Daniel Krob

CESAF: an Iterative & Collaborative Approach for Complex Systems Development

Introduction

Agile development methods are nowadays well established in software engineering. They refer to an approach to software development under which requirements and solutions evolve simultaneously through the collaborative effort of self-organizing cross-functional teams, including customer(s) and end-users(s), whose objective is to construct a software solution that answers as well as possible to its business needs. Agile approaches advocate adaptive planning, evolutionary development, early delivery and continuous improvement. They also encourage rapid and flexible responses to changes (see for instance [START_REF] Highsmith | Agile Project Management: Creating Innovative Products[END_REF], [START_REF] Runyan | Introduction to Agile Methods[END_REF] or [START_REF] Wilipedia | Agile software development[END_REF] for more details).

The term agile was popularized -in the software engineering area -by the Manifesto for Agile Software Development that goes back to 2001 (cf. [START_REF]Agile Manifesto[END_REF]). The values and principles proposed by this manifesto are especially the cornerstones of a broad range of modern software development frameworks, including Scrum and Kanban, which are among the most popular agile methodologies in these contexts (see [START_REF] Dingsoyr | A decade of agile methodologies: Towards explaining agile software development[END_REF] for a short history of agile methodologies). One shall finally point out that such agile approaches are nowadays widely used in the industry for non-critical software development: most of service industries (banks, insurances, retail, telecommunications, etc.) are typically using them on a daily basis for developing their business software capabilities.

The maturity of agile approaches in software development can be measured by the worldwide success and dissemination of agile frameworks at enterprise scale, such as SAFe (whose acronym stands for Scaled Agile Framework) which is the most famous one in this matter (see [START_REF] Knaster | SAFe® Distilled: Applying the Scaled Agile Framework for Lean Software and Systems[END_REF], [START_REF] Safe | Scaled Agile Framework[END_REF] or [START_REF] Safe | Model Based Systems Engineering[END_REF]). This last framework proposes a full body of knowledge for deploying and scaling agile methods at enterprise level: SAFe indeed explains how to use consistently agile approaches both at team, program, large solutions and enterprise portfolio levels, again still in the context of software development.

The same maturity does however not exist in the context of complex systems development where one can only find some poor and still quite recent references to agile systems engineering. The first attempt of extending the agile framework to the context of system development seems for instance to only go back to the end of 2012 when an IBM researcher, Hazel Woodcock, proposed a revisit of the Agile Manifesto for systems engineering (see [START_REF] Woodcock | The Agile Manifesto reworked for Systems Engineering[END_REF]). In the line of this seminal initiative, a working group of the International Council on Systems Engineering (INCOSE) started then -in 2014 -to work on agile systems engineering (see [START_REF]Agile systems[END_REF]) with a regular production on this subject, leading in particular to a first textbook which was published by B.P. Douglass in end 2015 (see [START_REF] Douglas | Agile Systems Engineering[END_REF]). Finally, one shall also point out a quite recent first attempt -that goes back to October 2017, as far as we could trace itof SAFe team that proposed a sketch of a Model-Based Systems Engineering agile framework. This last proposal is however reduced to a very small number of ideas, not detailed at all and clearly not very effective and not supported by returns on experiments from real system developments (see [START_REF] Safe | Model Based Systems Engineering[END_REF]). 1 CESAMES -15, rue La Fayette -75009 Paris -France -email : daniel.krob@cesames.net Moreover, these attempts for extending the agile paradigm to systems engineering clearly did not deeply penetrate the industry. In practice, most of the industrial development organizations are indeed not "agile" at all -taking here this term in the meaning of agile methods -and are still using quite classical more or less cascaded development approaches, based on a V-cycle organization with specialized teams working in silos, where customer-focus and transversal collaboration are usually weak. One may observe that this situation is probably due to the fact that physical systems are much more difficult to handle than software systems and that systems development frameworks are usually strongly constrained by regulatory issues, especially when dealing with critical systems.

Nevertheless, we can however see that some industrial companies have successfully started to deploy agile systems engineering (also called iterative systems engineering) approaches with outstanding outcomes. Two of these examples are SpaceX and Gripen that are quickly presented below.

 First of all, SpaceX was for instance able to apply an "agile" approach to a multidisciplinary industrial and complex system such as a launcher. Agility allowed SpaceX to significantly reduce program costs by transforming the classical unique single development cycle into a series of short design-build-test development cycles. During these short cycles, departments and disciplines are aligned on the same system view of the global product. All development actors involved in SpaceX engineering activities are also regularly synchronized to reduce over-costs and delays on each short iterative cycle. In the same way, customers' requirements are permanently tracked and verified in order to optimize all design items covering these requirements at each iterative designbuild-test cycle.Note finally that digital continuity is key to improve collaboration and to master dashboarding status at each cycle, allowing the three SpaceX locations, each of them mixing many teams and sub-teams, to work as a global team sharing all key information.

 In the same way, Saab applied successfully an iterative systems engineering approach for developing the JAS 39 E/F Gripen military aircraft, with a huge impact on design complexity. The design cost of this aircraft is indeed estimated at 14 B€ instead of 25 B€ for the Dassault Aviation Rafale. The Gripen success is considered as mainly linked to the efficiency of the transversal collaboration between functional & design teams, who used digital and functional mockup tools as pivot to synchronize.

These two examples are showing well the strategic importance of agility in the context of multiphysical complex industrial system development.

CESAF: CESAM Engineering Systems Agile Framework

History

CESAF2 is an agile model-based systems engineering framework, based both on real concrete applications & experiences in order to ensure practical applicability and on the CESAM model-based systems architecting framework (see [START_REF] Cesames | CESAM: CESAMES Systems Architecting Method -A Pocket Guide[END_REF] for more details).

We already have defined the fundamentals of such a framework which, even if not fully formalized, was already used in several real development contexts in order to find quickly an optimal system that answers to all stakeholders' needs. More specifically, we helped implementing since around three years several agile development loops in complex system development contexts such as automotive (e.g. development of new advanced driver assistance services), high tech (e.g. development of a new camera in Japan and of a smart site management system in China), energy (e.g. development of a smart energy distribution system) and aeronautics (e.g. mechanical engine design and manufacturing). In aerospace, the development of a new aircraft turbomachine was re-analyzed last year through a complete coarse grain agile loop achieved in 3 months: it allowed fixing the main known engineering and manufacturing issues through the understanding of the key relationships between product features and characteristics of the most important manufactured parts. A second experience at engine level confirmed the achievement of -20 % of lead time, as experienced with the first project with the same customer.

Finally, we are working -since mid 2018 -within one of the key aircraft manufacturers in order to implement an ambitious transformation program at enterprise level, dedicated to the deployment through engineering, manufacturing and customer services of an agile development framework relying on model-based systems engineering. Note that these different experiences are of course confidential since they were all deployed on real development projects.

Key Features

Feature 1: Product / Organisation Alignement

Preliminary definition of a product reference architecture:

The first key element to enable agile engineering is an adequate product architecture. In this matter, it is key to construct first a generic reference product decomposition where the product is recursively decomposed into logical components which are as independent as possible from a functional & logical point of view. Such a decomposition will help reducing the functional & logical interfaces that are existing between product components, and consequently the issues linked to the concurrent design and the integration of these components. Such a generic reference product decomposition shall in particular contribute to a maximalist ("150 %") high-level vision of the product of interest (or of a family of similar products if one is dealing with a product line approach) that include the definition of generic assets such as generic external & internal interfaces, generic needs and functional & constructional requirements, generic functional and logical architectures, etc. An example of such a generic reference architecture -that we constructed on the basis of publicly available documentation -is provided in Figure 1 for an Airbus single aisle aircraft. Note that these reference architectures are key for providing a standard & shared way of presenting and analyzing different design alternatives when relevant. They are also a natural support for impact analyses, as far as they have a double functional and constructional nature.

Product / organisation alignment:

Based on the previous reference product architecture, a second key element towards agile engineering is the alignment of the multidisciplinary activities and of the generic reference product architecture. Organization shall indeed replicate the product architecture in order to provide the best agility all over the development process. Traditional activity organization is indeed often a source of tunnel effect due to the fact that teams are not functionally independent, not sufficiently autonomous and even sometimes too big to be mastered in an agile process. It is therefore often key to reorganize the development teams in order to mimic the reference product architecture in the organization.

