Alexis Martin
email: alexis.martin@mnhn.fr

Emmanuelle Trouslard

Mélyne Hautecoeur

Jonathan Blettery

Camille Moreau

Thomas Saucède

Nadia Améziane

Guy Duhamel

Marc Eléaume

Ecoregionalisation and conservation of benthic communities in the French exclusive economic zone of Kerguelen

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The Kerguelen Plateau is divided into a northern part situated within a French exclusive economic zone (EEZ) and a southern part situated within an Australian EEZ. Benthic habitats are spatially structured, among other factors, by the proximity of the Polar Front [START_REF] Améziane | Biodiversity of the benthos off Kerguelen Islands: overview and perspectives[END_REF][START_REF] Park | Circulation and hydrography over the Kerguelen Plateau[END_REF]. Coastal ecosystems of the Kerguelen archipelago are relatively well-known, and a multi-year monitoring program allows permanent data acquisition [START_REF] Féral | Kerguelen Islands: a living laboratory to understand the benthic biodiversity of the Antarctic[END_REF][START_REF] Féral | PROTEKER: implementation of a submarine observatory at the Kerguelen Islands (Southern Ocean)[END_REF]. In contrast, because of access limitation, benthic ecosystems deeper than 100 m remain under-studied and poorly sampled [START_REF] Améziane | Biodiversity of the benthos off Kerguelen Islands: overview and perspectives[END_REF][START_REF] Hureau | Marine Research on the Kerguelen Plateau: from early scientific expeditions to current surveys under the CCAMLR objectives[END_REF]. The MD04 scientific expedition (1975, research vessel Marion Dufresne) [START_REF] Arnaud | Contribution à la bionomie marine benthique des régions antarctiques et subantarctiques[END_REF] and the POissons de KERguelen (POKER) 2 fish stock assessment survey (2010, fishing vessel Austral) [START_REF] Duhamel | Poker 2, 2ème campagne d'évaluation de la biomasse de poissons à Kerguelen[END_REF] contributed to provide comprehensive marine invertebrate datasets of these benthic ecosystems (Eléaume et al., 2011b). Studies using these datasets have mostly focused on systematics (e.g. [START_REF] Boury-Esnault | Les démosponges du plateau continental de Kerguelen-Heard[END_REF][START_REF] David | A new species of subantarctic Plexechinus and its phylogenetic position within the Holasteroida (Echinodermata: Echinoidea)[END_REF]Eléaume et al., 2011b;[START_REF] Monniot | Ascidies antarctiques et subantarctiques: morphologie et biogéographie[END_REF]. More recently, POKER 2 data has been used to better understand the spatial distribution of deepsea benthic habitats using a generalist approach that combines biological and environmental data (Eléaume et al., 2011a). Understanding deep-sea ecosystem structure represents a major scientific issue for conservation and sustainable exploitation of commercial species in the French EEZ. In addition to global change [START_REF] Smith | A synthesis of bentho-pelagic coupling on the Antarctic shelf: Food banks, ecosystem inertia and global climate change[END_REF], benthic ecosystems in the French EEZ of Kerguelen are impacted by an industrial deep-sea longline fishing activity, focusing today on Patagonian toothfish (Dissostichus eleginoides) [START_REF] Duhamel | Major fishery events in Kerguelen Islands: Notothenia rossi, Champsocephalus gunnari, Dissostichus eleginoides. Current distribution and status of stocks[END_REF].

To prevent potential impacts of longline fishing and following the evolution of international regulation, the 'Terres Australes et Antarctiques Françaises' (TAAF) administration transposed in 2014 CCAMLR Conservation Measures 22-06 and 22-07 about vulnerable marine ecosystems (VMEs) conservation in the French EEZ of Kerguelen (TAAF, 2014). Furthermore, the Muséum national d'Histoire naturelle (MNHN) started to develop in 2015 a permanent VME monitoring program to assess the real impact of the fishery on deep-sea benthic habitats [START_REF] Martin | New data acquisition protocol for benthos by-catch in the French fisheries of the Southern Ocean, presentation of the protocol and first preliminary results[END_REF].

Moreover, the Marine Reserve of Kerguelen, managed by the TAAF administration and originally limited to some coastal areas, has been recently extended to a large portion of deep-sea environments in the French EEZ in 2016 and 2017 (TAAF, 2017). Simple protected areas, and 'strictly protected areas' where fishing is prohibited, have been defined with quantitative objectives regarding the surface to be protected. 'Best available science' and 'expert knowledge' for various environmental components (e.g. marine mammals and birds, fish distribution patterns, benthic units, pelagic physical sub-regions) have been used to design protected areas. A five-days scientific workshop, based on discussions only, with experts from various institutions allowed the production of a report including all scientific elements used to define the protected areas [START_REF] Koubbi | Ecoregionalisation of the Kerguelen and Crozet islands oceanic zone. Part I: Introduction and Kerguelen oceanic zone[END_REF]. Conservation issues for deep-sea benthic ecosystems, were addressed using presence data of benthic marine invertebrates available for the area (Eléaume et al., 2011b), outputs of species distribution models initially produced for a former Marine Protected Area project in 2011 [START_REF] Martin | New data acquisition protocol for benthos by-catch in the French fisheries of the Southern Ocean, presentation of the protocol and first preliminary results[END_REF] and expert knowledge have been provided during the workshop by MNHN scientists [START_REF] Koubbi | Ecoregionalisation of the Kerguelen and Crozet islands oceanic zone. Part I: Introduction and Kerguelen oceanic zone[END_REF].

Here we (i) identify sub-regions characterised by homogeneous benthic assemblages using a measurable and repeatable methodology, and (ii) compare these sub-regions to designated protected areas in the French EEZ Marine Reserve to evaluate the relevance of the protection zones for deep-sea benthic habitat conservation.

