Feurer Denis

Mouelic Le

Stéphane

Raucoules Daniel

DETECTION OF THE SUBSIDENCE AFFECTING A SHOPPING CENTER IN MARSEILLES (France) USING SAR INTERFEROMETRY

Help of satellite radar interferometry for urban subsidence observation has been demonstrated for several years now. This monitoring tool is able to provide an assessment of the ground motion with a millimetric accuracy and a large spatial coverage. We present here a result of this technique applied to the monitoring of a small area : the «Grand Littoral» shopping centre complex and cinema multiplex in Marseilles, France. This construction work was one of the most important construction site of this last few years in France. Inaugurated in October, 1997, the multiplex had to close 6 of its 15 cinemas five months later because of collapsing risks due to important ground movements. It has been totally closed in July, 1999. The multiplex building demolition is currently under way. Finally, this "flop" represents a cost of 30 millions euros. 14 ERS images acquired between 1992 and 2000 had been processed in order to produce a set of 105 differential interferograms. We performed a recursive correction of orbital and topographic fringes using a FFT computation and a Digital Elevation Model provided by the French National Institute (IGN). The analysis of the interferograms series has allowed to detect unambiguously a signature of few pixels corresponding to the ground movement. From this study, we observed a ground deformation during 1997 to 1998, an overall stability during late 1998 to 1999 and again a deformation during late 1999 to 2000. This study shows that, in specific cases, traditional InSAR is able to provide valuable information on very localised ground deformation. It also shows the interest of a comprehensive study of the full ERS archive of this site in order to assess the stability of the ground before, when no ground-based measurements were available, during, and after the construction works.

INTRODUCTION

The "Grand Littoral" shopping centre complex and cinema multiplex of Marseilles was one of the most important construction work of this last years in France. It has been built on a former quarry, which provided materials for the neighbouring tilery. The construction work began in 1995, and the site had been inaugurated in 1997. In 1998, evidences of ground movements leaded to the closing of 6 cinemas out of the 15. The cinema multiplex had to entirely close in July, 1999. The demolition works began then, and the building is now totally pulled down. This affair was one of the most expensive for the insurers in France, the total cost representing more than 30 millions Euro. 14 ERS images acquired between 1992 and 2000 were at our disposal. The aim of this study is to check the feasibility of a monitoring of this phenomenon with SAR interferometry.

DATA PROCESSING

As already mentioned above, the data stack was consisting in 14 ERS-1&2 images, acquired between 1992 and 2000. The table of the Fig. 1 describes these images. We use the Gamma software for the interferograms computation [1]. After having co-registered each image upon a reference chosen in order to minimise the baseline distribution, all the interferograms with baselines smaller than +/-300 meters had been computed. The orbital fringes had been removed thanks to precise orbits from the Delft university [2] and a FFT computation of the phase ramp had been performed to refine the baselines. Orbital fringes computed from these baselines had been removed. A Digital Elevation Model from the French institute for cartography (IGN) had been used to remove the topographic fringes. The 105 interferograms had been visually checked and selected. Only 17 interferograms had been used for the final interpretation (see Fig. 2. ). A mean deformation map had been computed with a least square adjustment of the temporal series [START_REF] Mouelic | A least-squares adjustment of multi-temporal InSAR data -Application to the ground deformation of Paris[END_REF][START_REF] Usai | A Least Squares Database Approach for SAR interferometric data, Monitoring terrain deformations at Phlegrean Fields with SAR interferometry[END_REF]. For purpose of visualisation, the interferograms had been filtered with a Goldstein filter and geocoded [START_REF] Goldstein | Radar interferogram filtering for geophysical applications[END_REF].

____________________________________________________________

DETECTION OF THE MOVEMENT

The visual analysis of the set of interferograms upon the "Grand Littoral" zone reveals the presence of a particular signature. This signature appears on several independent interferograms (see Fig. 3. ), which ensures that this signal is not a tropospheric artefact. There is no correlation between the baseline and the magnitude of the signal, which ensures that the signal is neither a topographic artefact due to a DEM error. These different fringe patterns should hence be due to ground movement or movement of the buildings.

TEMPORAL MONITORING

The following figure represents the set of interferograms that had been used for the interpretation and the archive subset used in this study. According to this image, it appears that :

• almost the whole area covered by the shopping centre remains coherent for this period. The south-western part of the centre, where is located the cinema multiplex, shows a loss of coherence, • two zones present a ground motion : the south-western part, which moves away from the satellite, and the middle of the centre, which moves towards the satellite. This could be interpreted as a tip-over of the zone, but our interpretation must remain very cautious. First, this image is the linear average of all the movement that could have occurred during this period, and then, the temporal sampling of the phenomenon is too low to have a precise idea of the temporal behaviour of the zone. Finally, the shopping centre is fitted out with devices allowing the stability control of the buildings, which hampers an interpretation based only on the geomechanical characteristics of the site.

CONCLUSION

This study showed that, for the particular case of the "Grand littoral", SAR interferometry could be used to detect and monitor a building movement. However, this study did neither allow to precisely understand the history of the ground movement that caused the building damages nor detecting some precursors. Obtaining this kind of information would have required the exploitation of the full archive. In our case, unfortunately, information for the year 1993 could have been very precious. This study focuses once more on the potential of the ERS1&2 archive interferometric processing, which allows one to have information and measurements for the time period preceding the actual detection of the ground motion.

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Characteristics of the selected interferograms -presentation of the data set compared to the ERS1&2 archiveOf course, the temporal sampling given by 14 images irregularly acquired within this 8-year time span is too weak to allow a correct monitoring. However, by selecting within this data set a connected subset covering the whole period, it


Isabelle Badenhausser
email: isabelle.badenhausser@inrae.fr

Damien Fourcy

Mathilde Bertrand

Aurélien Pierre

Brigitte Bonneau

Jean-Louis Chapuis

Yann Rantier

Maurice Hullé

Do non-native plants affect terrestrial arthropods in the sub-Antarctic Kerguelen Islands?

Keywords: Biodiversity, biological invasions, species richness, vascular plants, functional traits

IB and MH conceived and designed research. IB, MB, AP conducted experiments. IB and BB identified arthropods. DF

INTRODUCTION

The Kerguelen Islands are located in the South Indian Ocean, and include a main island of 6675 km² and about sixty other islands (1 km²-200 km²) (Fig 1). Like the other sub-Antarctic islands, their terrestrial ecosystems are characterized by strong geographical and historical isolation.

They harbor a small number of sub-Antarctic species for both flora and fauna due to low levels of immigration from nearby continents and harsh environmental conditions [START_REF] Chown | Ecological biogeography of southern islands: species-area relationships, human impacts and conservation[END_REF]. Terrestrial ecosystems are very simple, natively lacking some taxonomic groups such as mammals, amphibians and reptiles. Low plant diversity at the species and botanical family levels characterizes the native flora, which is composed of 29 species of vascular plants [START_REF] Frenot | Human activities, ecoystem disturbance and plant invasions in subantarctic Crozet, Kerguelen and Amsterdam Islands[END_REF]. Native macro-arthropod communities are composed of 22 native insect species and two native spider species [START_REF] Hullé | The terrestrial macro-invertebrates of the sub-Antarctic Îles Kerguelen and Île de la Possession[END_REF], while micro-arthropod communities which are dominated by Oribatida mites and springtails are respectively composed of 24 and 18 native species (Deharveng and Travé 1981). It should be noted that micro-arthropod diversity may be underestimated and that some 'undiscovered' or cryptic species are likely to be found using recent advances in molecular biology in Antarctic [START_REF] Collins | Genetic diversity of soil invertebrates corroborates timing estimates for past collapses of the West Antarctic Ice Sheet[END_REF]. Macro-arthropod communities are unbalanced, some trophic groups being absent or not abundant such as herbivores, pollinators and decomposers such as flies dominate macro-arthropod communities due to the large amounts of marine mammal and seabird excretions [START_REF] Chown | Antarctic Entomology[END_REF].

Despite the geographical isolation of the Kerguelen Islands, many species among plants, arthropods and mammals have been introduced, voluntarily or involuntarily, due to human activities, and have established. For instance, there are currently as many non-native as native macro-arthropod species [START_REF] Hullé | The terrestrial macro-invertebrates of the sub-Antarctic Îles Kerguelen and Île de la Possession[END_REF], while few non-native micro-arthropod species have been recorded (Deharveng and Travé 1981;[START_REF] Greenslade | Exotic Collembola on subantarctic islands: pathways, origins and biology[END_REF]. For plants, the number of species in some sites historically frequented by humans has become three ACCEPTED MANUSCRIPT / CLEAN COPY 6/47 times greater than the number of native species [START_REF] Frenot | Biological invasions in the Antarctic: extent, impacts and implications[END_REF], having potentially important impacts on ecosystem processes.

Biological invasions are widely recognized as being one of the most significant threats to biodiversity and are expected to increase with global warming [START_REF] Shaw | Taxonomic homogenization and differentiation across Southern Ocean Islands differ among insects and vascular plants[END_REF][START_REF] Mcgeoch | Monitoring biological invasion across the broader Antarctic: A baseline and indicator framework[END_REF]. Sub-Antarctic islands are deeply affected by climate change [START_REF] Lebouvier | The significance of the sub-Antarctic Kerguelen Islands for the assessment of the vulnerability of native communities to climate change, alien insect invasion and plant viruses[END_REF] which makes their ecosystems more suitable to less stress-adapted non-native species [START_REF] Pertierra | Global thermal niche models of two European grasses shows high invasion risks in Antarctica[END_REF]). In addition, some established non-native species may become invasive [START_REF] Frenot | Biological invasions in the Antarctic: extent, impacts and implications[END_REF], exacerbating their impact on biodiversity and ecosystems due to the disruption of interaction networks (Dunn and Hatcher 2015). In general, invasive plants reduce the diversity of native plant species and are predicted to also negatively impact native arthropod diversity (review in [START_REF] Spafford | A systematic review of arthropod community diversity in association with invasive plants[END_REF] through different mechanisms such as the alteration of vegetation structure or the loss of some plant species which host specialist arthropods. Few native specialist arthropods are present on the Kerguelen Islands, e.g., the moth Embryonopsis halticella which feeds on Poa cooki (Crafford and Scholtz 1986), and the fly Calycopteryx moseleyi [START_REF] Tréhen | Organisation et dynamique des peuplements diptérologiques à Kerguelen. Mise en évidence de modifications liées à l'insularité[END_REF]), the weevils Bothrometopus angusticollis and Canonopsis sericea which feed on Pringlea antiscorbutica [START_REF] Voisin | Les Coléoptères de la province de Kerguelen (îles subantarctiques de l'océan Indien)[END_REF], and the loss of their host-plant might particularly affect them.

