAs, followed by Tukey's post hoc tests, were used to test for the effects of litter type (separated in NPL vs. NRL), litter species identity (10 litters), macroinvertebrate presence (fine-mesh bag [FMB] vs. coarse-mesh bag [CMB]), and their interactions on litter decomposition rate and microbial activity.

In order to further test whether litter decomposition and microbial activity differed between litter mixtures and single litter species, the relative litter mixture effects (RME) on litter decomposition rate and microbial activity were calculated. The RME was calculated as the relative difference between the observed litter decomposition rate/microbial activity (O) from the litter mixtures compared to those expected based on the respective single litter species treatments (E), following the formula (O -E) / E × 100% (Wardle and others 1997). Onesample Student's t-tests were used to test whether the RME were significantly different from zero.

For the two-species litter mixtures, two-way ANOVAs, followed by Tukey's post hoc tests, were used to test for the effects of litter mixing (NPL-NPL, NPL-NRL and NRL-NRL), macroinvertebrate presence (FMB vs. CMB), and their interactions i) on litter decomposition rate and microbial activity and ii) on the RME on litter decomposition rate and on microbial activity.

For a more detailed understanding of how the mixture of leaf litter affected the litter decomposition rate and microbial activity, we evaluated the effects of the mean litter traits (TraitCWM) and functional litter trait dissimilarities (TraitFD) of the eight measured litter traits (Supplementary Table S2). First, a principal component analysis (PCA) was conducted using the CWM or the FD values of the eight measured litter traits across the 45 two-species litter mixtures. CWM1 and CWM2, and FD1 and FD2 represented the two first components of the PCAs conducted using the CWM or the FD values across the litter mixtures, respectively.

Second, multiple linear regression models were performed in order to decipher the relative contributions of TraitCWM and TraitFD. In these models the effects of the TraitsCWM (i.e. CWM1 and CWM2), TraitsFD (i.e. FD1 and FD2), macroinvertebrate presence (FMB vs. CMB), and their interactions were tested i) on litter decomposition rate and microbial activity and ii) on the RME on litter decomposition rate and on microbial activity.

RESULTS

Litter traits

The PCA based on the element concentrations showed that the first PCA axis explained 50.2% of the variation and discriminated between the nutrient-rich litters (NRL) on the left and the nutrient-poor litters (NPL) on the right of the axis (Fig. 2).

Litter species incubated individually

The NRL exhibited two times more litter mass loss and microbial activity than the NPL (Table 1; Fig. 3). The presence of macroinvertebrates increased the litter mass loss and the microbial activity, but this effect was dependent on the litter type (litter type × mesh size interaction, Table 1). This significant interaction was explained by a stronger increase in litter mass loss and microbial activity in the NRL category (+59% litter mass loss and +33% microbial activity) compared to the NPL category (+23% litter mass loss and +28% microbial activity) (Fig. 3).

In addition, litter mass loss and microbial activity were significantly affected by litter species identity (Table 1). Within the NRL category, Alnus showed the highest litter mass loss compared to the four other species (Supplementary Fig. S1a), as well as a higher microbial activity than Carpinus, Acer, and Tilia (Supplementary Fig. S1b). Within the NPL category, Salix and Betula showed both a higher litter mass loss and a higher microbial activity than the three other species (Supplementary Fig. S1a andS1b). The positive effect of the macroinvertebrate presence on the litter mass loss also varied according to the litter species identity (litter species × mesh size interaction, Table 1), as the effects ranged from +68% for Alnus to an absence of effect for Fagus (Supplementary Fig. S1a).

Effects of mixing low-and high-quality litter

Litter mass loss and microbial activity increased according to the gradient NPL-NPL < NPL-NRL < NRL-NRL mixtures (Table 2; Fig. 4a and4c), with two times more litter mass loss and microbial activity in mixtures of two nutrient-rich litters compared to the mixtures of two nutrient-poor litters. The relative mixture effects (RME) on litter mass loss and on microbial activity showed a similar trend, with an increase in RME according to the same gradient (Table 2; Fig. 5). The NPL-NPL mixtures lost -3.5% litter mass compared with the expected values from the respective single litter species (Fig. 5a), while the observed microbial activity of the NPL-NPL mixtures did not differ significantly from the expected values (Fig. 5c). The NPL-NRL mixtures lost +5.3% litter mass than expected from the respective single litter species (Fig. 5a), while the observed microbial activity of the NPL-NRL mixtures did not differ from the expected values (Fig. 5c). The NRL-NRL mixtures showed +8.9% litter mass and +16.3% microbial activity than expected from the respective single litter species (Figs. 5a and5c).