As a consequence, a typical agile development organization shall be split into 2 -3 or more layers according to product complexity and constraints. A typical 3 layers organization will for instance be decomposed into a system level, a sub-system level and a part level (this last one taking into account the part manufacturing constraints, typically for mechanical components). In this organization, each development team shall therefore be in charge of a unique component of the reference product decomposition of the product of interest, all components being covered by the different development teams. Note finally that one also has to identify "architects" that shall be in charge of synchronizing the development teams, both horizontally between the different disciplines and vertically between the different product layers. The resulting organizational model is illustrated in Figure 2.

Feature 2: Iterative Collaboration between Layers and Disciplines

The second idea of our agile approach for complex systems engineering consists in iteratively defining the product by organizing its development into successive collaborative design loops of increasing length and granularity where the product relevancy, consistency and feasibility is assessed at more and more precise levels of analysis (see next figure). Each loop shall in particular cover and study several possible product variants at different levels of grain along their full development cycle, starting from top-level architecture up to manufactured part design and definition of manufacturing and maintenance concepts. The design choices shall be gradually frozen and deepened until achieving the full complete specification of the product and of its associated manufacturing and maintenance processes. All system stakeholders -in particular manufacturing & maintenance actors -shall be involved in these design loops in order to capture as early as possible their constraints.

Such a development model makes it possible to freeze quickly structuring choices related to system global performance, system architecture, external or internal interfaces, critical parts or technologies and development, manufacturing & maintenance processes, ensuring the technical and industrial feasibility of the target system and providing a good control of risks at any time.

During each design loop, all involved teams -that is say all engineering & manufacturing disciplines involved in a given product development -shall work collaboratively and share the same set of design drivers and hypotheses that will be progressively refined and frozen (see enabler 3). At the end of each design loop, a new product increment shall be generated through relevant models and tools (see enabler 4). Due to the progressive refinement of the design drivers, this approach provides a huge adaptability of the development process to new or updated requirements or constraints coming from various stakeholders. This approach also helps multidisciplinary teams to reach the global optimum choice of the product at each loop. The below Figure 3 illustrates such an approach with three design loops starting from a coarse grain high-level loop up to a fine grain detailed loop. Each design loop will then analyze a number of design alternatives, starting typically with a dozen alternatives in the first loop, reducing them to 2-4 main alternatives in the second loop, up to converging to only 1-2 key alternatives in the last loop. This can be achieved by using trade-offs techniques, with different levels of details to reach a global optimum (see the below Figure 4).

Note that requirements are usually often over-specified and in much too important number, due to the fact that all actors in the development chain are creating requirements to protect themselves by taking local margins of security. In an agile approach, each design loop shall therefore pay a specific attention to formalize the system requirements to what is just necessary: this objective relies on prioritization techniques managed through collaborative requirements reviews, involving regularly all key development actors, where allocations of performance are collaboratively managed and optimized when descending into product architecture. Since we are dealing with systems, any agile development process shall also take into account deeply the constraints coming from manufacturing and maintenance since these are the steps where the "real thing" is constructed and used. In an agile systems engineering process, manufacturing teams must therefore be involved in each design loop -as already pointed out previously -in order to analyze design requirements from their perspective and to evaluate/simulate their impact on manufacturing and assembly processes with the "good" industrial KPIs (e.g. lead time, NRC & RC).

Similarly support & services teams must be involved in order to evaluate the impact of design requirements on the maintenance processes using relevant KPIs (e.g. time and cost of maintenance operations).

Regular rituals, involving design, manufacturing and maintenance teams, and more generally all relevant stakeholders, shall therefore be regularly organized within each design loop in order to integrate all these different point of views in the design. Model-based systems engineering plays a key role in these rituals since they shall be balanced between a product and a project focus. A typical agile design ritual indeed consists in examining and discussing collectively more and more precise models of the product under development (see an automotive example in the next figure).

Feature 3: Iterative Key Design Driver's Management

The third key idea is the preliminary identification of the key design parameters on which each design loop rely. They shall form for each design loop a coherent set of interdependent characteristics, dealing with either product environment (e.g. mission profiles, use cases, customer & stakeholder requirements), either functional (e.g. presence of specific functions, behavioral performance) or constructional (e.g. mass, geometry of critical parts, technology) requirements, or manufacturing & maintenance constraints. These key design drivers also allow to explore design space and to identify potential architecting variants. The first loop focuses for instance on the structuring parameters of the product to analyze with highest priority. Note finally that these drivers can be derived from the analysis of a stakeholder need and functional & constructional requirement architecture, which are thus the first key models on which relies our agile approach. The Key Design Drivers (KDD) represent the hypotheses shared between all teams involved in an agile development, i.e. the shared design space. During a given design loop, teams work with a fixed range for each KDD, with freedom of design while their parameters are contained into the defined ranges, from each synchronization ritual to the next one. At each iteration, teams define and evaluate potential architecture variants within KDD ranges in order to progressive reduce these ranges at the end of the iteration. If no feasible architecture can be defined within the design space defined by the current KDDs, a synchronization activity will redefine new KDD ranges.

This approach ensures agility to adapt the design in a robust way by mastering the traceability from initial stakeholder requirements to shared design functional & constructional hypotheses.

Feature 4: DMU and FMU as Pivots for Transversal Synchronization

The last ingredient of an agile systems engineering process is to use Digital & Functional Mockup (DMU and FMU) as pivots for transversal synchronization in each design loop. The key idea is that at the end of each iteration, each team shall produce models (3D, thermal, mechanical resistance, thermodynamics, etc.) which are to be integrated in a shared DMU (for the geometrical aspects) and in a shared FMU (for the functional aspects), whose overall coherence is analyzed. Due to the need of synchronizing all disciplines at each product increment, the use of these digital models as pivots for the design is therefore key to support an agile approach.

Note that usually the functional digital mockup is not a single tool, but rather a series of interconnected tools (e.g. Capella, Simulink, FL Editor, etc.). A key difficulty is here to ensure the alignment of the system decomposition used in the functional digital mockup with the geometrical decomposition on that the digital mockup manages, which currently is still a matter of human expertise. Both tools have indeed different modeling constraints and traditionally have been imposed separately. Nevertheless, to implement agile engineering, functional and discipline integration through both functional oriented design tools and CAD is a key enabler. A key point is also that the FMU shall be designed to provide comprehensive system level predictive models, that is to say models integrating all key product dimensions, typically of multi-physical nature, that allow to simulate the system under design within its environment in order to validate hypotheses and manage trade-offs. The levels of detail and the representativeness of such models must necessarily be aligned with the granularity of the design loops. These models can be existing models (e.g. Simulink / Modelica models) or existing model aggregations via ad hoc tools (e.g. Optimus). Such system level simulation models are in particular key for evaluating design performances, validating the design choices, managing the initial trade-offs and helping to negotiate the customer

Figure 1 -

 1 Figure 1 -Reference functional-logical architecture of a single-aisle aircraft (Airbus A320)

Figure 2 -

 2 Figure 2 -Agile organization resulting from a product / organization alignment

Figure 3 -

 3 Figure 3 -Our agile development model based on successive and more & more detailed design loops

Figure 4 -

 4 Figure 4 -Examples of several architectures compared through a Pareto multi-criteria optimization approach

Figure 5 -

 5 Figure 5 -Example of a development ritual using model-based representations of a complex system

Figure 6 -

 6 Figure 6 -Examples of design drivers identified in the context of an innovative camera development, here used to define two product variants that were analysed during a first design loop

Figure 7 -

 7 Figure 7 -Illustrations of Digital and Functional Mockup views

CESAF is an acronym for CESAM Engineering Systems Agile Framework.

Conclusion

The CESAF framework has 4 key features, allowing an industrial company to successfully implement an agile / iterative systems engineering framework. These features are:

1. Product and organization alignment, 2. Iterative collaboration between layers and disciplines, 3. Iterative key design driver's management, 4. DMU and FMU as pivots for transversal synchronization.