Arthropods are highly dependent on plants [START_REF] Schaffers | Arthropod assemblages are best predicted by plant species composition[END_REF][START_REF] Joern | Ecological mechanisms underlying arthropod species diversity in grasslands[END_REF] and different components of plant community features affect them, such as plant composition, or morphological and physical attributes. For instance, plant height and lateral spread as well as leaf dimensions characterize plant architecture and determine the availability of different micro-habitat conditions allowing or not arthropods to choose optimal conditions of temperature, protection against wind, rain or predators [START_REF] Spafford | A systematic review of arthropod community diversity in association with invasive plants[END_REF][START_REF] Gardarin | Understanding plantarthropod interactions in multitrophic communities to improve conservation biological control: useful traits and metrics[END_REF]. By providing attachment points for spider webs, architectural traits may have an impact on predator hunting efficacy, depending on predator hunting strategy and on predator-prey ACCEPTED MANUSCRIPT / CLEAN COPY interactions [START_REF] Pearson | Invasive plant architecture alters trophic interactions by changing predator abundance and behaviour[END_REF]. Vegetation stature also influences the distribution of soil-dwelling arthropods likely through its correlation with litter quantity [START_REF] Gardarin | Understanding plantarthropod interactions in multitrophic communities to improve conservation biological control: useful traits and metrics[END_REF]. For herbivores, which have direct interactions with plants since they feed on them, [START_REF] Carmona | Plant traits that predict resistance to herbivores[END_REF] showed that morphological and physical plant traits may act as a physical barrier and are often more important for plant-herbivore interactions than chemical traits. For instance, traits related to the biomechanical properties, such as cuticle thickness, specific leaf area [START_REF] Ordonez | Functional differences between native and alien species: a global-scale comparison[END_REF], or leaf dry matter content (Deraison et al. 2015) have been shown to impact herbivore choice when selecting their food plant.

How arthropod communities reliant on native vegetation on sub-Antarctic islands, are impacted by non-native plants is yet to be tested [START_REF] Houghton | The impacts of non-native species on the invertebrates of Southern ocean islands[END_REF] and is difficult to predict. This is due to the absence or low richness of many insect groups [START_REF] Gressit | Subantarctic entomology and biogeography[END_REF][START_REF] Vernon | A comparative approach to the entomological diversity of polar regions[END_REF], and to the high interactions at play between native and non-native plants, native and non-native arthropods and between plants and arthropods [START_REF] Houghton | The impacts of non-native species on the invertebrates of Southern ocean islands[END_REF]. Traitbased approaches have been proposed as useful tools to study plant-arthropod interactions at the community level [START_REF] Lavorel | A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services[END_REF]e.g., Deraison et al. 2015;[START_REF] Provost | Traitmatching and mass effect determine the functional response of herbivore community to land use intensification[END_REF] and to understand community responses to biotic disturbance induced by non-native species [START_REF] Gross | Functional differences between alien and native species: do biotic interactions determine the functional structure of highly invaded grasslands?[END_REF][START_REF] Mouillot | A functional approach reveals community responses to disturbance[END_REF]. This study aimed to investigate the effects of plant communities dominated by native or non-native plant species on macro-and micro-arthropod communities on three islands of the Kerguelen archipelago. On each of the studied islands, we selected relatively large areas of the two types of vegetation, i.e., native and non-native, that were close to each other. By considering plant traits involved in plant-arthropod interactions, i.e., plant architecture and leaf attributes, we assessed whether the plant functional community structure differed between the two types of vegetation using complementary indices, the functional dispersion and the community-weighted mean of traits [START_REF] Mouillot | A functional approach reveals community responses to disturbance[END_REF].

Secondly, we estimated the taxonomic diversity of macro-arthropods and their abundances as

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well as the abundances of epigaeic micro-arthropods in both types of vegetation. We then investigated whether these components of arthropod communities differed between native and non-native plant communities and whether plant functional diversity and community-weighted means of traits explained a significant part of their variability.

MATERIAL AND METHODS

STUDY AREA AND SAMPLING DESIGN

The study was conducted during the summer 2017-2018 on three islands of the Golfe du Morbihan in the Kerguelen archipelago (48° 30'-50° S, 68° 27′-70° 35′ E): Île Australia, Île aux Cochons and Île Mayes (Fig. 1). Originally, plant communities were dominated by Acaena magellanica, Azorella selago, Festuca contracta and P. antiscorbutica. These plant assemblies covered Île Australia and Île Mayes until the 2010s. From these years, following climate change, non-native Poaceae and Asteraceae developed, in particular Poa pratensis and Taraxacum gr. ruderalis. These plants have expanded widely on Île Mayes, where meadows of P. pratensis covered large areas in 2016. On Île Australia, non-native Poaceae communities (P.

pratensis, Vulpia bromoides) were still localized in 2016. Île aux Cochons differed from the other two islands by the presence of rabbits (Oryctolagus cuniculus) until 1997, when they were eradicated [START_REF] Chapuis | Eradication of rabbits (Oryctolagus cuniculus) by poisoning on three islands of the subantarctic Kerguelen Archipelago[END_REF]. This mammal resulted in the rarefaction of P. antiscorbutica and A. selago, replaced by A. magellanica, which covered more than 90% of the island in 1997 [START_REF] Chapuis | Eradication of rabbits (Oryctolagus cuniculus) by poisoning on three islands of the subantarctic Kerguelen Archipelago[END_REF][START_REF] Chapuis | Une gamme d'îles de référence, un atout majeur pour l'évaluation de programmes de restauration dans l'archipel de Kerguelen[END_REF]. During the years 2000-2010, the increase in temperature and especially the low summer precipitations [START_REF] Lebouvier | The significance of the sub-Antarctic Kerguelen Islands for the assessment of the vulnerability of native communities to climate change, alien insect invasion and plant viruses[END_REF]) resulted in the significant regression of A. magellanica and the development of non-native Asteraceae (Taraxacum erythrospermum, T. gr. ruderalia) and Poaceae (in particular P. pratensis).

Currently, the communities dominated by native species, i.e., A. magellanica, occupy small ACCEPTED MANUSCRIPT / CLEAN COPY areas. All three islands have been colonized by mice, which have heavily predated and damaged their invertebrate fauna [START_REF] Chapuis | Une gamme d'îles de référence, un atout majeur pour l'évaluation de programmes de restauration dans l'archipel de Kerguelen[END_REF].

The sampling design consisted of areas of native-or non-native-dominated vegetation that were close to each other (a few tens of meters). It was implemented in herbaceous habitats which are the main habitats colonized by non-native plants. Sites corresponding to these criteria were preselected using a remote sensing based cartography of the vegetation on the three studied islands (Fourcy et al. 2018) (Fig. 2; see Online Resource 1 for a complete description of the classes). Vegetation maps were produced from a multispectral Pléiades image acquired in February 2016 (2 m ground spatial resolution) that we analysed by performing a supervised classification with machine-learning algorithms. The supervised classification was based on botanical surveys conducted in December 2016 on training zones on Île Australia, Île aux cochons and Île Mayes, and used as references for the machine-learning algorithms.

Within each island, we selected in the field five sites among the preselected sites where an area covered with >90% of native plant species was close to an area covered with >90% of non-native plant species. The percent coverage of vegetation was estimated visually at that step.

Then, we delineated a patch >150 m² within each of these areas and surveys were carried out within each patch. Distance between the two types of patch within a site ranged from 15 m to 58 m. This resulted in 30 vegetation patches: 3 islands, 5 sites per island, 2 patches per site (one non-native, hereafter "non-native patch", and one native, hereafter "native patch") (Fig. 2).

When selecting the sites, we controlled for abiotic conditions known to affect arthropods, such as the altitude, distance from the sea [START_REF] Hullé | The terrestrial macro-invertebrates of the sub-Antarctic Îles Kerguelen and Île de la Possession[END_REF], slope, dominant wind and sun exposures. All sites were at < 40 m altitude, > 50 m from the sea, with a slope <20%. Sun and dominant wind exposures differed between sites and islands but not between paired patches (Online Resource 2).

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Note that the sampling conditions varied during the study. Harsh weather conditions were observed in Île aux Cochons where a hailstorm occurred during one day and in Île Mayes where low temperatures (4.4°C on average) and snowstorm occurred during three days.

Conversely, sunny weather conditions were recorded throughout the sampling period in Île Australia.

PLANT SURVEYS AND TRAITS

A botanical survey was conducted within each of the 30 patches on the first day of the experiment (20 th December 2017 on Île aux Cochons, 29 th December 2017 on Île Australia, 9 th January 2018 on Île Mayes). We used five quadrats of 0.50 m x 0.50 m haphazardly located within each patch. In each quadrat, we recorded the number of plant species and we estimated a percentage cover of the quadrat surface for each plant species. We calculated plant species diversity and mean cover of each plant species per patch over the five quadrats.

To characterize plant community features, seven plant traits reflecting important functions for plants (Diaz et al. 2016), and involved in plant-arthropod interactions [START_REF] Carmona | Plant traits that predict resistance to herbivores[END_REF][START_REF] Gardarin | Understanding plantarthropod interactions in multitrophic communities to improve conservation biological control: useful traits and metrics[END_REF] were selected: plant height (PH) and plant width (PW), leaf length (LL) and leaf area (LA), leaf dry matter content (LDMC), specific leaf area (SLA), and leaf thickness (LT). In each island, 15-30 specimens of each plant species covering >10% of at least one vegetation patch were measured and one leaf per plant was sampled, excepting A. selago and P. antiscorbutica for which we only sampled a total of 2-5 leaves. Traits were measured following standard protocols [START_REF] Cornelissen | A handbook of protocols for standardised and easy measurement of plant functional traits worldwide[END_REF] and their values were averaged over the number of plant or leaf specimens per plant species and island.

ARTHROPOD SAMPLING

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On the first or second day of the experiment, we sampled arthropods in the 30 patches (one trapping session) using two types of trap to accurately estimate arthropod diversity: pitfall traps and yellow pans [START_REF] Southwood | Ecological methods, with particular reference to the study of insect populations[END_REF]. We placed five pitfall traps (7.5 cm diameter, 5 cm depth) and two yellow pans (20 cm diameter) per patch. Pitfall traps were placed haphazardly in the patch, while yellow pans were placed on the ground and located so that the distance was the highest between the native patch and the non-native patch within each site. Traps were filled with a preservative solution of water, a few drops of liquid soap and salt (10 gL -1 ). All traps within an island were set up on the same day and left in place for four trapping days. We stored all caught arthropods in the lab in a 70% ethanol solution. Macro-arthropods were identified to the lowest possible taxonomic level (usually species) following [START_REF] Hullé | Guide d'identification des invertébrés des Îles sub-Antarctiques Kerguelen et Crozet[END_REF]. We identified winged aphids to species level while we pooled all wingless aphids together.