Litter mass loss and microbial activity were respectively 41% and 30% higher with macroinvertebrates than without (Table 2; Fig. 4b and4d). The RME on litter mass loss and on microbial activity were also higher with macroinvertebrates than without (Table 2). Specifically, the litter mixtures exhibited synergistic effects in the presence of macroinvertebrates, with +7.3% litter mass loss and +9.9% microbial activity than expected from the respective single litter species (Figs. 5b and5d).

CWM-versus FD-trait control over litter decomposition rate and microbial activity

The PCA of the CWM traits showed that the first PCA axis (CWM1), explaining 50.2% of the variation, was determined by high scores of P and Ca concentrations, and, to a lower extent, by the scores of the K, Mg, Na, and S concentrations (Supplementary Fig. S2a). The low scores of the second PCA axis (CWM2), explaining 21.9% of the variation, were related to high values of N and C concentrations (Supplementary Fig. S2a). Regarding functional trait dissimilarity, high scores of the first PCA axis (FD1), explaining 28.5% of the variation, were related to the increase in dissimilarity in the Mg, S, and C concentrations, while the low scores were related to the increase in dissimilarity in the N and Ca concentrations (Supplementary Fig. S2b). The low scores along the second axis (FD2), explaining 22.0% of the variation, were largely dependent on the increase in dissimilarity in the K, Na, and P concentrations (Supplementary Fig. S2b).

When simultaneously evaluating the effects of the CWM and FD traits (Table 3), we found that both litter mass loss and microbial activity were mainly controlled by the CWM values (i.e. increasing mean nutrient concentrations within the litter mixture) compared to the FD values (i.e. increasing dissimilarity in nutrient concentrations within the litter mixture). In fact, CWM values (CWM1 + CWM2) explained respectively 9 and 15 times more of the overall variance in litter mass loss and in microbial activity than FD values (FD1 + FD2) (Table 3).

When simultaneously evaluating the effects of the CWM and FD traits on RME, we found that the RME on litter mass loss was controlled by the CWM (CWM1), the presence of macroinvertebrates, and the interaction between the FD and the presence of macroinvertebrates (FD2 × mesh size) (Table 3). Increasing CWM1 scores was related to higher RME (Fig. 6a).

The significant interaction between FD2 and mesh size (Table 3) showed that increasing FD in the initial K, Na, and P concentrations stimulated microbial-driven RME (Fig. 6d). Concerning the RME on microbial activity, it was found to be significantly affected only by CWM1 and the presence of macroinvertebrates (Table 3). Similar to what we observed for litter mass loss, increasing CWM1 scores was related to higher RME on microbial activity (Fig. 6e).

The RME on litter mass loss appeared to be more strongly correlated with the increase in P, Ca, Mg and Na concentrations than with the K, N, or S concentrations, and these relationships were more marked in the coarse-mesh litterbags in which macroinvertebrates were present, than in the fine-mesh litterbags in which macroinvertebrates were absent (Table 4).

The RME on microbial activity appeared to be more strongly correlated with the increase in Ca, K, and Mg concentrations than in N and P concentrations, and these relationships were also more marked in the presence than in the absence of macroinvertebrates (Table 4).

DISCUSSION

Niche complementarity hypothesis vs. mass-ratio hypothesis for explaining litter diversity effects

We found evidence of leaf litter diversity effects on decomposition rates, including additive, synergistic, and antagonistic effects, as previously shown in other studies (Kominoski and others 2007;Srivastava and others 2009;Lecerf and others 2011;Handa and others 2014). Surprisingly, the pattern observed in our study highlighted that the largest mixture effects occurred in mixtures containing the combination of two nutrient-rich litters (NRLs), rather than in mixtures of one nutrient-rich litter (NRL) and one nutrient-poor litter (NPL), as predicted in our second hypothesis (Fig. 1b). Such a finding is in agreement with the mass-ratio hypothesis, and emphasizes the fact that increasing the mean value of the nutrient pool in litter mixtures favors the occurrence of litter diversity effects. In any case our results pointed out that the litter diversity effects on decomposition were strongly litter-quality dependent. This finding is congruent with the recent study of [START_REF] Jabiol | Biodiversity and litter decomposition: a case study in a Mediterranean stream[END_REF] in which higher synergistic effects on litter decomposition occurred when Alnus glutinosa was combined with Fraxinus angustifolia (i.e. two NRLs) than when Alnus glutinosa was combined with Quercus ilex (i.e.

one NRL and one NPL).