Note that features 1 and 3 do strongly rely on systems engineering and architecting techniques, when feature 2 is rather an adaptation of agile and SAFe principles to industrial contexts and feature 4 refers to the digital tooling support.


domain: phys spi

text:
Gael Sebald

Tetsuya Uchimoto

Benjamin Ducharne

Toshiyuki Takagi

Magnetic control for high chromium steel creep ELyT Global Theme: Electromagnetic NDT Scientific topic: Electromagnetic instrumentation

Creep phenomenon is an important feature to assess in high temperature applications, although the correlations with microstructure and magnetic behaviour remain unclear. In this project, 12%Cr-Mo-W-V creep test samples (used in thermal power plants) are investigated using three electromagnetic inspection techniques. Magnetic parameters based on the results are then evaluated in comparison to the microstructure. Additionally, a modified Jiles-Atherton model has been used to numerically reproduce experimental results from Magnetic Incremental Permeability (MIP), Magnetic Barkhausen Noise (MBN) and standard B(H) measurements. All the three techniques exhibit different responses in understanding creep and the modelling parameters derived from the adapted Jiles-Atherton Model parameters are then correlated to the microstructure information. Coupling between the stress and magnetic field is the main and important feature of the ferromagnetic materials consisting of various small magnetic domains in its microstructure [START_REF] Schull | Non-destructive evaluation: theory, techniques and applications[END_REF] [START_REF] Ducharne | Phenomenological model of Barkhausen noise under mechanical and magnetic excitations[END_REF]. Conventional Eddy current testing has been extensively used for the ferromagnetic materials characterization but when it comes to creep damage detection, it becomes difficult to distinguish between the changes caused by the actual creep damage and from the signals generated by other sources like, cracks, surface roughness, hardness etc. In this research three different electromagnetic techniques are applied to the 12 different samples from three different categories with different temperature and stress treatments. Magnetic Incremental Permeability (MIP) is used to investigate samples as it is highly sensitive to stress. On the other hand, Magnetic Barkhausen Noise being sensitive to the mechanical changes in the materials, is also used to analyse the samples in addition to standard B(H) curve measurements. Finally, ferromagnetic hysteresis models such as dry friction quasi static model [START_REF] Ducharne | Low frequency modelling of hysteresis behaviour and dielectric permittivity in ferroelectric ceramics under electric field[END_REF], Preisach model [START_REF] Zhang | Preisach's model extended with dynamic fractional derivation contribution[END_REF], Jiles-Atherton model [START_REF] Zhang | Dynamic magnetic scalar hysteresis lump model, based on JilesAtherton quasi-static hysteresis model extended with dynamic fractional derivative contribution[END_REF], which are based on magnetic induction B versus applied magnetic field strength H, are tested to get the simulated data based on experiments. Having a physical interpretation, the J-A model [6] is chosen but it is modified to derive modelling parameters which are then evaluated against the microstructure of the test samples. Finally, experimental data obtained using different techniques applied to creep samples are presented, and the relevant ferromagnetic model is given. Fig. 1(a) below shows evolution of one of the J-A parameters 'K' vs. Precipitation number for differently treated samples. It is quite evident that the energy required (K) to break the pinning site is larger in case of higher number of precipitates.

Figure 1 (

 1 Figure 1(a): Evolution of J-A parameter 'K' vs. Precipitations


Ling Xiao

Ming-Hui Li

Jun Dai
email: jundai@scau.edu.cn

Mikael Motelica-Heino

Xu-Fei Chen

Jia-Long Wu

Lanfeng Zhao

Kexue Liu

Chi Zhang
email: zhangchi2012@scau.edu.cn

Assessment of earthworm activity on

Keywords:

Introduction

Heavy metal pollution in soil has been a major concern all over the world. The bioavailability of heavy metals plays a vital role in their toxicity and accessibility to organisms (Xiao et al., 2017a(Xiao et al., , 2017b)). Among the various chemical methods to evaluate heavy metal bioavailability in soils, diethylenetriaminepentaacetic acid

(DTPA) extraction is an effective and widely used one [START_REF] Zhang | Earthworm and organic amendment effects on microbial activities and metal availability in a contaminated soil from China[END_REF][START_REF] Han | Using sequential extraction and DGT techniques to assess the efficacy of plant-and manure-derived hydrochar and pyrochar for alleviating the bioavailability of Cd in soils[END_REF]. Hence, using DTPA extracted fraction to assess heavy metal bioavailability facilitates comparison with previous researches. Additionally bio-indicators such as earthworms have become increasingly popular and are now widely used to evaluate the sustainability of soil use [START_REF] Fusaro | Soil Biological Quality Index based on earthworms (QBS-e). A new way to use earthworms as bioindicators in agroecosystems[END_REF] and heavy metal contamination due to the consistent relationships between heavy metal concentrations in earthworm tissues and soils [START_REF] Suthar | Earthworms as bioindicator of metals (Zn, Fe, Mn, Cu, Pb and Cd) in soils: is metal bioaccumulation affected by their ecological category?[END_REF]. The accumulation of heavy metals in earthworm tissues is in fact associated not only with the bioavailable fraction of heavy metals in soil but also with the survival rate, individual biomass and ecological category of earthworms as well as soil quality parameters [START_REF] Suthar | Earthworms as bioindicator of metals (Zn, Fe, Mn, Cu, Pb and Cd) in soils: is metal bioaccumulation affected by their ecological category?[END_REF]. The bioavailability of heavy metals in soil is significantly associated to soil properties (i.e. pH, soil organic C and total N along with their components, such as microbial biomass C and N (C mic and N mic ), dissolved organic C and N (C dis and N dis ), mineralized C and N (C min and N min ) and so forth.) [START_REF] Li | Ion activity and distribution of heavy metals in acid mine drainage polluted subtropical soils[END_REF]Xiao et al., 2017aXiao et al., , 2017b)). Nevertheless, these soil properties can be modified by the activity of earthworms [START_REF] Gong | Earthworms modify soil bacterial and fungal communities through enhancing aggregation and buffering pH[END_REF]. For instance it was reported that earthworm activities affected the formation of soil aggregates and hence influenced soil pH, organic C and N along with their components [START_REF] Fonte | Influence of earthworm activity on aggregate-associated carbon and nitrogen dynamics differs with agroecosystem management[END_REF][START_REF] Gong | Earthworms modify soil bacterial and fungal communities through enhancing aggregation and buffering pH[END_REF]. Under the stress of heavy metal contamination, earthworm activities could also promote N transformation in soils [START_REF] Xu | Earthworms stimulate nitrogen transformation in an acidic soil under different Cd contamination[END_REF]. Globally earthworms are powerful regulators of soil processes, participating in the maintenance of soil structure and regulation of several ecosystem services (food production, nutrient cycling, contaminants retention and climate regulation) [START_REF] Liu | Earthworms coordinate soil biota to improve multiple ecosystem functions[END_REF]. They also constitute a major component in soil C and N function through their feeding, burrowing and casting activities [START_REF] Lavelle | Ecosystem engineers in a self-organized soil: a review of concepts and future research questions[END_REF]. Additionally they can modify heavy metal availability in soil [START_REF] Zhang | Earthworm and organic amendment effects on microbial activities and metal availability in a contaminated soil from China[END_REF] as they are relatively efficient accumulators of both essential and non-essential metals [START_REF] Wang | Bioaccumulation of heavy metals in earthworms from field contaminated soil in a subtropical area of China[END_REF][START_REF] Yuvaraj | Environment-friendly management of textile mill wastewater sludge using epigeic earthworms: bioaccumulation of heavy metals and metallothionein production[END_REF]. As the biochemical response of different earthworm species to soil contamination are quite different, they were often used as functional bio-indicators [START_REF] Jeyanthi | Comparative study of biochemical responses in three species of earthworms exposed to pesticide and metal contaminated soil[END_REF][START_REF] Sinkakarimi | Interspecific differences in toxicological response and subcellular partitioning of cadmium and lead in three earthworm species[END_REF]. It was reported that biota to soil accumulation factor (BSAF) of earthworms highlights the different "response" strategies of earthworms in contaminated soils [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF]De Vaufleury et al., 2013). It also reflects the different feeding, burrowing and casting behavior of earthworms in different ecological category [START_REF] Suthar | Earthworms as bioindicator of metals (Zn, Fe, Mn, Cu, Pb and Cd) in soils: is metal bioaccumulation affected by their ecological category?[END_REF]. Furthermore, it may indicate the distribution of heavy metals in different soil fractions [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF]. Earthworm has been proved to be able to change heavy metal bioavailability in soil, and thus have been frequently used for soil heavy metal remediation [START_REF] Zhang | Earthworm and organic amendment effects on microbial activities and metal availability in a contaminated soil from China[END_REF]. Due to different earthworm species, soil types as well as metallic elements, results differed to some extents according to the various studies. For instance, [START_REF] Wen | The role of earthworms (Eisenia fetida) in influencing bioavailability of heavy metals in soils[END_REF] and [START_REF] Lemtiri | Earthworms Eisenia fetida affect the uptake of heavy metals by plants Vicia faba and Zea mays in metal-contaminated soils[END_REF] found that addition of earthworms increased soil heavy metal bioavailability, while Li et al. (2019aLi et al. ( , 2019b) ) and reported addition of earthworms reduced it. The relationship between the accumulation of heavy metals in earthworms and heavy metal bioavailability in soil could gain a better insight in the role of internal drives of earthworms in relation to the fate of heavy metals in soil [START_REF] Suthar | Earthworms as bioindicator of metals (Zn, Fe, Mn, Cu, Pb and Cd) in soils: is metal bioaccumulation affected by their ecological category?[END_REF]. It was found that bioavailable heavy metals were positively correlated with BSAF heavy metals in earthworms [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF][START_REF] Wang | Exploring the bioavailability of nickel in a soil system: physiological and histopathological toxicity study to the earthworms (Eisenia fetida)[END_REF]. However, a negative association between heavy metal bioavailability and accumulation of heavy metals in earthworm tissues was also found by [START_REF] Kavehei | Effects of red earthworms (Eisenia fetida) on leachability of lead minerals in soil[END_REF]. Thus, there is still an uncertainty whether BSAF-heavy metals in earthworm tissues are true indicators of heavy metal bioavailability in contaminated soil. The possibility to use BSAF-heavy metals of earthworms together with biological indicators such as earthworm survival, growth and health has thus to be established. In particular, the association between BSAF-heavy metals of earthworms and soil properties, as well as their effect on heavy metal bioavailability in soil has not been clearly figured out.