We characterized macro-arthropods, i.e., insects and spiders, by calculating their taxonomic richness and abundance. For this purpose, we defined different groups of macroarthropods according to their origin status and trophic group according to [START_REF] Hullé | The terrestrial macro-invertebrates of the sub-Antarctic Îles Kerguelen and Île de la Possession[END_REF]. To calculate taxa richness, we first pooled for each patch all macro-arthropods collected either in pitfall traps or in yellow pans. Then, we calculated native, non-native and total taxa richness, which included taxa with unknown native or non-native status. We also calculated taxa richness of three trophic groups: decomposers (taxa feeding on plant material and omnivorous taxa), herbivores and predators. For macro-arthropod abundances, we considered native macro-arthropods and performed separate analyses for pitfall traps and for yellow pans, pooling all individuals sampled in each patch. We also analysed the abundances of decomposers in yellow pans, herbivores in yellow pans and predators in pan traps by pooling all individuals sampled in each patch (see [START_REF] Badenhausser | Do non-native plants affect terrestrial arthropods in the sub-Antarctic Kerguelen Islands?[END_REF] for a complete description of dataset). For micro-arthropods, we focussed on the abundances of Oribatida mites and Symphypleona springtails, which are generally epigaeic and tend to occur above or on the top of the ground ACCEPTED MANUSCRIPT / CLEAN COPY 12/47 surface [START_REF] Greenslade | Assessing the risk of exotic Collembola invading subantarctic islands: prioritising quarantine management[END_REF]. We analysed their abundances in pitfall traps by pooling all individuals sampled in each patch.

STATISTICAL ANALYSES

All statistical analyses were performed using the R statistical software (R Development Core Team 2020) version 4.0.3 and packages car [START_REF] Fox | An {R} Companion to Applied Regression, Third Edition[END_REF], emmeans [START_REF] Lenth | emmeans: Estimated Marginal Means, aka Least-Squares Means[END_REF], factoextra (Kassambara and Mundt 2020), FactoMineR [START_REF] Le | FactoMineR: An R Package for Multivariate Analysis[END_REF], FD [START_REF] Laliberté | A distance-based framework for measuring functional diversity from multiple traits[END_REF][START_REF] Laliberté | FD: measuring functional diversity from multiple traits, and other tools for functional ecology[END_REF]) and lme4 [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF].

Characterization of plant functional community structure

Two complementary components of the functional structure of plant communities were assessed, i.e., functional composition and functional diversity [START_REF] Laliberté | A distance-based framework for measuring functional diversity from multiple traits[END_REF][START_REF] Mouillot | A functional approach reveals community responses to disturbance[END_REF]. Plant functional composition was estimated using the community weighted mean index calculated for each trait, CWM, and functional diversity using the multitrait functional dispersion index, FDis. Both indices were calculated following [START_REF] Laliberté | A distance-based framework for measuring functional diversity from multiple traits[END_REF].

CWM is the mean trait value of the community, weighted by the relative cover of each plant species. It reflects the trait values of the dominant species in the community. It was computed separately for the seven measured traits (CWMTRAIT):

𝐶𝑊𝑀 𝑇𝑅𝐴𝐼𝑇 = ∑ 𝑝 𝑗 𝑇 𝑗 𝑛 𝑗=1
where n is the number of plant species in the vegetation patch, pj is the relative cover of species j in the patch (mean of species cover over the five quadrats per patch), and Tj is its mean trait value per island.

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FDis is the mean distance in multidimensional trait space of individual species to the centroid of all species. It takes into account species abundances weighting distances of individual species by their relative abundances. Its minimum value is 0, when the community is composed of only one species. High FDis value in plant communities reflects a strong disparity in the distribution of traits. By providing a greater number of niches and microhabitats for arthropods, high FDis is expected to result in greater arthropod diversity (e.g., Deraison et al. 2015). It was computed as:

𝐹𝐷𝑖𝑠 = ∑ 𝑎 𝑗 𝑧 𝑗 𝑛 𝑗=1 ∑ 𝑎 𝑗 𝑛 𝑗=1
where n is the number of plant species in the vegetation patch, aj is the cover of species j in the patch (mean of species cover over the five quadrats per patch), zj is the distance of species j to the weighted centroid of the [xij] (trait × species) matrix, c:

𝑐 = [𝑐 𝑖 ] = ∑ 𝑎 𝑗 𝑥 𝑖𝑗 𝑛 𝑗=1 ∑ 𝑎 𝑗 𝑛 𝑗=1
We performed a principal component analysis (PCA) on the CWMTRAIT of the seven selected traits to describe plant community features in the two types of patch and to evaluate how they correlated.

We used Linear Mixed Effects Models (LMM) fitted by log-likelihood criterion to test for the effect of the island and type of patch on plant species richness, the seven CWMTRAIT and

FDis. The interaction term between the type of patch and the island was included in the models since the effect of non-native plants may depend on the island and its invasion history. The site was included in the models as random effect to take into account for the effects of similar abiotic environmental conditions in the two patches per site. Models were simplified step by step by removing the interaction term and the main fixed effects if not significant (α = 0.05) using Wald Chi-square test, 𝜒 2 . Parameters of the final models were estimated using restricted maximum likelihood (REML), and least-squares means were calculated. Predicted means were compared ACCEPTED MANUSCRIPT / CLEAN COPY 14/47

using Tukey method and adequate contrasts. Model errors were inspected for normality, constant mean and variance. Model formula was:

𝑌 ~ (1|𝑆𝑖𝑡𝑒) + 𝐼𝑠𝑙𝑎𝑛𝑑 * 𝑇𝑦𝑝𝑒 𝑜𝑓 𝑝𝑎𝑡𝑐ℎ

Effects of plant communities on arthropod communities

The effects of plant communities on the richness and abundance of arthropods were analysed using LMM or Generalized Linear Mixed Effects Models (GLMM) depending on the distribution of the data and model errors, in order to satisfy the statistical assumptions of models. LMM was used for analysing i/ the taxa richness of macro-arthropods: native species, non-native taxa (sqrt-transformed), all taxa (sqrt-transformed), decomposers (sqrttransformed), herbivores and predators; ii/ the abundances of macro-arthropods: decomposers (log-transformed), herbivores (log-transformed), predators (log-transformed); iii/ the abundance of Oribatida mites (log-transformed). GLMM with Poisson errors was used for analysing i/ the abundance of native macro-arthropods and ii/ the abundance of Symphypleona springtails.

First, we tested for the effects of the type of patch and plant FDis on the response variables mentioned above. We included in the models the island, the type of patch and their two-way interaction, and plant FDis as fixed effects. The site was included as random effect in all models. The model had the following structure:

Y ~ (1|𝑆𝑖𝑡𝑒) + 𝐼𝑠𝑙𝑎𝑛𝑑 * 𝑇𝑦𝑝𝑒 𝑜𝑓 𝑝𝑎𝑡𝑐ℎ + 𝐹𝐷𝑖𝑠
Model simplification was conducted firstly by removing non-significant interaction term. Statistical assumptions were inspected, and model predictions and mean comparisons were performed as described above.

Second, we aimed to establish the relative importance of the functional composition of

RESULTS

CHARACTERIZATION OF PLANT COMMUNITIES AT THE PATCH SCALE

Main native plant species in the patches were A. magellanica, A. selago, F. contracta, and P.

antiscorbutica and main non-native species were P. pratensis generally associated with T. gr.

ruderalia, and T. erythrospermum. Non-native plant species covered on average >95% in nonnative patches (Fig. 3d, e, f) while native plant species covered ~ 90% in native patches (Fig. 3a,b,c). Total plant species richness per patch did not vary between native and non-native

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patches (LMM: p (>𝜒 1 2 ) = 0.1484). It was significantly lower (LMM: p (>χ 2 ) = 0.0230) on Île Australia (estimated mean ± SE, 3.1 ± 0.6 plant species) and Île Mayes (3.4 ± 0.6) than on Île aux Cochons (5.3 ± 0.8). These differences between islands were due to differences in nonnative plant species richness in the native patches, which was higher on Île aux Cochons than on Île Mayes than on Île Australia (LMM: Island: p (>χ 2 ) = 0.0001; Type of patch: p (>χ 2 ) = 0.0030; Island × type of patch: p (>χ 2 ) = 0.0321). Native plant species richness was the same in both types of patch and on the three islands (observed mean ± SE = 1.9 ± 0.3). The functional dispersion FDis did not vary between the types of patch but between islands (Table 1).

The PCA performed on CWMTRAITs showed that non-native and native patches formed distinct groups in the space of the two first PCA axes which accounted for 82.9% of the variance (Online Resource 3). The first PCA axis (60.1% of explained variance) mainly correlated to leaf dimensions, i.e., leaf length (CWMLL) (r = 0.96) and leaf area (CWMLA ) (r = 0.86). The second PCA axis (22.8% of explained variance) correlated to leaf thickness (CWMLT ) (r = 0.79).

All CWMTRAIT values differed significantly between native and non-native patches (Table 1

Macro-arthropod diversity and native macro-arthropod abundance

The same native macro-arthropod species with very few exceptions were collected in native and non-native patches within each island. They belonged to three trophic groups, i.e., decomposers, herbivores and predators. Taxa richness of native macro-arthropods was low and differed between islands (estimated means ± SE, Île Australia: 3.5 ± 0.4; Île aux Cochons: 2.7 ± 0.4; Île Mayes: 1.9 ± 0.4) (Table 2). The type of patch had no effect on the taxa richness of native macro-arthropods but plant FDis had a positive significant effect (Table 2) (LMM:

Parameter estimate ± SE, 6.31 ± 3.11) suggesting that a wide variety of food niches or microhabitats was favorable to native macro-arthropods. Taxa richness of non-native macroarthropods was roughly twice the taxa richness of native macro-arthropods and differed between islands (Île Australia: 7.6 ± 0.7; Île aux Cochons: 5.1 ± 0.6; Île Mayes: 4.3 ± 0.5) (Table 2). The type of patch and FDis had no effect on the taxa richness of non-native macroarthropods (Table 2). Total macro-arthropod richness per patch was the same in native and nonnative patches on Île Australia (native patches: 11.9 ± 1.3; non-native patches: 13.7 ± 1.3) and

Île aux Cochons (native patches: 9.2 ± 1.0; non-native patches: 7.6 ± 1.0) (Table 2). On Île Mayes, it was significantly lower in native patches (5.2 ± 0.8) relative to non-native patches (8.2 ± 1.0) (Table 2). FDis had a positive significant effect on total richness (Table 2).