Materials and methods

Approach and choice of the method

We used an ecoregionalisation method as a 'process method and output of identifying and mapping broad spatial patterns based on physical and biological attributes through classification methods used for planning and management purposes' [START_REF] Reygondeau | Definition of the Mediterranean eco-regions and maps of potential pressures in these ecoregions. In: Policy oriented marine Environmental Research in the Southern European Seas (PERSEUS)[END_REF]. The choice of this approach aims to meet the first main objective of this study, to characterise ecological sub-regions based on homogeneous benthic assemblages and to contribute to our understanding of deep-sea benthic ecology of the study area. Various ecoregionalisation methods have been developed and are commonly used in ecosystem-based spatial management studies (e.g. [START_REF] Berline | A connectivity-based eco-regionalization method of the Mediterranean Sea[END_REF][START_REF] Blasi | Vegetation science and the ecoregional approach: a proposal for the ecological land classification of Italy[END_REF][START_REF] Duranton | Contribution à l'étude de la bio-écologie de deux locustes péruviens, Schistocerca cf. interrita Scudder 1899 et Schistocerca piceifrons peruviana Lynch Arribalzaga 1903 (Orthoptera, Cyrtacanthacridinae)[END_REF][START_REF] Hill | Modelbased mapping of assemblages for ecology and conservation management: A case study of demersal fish on the Kerguelen Plateau[END_REF][START_REF] Hornsmann | Calculation of an Ecological Land Classification of Europe (ELCE) and its application for optimising environmental monitoring networks[END_REF][START_REF] Hunter | A multiscale, hierarchical, ecoregional and floristic classification of arid and semi-arid ephemeral wetlands in New South Wales, Australia[END_REF][START_REF] O'hara | Bioregionalisation of Australian waters using brittle stars Echinodermata: Ophiuroidea, a major group of marine benthic invertebrates[END_REF]. Given its relevance to our objectives, the generalised dissimilarity modelling (GDM) technique has been selected for analysis [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF]. GDM is a 'statistical technique for modelling spatial variation in biodiversity between pairs of geographical locations. It allows to make predictions and map biological patterns' [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF]. The method relates dissimilarity in community composition to environmental predictors. To perform the GDM and produce interpretable results in terms of benthic assemblages, a complete analysis pipeline developed using the R software (R Development Core Team, 2015) includes different steps, from dataset assembly to statistical analyses. This robust and broadly applied modelling technique (e.g. [START_REF] Ives | Generalized linear mixed models for phylogenetic analyses of community structure[END_REF][START_REF] Koubbi | Estimating the biodiversity of the East Antarctic shelf and oceanic zone for ecoregionalisation: Example of the ichthyofauna of the CEAMARC (Collaborative East Antarctic Marine Census) CAML surveys[END_REF][START_REF] Leathwick | Use of generalied dissimilarity modelling to improve the biological discrimination of river and stream classifications: River classification for conservation management[END_REF][START_REF] Leitao | Mapping beta diversity from space: Sparse Generalised Dissimilarity Modelling (SGDM) for analysing high-dimensional data[END_REF][START_REF] Rose | A data-driven method for selecting candidate reference sites for stream bioassessment programs using generalised dissimilarity models[END_REF], including the development of an analysis pipeline, provides a repeatable methodology allowing comparison between models.

Biological data

POKER is a program led by the MNHN, Paris, France, and has been designed to monitor commercial and by-catch fish species within the French EEZ of Kerguelen [START_REF] Duhamel | Major fishery events in Kerguelen Islands: Notothenia rossi, Champsocephalus gunnari, Dissostichus eleginoides. Current distribution and status of stocks[END_REF]. This monitoring program is based on systematic random stratified trawl surveys over the northern part of the plateau, from 85 to 1 000 m depth. The POKER 2 cruise (October 2010) included a marine invertebrate by-catch sampling program [START_REF] Duhamel | Major fishery events in Kerguelen Islands: Notothenia rossi, Champsocephalus gunnari, Dissostichus eleginoides. Current distribution and status of stocks[END_REF]. The 40 mm mesh bottom trawl used during POKER was designed for catching demersal fish, and benthic invertebrates collected as by-catch were limited to macro-epibenthic invertebrate. All organisms collected have been sorted, weighted, identified and photographed (Eléaume et al., 2011b). Furthermore, representative sub-samples of specimens have been collected and fixed in 96% ethanol to be later identified using molecular and anatomical approaches (Eléaume et al., 2011a). A set of presence/absence data for 111 taxa and morphotypes identified at various taxonomic levels has been produced for the 209 stations of the survey (Figure 1). The dataset is composed of 43% of species or morphotypes, 27% of genus, 2% of families, 9% of orders, 12% of classes and 7% of phyla.

Four types of grouping have been used to pool observations and produce ecoregionalisation models: life-history traits (trophic mode and motility), protection status and taxonomic identification. Scientific literature [START_REF] Gutt | Antarctic macro-zoobenthic communities: a review and an ecological classification[END_REF][START_REF] Shojaei | Developments in German Bight benthic ecology driven by climate change and anthropogenic utilisation[END_REF] and taxa listed by CCAMLR as VME bio-indicators (mostly engineer taxa and/or sessile and/or suspension feeders) (SC-CAMLR, 2009) have been used to define relevant groups and classify all the observations in specific tables.

Environmental data

Fifteen environmental variables representing sea floor and sea-surface conditions have been extracted or derived [START_REF] Guillaumot | Echinoids of the Kerguelen Plateau -occurrence data and environmental setting for past, present, and future species distribution modelling[END_REF] from available international databases [START_REF] Boyer | World Ocean Database 2013[END_REF][START_REF] Douglass | A Hierarchical classification of benthic biodiversity and assessment of protected areas in the Southern Ocean[END_REF][START_REF] Mccoy | Southern Ocean Sediments: Circum-Antarctic to 30°S[END_REF][START_REF] Smith | du 31 mars 2017 instituant un périmètre de protection autour de la réserve naturelle nationale des Terres australes françaises[END_REF], and processed to fit the postulated life span and observed bathymetric distribution of species and the POKER 2 spatial coverage [START_REF] Duhamel | Major fishery events in Kerguelen Islands: Notothenia rossi, Champsocephalus gunnari, Dissostichus eleginoides. Current distribution and status of stocks[END_REF] (Table 1). Environmental statistics were averaged over the 2005-2012 period and encompass depths ranging from 85 to 1 000 m with spatial extension from 46° to 52°S and 63° to 73°E. Spatial resolution was set to a common 0.1°. POKER 2 transect mid-points were used to assign environmental statistics to biological occurrences.