Surprisingly, TraitCWM proved to be a better predictor of litter diversity effects than TraitFD, for both the coarse-and fine-mesh litterbags, contrasting with our second hypothesis. Indeed, based on the well-developed literature on the drivers of the litter decomposition process (e.g. Hättenschwiler and others 2005;Cornwell and others 2008;Gessner and others 2010;Makkonen and others 2012;Vos and others 2013;Handa and others 2014;Garcia-Palacios and others 2017), we expected that litter diversity effects occur when there are increases in the nutrient concentration dissimilarity of the litter mixtures (i.e. according to the niche complementary hypothesis, Fig. 1b), while the mean nutrient concentration within the litter mixture must only explain the litter decomposition rate (i.e. according to the mass-ratio hypothesis, Fig. 1a). The results from the coarse-mesh litterbags showed no evidence of nutrient dissimilarity effect (TraitFD), conforming with the conclusion of Frainer and others (2015), which also highlighted, over a lower range of litter traits (N, P, and lignin), that no litter dissimilarity effect was involved in the litter diversity effects occurring in two-species mixtures.

In contrast, TraitFD was linked to the litter diversity effects for the fine-mesh litterbags. In this case, litter mixtures with contrasting nutrient concentrations may have improved the availability of different nutrient sources for microorganisms [START_REF] Schimel | Nitrogen transfer between decomposing leaves of different N status[END_REF]Handa and others 2014), as suggested by the niche complementarity hypothesis. Filamentous fungi, including the aquatic hyphomycetes that dominate fungal communities on decomposing leaves in streams (Krauss and others 2011), can indeed extend their hyphae over considerable distances in order to acquire remote resources that they transport to the locations of active hyphal growth [START_REF] Ritz | Fungal roles in transport processes in soils[END_REF]). It has previously been suggested that fungi-driven N transfer among litter species varying in their initial N concentration may contribute to litter mixture effects [START_REF] Schimel | Nitrogen transfer between decomposing leaves of different N status[END_REF]Vos and others 2013;Handa and others 2014). Thus, nutrients from a nutrient-rich litter may be translocated to another litter depleted in nutrient (i.e. a nutrient-poor litter; Schimel and Hattenschwiler 2007;Handa and others 2014). Therefore, our findings highlighted that both TraitCWM and TraitFD contributed to explain litter diversity effects driven by microorganisms alone. Interestingly, TraitFD did not explain litter diversity effects driven by microorganisms and macroinvertebrates together. In this case, it could be hypothesized that the additional nutrients provided by macroinvertebrate feces alleviated the nutrient limitation for microorganisms [START_REF] Wetzel | Death, detritus, and energy flow in aquatic ecosystems[END_REF][START_REF] Joyce | Shredder fecal pellets as stores of allochthonous organic matter in streams[END_REF], and consequently the TraitFD contribution to litter diversity effects.

Macroinvertebrates control over decomposition and litter diversity effects

Across all of the 45 litter mixtures, litter mixture decomposition was not found to be enhanced in the fine-mesh litterbags (i.e. additive effects), while the litter mixtures in the coarse-mesh litterbags lost on average 7.3% more mass than the litter in the single-species treatments (i.e. synergistic effects). The absence of litter mixture effects when macroinvertebrates were excluded suggests that the activity of microbial decomposers alone did not induce any effect of litter mixture on decomposition in the studied temperate stream.

Previous studies performed in lotic systems, where decomposition primarily involves microorganisms, have also reported a lack of a synergism (Ferreira and others 2012;Bruder and others 2014). For instance, similar to our findings, [START_REF] Jabiol | Biodiversity and litter decomposition: a case study in a Mediterranean stream[END_REF] reported that no effect of litter mixture was observed when detritivores were excluded during the litter decomposition in a Mediterranean stream in southern France. In a laboratory experiment, Swan and Palmer (2006b) also reported that litter mixture effects were contingent on the feeding activity of the isopod Caecidotea communis.