The native spiders, Neomaso antarcticus and Myro kerguelensis, represented ~90% of the abundances of native macro-arthropods collected in pitfall traps whatever the type of patch

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and the island, and >50% in yellow pans. Native macro-arthropod abundances in pitfall traps were significantly lower in native patches relative to non-native patches and differed between islands (Table 2, Fig. 4a). In addition, they increased significantly with FDis (Table 2) (GLMM:

Parameter estimate ± SE, 2.46 ± 1.09). The island, type of patch, and FDis had no significant effect on the abundances of native macro-arthropods collected in yellow pans (GLMM: Island: p (>χ 2 ) = 0.0966; Type of patch: p (>χ 2 ) = 0.9381; Island × Type of patch: p (>χ 2 ) = 0.2201;

FDis: p (>χ 2 ) = 0.1413).
Results of the comparison between the patch model and CWMTRAITs models showed that CWMPW and CWMLA models were better than the patch model in explaining the taxa richness of native macro-arthropods (Online Resource 4). In these models, CWMPW and CWMLA had opposite effects depending on the island (Online Resource 4). No CWMTRAIT model was selected as the best model relative to the patch model in explaining the non-native and total taxa richness, and the abundance of native species (Online Resource 4).

Diversity and abundance of trophic groups

Herbivores represented >95% of the counts whatever the island and type of patch (Online Resource 5). This group was mainly composed of four aphid species among which Myzus ascalonicus was dominant (>95% of winged aphids), and one thrips species Apterothrips apteris. Four native species represented other herbivores, which were very few (47 individuals in total). Singular fit occurred in LMM conducted on herbivore richness, which was not analysed. Herbivore abundance was similar or higher depending on the island in non-native patches relative to native (Table 2; Fig. 4b). Plant FDis had strong effects on herbivore abundance, which increased with increasing FDis (

EFFECTS OF PLANT COMMUNITIES ON MICRO-ARTHROPOD COMMUNITIES

Overall, 9845 micro-arthropods were collected in pitfall traps among which 9001 Oribatida mites and 844 Symphypleona springtails. Oribatida mites represented >80% of microarthropods on the three islands. Native patches had higher Oribatida mite (Fig. 5a) and Symphypleona springtail (Fig. 5b) abundances relative to non-native patches whatever the island (Table 2). FDis had a strong negative effect on Symphypleona springtails (GLMM:

Parameter estimate ± SE, -7.25 ± 1.00) while it had no effect on Oribatida mites (Table 2).

Results of the comparison between the patch model and CWMTRAITs models showed that CWMPW model was better than the patch model in explaining Symphypleona abundance, which decreased with increasing CWMPW whatever the island (Online Resource 4).

DISCUSSION

Our study shows opposite effects of non-native plants on macro-arthropods and microarthropods. Despite strong differences in the functional composition of plant communities, we didn't demonstrate any negative effect of non-native plants on native macro-arthropods. In fact, macro-arthropod diversity and abundance were similar or higher in non-native plant communities whatever their native or non-native status, and their trophic group. Conversely, micro-arthropods, i.e., Symphypleona springtails and Oribatida mites, abundances were higher in native plant communities relative to non-native. Finally, we found that plant functional diversity was similar in native and non-native plant communities, and benefited native macroarthropods while it had a null or negative effect on micro-arthropods.

FUNCTIONAL STRUCTURE OF NATIVE AND NON-NATIVE PLANT COMMUNITIES

Trait-based indices, i.e., CWMTRAITs and FDis, were used to characterize the functional composition and diversity of native and non-native patches. Traits associated with leaf structure ACCEPTED MANUSCRIPT / CLEAN COPY and quality were expected to be involved in trophic plant-arthropod interactions, and those with plant stature in non-trophic interactions through their effects on arthropod habitats [START_REF] Lavorel | A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services[END_REF][START_REF] Gardarin | Understanding plantarthropod interactions in multitrophic communities to improve conservation biological control: useful traits and metrics[END_REF]. All CWMTRAITs differed between the two types of patch, reflecting the very high cover of respectively native and non-native plant species in the patches and trait differences between dominant native and non-native species. Dominant native plant species, i.e., A. magellanica and F. contracta, had low stature and leaf dimensions, low SLA and high LDMC, indicating low relative growth rate, low nutrient content and slow resource capture. They contrasted with dominant non-native species, i.e., P. pratensis and T. gr.

ruderalia whose large stature and leaf dimensions, large SLA and low LDMC, correspond to fast-growth rate, high nutrient content and good light competitor (Diaz et al. 2016). Other studies also observed these characteristics in native and non-native plant communities (Ordonnez et al. 2010;[START_REF] Gross | Functional differences between alien and native species: do biotic interactions determine the functional structure of highly invaded grasslands?[END_REF]). We also found that FDis did not differ between native and non-native patches. This reflects the fact that native and non-native species did not mix or mixed very little within the patches. It also reflects similar range of trait disparity between the two dominant species within each type of patch, i.e., between a grass species (F. contracta in native patches and P. pratensis in non-native patches) and an herb species (A. magellanica in native patches and T. gr ruderalia in non-native patches).

NON-NATIVE PLANTS HAVE NO NEGATIVE EFFECT ON NATIVE MACRO-

ARTHROPODS

Plant FDis significantly contributed to explaining the effect of plants on native macroarthropods, which benefited as expected from a greater diversity of plant resources and microhabitats in both native and non-native vegetation. Differences in the composition of native and non-native vegetation, did not lead to negative effects on native macro-arthropods. Although this result does not match the general expectation (review in [START_REF] Spafford | A systematic review of arthropod community diversity in association with invasive plants[END_REF], it is

ACCEPTED MANUSCRIPT / CLEAN COPY 22/47
interesting to note that it is in agreement with the only published comparative study on the effect of non-native plants on invertebrates at the community scale, and in the context of sub-Antarctic islands [START_REF] Gremmen | Impact of the introduced grass Agrostis stolonifera on vegetation and soil fauna communities at Marion Island, sub-Antarctic[END_REF]. In this study, which focussed on soil fauna communities, native macro-invertebrate abundance was similar or higher in vegetation dominated by the non-native grass Agrostis stolonifera compared to vegetation dominated by A. magellanica. Obviously, non-native plants would have had a negative impact on specialist native macro-arthropods feeding on some particular native plant species. However, highly specialist native macroarthropods were scarce in our study, probably because we focussed on herbaceous habitats, while specialist species were more likely to be found in fellfield habitats [START_REF] Hullé | The terrestrial macro-invertebrates of the sub-Antarctic Îles Kerguelen and Île de la Possession[END_REF]. Two hypotheses may explain the lack of negative effects of non-native plants on native macro-arthropods. First, the spatial scale of our paired sampling design, i.e., the short distances separating native and non-native patches could result in native macro-arthropod communities being composed of the same species in both types of patch. Indeed, the mosaic landscape and entangled patterns of native and non-native plant communities observed on the islands studied (Fig. 2) may allow between-patch movements of individuals either actively walking, or flying, depending on species dispersal abilities, or passively carried by wind from and to nearby areas [START_REF] Schooley | Finding habitat patches and directional connectivity[END_REF]. At the scale of the patch, higher abundances of some macroarthropod species in non-native patches may be related to better local conditions provided by the non-native plants. Similar small-scale distribution patterns in arthropods depending on plant species within the vegetation mosaic have been described (e.g., [START_REF] Coulson | Microscale distribution pattern in high Artic soil microarthropod communities: the influence of plant species within the vegetation mosaic[END_REF]. A second hypothesis is that interactions between non-native and native macro-arthropods play a greater role than plant-arthropod interactions in shaping native macro-arthropods. Indeed, an important feature of macro-arthropod communities was that non-native species outnumbered native in both types of vegetation, as already highlighted on Kerguelen archipelago [START_REF] Frenot | Biological invasions in the Antarctic: extent, impacts and implications[END_REF].

For instance, the predatory beetle M. soledadinus had a strong impact on native invertebrates ACCEPTED MANUSCRIPT / CLEAN COPY e.g., the flies Anatalanta aptera and C. moseleyi, resulting in their local extinction on some coastal habitats of the Kerguelen Islands [START_REF] Lebouvier | Spotlight on the invasion of a carabid beetle on an oceanic island over a 105-year period[END_REF]). In our surveys, M. soledadinus were few and could not have any impact on native arthropods. Dominant non-native taxa in our study were the aphids, which are sap-feeders and the thrips A. apteris, which feeds on the content of leaf cells (Karban and Strass 1994). By occupying previously vacant or unsaturated ecological niche [START_REF] Russel | Invasive alien species on islands: impacts, distribution, interactions and management[END_REF][START_REF] Houghton | The impacts of non-native species on the invertebrates of Southern ocean islands[END_REF], they probably don't compete with native species. Since data is missing from locations on Kerguelen Islands that do not host nonnative invertebrates [START_REF] Hullé | The terrestrial macro-invertebrates of the sub-Antarctic Îles Kerguelen and Île de la Possession[END_REF] against which to compare the diversity and abundance observed in our surveys, it is difficult to conclude on this hypothesis.

THE ABUNDANCE OF MACRO-ARTHROPODS OF DIFFERENT TROPHIC GROUPS IS HIGHER IN NON-NATIVE VEGETATION

Non-native plants also had no negative effect on macro-arthropods belonging to different trophic groups, i.e., herbivores, decomposers and predators whose diversity and abundance were similar or higher in non-native patches. The aphid M. ascalonicus and the thrips A. apteris much dominated among herbivores. Both species being polyphagous, they were able to feed on native and non-native species such as A. magellanica and T. gr ruderalia [START_REF] Karban | Colonization of new host-plant individuals by locally adapted thrips[END_REF][START_REF] Hullé | Aphids of sub-Antarctic Îles Crozet and Kerguelen: species diversity, host range and spatial distribution[END_REF]. Plant FDis had an important contribution to explaining the abundance of macro-herbivores, which benefited as expected from diversified resources [START_REF] Carmona | Plant traits that predict resistance to herbivores[END_REF][START_REF] Moretti | Linking traits between plants and invertebrate herbivores to track functional effects of land-use changes[END_REF]. This result highlights that the traits we selected were involved in herbivore-plant interactions, as also suggested by the strong positive effects of CWMSLA and CWMLL on herbivore abundance. Decomposers mainly comprised omnivorous taxa, which feed mostly on carcases or faeces of mammals and seabirds. Their weak trophic link with plants might explain their moderate response to native and non-native vegetation, as well as to plant

FDis. Predator diversity and abundance were not impacted by plant FDis. Predators, i.e., three ACCEPTED MANUSCRIPT / CLEAN COPY 24/47 spiders, were more abundant in non-native patches relative to native, perhaps partly due to differences in prey availability, which is an important factor driving spider dynamics [START_REF] Pearson | Invasive plant architecture alters trophic interactions by changing predator abundance and behaviour[END_REF]). Indeed, these species have a very large diet consisting mainly of small insects (larvae, adults) [START_REF] Ysnel | Données sur le cycle biologique de quelques araignées des terres australes françaises (Kerguelen, Crozet)[END_REF], whose abundances were higher on non-native patches. This result may contribute to explain higher abundances of native macro-arthropods in non-native patches since the native spiders, N. antarcticus and M. kerguelensis, represented ~90% of their abundance.