Prior to modelling, environmental factors have been compared using the Spearman correlation test. When two factors were highly correlated, the one with the highest spatial resolution or the best ecological explanatory power was selected for modelling. The other factor was removed from the analysis. A threshold of rs ≤ 0.66 has been defined to consider the factors to be not correlated [START_REF] Mukaka | Statistics corner: A guide to appropriate use of correlation coefficient in medical research[END_REF].

Statistical analyses

GDM technique is based on analysis of biological dissimilarity between sampling stations according to the variation of related environmental factors [START_REF] Ferrier | Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment[END_REF].

GDM technique takes into account biological and environmental datasets and produces a dissimilarity matrix that best reflects biological/ environment interactions. The process includes various steps. First, a dissimilarity matrix is computed from each of the presence/absence biological tables to obtain the biological distance between pairs of stations. The Jaccard Index is used [START_REF] Goslee | The ecodist package for dissimilarity-based analysis of ecological data[END_REF]. Then, each dissimilarity matrix , 1989) is fitted, with the biological dissimilarity as the explained value and the environmental statistics as the explanatory factors. An iterative modelling process is run to select the best GLM and the best set of environmental factors, using the deviance explained as testing criteria [START_REF] Mccullagh | Binary data[END_REF].

Models are used to predict biological dissimilarity over the whole study area using all environmental predictors as available across the study domain. Each ecoregionalisation model results therefore in a new distance matrix where the biological distance index between all pairs of pixels is calculated from the GLM. Predictions are limited to the regions that present environmental conditions similar to those observed at the sampling stations. In addition, data gaps are not compensated by interpolation techniques to allow a critical evaluation of the quality of the modelling outputs.

Modelled predictions are then clustered using partitioning around medoids (PAM), an algorithm that accommodates very large matrices [START_REF] Kaufman | Clustering by means of medoids. Data Analysis based on the L1-Norm and Related Methods[END_REF]. Each clustering is iterated ten times, starting with three a priori clusters, and finishing the process at twelve clusters. The number of clusters that maximises the 'Sih' silouhette index [START_REF] Rousseeuw | Silhouettes: a graphical aid to the interpretation and validation of cluster analysis[END_REF] is selected.

For each ecoregionalisation model, ecological sub-regions or ecotypes are characterised. Ecotypes are here defined as spatially delimited areas with shared similar environmental factors and benthic assemblages. For this study, the spatially delimited areas are the areas obtained from the clustering of model predictions. To compare the spatial structuring of the models, a principal component analysis (PCA) is applied to the clustering of the predictions. The pixels are set as the individuals of the analysis. The cluster values, associated to each pixel according to the ecoregionalisation models, are set as the variables. Environmental envelope of each ecotype is summarised using environmental statistics of pixels attributed to each cluster, and represented in a series of boxplots.

Benthic assemblages of ecotypes are the taxa detected in sampling stations located within the spatially delimited areas. To characterise species assemblages in a given spatial area, many Figure 1 techniques are commonly used (e.g. [START_REF] Johnson | Freshwater biomonitoring using individual organisms, populations, and species assemblages of benthic macroinvertebrates[END_REF][START_REF] Kremen | Assessing the indicator properties of species assemblages for natural areas monitoring[END_REF][START_REF] Pardo | Species assemblages as descriptors of mesohabitats[END_REF][START_REF] Ward | Selecting marine reserves using habitats and species assemblages as surrogates for biological diversity[END_REF][START_REF] Wright | On the meaning and measurement of nestedness of species assemblages[END_REF]. For this study, two methods were applied. First, the ecotypes are compared using a PCA applied to the presence/absence data of the taxa detected within the clusters. The clusters are set as the individuals of the analysis while the taxa are set as the variables. Furthermore, the IndVal index is used [START_REF] Dufrêne | Species assemblages and indicator species: the need for a flexible asymmetrical approach[END_REF]. Indval attributes a value to each taxon or group of taxa in a given area. This value is the product of a specificity component defined as 'the probability that the surveyed site belongs to the target site group given the fact that the species has been found', and a sensitivity component defined as 'the probability of finding the species in sites belonging to the site group' (italics are quoted from [START_REF] Dufrêne | Species assemblages and indicator species: the need for a flexible asymmetrical approach[END_REF]. The higher the value of the IndVal index attributed to a taxon or a combination of taxa, the higher its value as a bioindicator. IndVal statistical significance is also tested and a p value is provided [START_REF] Dufrêne | Species assemblages and indicator species: the need for a flexible asymmetrical approach[END_REF]. For this study, we performed an IndVal analysis by testing bioindicator values for all taxa that were detected in the various ecotypes of each ecoregionalisation model. We also performed the analysis by testing bioindicator values for all combinations of two and three of those taxa. We assume that testing bioindicator values of larger combinations of taxa (i) would be contradictory with the concept of bioindicators, and (ii) may produce results difficult to interpret. Furthermore, we selected the most robust bioindicators according to a first threshold of p value ≤0.05 and a second threshold of IndVal ≤ 0.5 [START_REF] Dufrêne | Species assemblages and indicator species: the need for a flexible asymmetrical approach[END_REF].

The models are compared by considering as a set of complementary quality indices (i) the number of ecotypes obtained, (ii) the number of taxa used to compute each model, (iii) the clusterisation Sih index, (iv) the number of computed IndVal bioindicators and (v) the number of clusters including bioindicators. According to the parsimony concept, we assume that an informative model must maximise the level of ecological information in the output while using the most limited biological information in the input. For this study, we assume that the best informative model must be based on a lower number of taxa but include a higher number of ecotypes.

Ecotypes are projected onto a map using QGIS (QGIS Development Team, 2017) and surface overlap with marine reserve strictly protected areas (MRSPAs) is computed. Percentage of each ecotype located within MRSPAs is here interpreted as a measurement of the protection level of each ecotype. This level of protection is contextualised according to the degree of rarity of each ecotype calculated as the percentage of each ecotype in the whole study area. These results are used in a preliminary evaluation of the relevance of the protection zones for the conservation of deep-sea benthic assemblages.

Results

Six out of 15 uncorrelated environmental factors are retained for modelling: depth, surface temperature mean, sea-surface salinity amplitude, chlorophyll-a summer mean, slope and sediments.