The overall goal of this research was to investigate effects of earthworm activity on heavy metal bioavailability in soils using their BSAF-heavy metals. Based upon the above rationale, a microcosmic laboratory experiment was set up with two species of earthworms (i.e. Amynthas corticis (A. corticis) and Amynthas robustus (A. robustus)) and for two types of soils (i.e. based on different contamination levels and soil properties) aiming to 1) evaluate the contribution of BSAF-heavy metals of earthworms to soil properties (pH, soil C and N along with their components); 2) explain the contribution of BSAF-heavy metals of earthworms to heavy metal bioavailability in soil assessed with DTPA extracted heavy metals; 3) assess the association between BSAF-heavy metals of earthworms and soil properties along with their effect on soil heavy metal bioavailability. This study is beneficial to the assessment of soil contamination by using earthworms as bio-indicators and hence can improve future soil heavy metal remediation based on earthworm applications.

Materials and methods

Samplings

The study sites are located close to Dabaoshan mine (24°30′ N and 113°45′ E, Guangdong province, South China) which has been operated for 40 years and along Yanghe River that has been seriously polluted by acid mine drainage. Farmlands around Yanghe River have been severely contaminated since it was the main water resource for irrigation. Soil samples were collected from agricultural fields in the upstream (Site A) and downstream (Site B), which were 3 km and 10 km respectively away from the mine cluster. Three soil samples (2 × 2 m) of the surface horizons (0-20 cm) from three paddy rice fields (Spacing is 10 m) were collected at each site. The main physico-chemical properties along with the total and DTPA extraction of metal contents in the soil samples are shown in Supplementary materials (STable1 and STable 2). Generally, soils in both site A and B are quite acid. Soil pH values, organic C and total N contents in site A were significantly higher (P < 0.05) than those in site B, while cation exchange capacity (CEC), clay content, and saturated water were much lower than those in site B (P < 0.05). Iron and manganese contents for site A were 3.49 and 1.40 times higher than those in site B (P < 0.05). Total contents of Zn, Cd, Pb and Cu in site A were 1.10, 2.31, 1.25 and 1.75 folds as those in site B, respectively. Similarly DTPA extraction contents of Zn, Cd, Pb and Cu in site A were 1.07, 2.10, 1.25 and 0.94 times as those in site B (P < 0.05), respectively.

A. corticis (C) and A. robustus (R) were collected by hand from the uncontaminated fields in Qingyuan, Guangdong Province, Southern China. The soil are well managed and uncontaminated according to our investigation(STable3). According to their physiological characteristics, the earthworm specimens were sent to South China Botanical Garden (Chinese Academy of Sciences) for their accurate identification. Initial heavy metal contents in earthworm tissues is given in STable 3. Generally, there was no significant difference in Cu, Pb and Zn accumulation between these two species of earthworms. However, probably due to higher individual biomass of A. robustus, Cd content in A. robustus was significantly higher than that in A. corticis (P < 0.01). Adult earthworms with clitellate were selected and pre-cultured in the soil at 25 °C for one month. In each pot, 8 earthworms of the same species were introduced in 2 kg soil. Average fresh weights of A. corticis and A. robustus were 0.67 g and 2.02 g, respectively.

The rice straws sampled from the teaching farms of South China Agricultural University were cut into 2 cm segments, soaked into water for one week to remove tannins along with other harmful substances, and applied to pots to feed the earthworms. Organic C and total N contents along with C: N ratio of rice straws was 61.2%, 1.24% and 49.3:1, respectively. Initial heavy metal concentration rice straw are shown in Supplementary (STable 3).

Experimental design

2 kg of soil with field moisture was placed in 2 L pots (10 × 18 × 15 cm). Orthogonal experiments were designed based on soil type and earthworms species. In total, there were six treat ments including two non-incubated controls as following (1) Soil A without earthworms (AS); (2) Soil A with A. corticis (AC); (3) Soil A with A. robustus (AR); (4) Soil B without earthworms (BS); (5) Soil B with A. corticis (BC); and (6) Soil B with A. robustus (BR). Four replicates were set for each treatment, and 100 g rice straw (dry weight) was placed on soil surface to feed earthworms.

All pots were placed in the dark and the room temperature was kept at 25 °C for 120 days. Soil moisture was adjusted to field capacity every three days. Living earthworms were collected, counted and weighted at the end of cultural period. Collected earthworms were used to determine their metal accumulation. Soil were mixed and divided into two halves, one of which was air-dried and passed through 2 mm and 0.149 mm sieves for further physical and chemical analysis, while the other one was stored in 4 °C refrigerator at field moisture for the analysis of microbial biomass C and N, dissolved organic C and N along with mineralized C and N.

Chemical and biological analyses

Soil pH was determined using a 1:2.5 soil-to-water ratio. Clay (<0.002 mm) content was determined by the pipette method. Organic C was determined by the dichromate digestion method and total N by Kjeldahl digestion [START_REF] Sparks | Methods of Soil Analysis: Chemical Methods[END_REF]. The cation exchange capacity was determined by the method of NH 4 OAC (1 mol L -1 , pH 7.0) exchange and Kjeldahl digestion [START_REF] Sparks | Methods of Soil Analysis: Chemical Methods[END_REF].

Soil organic carbon mineralization rate (C min ) was measured by the method of NaOH absorption, and the amount of CO 2 in the microbial carbon source mineralization was determined under the standard condition [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF]. The soil mineralized nitrogen (N min ) was extracted by 0.01 mol•L -1 CaCl 2 , and the content of ammonia nitrogen and nitrate nitrogen were measured by AAS flowing analyzer, and the value of N min was equal to the sum of ammonia nitrogen and nitrate nitrogen contents. Microbial biomass C and N were determined by the chloroform fumigation extraction method [START_REF] Vance | An extraction method for measuring soil microbial biomass C[END_REF]. Microbial biomass C (C mic ) and N (N mic ) were calculated according to equations: C mic = Ec/0.38, and N mic = En/0.45 (Jenkinson, 1988), where Ec and En are the difference between extractable C and N from fumigated and non-fumigated samples.

Total Cu, Zn, Pb, Cd contents in soil were determined by flame atomic absorption spectroscopy (FAAS) or graphite furnace atomic absorption spectrometry (GFAAS) after digestion of 1.0 g soil sample with H 2 O 2 , HF, HNO 3 and HClO 4 [START_REF] Amacher | Nickel, cadmium, and lead[END_REF]. Meanwhile, DTPA extraction of soil Cu, Zn, Pb and Cd was conducted based upon [START_REF] Lindsay | Development of a DTPA soil test for zinc, iron, manganese, and copper 1[END_REF].