THE ABUNDANCE OF MICRO-ARTHROPODS IS LOWER IN NON-NATIVE

VEGETATION

Non-native plants had negative effects on the abundance of both Oribatida mites and Symphypleona springtails. This result was also observed at Marion Island where Symphypleona springtails and one dominant Oribatida species had higher abundances on the native plant species A. magellanica than on the non-native A. stolonifera [START_REF] Gremmen | Impact of the introduced grass Agrostis stolonifera on vegetation and soil fauna communities at Marion Island, sub-Antarctic[END_REF].

Several studies conducted in the sub-Antarctic islands, concluded that both mites and springtails were mainly controlled by abiotic factors, in particular plant or soil moisture and temperature [START_REF] Travé | Structure des peuplements de microarthropodes à Kerguelen. Essai de synthèse[END_REF][START_REF] Mcgeoch | Species and community responses to short-term climate manipulation: Microarthropods in the sub-Antarctic[END_REF]). Local plant community can have indirect effects on microarthropods through the effects of plant traits on local abiotic conditions, which in turn may affect micro-arthropods. For instance, such mechanisms were suggested to explain springtail responses to different plant functional groups (Eisenhauer et al. 2011). In this study, lower densities of springtails in the legume group were explained by higher plant biomass production, which, by depleting soil water content, directly affected springtails; it also depleted nutrients in the soil, which in turn negatively affected fungal growth, i.e., the amount of food available to springtails. Interestingly, FDis had a strong negative effect on Symphypleona springtails and no effect on Oribatida mites. These finding echoes that of [START_REF] Milcu | Functionally and phylogenetically diverse plant communities key to soil biota[END_REF] who found the ACCEPTED MANUSCRIPT / CLEAN COPY same results, and suggested that the negative effect of FD on springtail abundance was mediated by its negative effect on root biomass. In contrast to macro-arthropods, micro-arthropods are less mobile and therefore they might not be able to reach the native patches such as macroarthropods can do to find more resources. Further investigations on the relationship between plants, soil micro-arthropods and their main food resources, i.e., soil microbes, could allow understanding the mechanisms generating the results we observed. Indeed, we may hypothesize that the negative effects of non-native plants on Oribatida mites and Symphypleona springtails resulted at least partly from changes in the microbial networks in links to changes in plant communities [START_REF] Boeddinghaus | Plant functional trait shift explain concurrent changes in the structure and function of grassland soil microbial communities[END_REF]Karimi et al. 2019).

CONCLUSION

Our study contributes to document the under-explored question of the interactions between plants and arthropods, in the context of biological invasions in the sub-Antarctic islands. This is particularly true for micro-arthropods, where the drivers of community assemblages, interspecific interactions, species feeding preferences and ecology are largely unknown [START_REF] Houghton | The impacts of non-native species on the invertebrates of Southern ocean islands[END_REF]. Our results point out the importance of conducting studies at different spatial scales and repeated over time, which could allow understanding the mechanisms by which non-native plants change native communities and modify the dynamics of both plant and arthropod communities. This would also make it possible to assess the long-term consequences for species conservation and ecosystem functioning. Indeed, by changing abundances of macro-arthropods belonging to different trophic groups and by depleting micro-arthropods, which are essential for nutrient cycling and primary production in terrestrial ecosystems, our findings suggest that non-native plant species may alter trophic interactions and whole ecosystem functioning in sub-Antarctic islands. 2 for GLMM results).

ACCEPTED MANUSCRIPT / CLEAN COPY

Predictions are on a log scale. Shown are the results of the comparison between native and nonnative patches (additive effect: p is the p-value of Chi-square test, χ 2 in LMM or GLMM)

ACCEPTED MANUSCRIPT / CLEAN COPY ACCEPTED MANUSCRIPT / CLEAN COPY

  plant communities, namely CWMTRAITs and other components of plant community such as plant ACCEPTED MANUSCRIPT / CLEAN COPY species identity, in shaping arthropod communities. However, collinearity issues occurred when including the type of patch and single CWMTRAITs in the same statistical models because CWMTRAITs and the type of patch were highly correlated (VIF values >2)(Zuur et al. 2010). For this reason and as an exploratory analysis, we have limited the analysis of the effects of CWMTRAITs on arthropods to a comparison of models including either the type of patch or single CWMTRAITs. As described above, we run LMM or GLMM to model macro-arthropod taxa richness and abundances, and micro-arthropod abundances. Model formula for the patch model was: 𝑌 ~ (1|𝑆𝑖𝑡𝑒) + 𝐼𝑠𝑙𝑎𝑛𝑑 * 𝑇𝑦𝑝𝑒 𝑜𝑓 𝑝𝑎𝑡𝑐ℎ Model formula for the CWMTRAIT models was: 𝑌 ~ (1│𝑆𝑖𝑡𝑒) + 𝐼𝑠𝑙𝑎𝑛𝑑 * 𝐶𝑊𝑀 𝑇𝑅𝐴𝐼𝑇 After model simplification as described above, an information-theoretic approach (AICbased approach corrected for small sample size; AICc; Burnham and Anderson 2002) was used to compare for each CWMTRAIT the simplified patch model and CWMTRAIT model. AICc difference (delta AICc) between the two models was calculated. If |delta AICc| < 2, models were not significantly different, while if |delta AICc| > 2 they differed, the best model being the one with the smallest AICc.

Fig. 1 Fig. 2 Fig. 3 Fig. 4

 1234 FIGURE CAPTIONS

Fig. 5

 5 Fig. 5 Model predictions ± SE of the effect of the type of patch (native in green, non-native in

Fig. 1 ACCEPTED

 1 Fig. 1

  Fig. 2

Fig. 4 ACCEPTED

 4 Fig. 4

Fig. 5

 5 Fig. 5

  to native species (560 in pitfall traps, 225 in yellow pans), 49 224 to non-native taxa and 108 specimens were not classifiable (70 Smittia sp., 36 Siphonaptera, one Ixodes spp., one Thysanoptera). One non-native taxa was numerically dominant, i.e., the aphids which

	represented >90% of the total counts whatever the type of patch and island (see Badenhausser
	(2021) for a complete description of dataset).
	EFFECTS OF PLANT COMMUNITIES ON MACRO-ARTHROPOD COMMUNITIES
	Overall, we collected nine native macro-arthropod species, 16 non-native taxa (among which

). Native patches were characterized by low-stature plants, thick and small leaves, small SLA, and large LDMC. Non-native patches were characterized by large stature plants (+33% CWMPH; +36% CWMPW), thiner leaves (-45% CWMLT) with larger dimensions (+103% CWMLL; +80% CWMLA), larger SLA (+38% CWMSLA) and smaller LDMC (-15% CWMLDMC). All CWMTRAIT values but CWMSLA also differed between islands (Table

1

).

13 species), and four taxa not classifiable as native vs non-native species (Smittia sp., Ixodes spp., Siphonaptera, Thysanoptera). Among macro-arthropods, 785 of the collected individuals ACCEPTED MANUSCRIPT / CLEAN COPY belonged

Table 2

 2 with increasing CWMLL and CWMSLA whose single effects were selected in the simplified models (Online Resource 4).

	Decomposers (feeding on plant material and omnivorous pooled) represented ~1% of
	the macro-arthropods (Online Resource 5). This group was the richest with 13 taxa, among
	which the sciarid Lycoriella sativae represented half of the counts. Decomposer taxa richness
	was the same in both types of patch on Île Australia (estimated means ± SE, native patches: 4.4
	± 0.8, non-native patches: 5.1 ± 0.9), and Île aux cochons (native patches: 3.2 ± 0.7, non-native
	patches: 2.4 ± 0.6). It was higher in non-native patches relative to native patches on Île Mayes

) (LMM: Parameter estimate ± SE, 11.22 ± 3.58). In addition, CWMLL and CWMSLA models explained significantly more variability in herbivore abundance relative to the patch model (Online Resource 4). Herbivore abundance ACCEPTED MANUSCRIPT / CLEAN COPY increased (native patches: 1.7 ± 0.5, non-native patches: 3.0 ± 0.7) (Table

2

). The same results were observed for decomposer abundance (Table

2

, Fig.

4c

). FDis had no effect on decomposer taxa richness and abundance (Table

2

). CWMLA and CWMLDMC models better modeled decomposer taxa richness than the patch model, and the effects of CWMLA and CWMLDMC differed between islands (Online Resource 4).

Predators represented 2.6% of the macro-arthropods collected in pitfall traps (Online Resource 5). Predator trophic group was composed of two native spider species, N. antarcticus, M. kerguelensis, and two non-native species, the spider Tenuiphantes tenuis, and the carabid beetle, Merizodus soledadinus (only 2.0% of the predators). Predator richness was the same in native and non-native patches on Île Australia (native patches: 3.0 ± 0.3; non-native patches:

Table 2 )

 2 . Predators were significantly more abundant in non-native patches relative to native ones on Île Australia and Île Mayes (Table2, Fig.4d). On Île aux Cochons, they were as numerous in both types of patch (Table2, Fig.4d). Neither FDis nor CWMTRAITs had any effect on predator richness and abundance (Table2, Online Resource 4).

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Table 1 .

 1 Effect of island, type of plant patch (native or non-native) and their two-way interaction on the plant community functional diversity (FDis) and the community-weighted means of plant traits (CWMTRAIT). CWMPH = plant height, CWMPW = plant width, CWMLL = leaf length, CWMLA = leaf area, CWMLT = leaf thickness, CWMSLA = specific leaf area, CWMLDMC = leaf dry matter content. Values and significance of Type II Wald Chi-square tests, χ 2 , realized on fixed effects tested in LMM models (Island: 𝜒 2 2 Type of patch: 𝜒 1 2 , Island × Type of patch: 𝜒 2 2 ). Significant fixed effects are in bold. We also show predicted means ± standard errors (α = 0.05) of FDis and CWMTRAIT's for each island × type of patch combination.