Twenty-one distinct benthic ecoregionalisation models are produced and projected onto a map also showing MRSPA locations. Pixel size indicates the geographical scale of predictions and blank areas indicate missing uninterpolated data.

Thirteen models have been computed using taxa at various identification levels: Actiniaria (Figure 2a), Annelida (Figure 2b), Arthropoda (Figure 2c), Ascidiacea (Figure 2d), Asteroidea (Figure 2e), Cnidaria (Figure 2f), Corals (Figure 2g), Echinodermata (Figure 2h), Echinoidea (Figure 3a), Holothuroidea (Figure 3b), Mollusca (Figure 3c), Ophiuroidea (Figure 3d) and Porifera (Figure 3e). A complete benthic ecoregionalisation model has also been computed by pooling all 111 taxa occurrence data (Figure 3f). Six models have been computed using life-history traits: motility includes sessile (Figure 3g) and vagile (Figure 3h); organisms feeding mode includes detritivore (Figure4a), necrophagous (Figure4b), predator (Figure4c) and suspension feeders (Figure4d). A single model was obtained using CCAMLR VME taxa (Figure 4e).

Although number of ecotypes can differ between models, distribution patterns display similar spatial structures. In models with a lower number of ecotypes, distribution patterns of benthic assemblages are mainly structured according to depth. For example, the suspension feeder model (Figure 4d) includes only three ecotypes, with ecotype 2 associated with habitats shallower than 250 m depth and restricted to the immediate vicinity of the Kerguelen archipelago, ecotype 1 associated with habitats deeper than 250 m and largely distributed over the Kerguelen Plateau, and ecotype 3 which appears to be strictly associated with habitats located in a limited shallower area northeast of the archipelago, which has been shown to be a primary productivity peak zone with a high level of chlorophyll-a concentration (NASA Goddard Space Flight Center, Ocean Biology Processing Group (2014). Ecoregionalisation models with a higher number of ecotypes tend to show more detailed rather than strongly contrasting distribution pattern structures. For example, the predator model (Figure 4c) results in six ecotypes that depict with more details both deeper and shallower areas, but shows the same threshold in biological dissimilarity around 250 m depth.

The same holds true for ecoregionalisation models using life-history traits and taxa. Furthermore, except the two poorly detailed maps based on Mollusca (Figure 3c) and Detritivore (Figure 4a) ecoregionalisations, all maps show the same threshold of biological dissimilarity around 250 m depth.

The similarity between the distribution pattern structures of the various ecoregionalisation models is confirmed by the PCA. The PCA results in the first two axes representing 58.79% and 9.40% of the variance respectively (Figure 5). While not included in the analysis, clusters of pixels identified using the CCAMLR VME dataset are consistent with the distribution of the PCA individuals obtained with the other ecoregionalisation models (Figure 5). Furthermore, (i) all the ecoregionalisation models appear to be significantly correlated to the first axis of the PCA with p values close to zero, and (ii) most of the ecoregionalisation models appear to be strongly correlated, 13 out of 19 models exceeding a correlation value of 0.75 (Table 2). The highest correlation value is 0.93, the lowest correlation value is 0.28, the median value is 0.83 and the mean is 0.74 (Table 2).

Linkage and nesting effects in ecotype distribution patterns across life-history trait and taxabased ecoregionalisation models are probably also due to the fact that the biological data used is not independent between groups. Life history trait groupings are derived from taxonomic groupings and may correlate in some cases. Asteroidea, for example, are all vagile organisms and most are predators. One can therefore expect some degree of nestedness between results derived from modelling Asteroidea, vagile or predator groups. However, this may explain only a part of linkage and nesting relations. Indeed, similar observations can be done by comparing ecoregionalisations computed with independent biological datasets. Comparing Actinaria (Figure 2a) versus Echinodermata (Figure 2h) ecoregionalisation, and sessile (Figure 3g) versus vagile (Figure 3h) ecoregionalisation are relevant examples.

Across all ecoregionalisation models, the number of ecotypes varies from 3 to 8 with a mode at 4 and a mean at 4.1 (Table 3). For the taxa-based ecoregionalisation models, the number of ecotypes varies from 3 to 5, with a mode at 3 and a mean of 3.53. For the life-history-traits-based models, the number of ecotypes varies from 3 to 8, with a mode at 3 and a mean of 4.6. The maximum number of ecotypes is found when models are run using vagile or CCAMLR VME groups.

The number of ecotypes across models is not related to the number of taxa used to fit the models. For example, CCAMLR VME ecoregionalisation results in eight ecotypes and is supported by 23 taxa (Table 3). In contrast, Asteroidea ecoregionalisation results in four ecotypes, supported by 30 taxa, and the complete ecoregionalisation, supported by 111 taxa, results in four ecotypes.

The number of taxa used in models varies from 2 for Annelida to 111 for Complete (Table 3). Taxabased models include from 2 (Annelida) to 52 (Echinodermata) taxa, and life-history-traits-based models include 10 (necrophagous) to 52 (vagile) taxa. Across all models, the mean is 21.6 taxa, the mode is 10 and the median is 10.

The Sih index associated to each clustering varies from 0.67 (necrophagous) to 0.93 (Ascidiacea and suspension feeders), with a mean at 0.77 across all groupings (Table 3).

For taxa-based models, the number of IndVal bioindicators varies from 0 to 8 combinations of taxa (Table 3). The highest value is obtained for Echinodermata model. For life-history-traits-based models, the number of IndVal bioindicators varies from three combinations of taxa for the necrophagous ecoregionalisation to 29 for predator. The CCAMLR VME ecoregionalisation includes 12 combinations of taxa as IndVal bioindicators for three clusters. Across all models, the mean of IndVal bioindicators is 7.19, the minimum is 0, the maximum is 39, the mode is 0 and the median is 1.

The number of clusters including IndVal bioindicators within each ecoregionalisation model varies from 0 to 3 (Table 3). No ecoregionalisation model includes IndVal bioindicators for all of its ecotypes.