After being counted and weighted, earthworms were transported into Petri dishes with one filter paper. A few drops of distilled water were applied to keep earthworms moister. They were kept at 25 °C for 7 days and filter papers were changed daily to allow a complete evacuation of their gut contents. Earthworms were then killed in liquid nitrogen and further oven-dried at 100 °C for 24 h. Heavy metal content in earthworms was determined using 0.2 g of dried and crushed earthworm sample. The sample was mixed with 8 mL concentrated HNO 3 and 2 mL concentrated HClO 4 for 12 h and digested at 250 °C for 2 h. After cooling, the solution was made up to 50 mL using ultra-pure distilled water [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF]. Metal contents in earthworms were determined by ICP-OES (Varian 710-ES). The BSAF of a metal in the earthworms was calculated by the following formula: BSAF = the metal content in earthworm tissue/total metal content in soil [START_REF] Cortet | The use of invertebrate soil fauna in monitoring pollutant effects[END_REF].

Statistical analysis

All data were expressed as mean ± standard deviation. Multivariate analysis was carried out using R and CANOCO 5.0 (R Develop ment Core Team, 2009;[START_REF] Sadyś | Application of redundancy analysis for aerobiological data[END_REF]. Univariate analysis was conducted using SAS 8.0 software. One-way or two-way ANOVA was applied if the data were in a normal distribution. Kruskal Wallis and Scheiner-Ray-Hare tests were used if the data did not conform to the normal distribution. Multiple comparisons were also conducted by means of Duncan's test, when significant difference was found by means of one-way ANOVA. Residual normality and homoscedasticity were verified using Kolmogrov-Smirnov and Bartlett tests. To gain further insight into the growth of earthworms and metal accumulation in earthworm tissues as well as the influence of soil C and N parameters on heavy metal availability in soil, redundancy analysis (RDA) was used with the CANOCO 5.0 software. In order to analyze the influence of earthworms on soil properties, survival number, biomass, and BSAF-heavy metal values of earthworms were set as explanatory variables, while soil organic C and N, along with their components were set as response variables. For analyzing the influence of earthworms on metal bioavailability, survival number, biomass, along with BSAF values of earthworms were used as explanatory variables while DTPA extracted metals were used as responsible variables. To maintain the consistency of the results, original data were transformed to lg (x+1) for correlation coefficients and stepwise regression analysis.

Results

Growth, metal accumulation and heavy metal bio-concentration factor of earthworm

Characteristics of earthworm growth

Biomass of A. corticis significantly decreased by 70.6% (AC) and 64.2% (BC) in both soils (p < 0.05), respectively. In contrast biomass of A. robustus in soil A was fairly stable (p > 0.05), while it decreased by 32.1% in B soil (Table 1). Generally, the quantity of the two earthworm species decreased in all treatments. Compared to A. robustus, the quantity of which decreased by 25% (P < 0.05) and 12.5% (P > 0.05) in soil A and B, respectively, the numbers of A. corticis decreased more drastically by 50% (P < 0.05) and 62.5% (P < 0.05), respectively in soil A and soil B.

Accumulation and BSAF of metals in earthworm tissue

After 120 days of cultivation, the accumulation of Zn, Cd, Pb and Cu in A. corticis tissue increased by 1.99-2.98, 1.34-2.82, 27.0-87.0 and 13.4-15.8 times, respectively, compared to their initial concentrations in that earthworm. The accumulation of Zn, Cd, Pb and Cu in A. robustus increased by 0.79-0.81, 1. 75-1.88, 124-233 and 4.23-6.53 earthworm species and soil type (Table 2). Zn accumulation in earthworm tissue was species dependent. Accumulation of Zn in A. corticis was significantly higher than that of A. robustus (p < 0.05). Cd accumulation in earthworm tissue however, was related to soil types. Cd contents in both earthworm species cultivated in soil A were significantly higher than those in soil B (P < 0.05). Although the initial content of Cd in A. robustus was significantly higher than that in A. corticis (STable3), after 120 day of incubation in heavy metal contaminated soil, the contents of Cd in both earthworms were similar (Table 2). It was suggested that A. corticis may readily accumulate Cd from contaminated soil. According to two-way ANOVA, accumulations of Pb was significantly related to earthworm species (P < 0.05), while accumulation of Cu was significantly affected by the interactive effect of soil type and earthworm species (P < 0.05). Cd-BSAF ranged from 4.38 to 11.5, and the highest value occurred in treatment with A. robustus in soil B. Zn-BSAF were 1.39-4.57, and Zn-BSAF of A. corticis was significantly higher than that of A. robustus in both soils (P < 0.05). Pb-BSAF ranged from 0.222 to 11.4. Pb-BSAF of A. robustus was significantly higher than that of A. corticis in both soils. Cu-BSAF were 0.15-0.48. Two-way ANOVA indicated a significant interaction occurred in Cu-BSAF between earthworm species and soil types (P < 0.05).

Soil pH, organic C and N along with their components

After 120 days of incubation, variations of soil properties in the different treatment are given in Table 3. Soil pH ranged from 3.83 to 5.23 in the soils treated with earthworms. Generally, the addition of earthworms could reduce soil pH to some extent. In particular, the addition of A. robustus significantly decreased soil pH by 0.21-0.45 units (P < 0.05). Two-way ANOVA showed that soil pH was significantly associated with earthworm species and soil type.

Though the addition of earthworms did not change soil C org significantly (P > 0.05) in soil A, it changed C min and C mic significantly (P < 0.05). In soil A, adding A. corticis and A. robustus increased C min by 27.0% and 28.3% in average, respectively, while incubation with A. corticis and A. robustus respectively reduced C mic by 19.9% and 44.4% in average. In soil B, the addition of A. corticis and A. robustus significantly increased soil C org by 26.2% and 23.8%, respective (P < 0.05). Incubation with two species of earthworms also resulted in a significant increase in C mic (44.5% vs.61.3% respectively for A. corticis and A robustus). Independent t tests showed that soil type also affected organic C and its components. C org was significantly higher in treatments with earthworms in soil B than that in soil A (P < 0.05). C min in all treatments with soil A was significantly higher than that with soil B (P < 0.05), while C mic in treatment with soil A was significantly lower than that with for soil B (P < 0.05). Two-way ANOVA also indicated that interactive effect between soil type and earthworm species occurred in C org and C mic (P < 0.05).

Addition of earthworms significantly changed soil N tot and its components in both soils (P < 0.05). The addition of A. corticis and A. robustus significantly increased N tot in soil A by 11.6% and 17.0%, respectively. The addition of A. corticis and A. robustus significantly increased N tot in soil B by 9.6% and 13.9%, respectively (P < 0.05). The addition of A. robustus in both soils resulted in a significant increase of N dis (P < 0.05). Incubation with two species of earthworms significantly increased N min for both soils (P < 0.05), while incubation with these earthworms significantly reduced N mic (P < 0.05). Based upon independent t-test, N dis was found to be significantly higher in treatment with A. robustus in soil B than that in soil A. N min was significantly higher (more than 2 times) in all treatment with soil B than that in soil A (P < 0.05).

Heavy metal bioavailability

DTPA extracted Zn, Cd, Pb and Cu were 12.3-21.8, 0.115-0.4, 37.2-52.1 and 61.9-104.5 mg kg -1 , respectively (Table 4). DTPA extracted Zn was significantly higher in treatment AR than AS, indicating that A. robustus increased the soil bioavailability of Zn. DTPA extracted Cu increased by 4.0 and 5.0 mg kg -1 in treatments with A. corticis in soil A. In soil B, Cu-DTPA content was significantly higher in treatment BR compared to BS (P < 0.05), indicating that A. robustus significantly increased bioavailability of Cu. Compared to BS, Cu-DTPA in treatment BR increased by 9.6%.

Multivariate statistical analysis of the effect of earthworms on soil properties

After 999 times Monte Carlo test, Pb-BSAF and Cu-BSAF were selected as significant factors affecting the variations of soil properties (Fig. 1). Cu-BASF and Pb-BSAF reflected 51.9% of total eigenvalue. Explaining 38.5% of changes in soil pH, organic C and N along with their components, Cu-BSAF was the major factor that affected soil property changes. Pb-BSAF explained 13.3% of soil property variations. The axis 1 of RDA, positively associated with Cu-BSAF, indicated 40% of the dependent variable changes. Axis 1 represented the effect of Cu accumulation capacity of earthworms on soil properties. Axis 2 was positively correlated with Pb-BSFA, reflecting 11.9% of dependent variable variations. Axis 2 represented the impact of Pb accumulation capacity of earthworms on soil properties. Moreover, distribution of the treatments along axis 1 suggested that Cu-BSAF played a major role in variations of soil properties, Cu-BSAF of earthworms being associated with the interactive effect of soil types and earthworm species. Distribution of the treatments along axis 2 suggested that Pb-BSAF also had a great impact on soil property changes and was related to earthworm species.