					Île Australia	Île aux cochons	Île Mayes
	Variable	Fixed factor	χ 2	p (>χ 2 )	Native	Non-native	Native	Non-native	Native	Non-native
					patch	patch	patch	patch	patch	patch
	FDis	Island	9.16	0.0103
		Type of patch	1.47	0.2258 0.081 ± 0.023 0.081 ± 0.023 0.158 ± 0.023 0.158 ± 0.023 0.155 ± 0.023 0.155 ± 0.023
		Island × Type	1.28	0.5255
		of patch
	CWMPH	Island	235.34 < 0.0001
	(cm)	Type of patch 117.88 < 0.0001	16.8 ± 0.9	23.9 ± 0.9	21.5 ± 0.9	25.2 ± 0.9	25.3 ± 0.9	35.5 ± 0.9
		Island × Type	34.62	< 0.0001
		of patch
	CWMPW	Island	6.36	0.0416
	(cm)	Type of patch 10.18	0.0014	15.4 ± 1.8	20.7 ± 1.8	11.7 ± 1.8	17.1 ± 1.8	16.8 ± 1.8	22.2 ± 1.8
		Island × Type	0.58	0.7479
		of patch

  Table 2 for GLMM results), (b) herbivores (yellow pan counts), (c) decomposers (yellow pan counts) and (d) predators (pitfall trap counts) (see Table 2 for LMM results). Predictions are on a log scale.Shown are the results of the comparison between native and non-native patches (additive effect: p is the p-value of Chi-square test, χ 2 in LMM or GLMM; interactive effect with the island: p is the p-value for comparing the estimates of native vs non-native patches within each island with Tukey's method and paired contrasts)

ACKNOWLEDGMENTS

The authors thank Peter Convey and two anonymous referees for helpful comments on an earlier version of the manuscript. The authors thank M. Lebouvier and G. Le Provost for useful comments on an earlier version of the manuscript. The authors thank all the contributors involved in the long-term plant and arthropod monitoring carried out within the framework of the programme 136 (IPEV). This research was supported by the 'Institut Polaire Français Paul-Emile Victor' (IPEV, programme 136), the CNRS (Zone-Atelier de Recherches sur l'Environnement Antarctique et Subantarctique).

The research was supported by the 'Institut Polaire Français Paul-Emile Victor' (IPEV, programme 136 subanteco) and the CNRS (Zone-Atelier de Recherches sur l'Environnement Antarctique et Subantarctique).

datasets generated and analysed during the current study are available in the Data INRAE repository, https://doi.org/10.15454/DPILL6 CODE

Data

Analyses. R package version 1.0.7.

http://www.sthda.com/english/rpkgs/factoextra

Crafford JE, Scholtz CH (1986) Impact of Embryonopsis halticella Eaton larvae (Lepidoptera: Yponomeutidae) feeding in Marion Deharveng L, [START_REF] Travé | Structure des peuplements de microarthropodes à Kerguelen. Essai de synthèse[END_REF]

Supplementary Information

Additional Supporting information may be found in the online version of this article: ACCEPTED MANUSCRIPT / CLEAN COPY

Online


Laura Ordonez Magro
email: laura.ordonezmagro@gmail.com

Joël Fagot

Jonathan Grainger

Arnaud Rey

Laura Ordonez Magro

IN PRESS/ Learning & Behavior On the role of interference in sequence learning in Guinea baboons (Papio papio)

Keywords: statistical learning, sequence learning, memory, forgetting, interference

It is nowadays well-established that decay and interference are two main causes of forgetting. In the present study, we specifically focus on the impact of interference on memory forgetting. To do so, we tested Guinea baboons (Papio papio) on a visuo-motor adaptation of the Serial Reaction Time task in which a target sequence is repeated, and a random sequence is interposed between repetitions, a similar situation as the one used in the Hebb repetition paradigm. In this task, one three-item sequence, the repeated sequence, was presented every second trial and interleaved with random sequences. Interference was implemented by using random sequences containing one item that was also part of the repeated sequence. In a first condition, the overlapping item was located at the same position as the repeated sequence. In a second condition, the overlapping item was located at one of the two other positions. In a third condition, there was no overlap between repeated and random sequences. Contrary to previous findings, our results reveal similar learning slopes across all three conditions, suggesting that interference did not affect sequence learning in the conditions tested. Findings are discussed in the light of previous research on sequence learning and current models of memory and statistical learning.

According to the influential model of statistical learning PARSER [START_REF] Perruchet | PARSER : A Model for Word Segmentation[END_REF], when a to-be-learned sequence of items is not frequently repeated, the memory trace of this percept rapidly vanishes as a consequence of spontaneous decay and interference with similar material. In the present study, we specifically focus on the impact of interference on the learning of sequential material. In other words, we examine whether and how similarities between random and repeated visuo-spatial sequences induce interference in active representations in working memory and in the creation of sequential long-term memory representations. Indeed, when learning sequences in everyday life (e.g., sequencing movements in typing or sports, when playing an instrument, or sequencing sounds in speech), there is generally more or less overlap between the to-be-learnt items composing a sequence (e.g., movements or sounds), which is likely to induce interference between sequences and consequently to affect learning.

The effect of interference on sequence learning has been studied in humans by [START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF] with the Hebb repetition paradigm. In that study, the authors examined to what extent item-overlap is likely to affect learning in adults. Participants were presented with a repeated Hebb sequence of seven single-syllable words which were interposed by either nonoverlapping or fully overlapping filler sequences. The fully overlapping filler sequences were composed of exactly the same items as those contained in the Hebb sequence, but in different orders (e.g., Hebb sequence = "flea, vase, disc, moss, shed, curb, soup"; filler sequence = "shed, disc, flea, soup, moss, curb, vase"), while the non-overlapping filler sequences did not share any item with the Hebb sequence (e.g., Hebb sequence = "flea, vase, disc, moss, shed, curb, soup"; filler sequence = "cow, cart, wink, seam, coin, arch, grown"). Results revealed reliable learning in the non-overlapping condition, while no learning was observed in the fully overlapping condition, suggesting that interference significantly affected learning. However, in our view, full overlap between items is very rare in natural learning situations. Taking the example of vocabulary acquisition in infants, not all words of a given language are anagrams of each other (e.g., eat and tea) and rather do, in many cases, share at most some phonemes only (e.g., / kɒpi / [copy] versus / kɒfi / [coffee]). As a consequence, mouth movements during speech production of different words are not systematically permutations of the same movements. The same is true for the acquisition of non-linguistic sequences such as sequences of movements when tapping out a phone number, learning to drive a car, or when playing an instrument.

As far as we know, only one study in humans used a semi-overlapping design, meaning that random and repeated sequence shared some but not all items [START_REF] Saint-Aubin | Learning multiple lists at the same time in the Hebb repetition effect[END_REF]. However, the aim of that study being different from ours, the degree of item-overlap between sequences was not controlled for and therefore does not allow to draw conclusions about the effect of interference on sequence learning.

The aim of the present study is thus to examine the impact of interference on sequential statistical learning by proposing a semi-overlapping design, which is more similar to what is usually observed in natural learning situations. To do so, we created an adaptation of the Serial Reaction Time task [START_REF] Nissen | Attentional requirements of learning : Evidence from performance measures[END_REF] combined with the advantage of the Hebb repetition paradigm allowing to measure the impact of interference by inserting semi-overlapping random sequences between repeated sequences. In the present study, we tested a population of Guinea baboons in order to examine the impact of interference on sequence learning by controlling for language-related refreshing mechanisms, which are absent in non-human primates. Baboons were presented with sequences of three target locations on the touch screens. One random sequence was inserted between the presentations of the repeated sequence. In a first condition (Condition 1), the random sequences contained one location that was part of the repeated sequence, and this location was always presented at the same position (e.g., repeated sequence = 7 6 2, random sequence = 7 9 1 or 9 6 1 or 1 9 2, etc., random sequences were presented in random order). In a second condition (Condition 2), random sequences again contained one item of the repeated sequence but this time, it was located at one of the two remaining positions compared to the repeated sequence (e.g., repeated sequence = 7 6 2, random sequence = 9 7 1 or 9 1 7, etc., and the same for the two other locations 6 and 2, again presented in random order). In the third condition (Condition 3), we used a non-overlapping design, meaning that different locations were used for the repeated and the random sequences (e.g., repeated sequence = 7 6 2, random sequence = 4 9 1). In the present study, we hypothesize that the representation of the items of the repeated sequence and the connections between these items within the sequence [START_REF] Burgess | A revised model of short-term memory and long-term learning of verbal sequences[END_REF] may interfere with the item representations of the overlapping random sequences (in Condition 1 and 2) as a consequence of overwriting of features shared by these sequences (Oberauer & Kliegel, 2006). More precisely and according to previous studies [START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF][START_REF] Smalle | Can Chunk Size Differences Explain Developmental Changes in Lexical Learning[END_REF], we expected to observe weaker or even no reliable learning in the two overlapping conditions but reliable learning in the non-overlapping condition. Moreover, we expected stronger interference in the "same-position" condition (Condition 1) compared to Condition 2. Indeed, imagine the following repeated sequence: 7 6 2, the co-occurrence between e.g., 7 and 6 should be more affected when being alternately presented with the following random sequence: 7 1 9 (condition 1) compared to e.g., 1 9 7 (condition 2), because 7 at the first position can here be associated to 6 or to 1, leading to an unstable representation of this association.

Method

Participants

We tested 25 Guinea baboons (Papio papio, 16 females) living in a social group at the CNRS primate facility in Rousset (France). The baboons were housed in a 700 m² outdoor enclosure with access to indoor housing.

Apparatus

Baboons had free access to fourteen Automated Learning Devices for Monkeys (ALDM, Fagot & Bonté, 2010;[START_REF] Fagot | Automatic testing of cognitive performance in baboons maintained in social groups[END_REF] equipped with touch screens and a food dispenser. When entering the ALDM test box, baboons were identified by microchips implanted in each arm. The system saved the last trial the baboon had achieved before leaving the box, allowing him to continue the task later on where it had stopped. The experiment was controlled by E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA). All the baboons were familiar with touch screen experimentation.

Materials and procedure

To begin a trial, baboons must press the yellow fixation cross 1 centered at the bottom of the screen. After pressing the cross, they saw a black screen that was divided into an invisible matrix of (3x3) cells, each containing a white cross in their center (see Figures 1A and1B). In this task, baboons needed to touch sequentially one red circle that moved on the screen in sequences of three target locations. When the baboon touched the first target position, it disappeared and was replaced by the white cross. The red circle then appeared at the second position and must again be touched before being presented with the last position of the sequence, where a last touch was required. Reward (grains of dry wheat) was delivered at the end of each sequence of three correct touches. In case of an error (i.e., the participant touched another location than the target one or failed to touch the screen within 5,000 ms), a green screen was displayed for 3,000 ms as a marker of failure.

1 Thus, the inter-stimulus-interval depends on the moment the baboon decides to press the yellow fixation cross. As the length of the inter-stimulus-interval can help subjects to separate trials and eventually help to minimize interference between trials, it is important to mention that we deliberately decided to let the baboons initiate the next trials in order to make sure that they are alert after having been rewarded with the grains. For a video, see https://osf.io/7h8cd/?view_only=ec264f23c01f4b94b4571543b494d867 The experiment was therefore composed of three conditions: Two overlapping conditions (Condition 1 and 2) and one non-overlapping condition (Condition 3). Each baboon completed all three conditions and the order of Conditions 1 and 2 was randomized.