The number of IndVal bioindicators for each model and the number of ecotypes including IndVal bioindicators are not related to (i) number of taxa supporting the modelling process or (ii) number of resulting ecotypes. For example, only four IndVal bioindicators could be computed in a unique ecotype for the suspension feeder ecoregionalisation, with 21 taxa used for the GDM and three ecotypes which could be characterised (Table 3). This can be compared to the detritivore ecoregionalisation, built with only 18 taxa and resulting in three ecotypes, but including 18 combinations of taxa as IndVal bioindicators for one ecotype of the model.

According to the comparison of the quality indices, we consider that the most parsimonious and informative benthic ecoregionalisation model is obtained using CCAMLR VME grouping. While using only 23 taxa, which is close to the mean value of 21.6 and considerably lower than the maximum value of 111 taxa, the CCAMLR VME model displays a maximum value of 8 ecotypes, which is much higher than the mean value of 4.1. Furthermore, the clusterisation appears to be sufficiently robust with a strong Sih index of 0.77 equivalent to the mean obtained for the whole set of outputs (Table 3). Moreover, the CCAMLR VME ecoregionalisation includes 12 IndVal bioindicators, higher than the mean of 7.19 and considerably higher than the mode of 1. Finally, this model is retained for further analyses of the effect of environmental factors on biological dissimilarity, and to evaluate the relevance of the protection zones of the new Marine Reserve. Projection onto a map of VME ecoregionalisations (Figure 4e) shows a homogeneous geographical distribution for each of the eight benthic assemblages: no strong fragmentation can be observed, most of the pixels appearing to be located in continuous areas of pixels with same computed ecotype. This strong spatial structuring can be observed both on the plateau and the southwestern Skiff Bank seamount.

Ecotypes 4, 5, 6 and 7 are almost entirely located within the largest MRSPA, close to the Kerguelen archipelago (Figure 4e). Their geographical distribution is structured along a west-east axis around the islands. In deeper areas, the ecotypes 1, 2 and 3 are located all over the plateau and the Skiff Bank, while ecotype 8 remains mainly present in a limited area in the north (Figure 4e). Furthermore, ecotypes 1, 2 and 3 are partly covered by three distinct MRSPAs but ecotype 8 appears to be located mostly outside all MRSPAs.

The effect of environmental factors on biological dissimilarity shows contrasted results (Figure 6). For the CCAMLR VME ecoregionalisation model, depth has a strong continuous effect from 1 000 to 200 m depth approximately (Figure 6a). A strong positive linear correlation between chlorophylla concentration and biological dissimilarity can also be noted (Figure 6b). Other environmental factors, slope (Figure 6c), salinity (Figure 6d), surface temperature mean (Figure 6e) and sediments (Figure 6f) show more limited effects, with various thresholds.

Environmental envelopes as summarised in Figure 7 show that depth appears to be a main discriminating factor, with a threshold value around 250 m (Figure 7a). A first group includes ecotypes 4, 5, 6 and 7 at depths shallower than 250 m. All display a reduced dispersion of depth value around the median. Ecotype 8 shows a median depth ~250 m and displays a larger dispersion of depth value, from ~85 to ~420 m. A third deeper group with medians depth ~490, 550 and 360 m is composed of ecotypes 1, 2 and 3. All display a comparatively large dispersion of depth value.

Slope is also a good discriminating factor with ecotypes 4, 5, 6, 7 and 8 characterised by small median values and reduced dispersion, and ecotypes 1, 2 and 3 characterised by higher median values and comparatively large dispersion (Figure 7b).

Chlorophyll-a concentration also proved to be a relevant explanatory factor (Figure 7c). Ecotypes 6 and 7 are characterised by comparatively higher values of chlorophyll-a concentration, all other ecotypes (1, 2, 3, 4, 5 and 8) being characterised by comparatively lower values. In contrast with other factors, chlorophyll-a concentration distribution seem homogeneous across ecotypes in terms of dispersion around the median.

Other environmental factors, salinity (Figure 7d), surface temperature mean (Figure 7e) and sediments (Figure 7f), do not appear to be relevant discriminating factors.

PCA results show a strong structuring in the composition of the benthic assemblages of the VME ecoregionalisation ecotypes. The two first axes of the analysis represent 43.84% and 32.64% respectively of the total variance (Figures 8 and9). The first axis allows to distinguish the ecotype 6, located in a high productivity area, and characterised by (i) the presence of Molgula pedunculata Herdman, 1881 (Ascidiacea Blainville, 1824), and (ii) a higher probability to observe Ptilocrinus amezianeae [START_REF] Eléaume | a. A large new species of the genus Ptilocrinus (Echinodermata, Crinoidea, Hyocrinidae) from Antarctic seamounts[END_REF]Roux, 2011 (Crinoidea Miller JS, 1821) and Scleractinia Bourne, 1900 organisms (e.g. Flabellum Lesson, 1831) (Figures 8 and9). Furthermore, various associations of Molgula pedunculata and specific sets of Actiniaria and Porifera Grant, 1836 organisms are identified as relevant bioindicators of ecotype 6 according to the IndVal analysis (Table 4).

The second axis of the analysis allows to distinguish deep and shallower ecotypes (Figure 8). Shallower ecotypes 4, 5 and 7 are characterised by a higher observed biodiversity with the presence of Cidaridae Gray, 1825, Glyphoperidium bursa Roule, 1909 (Actiniaria), Gorgonocephalus Leach, 1815(Euryalida Lamarck, 1816), Umbellula Gray, 1870 (Pennatulacea Verrill, 1865), Alcyonacea Lamouroux, 1812 (Cnidaria Verrill, 1865) and various sets of Actiniaria and Porifera organisms (Figure 9). Biondincators could be obtained only for ecotype 7. Deep ecotypes 1, 2, 3 and 8 are characterised by the presence of two Euryalida species, Asteronyx loveni Müller & Troschel, 1842 and Astrotoma agassizii Lyman, 1875, and by the presence of Funiculinidae Gray, 1870 (Pennatulacea) (Figure 9). Asteronyx loveni is identified as a relevant bioindicator of ecotype 8 according to the IndVal analysis (Table 4). Proportion of VME ecotypes located within a MRSPA varies from 11.66% (ecotype 2) to 80.6% (ecotype 5) with a mean of 45.11% across all ecotypes (Figure 10). Each VME ecotype represent from 3.13% (ecotype 6) to 27.31% (ecotype 1) of the total study area (Figure 11).