In homogenous species mixtures comprising litters of a single litter category, macroinvertebrates have little choice to select among different qualities of leaves, as opposed to the choice offered between leaf litter species in heterogeneous litter mixtures (Swan and Palmer 2006b). However, as macroinvertebrates can simultaneously exploit multiple litter species to meet their elemental demands [START_REF] Leroy | Litter quality, stream characteristics and litter diversity influence decomposition rates and macroinvertebrates[END_REF], combining two NRLs that nutritionally complement one another might stimulate the feeding activity of detritivores (Vos and others 2013). For example, the mixing of one P-rich with one N-rich species in the present study (such as Acer platanoides and Alnus glutinosa, respectively) increased litter mass loss of the mixture by 14%, probably due to the increased nutritional value of such litter mixing. In contrast, mixing two P-rich species (e.g. Acer platanoides and Carpinus betulus) increased litter mass loss of the mixture by only 4%, while mixing two P-poor species (e.g. Castanea sativa and Fagus sylvatica) decreased litter mass loss of the mixture by 10%. Alternatively in NRL+NPL mixtures, high detritivore density may be promoted by the co-occurrence of a suitable resource, such as that provided by the nutrient-rich litter, and a complex and structured habitat provided by the nutrient-poor litter (Sanpera-Calbet and others 2009; Jabiol and others 2014).

Litter nutrients control over decomposition and litter diversity effects

In agreement with our predictions and the literature, nutrient-rich litters decomposed faster than nutrient-poor litters, confirming that nutrient concentration is an important determinant of leaf litter decomposition (Kaspari and others 2008;Makkonen and others 2012; Garcia-Palacios and others 2016). The rates of litter decomposition observed in the present study were similar to those reported in other studies concerning temperate streams (e.g. Lecerf and others 2007;[START_REF] Schindler | Functional leaf traits and biodiversity effects on litter decomposition in a stream[END_REF]Frainer and others 2015). For instance, Lecerf and others (2007) reported 80% and 42% mass loss for Alnus glutinosa (i.e. a nutrient-rich litter) and Quercus robur (i.e. a nutrient-poor litter), respectively, after 34 days of decomposition in a temperate stream in central France. The results of the present study were also similar to the 16.5% mass loss for Fagus sylvatica (i.e. a nutrient-poor litter) after 30 days of decomposition obtained in the same Petit Hermitage stream in February 2005 (Piscart and others 2009).

Moreover, our results also highlighted the important role of nutrients not usually considered in litter diversity experiments. The first principal component of the CWM-trait PCA was the main driver of litter diversity effects in both fine-and coarse-mesh litterbags, and was driven by P, Ca, K, Mg, Na, and S. Their relative content in the leaves is closely related to one other (Garcia-Palacios and others 2016), hampering a straightforward interpretation of combination of elements. Nevertheless, when evaluating the individual contributions of litter nutrients, Ca and Mg appeared to be important drivers of litter diversity effects compared to N (and to P only for microbial activity), suggesting for the first time that these two nutrients may play an important role in litter diversity effects in addition to, or independently of, the P or N content. The Ca content is known to positively affect the growth and activity of aquatic hyphomycetes, by enhancing the fungal capacity to transfer N between distinct litter types [START_REF] Jenkins | The influence of water chemistry on the enzymatic degradation of leaves in streams[END_REF]. Meanwhile, Ca and Mg are known to be essential elements in the diet of macroinvertebrates, since they are required for enzymatic reactions, nerve connections, muscle function, and skeleton formation (National Research Council 2005).

Moreover, in some temperate streams, crustaceans could represent up to 95% of shredder biomass (Piscart and others 2011), and the Ca content of leaves could be a significant source of Ca for their cuticles [START_REF] Cairns | A review of the influence of low ambient calcium concentrations on freshwater daphniids, gammarids, and crayfish[END_REF], especially in streams with a low Ca content in the water [START_REF] Glazier | Springs as model systems for ecology and evolutionary biology: a case study of Gammarus minus Say (Amphipoda) in mid-Appalachian springs differing in pH and ionic content[END_REF], such as in Brittany, where the present study was conducted.

CONCLUSION

As expected, increasing mean nutrient concentration and macroinvertebrate presence favored both microbial activity and litter decomposition rate. In addition, we experimentally demonstrated for the first time that the mass-ratio hypothesis (i.e. TraitCWM) overrules the niche complementarity hypothesis (TraitFD) as a driver of litter diversity effects in a temperate stream.