Multivariate statistical analysis of influence of earthworms on heavy metal bioavailability

Through forward selection by 999 times of Monte Carlo test, Pb-BSAF and Cu-BSAF were selected as the significant factors influencing the bioavailability of heavy metals in soils (Fig. 2). Pb-BSAF and Cu-BSAF explained 51.7% of the total eigenvalues. This result is coupled with the effect of earthworms on soil properties. Cu-BSAF explained 45.1% of DTPA extracted metals and hence has a dominant effect on metal bioavailability. Pb-BSAF reflected 6.6% of DTPA extracted metals and also affected soil metal bioavailability significantly. Axis 1 was positively correlated with Cu-BSAF, explaining 47.6% of dependent variable changes. Hence, axis 1 represented the influence of Cu accumulation capacity of earthworms on heavy metal availability. Axis 2 was positively associated with Pb-BSAF, explaining 4.1% of dependent variable changes. Axis 2 reflected the impact of Pb accumulation capacity on heavy metal bioavailability. Distribution of treatments along Axis 1 indicated that Cu-BSAF had a major impact on metal bioavailability and was associated with soil types. Distribution of treatments along Axis 2 demonstrated that Pb-BSAF also affected metal bioavailability and was related to earthworm species.

Based upon the relationship between soil properties and BSAF-heavy metals of earthworms along with the relationship between DTPA extracted heavy metals and BSAF-heavy metals, it is obvious that earthworms could affect soil properties (i.e. C components and pH) and hence influenced DTPA extracted heavy metals. Stepwise regression was employed to analyze the comprehensive effect of soil properties and BSAF-heavy metals of earthworms (Table 5). In general, DTPA extracted heavy metals in soil could be regressed by soil The different letters indicate significant differences in different treatment (P < 0.05).

U N C O R R E C T E D P R O O F

Table 3

Variation of soil organic C and N along with their components in treatments (mean ± S.D., n = 4).

Treatment pH C org C dis C min C mic N tot N dis N min N mic g•kg -1 mg•kg -1 mg C-CO 2 •g -1 soil per day mg•kg -1 g•kg -1 mg•kg -1 mg•kg -1 mg•kg

Discussion

Characteristics of earthworms in different species

A. corticis and A. robustus are quite different in terms of behavior. A corticis is a typical epigeic species, which lives on the surface of soil (0-5 cm) and tends not to make burrows [START_REF] Xiao | Earthworms suppress thrips attack on tomato plants by concomitantly modulating soil properties and plant chemistry[END_REF]. Biomass of mature individual of A. corticis was about 1/4 of A. robustus (Table 1). As an endogeic species [START_REF] Lin | The impact on the soil microbial community and enzyme activity of two earthworm species during the bioremediation of pentachlorophenol-contaminated soils[END_REF], A. robustus feeds on soil mineral particles and makes burrows through soil to Xiao et al. Ecotoxicology and Environmental Safety xxx (xxxx) xxx-xxx move around [START_REF] Lin | Enhancement effect of two ecological earthworm species (Eisenia foetida and Amynthas robustus E. Perrier) on removal and degradation processes of soil DDT[END_REF][START_REF] Xu | Removal of persistent DDT residues from soils by earthworms: a mechanistic study[END_REF]. Not only the biomass of A. robustus individual was higher than that of A. corticis, but also the survival rate of A. robustus was higher than that of A. corticis (Table 1). These results indicated that A. robustus is more tolerant to heavy metal contamination compared to A. corticis, probably due to its higher individual biomass. In addition, the burrowing capacity of endogeic species, e.g. A. robustus enhances their toleration to heavy metal contamination [START_REF] Lukkari | Effects of heavy metals on earthworms along contamination gradients in organic rich soils[END_REF]. Therefore, under the stress of heavy metal contamination, the variation of biomass and survival rates of A. robustus were more stable than that of A. corticis.

U N C O R R E C T E D P R O O F L.
The accumulation of heavy metals differed significantly in earthworm tissues differed for A. corticis and A. robustus (P < 0.05, Table 2). These results indicated that the species of earthworms affected heavy metal accumulation in their tissue. [START_REF] Suthar | Earthworms as bioindicator of metals (Zn, Fe, Mn, Cu, Pb and Cd) in soils: is metal bioaccumulation affected by their ecological category?[END_REF] also observed species specific metal accumulation pattern. They found that endogeic earthworms accumulated more heavy metals in their tissue than anecic earthworms. Species-specific ingestion behavior, burrowing habit as well as earthworm niche structure have a great impact on heavy metal accumulation in earthworm tissues [START_REF] Suthar | Earthworms as bioindicator of metals (Zn, Fe, Mn, Cu, Pb and Cd) in soils: is metal bioaccumulation affected by their ecological category?[END_REF][START_REF] Wang | Bioaccumulation of heavy metals in earthworms from field contaminated soil in a subtropical area of China[END_REF]. However, it should be noted that the accumulation patterns is element specific. For instance, Zn-BSAF and Cd-BSAF of A. corticis were significantly higher than those of A. robustus though Pb-BSAF of A. corticis was significantly lower than that of A. robustus, indicating that heavy metal accumulation capacity of earthworms was element dependent. As an essential element, the accumulation of Zn in plants was relatively high [START_REF] Wang | Arbuscular mycorrhizae alleviate negative effects of zinc oxide nanoparticle and zinc accumulation in maize plants-a soil microcosm experiment[END_REF]. Cd tends to be the most mobile element among the studied heavy metals in soils, hence it could be more accessible to plants [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF]. As the major origin of soil organic matter is the plants growing on it [START_REF] Wang | Soil aggregates and organic carbon affected by the land use change from rice paddy to vegetable field[END_REF], Cd and Zn in soil organic matter are relatively high. In addition, exogenous Cd and Zn are easily absorbed into soil organic matter [START_REF] Fan | Effect of organic matter on sorption of Zn on soil: elucidation by Wien effect measurements and EXAFS spectroscopy[END_REF][START_REF] Zhou | Effects of organic matter fraction and compositional changes on distribution of cadmium and zinc in long-term polluted paddy soils[END_REF], which A. corticis feed on, may lead to the accumulation of Cd and Zn. Higher accumulation of Pb in A. robustus than that in A. corticis was also found because of their different feeding behavior.

A. robustus mainly feed on soil particles which were the main sink of Pb [START_REF] Li | Ion activity and distribution of heavy metals in acid mine drainage polluted subtropical soils[END_REF]. An interactive effect between soil type and earthworm species could be also observed in the variation of Cu-BSAF, indicating that different earthworm species played an important role in Cu accumulation in earthworms for the two types of soil. Based upon the above discussion, BSAF was found to be a good indicator to evaluate the ecological characteristics of earthworms. It also partially reflected interactive effect of earthworm species and soil types. Therefore, it is reasonable to use BSAF to assess the influence of earthworms on soil properties and heavy metal bioavailability.

Soil property variations and their influencing factors

Soil pH in treatment AR and BR was significantly lower than that in the control, indicating that A. robustus could significantly reduce soil pH. [START_REF] García-Montero | Impact of earthworm casts on soil pH and calcium carbonate in black truffle burns[END_REF] found that earthworms (Prosellodrilus sp.) increase soil pH by their cast production and activity, which is quite different from our results. However the effect earthworms on soil pH should depend on site-specific factors including pH and category of the original soil [START_REF] Cheng | Effects of earthworms on Zn fractionation in soils[END_REF][START_REF] Yu | Earthworm-mycorrhiza interaction on Cd uptake and growth of ryegrass[END_REF][START_REF] Oo | Soil properties and maize growth in saline and nonsaline soils using cassava-industrial waste compost and vermicompost with or without earthworms[END_REF]F Li et al., 2019aF Li et al., , 2019b)). RDA showed that soil pH and Cu-BSFA were significantly correlated with Axis 1, suggesting Cu-BSFA which reflected the influence of both earthworm species and soil types had a major impact on soil pH change.