Discussion

Quality of the models

Assessing the minimum number of biological records required to compute ecological models constitutes a major methodological issue, especially for data-poor areas such as freshwater and marine habitats (e.g. [START_REF] Bevilacqua | The use of taxonomic distinctness indices in assessing patterns of biodiversity in modular organisms[END_REF][START_REF] Fraschetti | Conservation of Mediterranean habitats and biodiversity countdowns: what information do we really need?[END_REF][START_REF] Guillaumot | Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau[END_REF][START_REF] Ramos-Merchante | Macroinvertebrate taxa richness uncertainty and kick sampling in the establishment of Mediterranean rivers ecological status[END_REF][START_REF] Van Proosdij | Minimum required number of specimen records to develop accurate species distribution models[END_REF]. Our study constitutes a contribution to solve this issue using an ecoregionalisation approach.

The quality indices for each ecoregionalisation model show that (i) number of ecotypes is independent from the number of taxa included in a given model, and (ii) number of IndVal bioindicators and number of ecotypes including IndVal bioindicators are independent from both the number of sampled taxa and the number of ecotypes resulting from the modeling process.

In the light of this analysis, the biological indicator value of the benthic assemblages appears to be more efficient for ecoregionalisation and ecotypes characterisation than the number and the diversity of sampled taxa. Indeed, increasing the quantity of biological observations by pooling specialist and ubiquitous species, can result in a decrease of model statistical power. This is exemplified here by the highly informative VME model.

Distribution patterns

Due to the field constraints and availability of both biological and environmental data, most of the modelling studies of the deep-sea invertebrates in the Southern Ocean are species-centred and based on species distribution modelling (SDM) techniques (e.g. [START_REF] Guillaumot | Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau[END_REF]Hibbert, 2016;[START_REF] Marshall | Species distribution modelling to support marine conservation planning: The next steps[END_REF]. In our study, we attempt to go further by modeling benthic assemblages. Our results represent a significant improvement and the first benthic ecoregionalisation models available for the French EEZ of Kerguelen.

One main difficulty in analysing these results lies in the interpretation of ecotypes in terms of ecological relationships between species. In coastal areas, fine-scale in situ observations allow assessing benthic assemblage composition and interactions. Main factors accounting for spatial distribution patterns of macro-zoobenthic communities on the Antarctic shelf are listed in [START_REF] Gutt | Coexistence of macro-zoobenthic species on the Antarctic shelf: An attempt to link ecological theory and results[END_REF]: competition relations (e.g. [START_REF] Dauby | Potential impact of the main benthic amphipods on the eastern Weddell Sea shelf ecosystem (Antarctica)[END_REF][START_REF] Gutt | On the distribution and ecology of holothurians in the Weddell Sea (Antarctica)[END_REF]Gutt andStarmans, 1998), food availability (e.g. Hibbert, 2016;Gutt and Schikan, 1998;[START_REF] Gutt | Structure and biodiversity of megabenthos in the Weddell and Lazarev Seas (Antarctica): ecological role of physical parameters and biological interactions[END_REF], space availability (e.g. [START_REF] Barnes | Shallow benthic fauna communities of South Georgia Island[END_REF][START_REF] Cattaneo-Vietti | Spatial and vertical distribution of benthic littoral communities in Terra Nova Bay[END_REF][START_REF] Gutt | Structure and biodiversity of megabenthos in the Weddell and Lazarev Seas (Antarctica): ecological role of physical parameters and biological interactions[END_REF], seasonal changes affecting populations (e.g. [START_REF] Jazdzewski | Seasonal fluctuations of vagile benthos in the uppermost sublittoral of a maritime Antarctic fjord[END_REF], predation relations (e.g. [START_REF] Dayton | Toward an understanding of community resilience and the potential effects of enrichments to the benthos at McMurdo Sound, Antarctica[END_REF][START_REF] Dayton | Biological accommodation in the benthic community at McMurdo Sound, Antarctica[END_REF][START_REF] Mcclintock | Ecology of Antarctic marine sponges: an overview[END_REF] and disturbance effects [START_REF] Dayton | Role of biological disturbance in maintaining diversity in the deep sea[END_REF][START_REF] Gutt | Coexistence of macro-zoobenthic species on the Antarctic shelf: An attempt to link ecological theory and results[END_REF]. Such a level of analysis still remains inaccessible for deeper areas of the Kerguelen EEZ. However, persistence of linkage and nesting effects over all models may be interpreted as a strong indication that ecotypes and associated benthic assemblages may be understood as benthic landscape units. These benthic landscape units may constitute a relevant spatial framework for the northern Kerguelen Plateau to stratify (i) upcoming deep-sea benthic samplings, and (ii) further studies on benthic communities and ecosystems.

Effect of environmental factors

When considering a large geographical scale and a large range of bathomes, depth appears to be a key criterium to describe the spatial distribution of the deep-sea benthic invertebrates of the Southern Ocean (Brandt et al., 2007a, Brandt et al., 2007b), which includes mostly eurybatic species [START_REF] Arnaud | Quantitative distribution of the shelf and slope molluscan fauna (Gastropoda, Bivalvia) of the eastern Weddell Sea (Antarctica)[END_REF][START_REF] Arntz | Antarctic zoobenthos[END_REF][START_REF] Zimmermann | New results of the '"EPOS" leg 3 cruise to Antarctica: Horizontal and vertical distribution of isopods (Crustacea) in the eastern Weddell Sea[END_REF]. It is widely recognised that depth is a proxy of a series of drivers of Southern Ocean deep-sea benthic ecology, such as food availability (Hibbert, 2016;[START_REF] Gutt | Structure and biodiversity of megabenthos in the Weddell and Lazarev Seas (Antarctica): ecological role of physical parameters and biological interactions[END_REF][START_REF] Gutt | Coexistence of macro-zoobenthic species on the Antarctic shelf: An attempt to link ecological theory and results[END_REF]Hibbert, 2016;[START_REF] Piepenburg | The benthos off King George Island (South Shetland Islands, Antarctica): further evidence for a lack of a latitudinal biomass cline in the Southern Ocean[END_REF] or physical factors, which are known in coastal habitats to affect the biology of organisms (Ahn, 1994;[START_REF] Chiantore | Antarctic scallop (Adamussium colbecki) spatial population variability along the Victoria Land Coast, Antarctica[END_REF][START_REF] Dayton | Toward an understanding of community resilience and the potential effects of enrichments to the benthos at McMurdo Sound, Antarctica[END_REF][START_REF] Picken | Reproductive adaptations of Antarctic benthic invertebrates[END_REF][START_REF] Thrush | Functional role of large organisms in intertidal communities: community effects and ecosystem function[END_REF]. In this study we identify environmental variables other than depth as driving factors for benthic assemblage structuring in the northern Kerguelen Plateau.