In fact, in strong contrast to our expectations, the combination of two nutrient-rich litters yielded the highest litter mixture effects. In addition, the synergistic effects of litter mixing were mainly evident in the presence of macroinvertebrates. Both community weighted mean traits (TraitCWM) and the functional dissimilarity of litter traits (TraitFD) contributed to explain the litter mixture effects. There was no support for TraitFD explaining litter diversity effects in the presence of macroinvertebrates, while both TraitCWM and TraitFD contributed to explain litter mixture effects driven by microorganisms alone. Finally, Ca and Mg, which are often neglected in decomposition studies in streams, were found to be important drivers of litter mixture effects. Table 1. Effects of litter quality (NPL vs. NRL), litter species identity (10 species), litterbag mesh size (fine-mesh vs. coarse-mesh litterbags), and their interactions on litter decomposition rate and microbial activity in the single-species litterbags. d.f. = degrees of freedom, %SS = percentage of type III sums of squares. F-values and associated P-values (with the respective symbols * for P < 0.05, ** for P < 0.01, and *** for P < 0.001) are indicated. on microbial activity (panels e, f, g and h) as a function of community weighted mean traits (CWM1 and CWM2 from the PCA using the CWM values, Supplementary Fig. S2a) and functional trait dissimilarities (FD1 and FD2 from the PCA using the FD values, Supplementary Fig. S2b) in fine-mesh (white symbol) and coarse-mesh (grey symbol) litterbags. The RME on litter decomposition rate and on microbial activity are indicated as the relative difference between the observed and the expected values from the respective single litter species treatments. Significant relationships are indicated with dotted (fine-mesh litterbags) or grey (coarse-mesh litterbags) lines.

TABLES

  and macroinvertebrate population due to their aggregative behavior (Presa-Abós and others 2006), enhancing therefore the decomposition of litter mixtures (Sanpera-Calbet and others 2009).

  dissimilarity (FD) of litter mixtures according to Rao's quadratic entropy (Botta Dukat 2005; Epps and others 2007) as: TraitFD = ∑ ∑ 𝑝𝑖𝑝𝑗 * 𝑑𝑖𝑗 n j=1 n i=1

Fig. 2 .Fig. 3 .Fig. 4 .Fig. 5 .Fig. 6 .

 23456 Fig. 2. Principal component analysis (PCA) based on the chemical element concentrations of

Table 2 .

 2 Effects of litter quality mixing (NPL-NPL, NPL-NRL and NRL-NRL), litterbag mesh size (fine-mesh vs. coarse-mesh litterbags), and their interactions i) on litter decomposition rate and microbial activity and ii) on relative mixture effects (RME) on litter decomposition rate (LD) and on microbial activity (MA). d.f. = degrees of freedom, %SS = percentage of type III sums of squares. F-values and associated P-values (with the respective symbols * for P < 0.05, ** for P < 0.01, and *** for P < 0.001) are indicated.

			Litter decomposition	Microbial activity
		d.f.	%SS	F-value	%SS	F-value
	Litter quality (LQ)	1	59.1	854.8 ***	60.0	239.7 ***
	Litter species (LS)	8	12.6	22.8 ***	11.3	5.6 ***
	Mesh size (MS)	1	15.8	228.9 ***	11.1	44.3 ***
	LQ × MS	1	6.6	95.9 ***	1.4	5.7 *
	LS × MS	8	1.6	3.0 **	1.1	0.6
	Residuals	60	4.2		15.0

Table 3 .

 3 Effects of community-weighted mean traits (CWM), functional trait dissimilarities (FD), litterbag mesh size (fine-mesh vs. coarse-mesh litterbags), and their interactions i) on litter decomposition rate and microbial activity and ii) on relative mixture effects (RME) on litter decomposition (LD) and on microbial activity (MA). CWM1 and CWM2, and FD1 and FD2 represented the two first components of the PCAs conducted using the CWM or the FD values in Supplementary Fig. S2. d.f. = degrees of freedom, %SS = percentage of type III sums of squares. F-values and associated P-values (with the respective symbols * for P < 0.05, ** for P < 0.01, and *** for P < 0.001) are indicated.