Earthworms changed C and N transportation in soil [START_REF] Hoang | Earthworm burrows: kinetics and spatial distribution of enzymes of C-, N-and P-cycles[END_REF][START_REF] Sheehy | Earthworm Lumbricus terrestris mediated redistribution of C and N into large macroaggregate-occluded soil fractions in fine-textured no-till soils[END_REF]. Two-way ANOVA showed a significant influence of earthworm on C min (P < 0.01, Table 3), suggesting that earthworms affected soil C and N components by influencing soil microbial activities. Earthworm intestinal microorganisms were associated with redistribution of C and N in soil [START_REF] Sheehy | Earthworm Lumbricus terrestris mediated redistribution of C and N into large macroaggregate-occluded soil fractions in fine-textured no-till soils[END_REF]. A. corticis could increase soil C mineralization through their promotion of soil respiration [START_REF] Snyder | Competition between invasive earthworms (Amynthas corticis, Megascolecidae) and native North American millipedes (Pseudopolydesmus erasus, Polydesmidae): effects on carbon cycling and soil structure[END_REF]. Enzymes associated with C mineralization, for instance, β-glucosidase, cellobiohydrolase, xylanase were activated in burrows of endogeic earthworm like A. robustus [START_REF] Hoang | Earthworm burrows: kinetics and spatial distribution of enzymes of C-, N-and P-cycles[END_REF]. The present study showed that the effects of A. corticis and A. robustus on C min in soil were similar (Fig. 1), indicating that although the feeding behavior and burrowing behavior of these two earthworms were different, due to their different ecological category, their influence on C min were similar. C min was negatively correlated with Cu-BSAF, indicating that soil C min might be also associated with the interactive effect between soil types and earthworm species. C min in soil A was quite higher than that in soil B (Table 3 and Fig. 1), which is in line with higher heavy metal content in soil A than that in soil B, suggesting that soil heavy metal contamination might stimulate C mineralization to some extent. Similar phenomenon has been reported in previous studies (Flieβbach et al., 1994;[START_REF] Dinesh | Engineered nanoparticles in the soil and their potential implications to microbial activity[END_REF]. Two-way ANOVA showed that soil N and its components were associated with earthworm species. In particular, soil N dis was positively correlated with Pb-BSAF (Fig. 1), demonstrating that earthworms with higher Pb-BSAF (i.e. A. robustus) tends to impact N dis greater than those with lower Pb-BSAF (i.e. A corticis). So far, several possible mechanisms explaining endogeic earthworm-stimulated N dis have been put forward. Burrowing activity of earthworms could mediate soil aeration, modify soil moisture, decompose organic matter, and hence change the microbial communities and N cycles [START_REF] Kim | Interactions between earthworm burrowing, growth of a leguminous shrub and nitrogen cycling in a former agricultural soil[END_REF]. Earthworm epidermal mucus contains NO 3 --N and NH 4 + -N, which could directly increase soil dissolved N content by burrowing activities [START_REF] Zhang | Influence of earthworm mucus and amino acids on tomato seedling growth and cadmium accumulation[END_REF][START_REF] Xu | Earthworms stimulate nitrogen transformation in an acidic soil under different Cd contamination[END_REF]. Through burrowing activities, endogeic earthworms could have enhanced microbial activity and accelerate N mineralization [START_REF] Ernst | Decomposition and mineralization of energy crop residues governed by earthworms[END_REF].

RDA showed that 51.9% of soil property changes could be explained by Cu-BSAF and Pb-BSAF of earthworms, suggesting that heavy metal accumulation capacity of earthworms played an important role in soil property variations. Compared to biomass and survival number of earthworms, the species along with soil types were the most significant factors affecting soil properties. Thus, further studies regarding effect of earthworms on soil properties should consider intrinsic features of earthworm ecological category (i.e. BSAF of heavy metals).

Influence of earthworms on DTPA extracted heavy metals

Zn-DTPA in treatment AR along with Cu-DTPA in treatment AR and BR were significantly higher than those of control (P < 0.05), indicating that A. robustus increased Zn and Cu availability in soil. Nevertheless, A. corticis increased Cu availability in soil A. These results demonstrated that A. robustus might have stronger capability in activating heavy metal availability than A. corticis. This evidence has been supported by previous study that earthworms of endogeic species activated heavy metal availability [START_REF] Dai | Heavy metal accumulation by two earthworm species and its relationship to total and DTPA-extractable metals in soils[END_REF]. Heavy metals combined with soil minerals might be released during intestinal digestion by endogeic earthworms [START_REF] Sizmur | Do earthworms impact metal mobility and availability in soil? -a review[END_REF]. Compared to epigeic earthworms, endogeic earthworms tend to make burrows and move frequently in soil [START_REF] Lin | Enhancement effect of two ecological earthworm species (Eisenia foetida and Amynthas robustus E. Perrier) on removal and degradation processes of soil DDT[END_REF][START_REF] Xu | Removal of persistent DDT residues from soils by earthworms: a mechanistic study[END_REF]., and hence have greater impact on heavy metal bioavailability in soil.

RDA result showed that Cu-BSAF and Pb-BSAF of earthworms could explain 51.7% of DTPA extracted heavy metal changes, indicating that Cu and Pb accumulation capacity of earthworms were the most significant factors impacting heavy metal bioavailability in soil. As BSAF -Pb and Cu-BSAF respectively reflected the effect of earthworm species and the interactive effect between soil type and earthworm species, these results demonstrated that heavy metal bioavailability was affected not only by earthworm species but also by the interaction of earthworms with the studied soils. Significant differences Xiao et al. Ecotoxicology and Environmental Safety xxx (xxxx) xxx-xxx occurred between soil A and soil B in soil properties (i.e. pH, SOC, total N, clay, CEC, total Fe and total Mn) and DTPA heavy metals (STable 1 and 2), which might result in an interactive effect with earthworm species and subsequently affected heavy metal bioavailability in soil. For instance, pH was reported to be the most important factors affecting earthworm survival and hence leading to the change of heavy metal accumulation in earthworm and heavy metal bioavailability in soil [START_REF] Bradham | Effect of soil properties on lead bioavailability and toxicity to earthworms[END_REF]. CEC was also recognized as a significant factor influencing heavy metal toxicity to earthworms and further impacting bioavailable heavy metal in soil [START_REF] Lanno | Effects of soil properties on the toxicity and bioaccumulation of lead in soil invertebrates[END_REF].

U N C O R R E C T E D P R O O F L.
It should be noted that Cu-BSAF and Pb-BSAF could explain 51.9% and 51.7% of soil property and DTPA extracted heavy metal changes respectively by means of RDA, and these two analyses were similar (Figs. 1 and2). These results suggested that the accumulation of Cu and Pb in earthworms have similar impact on soil properties and heavy metal bioavailability. On one hand, the accumulation of Cu and Pb in earthworms affected soil properties and hence influenced DTPA extracted heavy metals. Stepwise regression indicated that increase of pH, C min and N dis could enhance heavy metal availability in soil (Table 5), and these soil properties were all associated with earthworm activities based upon our above analysis. The increase of C min indicated an increase of organic C decomposition, which is conducive to heavy metal availability [START_REF] Zhang | Earthworm and organic amendment effects on microbial activities and metal availability in a contaminated soil from China[END_REF]. The change of soil pH altered heavy metal bioavailability in soil (Xiao et al., 2017a(Xiao et al., , 2017b)). The decomposition of soil urea was proved to be associated with soil heavy metal bioavailability [START_REF] Li | Heavy metal removal by biomineralization of urease producing bacteria isolated from soil[END_REF]. These changes of soil properties reflected the synergetic effect of earthworms and microorganisms, which finally led to an augment of heavy metal bioavailability [START_REF] Liu | Interaction effects and mechanism of Pb pollution and soil microorganism in the presence of earthworm[END_REF]. On the other hand, it should be noticed that the Pb-BSAF explained 13.3% of soil property change while it only explained 6.6% of DTPA extracted heavy metals in soil, while Cu-BSAF explained 38.5% of soil property change and 45.1% of DTPA extracted heavy metals in soil. These results implied that earthworm activity have a great impact on the variation of soil properties and DTPA extracted heavy metals, while the interaction between different earthworm species and soil types played a dominant role in soil properties and heavy metal changes.