Cross examination of GLM effect curves of the VME ecoregionalisation model, and analysis of environmental statistics (Figure 7) help understand the relative effects of each environmental factor on benthic assemblage composition and in particular help better understand the 250 m depth threshold.

In the southern Kerguelen Plateau Heard Island and McDonald Islands (HIMI, Australian EEZ), depth, distance to canyon and concentration of particulate organic carbon (POC, used as a proxy of food availability) appear to be the main factors allowing for prediction of VME taxa biomasses using SDM techniques (Hibbert, 2016).

In the shallower areas of the northern Kerguelen Plateau, at depths ranging between 100 and 250 m, chlorophyll-a concentration appears to be the main factor driving the distribution of benthic assemblages. Chlorophyll-a is a proxy for a combination of several factors, including productivity and spatial distribution of surface currents [START_REF] Park | Circulation and hydrography over the Kerguelen Plateau[END_REF][START_REF] Park | Circulation and hydrography over the Kerguelen Plateau[END_REF] and may account for the west-east gradient of benthic assemblage variability (Figure 4e). Chlorophyll-a concentration is the only environmental factor that shows a significant eastward increase, which is strongly correlated to the pattern of the spatial distribution of ecotypes. This effect is also abserved on the faunal composition of the benthic assemblages. The shallower habitats include a higher observed biodiversity in comparison to the deeper habitats. Furthermore, the shallower habitats can also be distinguished from each other according to the relation between the chlorophyll-a concentration and their faunal composition. This is in line with previous results obtained for the southern Kerguelen Plateau, where a strong effect of POC concentration on the VME taxa biomass has been described (Hibbert, 2016).

In areas deeper than 250 m on the plateau and southwestern Skiff Bank seamount, depth remains the main environmental factor accounting for spatial distribution of benthic assemblages (Figure 4e). Slope appears as a secondary important factor and contributes to the definition of ecotype 8. This ecotype, including the only bioindicator in the deeper habitats, is located within a deep area, but presenting an original physical structure with a flat surface and a relatively low slope (Figure 4e). Other environmental factors are comparatively weakly significant.

Evaluation of the Marine Reserve

Using different modelling techniques, previous studies have been realised to assess the vulnerability of the southern Kerguelen Plateau benthic ecosystems and guide the creation of the HIMI marine protected area (MPA) within the Australian EEZ [START_REF] Welsford | Demersal fishing interactions with marine benthos in the Australian EEZ of the Southern Ocean: an assessment of the vulnerability of benthic habitats to impact by demersal gears. The Department of the Environment[END_REF]Hibbert, 2016). A recent assessment has also been produced for demersal fish [START_REF] Hill | Modelbased mapping of assemblages for ecology and conservation management: A case study of demersal fish on the Kerguelen Plateau[END_REF]. For the French EEZ, a preliminary evaluation of the Marine Reserve falls into the scope of this study. Needless to say, it is much too soon to evaluate its efficiency in terms of stock or effective ecosystem protection. But it is possible to assess the actual comprehensiveness, adequacy and representativeness of the Marine Reserve albeit if only roughly. Comprehensiveness, adequacy and representativeness are three main concepts that have been retained as key concepts by CCAMLR in designating MPAs [START_REF] Welsford | The Heard Island and McDonald Islands marine reserve and conservation zone: a model for Southern Ocean marine reserves[END_REF]. Comprehensiveness ensures that MPAs will include the full range of ecosystems recognised in an area; adequacy ensures that MPAs will have the required level of protection for diversity of life; representativeness ensures that areas protected reflect biotic diversity from a given region. In the case of the TAAF National Marine Reserve, our results are restricted to the northern Kerguelen Plateau. We certainly appreciate the fact the whole Kerguelen Plateau should be taken into account when assessing protected areas efficiency.

To do this will require integration of southern and northern Kerguelen Plateau benthic occurrences, which is well beyond the scope of this study. Adequacy and representativeness are difficult to assess separately from our results. Here we combine these two concepts in one, 'representativity', that may be defined as a level of protection as a function of relative spatial coverage and is calculated as the proportion of each ecotype included in the MRSPA.

As shown in Figure 5, all of the recognised ecotypes have been included in the MRSPA therefore complying with comprehensiveness as understood here.

The calculation of the percentage of each VME ecotype located within an MRSPA shows contrasting results. Shallower ecotypes (4, 5, 6 and 7) ranging from 100 to 250 m depth benefit from higher rates of protection, i.e. 61.96% of protection for ecotype 4, up to 80.60% for ecotype 5 (Figure 10). Ecotypes deeper than 250 m appear to benefit from lower levels of protection, i.e. 11.66% for ecotype 2 up to 24.86% for ecotype 1 (Figure 10).

Comparison with the degree of rarity of each VME ecotype allows a better interpretation. Deeper ecotypes 1 and 2 are protected at a lower level but remain the most common and represented VME benthic assemblages of the French EEZ, representing 27.31% and 26.16% of the whole area (Figure 11). The shallower ecotypes (4, 5, 6 and 7) are well protected by the Marine Reserve and remain the least represented assemblages, from 3.13% for ecotype 6 to 10.22% for ecotype 5 (Figure 11). Both deeper ecotypes 4 and 8 are relatively poorly represented in the French EEZ (11.21% and 8.01%) and also poorly protected by the Marine Reserve (12.24% and 12.38%) (Figure 11). Representativity appears to be unbalanced and some ecotypes could benefit from receiving a better level of protection.