Thirteen baboons began with the first condition and subsequently completed the second one, while twelve baboons completed the second and then the first condition. The non-overlapping condition was part of a previous study conducted in our lab (Ordonez Magro et al., revision submitted). Each condition began with a random phase during which baboons were presented with three blocks of 100 random sequences of three positions. This phase was used as a familiarization phase at the beginning of the experiment as well as a phase allowing to "clean up" the memory trace of the previously learned repeated sequence to avoid interference between to-be-learned repeated sequences across conditions. The test phase began when the baboon achieved a performance higher than 80% correct within three consecutive blocks of 100 random trials. Response times (RTs) between the appearance of the red circle and the participant's touch of each of the three positions for each sequence was recorded and served as a dependent variable. The present task follows a 1-filler design, meaning that the presentations of the repeated sequence were spaced by one random sequence (i.e., randomrepeated-random-repeated-…). Baboons performed 5 blocks of 100 trials, resulting in 250 presentations of the repeated sequence plus 250 presentations of the random sequence. To perform the 500 trials, baboons visited the test boxes in Condition 1 about 8.70 times in average, with an average of 65.96 trials per visit. In Condition 2, they visited the test boxes about 7.35 times with an average of 74.35 trials per visit. Finally, in Condition 3, baboons visited the test boxes about 8.12 times with an average of 69.85 trials per visit.

To avoid learning effects across conditions, participants were presented with a different repeated sequence in each condition (see Table 1 for an example). Repeated sequences were matched across positions for motor difficulty. To do so, a random phase of sequence production of six positions for 1,000 trials was conducted on thirteen baboons.

Based on these random trials, a baseline measure for all possible transitions from one location to another was computed by calculating mean RTs for each transition (e.g., from position 1 to 9), leading to a 9x9 matrix of mean RTs (see Appendix A).

In this experiment, and in accordance with the serial recurrent network (SRN; [START_REF] Elman | Finding Structure in Time[END_REF] model, learning was measured by comparing RTs between the three positions of the sequence. In a repeating three-position sequence such as e.g., 7 6 2, location 7 is always followed by location 6 and location 6 is always followed by location 2. Thus, location 7 (Position 1) is less predictable over trials, because of the previously presented random sequences, compared to locations 6 (Position 2) and 2 (Position 3) which benefit from the systematic presence of 7 or 7 6 presented just before. Thus, if learning takes place, we should observe faster RTs for Positions 2 and 3 compared to Position 1.

To be able to assess learning accurately, the mean RTs of the transition between Position 1 and 2 (transition 1) and the transition between Position 2 and 3 (transition 2) of the repeated sequences should be comparable. In our previous, study (Ordonez Magro et al., revision submitted), we created our repeated sequences by computing the RTs for the first and the second transition of all possible 504 triplets of the matrix and retained the triplets with the smallest difference of RTs (going from 2.06 ms to 15.55 ms), by making sure to choose sequences that were not too similar to each other (e.g., avoiding that all sequences begin with the same location). For the present study, we used novel sequences and thus retained the next five triplets of the list with the smallest difference of RTs (going from 6.97 ms to 18.22 ms), again making sure that they are not too similar too each other.

To create the random sequences of Condition 1, we computed, for each location, all sequences containing the given location at the same position and retained only those which did not contain the two remaining locations. For example, taking the repeated sequence 7 6 2, we computed all sequences starting with 7 and retained those which did not contain 6 and 2.

We did the same for the location 6 and the location 2. This gave us a total of 90 different random sequences for Condition 1. Regarding Condition 2, we computed, for each location, all sequences that did contain the given location but at a different position and again retained those which did not contain the two remaining locations of the repeated sequence. Taking the example of the repeated sequence 7 6 2, we computed all random sequences containing the location 7 at position two or three and retained those which did not contain 6 and 2. We did the same for locations 6 and 2. This gave us a total of 180 different random sequences for Condition 2. The presentation order of the random sequences was randomized across trials.

Table 1. Example of sequences presented in the three conditions for a given participant. Digits correspond to the nine screen locations. Repeated sequences are bolded, and the underlined digits correspond to the overlapping items.

Participant

Condition 1 Condition 2 Condition 3 Participant 1 6-3-2 6-9-4 6-1-2 6-9-4 3-5-4 6-9-4 3-9-7 6-9-4 … 9-1-6 7-6-2 4-9-7 7-6-2 1-8-7 7-6-2 8-2-1 7-6-2 … 1-4-6 5-8-3 7-9-4 5-8-3 9-7-2 5-8-3 2-6-4 5-8-3 …

Analyses

The ability of monkeys to learn the sequence was assessed by looking at the evolution of RTs on the three positions by computing linear regression analyses. The slope of the repeated sequence for the less predictable Position 1 and the mean slope of the predictable Positions 2 and 3 of the linear regression was used as an index of learning (see Figure 2 for an example. To see the learning slopes for each baboon, each position, and each condition, see https://osf.io/6cpfm/?view_only=35017b02cc2947688636ee7620e1906f).

Results

We retained the seventeen baboons (13 females, age range 3.58 -25.16 years) who completed all three conditions (for the full data set, see https://osf.io/6cpfm/?view_only=35017b02cc2947688636ee7620e1906f). Like in previous studies on regularity extraction in non-human primates [START_REF] Malassis | Non-adjacent Dependencies Processing in Human and Nonhuman Primates[END_REF][START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF][START_REF] Rey | Regularity Extraction Across Species : Associative Learning Mechanisms Shared by Human and Non-Human Primates[END_REF][START_REF] Rey | Learning higher-order transitional probabilities in nonhuman primates[END_REF]Tosatto et al., in press), analyses were only conducted on the repeated sequences (but see Appendix C, for some analyses on the random sequences). For these sequences, baboons obtained a mean accuracy level of 99.0% (SD=19.0%) in Condition 1 (item-overlap at the same position), of 99.1% (SD=15.8%) in Condition 2 (item-overlap at a different position), and of 98.4% (SD=15.7%) in Condition 3 (no overlap) 2 . Incorrect trials were removed from the data set. We then removed RTs greater than 800 ms and subsequently conducted a recursive trimming procedure excluding RTs greater than 2 standard deviations from the mean for each of the three possible positions in a block of 50 trials and for each baboon (see Appendix B for the mean RTs for both repeated and random sequences as a function of block and condition). To measure learning of the repeated sequence, we looked at the evolution of RTs at each of the three positions in the sequence. Previous studies have shown that learning of the sequence leads to faster RTs on the predictable positions (i.e., Positions 2 and 3) compared to the less predictable position (i.e., Position 1) due to stronger contextual information [START_REF] Elman | Finding Structure in Time[END_REF][START_REF] Malassis | Non-adjacent Dependencies Processing in Human and Nonhuman Primates[END_REF][START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF][START_REF] Rey | Regularity Extraction Across Species : Associative Learning Mechanisms Shared by Human and Non-Human Primates[END_REF][START_REF] Rey | Detection of regularities in a random environment[END_REF].

More precisely, as a measure of learning we computed, for each baboon, a slope on the RTs for each of the three positions of the repeated sequence and for each condition. We averaged the slopes of Position 2 and 3 for which learning was possible and compared them 2

As this condition was the same as in our previous study, we only retained the 500 first trials in order to compare the results across experiments.

with the slope of the less predictable Position 1 3 . We ran a 3 (Condition: Condition 1, Condition 2, and Condition 3) x 2 (Position: Position 1 versus mean of (Position 2 + 3)) repeated measures ANOVA on the learning slopes (see Figure 3). Bayesian Factors (BF) are also reported, as a major advantage of a Bayesian statistical framework is that it allows to quantify evidence in favor but also against the presence of an effect, and hence both positive and null effects can be reliably interpreted [START_REF] Kruschke | The time has come : Bayesian methods for data analysis in the organizational sciences[END_REF][START_REF] Wagenmakers | A practical solution to the pervasive problems ofp values[END_REF]. The Bayesian model comparison approach directly compares the null hypothesis to the alternative hypothesis (i.e., the effect of interest) and assesses evidence for the null effect and the effect of interest simultaneously [START_REF] Dienes | Using Bayes to get the most out of non-significant results[END_REF]. Results are interpreted using the Bayes factor (BF), which reflects the likelihood ratio of two compared models. The BF 10 is used to determine the likelihood ratio of the alternative model relative to the null model. A BF 10 > 3 provides anecdotal evidence; a BF 10 > 10 provides strong evidence, and a BF 10 > 100 provides decisive evidence for the alternative hypothesis, while a BF 01 < 0.33 provides anecdotal evidence; a BF 01 < 0.10 provides strong evidence, and a BF 01 < 0.01 provides decisive evidence for the null hypothesis [START_REF] Jeffreys | Theory of probability[END_REF]. The analyses were conducted with the JASP software package (JASP Team., 2021), using default settings for Cauchy prior distribution and the Monte Carlo Markov Chain Method for parameter estimation [START_REF] Wagenmakers | Bayesian inference for psychology. Part II : Example applications with JASP[END_REF].

Analyses revealed no significant main effect of Condition (F(2,32) = 0.367, p = .696, η 2 = .022, BF 01 = 0.12), a significant main effect of Position (F(1,16) = 27.184, p < .001, η 2 = .629, BF 10 > 100) with a steeper mean learning slope for Positions 2 and 3 (Mean = -.139, SD = .114) compared to Position 1 (Mean = -.020, SD = .108), and no significant interaction between Condition and Position (F(2,32) = 1.599, p = .218, η 2 = .091, BF 01 = 0.33). Thus, our 3

We compared the learning slope of Position 1 with the mean slope of Position 2 + 3 because if learning takes place, we should observe a steeper learning slope for the predictable Positions 2 and 3 compared to the less predictable Position 1. Moreover, averaging both predictable positions allowed us to obtain more data points and thus more robust results. findings showed faster mean RTs for the predictable Positions 2 and 3 compared to the less predictable Position 1 for all three conditions, and crucially, the mean RTs of the predictable Positions 2 and 3 were significantly different from zero (Condition 1: Mean = -0.151, CI [-0.103, -0.199]; Condition 2: Mean = -0.144, CI [-0.097, -0.191]; Condition 3: Mean = -0.122, CI [-0.055, -0.189]), while this was not the case for Position 1 (Condition 1: Mean = 0.011, CI [0.058, -0.036]; Condition 2: Mean = -0.045, CI [0.004, -0.086]; Condition 3: Mean = -0.026, CI [0.036, -0.088]). Thus, findings revealed that learning occurred for all three conditions and the absence of a main effect of Condition as well as of an interaction between Condition and Position indicates that learning was similar across all conditions. In sum, it seems that interference did not affect learning significantly in the conditions tested in the present study using a semi-overlapping design.