Conclusion

The addition of earthworms decreased soil pH, increased soil C min , and increased soil N along with its components (i.e. N dis and N min ). The addition of A. robustus enhanced Zn-DTPA in soil A along with Cu-DTPA in both soil A and soil B, while addition of A. corticis increased Cu-DTPA in soil A. Cu-BSAF and Pb-BSAF could reflect 51.9% of soil property change, and they could explain 51.7% of DTPA extracted heavy metal variation. Pb-BSAF, indicating the influence of earthworm activities of different species, had a great impact on soil property and heavy metal bioavailability change. Nevertheless, Cu-BSAF, reflecting the interaction between earthworm species and soil types, had a dominant effect on the variation of soil property and heavy metal bioavailability. RDA and stepwise regression analysis indicated that earthworm activities changed soil properties (i.e. pH, C min and N dis ) and thus enhanced heavy metal bioavailability. Collectively these results imply that BSAF is an important indicator for evaluating the effect of earthworm activity on soil heavy metal bioavailability and assessing the capacity of earthworms for soil heavy metal remediation. Further study may consider a field investigation to evaluate the contribution of BSAF-heavy metals of earthworms on heavy metal bioavailability for in situ condition.

CRediT authorship contribution statement

Declaration of competing interest

We declare that we have no conflicts of interest to this work.

  U N C O R R E C T E D P R O O F L.Xiao et al. Ecotoxicology and Environmental Safety xxx (xxxx) xxx-xxx

A

  and B referred to the treatments using soil A and soil B. S, C and R referred to treatment with no earthworm (control), A. corticis, and A robsutus. The different capital letters indicate significant differences in treatment of soil A (P < 0.05). The different lower letters indicated significant differences in treatment of soil B (P < 0.05). The different superscript letters indicated significant difference in different soil type with same earthworm species.

Fig. 1 .

 1 Fig. 1. Redundancy analysis (RDA) of soil properties and BSAF-heavy metals of earthworms. Squares and circles referred to soil treated with A. corticis and A. robustus, respectively. Color white and grey referred to soil A and soil B. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 .

 2 Fig. 2. Redundancy analysis (RDA) of DTPA extracted heavy metals in soil and BSAF-heavy metals of earthworms. Squares and circles referred to soil treated with A. corticis and A. robustus, respectively. Color white and grey referred to soil A and soil B. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Ling Xiao :

 Xiao Formal analysis, Data curation, Writing -original draft, Writing -review & editing. Ming-hui Li: Data curation, Formal analysis, Writing -original draft, Writing -review & editing. Jun Dai: Conceptualization, Methodology, Formal analysis, Funding acquisi tion, Writing -review & editing. Mikael Motelica-Heino: Formal analysis, Writing -review & editing. Xu-fei Chen: Data curation, Methodology. Jia-Long Wu: Data curation, Writing -original draft.. Lanfeng Zhao: Data curation. Kexue Liu: Data curation. Chi Zhang: Conceptualization, Methodology, Formal analysis, Data curation, Writing -original draft, Writing -review & editing, Funding acquisition.

Table 1

 1 Variation of earthworm biomass and quantity (mean ± S.D., n = 4).

	times, respectively, compared to their initial concentrations. Heavy metal ac-cumulation in earthworm tissue was associated with
			Biomass		Quantity
			g•pot -1		worms•pot -1
			0 d	120 d	0 d	120 d
	A. corticis A. robustus	soil A soil B soil A soil B	5.34 ± 0.07 Aa 5.31 ± 0.08 Aa 16.2 ± 0.09 Aa 16.2 ± 0.11 Ab	1.57 ± 0.88 Ab 1.90 ± 1.59 Ab 16.6 ± 1.51 Aa 11.0 ± 3.00 Bb	8 ± 0 Aa 8 ± 0 Aa 8 ± 0 Aa 8 ± 0 Aa	4 ± 3 Ab 3 ± 2 Bb 7 ± 1 Aa 6 ± 1 Ab

Different capital letters indicate the significant difference (P < 0.05) between the treatments with different soil and same earthworm, Different lower letters indicate the significant difference (P < 0.05) in the same treatment before and after incubation.

Table 2

 2 Variation of earthworm metal accumulation and BSAF in treatments (mean ± S.D., n = 4).

	Metals accumulation

A and B referred to the treatments using soil A and soil B. C and R referred to treatment with A. corticis, and A robsutus.

  referred to the treatments using soil A and soil B. S, C and R referred to treatment with no earthworm (control), A. corticis, and A robsutus. The different capital letters indicate significant differences in treatment of soil A (P < 0.05). The different lower letters indicated significant differences in treatment of soil B (P < 0.05). The different superscript letters indicated significant difference in different soil type with same earthworm species.

	L. Xiao et al.						Ecotoxicology and Environmental Safety xxx (xxxx) xxx-xxx
	Table 4 DTPA extracted heavy metals in soil of treatments (mean ± S.D., n = 4). Expand
	Treatment				Zn-DTPA	Cd-DTPA	Pb-DTPA	Cu-DTPA
					mg•kg -1	mg•kg -1	mg•kg -1	mg•kg -1
	Two way ANOVA	AS AC AR BS BS BR	Earthworm Soil Earthworm*Soil	p p p	19.5 ± 0.892B a 20.4 ± 0.251AB a 21.1 ± 0.665A a 13.5 ± 0.536a b 14.3 ± 1.570a b 14.6 ± 0.781a b 0.008 <0.0001 0.745	P R O O F 0.333 ± 0.024A a 50.1 ± 2.19A a 0.335 ± 0.015A a 50.2 ± 1.11A a 0.345 ± 0.022A a 48.1 ± 0.850A a 0.140 ± 0.006a b 40.2 ± 1.39a b 0.135 ± 0.014a b 40.1 ± 2.20a b 68.6 ± 2.38ab b 96.0 ± 2.97B a 100 ± 2.86A a 101 ± 1.69A a 64.8 ± 2.41b b 0.133 ± 0.010a b 38.3 ± 0.871a b 71.0 ± 3.01a b 0.784 0.215 0.0002 <0.0001 <0.0001 <0.0001 0.39 0.922 0.922
	AS AC AR BS BC BR Earthworm Soil Earthworm*Soil A and B U N C O R R E C T E D 5.12 ± 0.121A a 20.3 ± 4.86A a 26.8 ± 15.9A a 5.05 ± 0.123AB a 19.2 ± 1.10A b 24.0 ± 2.35A a 4.91 ± 0.114B a 20.5 ± 0.266A b 18.0 ± 2.60A b 4.49 ± 0.090a b 17.2 ± 1.26b a 21.3 ± 9.72a a 4.31 ± 0.208ab b 21.7 ± 1.37a a 28.7 ± 11.3a a 4.04 ± 0.206b b 21.3 ± 0.563a a 31.7 ± 9.69a a Two way ANOVA p 0.006 0.06 0.353 p <0.0001 0.362 0.381 p 0.247 0.012 0.103	23.3 ± 3.42B a 29.6 ± 4.22A a 29.9 ± 3.40A a 15.7 ± 2.90a b 19.6 ± 3.74a b 17.2 ± 1.76a b 0.004 <0.0001 0.204	67.5 ± 4.03A a 54.1 ± 5.06B a 37.5 ± 6.51C b 35.7 ± 5.20b b 51.6 ± 4.23a a 57.6 ± 10.0a a 0.696 0.167 <0.0001	1.12 ± 0.042B a 1.25 ± 0.070A a 1.31 ± 0.052A a 1.15 ± 0.016b a 1.26 ± 0.114a a 1.31 ± 0.023a a <0.0001 0.655 0.638	43.1 ± 10.4 B a 50.9 ± 5.55 AB a 58.9 ± 6.72 A b 44.9 ± 21.1b a 62.4 ± 8.89b a 90.5 ± 15.0a a 0.004 0.033 0.205	9.65 ± 4.08B b 19.5 ± 2.09A b 23.2 ± 5.85A b 25.9 ± 4.81c a 48.5 ± 16.4b a 88.3 ± 17.4a a <0.0001 <0.0001 0.798	-1 30.6 ± 1.23A a 19.0 ± 4.94B a 5.00 ± 4.80C a 32.9 ± 14.4a a 9.15 ± 7.48b a 16.0 ± 15.3ab a <0.0001 0.745 0.902

Table 5

 5 Stepwise regression between DTPA extracted heavy metals, soil organic C and N along with the components as well as BSAF of earthworms.

	Expand
	Element	Equations

Acknowledgements

This work was financially supported by the Natural Science Foundation of China (Grant No. 41201305 and 41601575), National Science and Technology Fundamental Resources Investigation Program of China (2018FY100300), National Key Research and Development Program of China (2016YFD0201301 and 2016YFD0201200) and China Scholarship Council in China. The author Dr. Ling Xiao thanks Chengqian Gong for his consistent support.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.ecoenv.2020.110513.