			Litter decomposition	Microbial activity	RME on LD	RME on MA
		d.f. %SS	F-value	%SS	F-value	%SS	F-value	%SS	F-value
	CWM1	1	48.8 2693.2 ***	55.1 1344.1 ***	15.4	81.8 ***	23.1 129.3 ***
	CWM2	1	1.6	86.4 ***	1.6	38.1 ***	0.3	1.4	0.3	1.7
	FD1	1	3.7	202.3 ***	1.8	43.2 ***	0.2	0.8	0.2	1.1
	FD2	1	1.9	107.1 ***	1.4	33.9 ***	3.7	19.9 ***	0.5	2.9
	Mesh size (MS)	1	30.8 1704.0 ***	22.9 559.5 ***	11.5	60.8 ***	13.3	74.6 ***
	CWM1 × MS	1	6.0	332.0 ***	1.9	46.5 ***	0.3	1.3	0.0	0.3
	CWM2 × MS	1	0.1	3.4	0.3	7.1 **	0.7	3.6	0.1	0.3
	FD1 × MS	1	0.7	40.1 ***	0.7	17.5 ***	0.4	2.0	0.2	1.2
	FD2 × MS	1	0.1	5.3 *	0.1	2.7	1.6	8.3 **	0.0	0.2
	Residuals	350	6.3		14.3		66.0		62.2

Table 4 .

 4 Relationships between CWM values of chemical element concentrations and relative mixture effects (RME) on litter decomposition rate and on microbial activity in fine-mesh and coarse-mesh litterbags. Adjusted R 2 and associated P-values (* P < 0.05, ** P < 0.01, *** P < (a) Hypothetical relationship between the different categories of litter mixtures and the litter decomposition rate of these litter mixtures. According to the mass-ratio hypothesis, the decomposition rate increases with the increase in CWM values in nutrient concentration within a litter mixture. (b) Hypothetical relationship between the different categories of litter mixtures and the intensity of litter diversity effects occurring in these litter mixtures. According to the niche complementary hypothesis, the intensity of litter diversity effects increases with the increase in FD values in nutrient concentration, which would be shown by an increase in litter decomposition rate in the present study. More precisely, this increase in decomposition rate, due to synergistic effects between the two litters in mixtures, will be higher in litter mixtures

	0.001) are indicated.
		C	N	P	Ca	K	Mg	Na	S
	RME on litter decomposition
	Fine-mesh litterbags	0.19 **	0.19 **	0.28 *** 0.26 *** 0.14 **	0.21 **	0.18 **	0.09 *
	Coarse-mesh litterbags	0.17 **	0.12 *	0.27 *** 0.35 *** 0.24 *** 0.30 *** 0.33 *** 0.15 **
	RME on microbial activity
	Fine-mesh litterbags	0.12 *	0.02	0.13 *	0.27 **	0.26 *** 0.31 *** 0.27 *** 0.21 ***
	Coarse-mesh litterbags	0.07	0.16 **	0.27 *** 0.41 *** 0.30 *** 0.43 *** 0.16 **	0.16 **

exhibiting intermediate nutrient concentration compared to litter mixtures exhibiting low or high nutrient concentrations (Fig.

1a

). NPL = nutrient-poor litter, NRL = nutrient-rich litter.

For both relationships, red dotted lines illustrate hypothetical increases in both litter decomposition rate and litter diversity effects mediated by macroinvertebrate presence.

NPL+NPL NPL+NRL NRL+NRL Litter decomposition rate Litter mixtures Functional dissimilarity (FD) in nutrient concentration Community-weighted mean (CWM) in nutrient concentration NPL+NPL NPL+NRL NRL+NRL Litter mixtures

	Low	Intermediate	High	Low	High	Low

ACKNOWLEDGMENTS

The chemical analyses were performed at the Plateforme d'Analyses Chimiques en Ecologie (PACE, LabEx Centre Méditerranéen de l'Environnement et de la Biodiversité, Montpellier, France), as well as at the PLateforme AnalYtique (PLAY, UMR CNRS 6553 ECOBIO, Rennes, France). We thank Raphaëlle Leclerc, Bruno Buatois and Nicolas Barthes for technical assistance during the chemical analyses as well as Pierre Mariotte for his reviewing of the English. This study was funded by the Selune project (project number 1 053 864) and by the Rennes Metropole's project AIS 2015. The study was also supported by the LTSER France Zone Atelier Armorique. Finally, the authors declare no conflict of interest.

AUTHORS' CONTRIBUTIONS

M.S., H.R.P., N.L.B. and C.P. conceived and performed the experiments. M.S. analyzed the data and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.