Discussion

In the present study, we examined the impact of interference on the learning of item sequences. According to the influential model of statistical learning PARSER [START_REF] Perruchet | PARSER : A Model for Word Segmentation[END_REF] and previous experimental studies on sequence learning [START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF][START_REF] Smalle | Can Chunk Size Differences Explain Developmental Changes in Lexical Learning[END_REF], the learning of sequences of items is significantly hindered in situations in which interference is caused by the presentation of similar sequences. Note that most studies in humans use either fully overlapping random and repeated sequences (e.g., Attout et al., 2020;[START_REF] Bogaerts | The involvement of long-term serial-order memory in reading development : A longitudinal study[END_REF][START_REF] Hebb | Distinctive features of learning in the higher animal[END_REF][START_REF] Szmalec | Order or disorder ? Impaired Hebb learning in dyslexia[END_REF], meaning that random sequences contain all items of the repeated sequence, but in a different order, or nonoverlapping sequences (e.g., [START_REF] Hitch | Slave to the rhythm : Experimental tests of a model for verbal short-term memory and long-term sequence learning[END_REF][START_REF] Ordonez Magro | Short-and long-term memory determinants of novel word form learning[END_REF], 2020, 2021;[START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF][START_REF] Smalle | Can Chunk Size Differences Explain Developmental Changes in Lexical Learning[END_REF], meaning that the repeated and the random sequences are composed of different items. To our knowledge, only one study in humans uses a semioverlapping design, meaning that random and repeated sequence share some but not all items [START_REF] Saint-Aubin | Learning multiple lists at the same time in the Hebb repetition effect[END_REF]. Moreover, previous studies in non-human primates examining the nature of regularity extraction in sequence learning [START_REF] Malassis | Non-adjacent Dependencies Processing in Human and Nonhuman Primates[END_REF][START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF][START_REF] Rey | Regularity Extraction Across Species : Associative Learning Mechanisms Shared by Human and Non-Human Primates[END_REF][START_REF] Rey | Learning higher-order transitional probabilities in nonhuman primates[END_REF]Tosatto et al., in press) usually use a design similar to the one introduced by [START_REF] Saffran | Statistical learning by 8-month-old infants[END_REF], where baboons are repeatedly exposed to one and the same sequence of locations. This design however does not allow researchers to measure the impact of interference on sequence learning. The present study therefore proposes a learning paradigm mixing repeated and random sequences with a semi-overlapping design in nonhuman primates. In our semi-overlapping design, repeated and random sequences shared only one item, either located at the same position (Condition 1) or on one of the two other positions (Condition 2). This design is, in our opinion, more representative of what generally occurs in natural learning situations like, for instance, learning to play an instrument, the acquisition of sports, learning to drive a car, sequencing speech sounds, etc. Moreover, the present study is the first one to use a semi-overlapping learning paradigm in non-human primates, allowing us to measure interference in memory while controlling for language-related factors.

In contrast to previous studies [START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF][START_REF] Smalle | Can Chunk Size Differences Explain Developmental Changes in Lexical Learning[END_REF] and to what we predicted on the basis of these studies, we did not observe a negative impact of interference on sequence learning. Indeed, our analyses showed that learning slopes were comparable across conditions. One potential explanation for this finding may be the fact that we used a semi-overlapping design rather than a fully overlapping design used in the previous studies.

Indeed, a full overlap between all items of a sequence undeniably causes more interference as compared to partial overlap. However, our findings, even if unexpected, are not totally surprising, given that such interference would be a major handicap for learning in natural situations (for a discussion, see also [START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF].

Moreover, according to PARSER [START_REF] Perruchet | PARSER : A Model for Word Segmentation[END_REF], the impact of interference may become practically negligible for a percept, once it has been encountered repeatedly and its representation in long-term memory has become relatively robust. Thus, our finding that interference does not affect learning is compatible with the predictions of PARSER and with what is observed in natural learning situations involving a high level of interference.

The absence of a significant difference in learning the semi-overlapping repeated sequences (Conditions 1 and 2) compared to the non-overlapping repeated sequences (Condition 3) can also be explained in terms of frequency of occurrence. Indeed, in our semioverlapping conditions the random sequences contained an item that was also part of the repeated sequence, which certainly increased the frequency of occurrence (during the experiment) of this given overlapping item. Thus, it is possible that the repetition of the same item in the random sequence might have strengthened its representation in long-term memory, which in turn would have facilitated the learning of item-item associations (i.e., co-occurrences between the three positions), via associative learning mechanisms [START_REF] Majerus | Two distinct origins of long-term learning effects in verbal short-term memory[END_REF][START_REF] Perruchet | Implicit learning and statistical learning : One phenomenon, two approaches[END_REF][START_REF] Perruchet | PARSER : A Model for Word Segmentation[END_REF]. This hypothesis is in line with sequence learning models [START_REF] Page | The Primacy Model : A New Model of Immediate Serial Recall[END_REF][START_REF] Perruchet | PARSER : A Model for Word Segmentation[END_REF], according to which learning is strongly dependent on repetition, a major component determining consolidation in long-term memory. There is indeed some evidence in our data that supports this hypothesis (see Figure 3). Even if the difference between conditions is not significant, there was a slightly steeper learning slope for Positions 2 and 3 for the overlapping conditions (Condition 1 and 2, -0.151 and -0.141, respectively) compared to the non-overlapping condition (Condition 3, -0.122) suggesting that the presentation of an overlapping item in the random sequences may rather have facilitated than perturbed the learning of the repeated sequence. Moreover, the observed absence of interference between random and repeated sequences can also be explained in terms of context signals. Indeed, according to the model of long-term learning proposed by [START_REF] Burgess | A revised model of short-term memory and long-term learning of verbal sequences[END_REF], each new sequence is associated with a new set of context nodes, while a repeated sequence is associated with its own set of context signals and these connections are further strengthened with each repetition. Based on this, our random and repeated sequences, even if they had one item in common, might have been associated with different context nodes, consequently limiting the interference between random and repeated sequences.

Finally, our observations are in line with several previous studies on non-human primates, showing that monkeys like tamarins, macaques, and baboons can extract regular patterns from continuous sequences allowing them to learn predictable motor sequences by using statistical cues [START_REF] Hauser | Segmentation of the speech stream in a nonhuman primate : Statistical learning in cotton-top tamarins[END_REF][START_REF] Heimbauer | A Serial Reaction Time (SRT) task with symmetrical joystick responding for nonhuman primates[END_REF][START_REF] Locurto | Characteristics of implicit chaining in cotton-top tamarins (Saguinus oedipus)[END_REF][START_REF] Locurto | Implicit chaining in cottontop tamarins (Saguinus oedipus) with elements equated for probability of reinforcement[END_REF][START_REF] Malassis | Non-adjacent Dependencies Processing in Human and Nonhuman Primates[END_REF][START_REF] Minier | The Temporal Dynamics of Regularity Extraction in Non-Human Primates[END_REF][START_REF] Procyk | The effects of sequence structure and reward schedule on serial reaction time learning in the monkey[END_REF][START_REF] Rey | Regularity Extraction Across Species : Associative Learning Mechanisms Shared by Human and Non-Human Primates[END_REF][START_REF] Rey | Learning higher-order transitional probabilities in nonhuman primates[END_REF]Tosatto et al., in press;[START_REF] Wilson | Auditory artificial grammar learning in macaque and marmoset monkeys[END_REF][START_REF] Wilson | Mixed-complexity artificial grammar learning in humans and macaque monkeys : Evaluating learning strategies[END_REF].

In sum, the present study used a semi-overlapping design to examine the influence of interference on memory forgetting in conditions that better mimic natural learning conditions compared to prior studies. In the present work, we observed that interference has not such a deleterious impact on sequence learning as would be expected based on the results of prior research. In contrast to the observation of previous studies, suggesting that no learning is possible when interference occurs [START_REF] Page | Repetition-spacing and item-overlap effects in the Hebb repetition task[END_REF][START_REF] Smalle | Can Chunk Size Differences Explain Developmental Changes in Lexical Learning[END_REF]

Figure 1A .

 1A Figure 1A. Representation of the touch screen with the nine locations and example of

Figure 1B .

 1B Figure 1B. Representation of the touch screen with the nine locations and example of

Figure 2 .

 2 Figure 2. Evolution of RTs for all three positions of the repeated sequence and mean

Figure 3 .

 3 Figure 3. Mean slopes for Positions 1 and the average for Positions 2 and 3 for each

  , our findings are easy to reconcile with what is observed in natural sequence learning situations, where learning still takes place in an interfering environment.

		t	df	p
	Random sequences
	Condition1	2.353	16	<.05
	Condition2	0.705	16	0.491
	Condition3	1.930	16	0.072
	Repeated sequences
	Condition1	-4.703	16	<.001
	Condition2	-6.832	16	<.001
	Condition3	-3.122	16	<.01

This work was supported by the CHUNKED ANR project (#ANR-17-CE28-0013-02) and the ERC advanced grant (#POP-R 742141).

Declarations

 Funding: This work was supported by the CHUNKED ANR project (#ANR-17-CE28-0013-02) and the ERC advanced grant (#POP-R 742141).

 Conflicts of interest: The authors have no conflicts of interest to declare that are relevant to the content of this article.

 Ethics approval: This research adhered to the applicable French rules for ethical treatment of research animals and received ethical approval from the French Ministery of Education (approval APAFIS#2717-2015111708173794 10 v3).

 Consent to participate: Not applicable.

 Consent for publication: Not applicable.

 Availability of data and materials: The datasets generated and analyzed during the current study are available in the OSF repository, https://osf.io/6cpfm/?view_only=35017b02cc2947688636ee7620e1906f.

 Code availability: Not applicable.

 Authors' contributions: Laura Ordonez Magro conceptualized the design, collected the data, conducted the data analyses, and wrote the paper; Joël Fagot conceptualized the design; Jonathan Grainger helped writing the paper; Arnaud Rey conceptualized the design, conducted data analyses, and helped writing the paper.

Open Practices Statements

The data and materials for all experiments are available on https://osf.io/6cpfm/?view_only=35017b02cc2947688636ee7620e1906f and none of the experiments was preregistered. Note that, paired t-test on the regression slopes showed steeper regression slopes for repeated compared to random sequences for the three conditions, confirming our analyses that learning takes place in all three conditions.

Condition 1: t(16) = -6.87, p < .001

Condition 2: t(16) = -6.06, p < .001

Condition 3: t(16) = -4.84, p < .001

Moreover, paired t-tests showed that the regression slopes for the random sequences are all positive and significantly larger than zero for Conditions 1 and not significantly different from zero for Condition 2 and 3, indicating that learning did not take place for the random sequences. As to the repeated sequences, analyses show that the slopes for all three conditions are negative and significantly smaller than zero, indicating that learning took place for the repeated sequences.