Conclusion

Our results show that increasing the number of taxa in models may have adverse consequences and may decrease the quality of analyses. Analysing datasets restricted to relevant groupings results in more informative ecoregionalisation models. Here we show that VME-based ecoregionalisation unexpectedly appears more informative than the model based on the complete dataset. This finding is of particular interest because VME organisms are regularly collected by longliners and can constitute a source of relevant data for modelling benthic ecosystems at a larger scale, including CCAMLR areas.

In this study we show that three major environmental factors shape deep-sea benthic assemblages in the northern Kerguelen Plateau. At depths shallower than 250 m, chlorophyll-a concentration appears to be the main environmental driver. In deeper areas, depth and slope remain the main driving factors. However, other factors that are essential to larval and food transport, larval recruitment and organism existence like current velocity and direction, substratum type, resuspended sediment containing organic matter as potential food source are not sufficiently detailed here. These factors are partly captured in other factors used here and need to be further developed and tested.

Our approach uses a repeatable method and may help evaluate Marine Reserve protection levels of benthic ecosystems. Our preliminary assessment, based on VME-based ecoregionalisation, shows that poorly represented shallower ecotypes (4, 5, 6 and 7) and deeper common ecotypes (1, 2) are given a high level of protection. Two relatively deep and poorly represented ecotypes (3, 8) receive comparatively lower level of protection. Our results partly support MRSPAs as established by expert knowledge [START_REF] Koubbi | Ecoregionalisation of the Kerguelen and Crozet islands oceanic zone. Part I: Introduction and Kerguelen oceanic zone[END_REF]. However, more ecotypes than expected have been identified suggesting that expert knowledge should be supplemented with more standardised, quantified approaches when possible. Additional ecoregionalisation results could be used as information layers for an integrative assessment of other conservation issues such as connectivity between ecotypes. Bioindicator organisms could be used as proxies for the design and management of protected areas.

In the near future, existing datasets will be completed by additional biological data collected during longline fishing activities [START_REF] Martin | New data acquisition protocol for benthos by-catch in the French fisheries of the Southern Ocean, presentation of the protocol and first preliminary results[END_REF] as well as POKER cruises [START_REF] Martin | Benthos bycatch study and benthic cameras deployment during the Poker 4 fish biomass survey for habitat characterization in the Kerguelen Plateau. In: Poker 4, 4ème campagne d'évaluation de la biomasse de poissons à Kerguelen[END_REF]. Resulting ecoregionalisation models are expected to depict in more detail and help better assess the spatial distribution of benthic ecosystems over the northern Kerguelen Plateau (French EEZ), along the slope where natural resources are currently exploited, and over the southern Kerguelen Plateau (HIMI, Australian EEZ). Environmental data need to be improved in terms of coverage existing layers. Some important variables such as substrate composition, resuspended organic matter, or current velocity and direction are missing and need to be assessed. To complement this study, which constitutes a first step towards understanding the deep-sea benthic ecology of the Kerguelen EEZ, the future work based on the new datasets may include (i) improved analysis of the faunal composition of the benthic assemblages, (ii) improved comparisons between ecoregionalisation models to allow a (iii) better interpretation about the effects of environmental factors on the benthic assemblage distribution and composition.

Figure 1 :

 1 Figure 1: Geographical distribution of POKER 2 cruise sampling stations over the French exclusive economic zone of Kerguelen.

  Figure 2:

Figure 2 :

 2 Figure 2: Benthic ecoregionalisation projected into maps showing ecotypes geographical distribution for (a) Actiniaria, (b) Annelida, (c) Arthropoda, (d) Ascidiacea, (e) Asteroidea, (f) Cnidaria, (g) Corals and (h) Echinodermata, Marine Reserve (MR) and strictly protected areas (SPAs).

Figure 3 :

 3 Figure 3: Benthic ecoregionalisation projected into maps showing ecotypes geographical distribution for (a) Echinoidea, (b) Holothuroidea, (c) Mollusca, (d) Ophiuroidea, (e) Porifera, (f) all taxa, (g) vagile organisms, and (h) sessile organisms, Marine Reserve (MR) and strictly protected areas (SPAs).

  Figure 3:

Figure 4 :

 4 Figure 4: Benthic ecoregionalisation projected into maps showing ecotypes geographical distribution for (a) detritivore organisms, (b) necrophagous organisms, (c) predator organisms, (d) suspension feeders, and (e) CCAMLR vulnerable marine ecosystems indicators, Marine Reserve (MR) and strictly protected areas (SPAs).

  Figure 4:

Figure 5 :

 5 Figure 5: PCA applied to the clusterisation of the models predictions, graph of individuals; individuals = pixels, variables = ecotypes from feeding mode, motility and taxa ecoregionalisations, supplementary variable = VME ecoregionalisation clusters Figure 5

  Figure 6:

Figure 6 :

 6 Figure 6: GLM curves of effects of environmental factors on biological dissimilarity from the CCAMLR VME ecoregionalisation model.

  Figure 7:

Figure 7 :

 7 Figure 7: Boxplots of environmental statistics for ecotypes derived from the CCAMLR VME ecoregionalisation model.

Figure 8 :Figure 9 :

 89 Figure 8: PCA applied to the biological composition of the VME ecoregionalisation ecotypes, graph of individuals; individuals = VME ecotypes, variables = taxa presence data.

Figure 10 :

 10 Figure 10: Proportion of each ecotype derived from VME ecoregionalisation modelling located within a strictly protected area; * = ecotypes deeper than 250 m, ° = ecotypes shallower than 250 m.

Figure 11 :

 11 Figure 11

Table 1 :

 1 Environmental factors used for the ecoregionalisation modelling with references.

	Environmental	Unit	Original	Time	References
	predictors		spatial	period
			resolution
	Depth	meter	30 seconds	Guillaumot et al. (2016), derived from
			of arc	Smith and Sandwell (1997)
	Average of surface	°C	0.1°2005-2012 World Ocean Database (2013)
	temperature
	Variance of surface	variance	0.1°2005-2012 World Ocean Database (2013)
	temperature
	Average of temperature	°C	0.1°2005-2012 Guillaumot et al. (2016), derived from
	near sea floor			World Ocean Circulation Experiment
				(2013) sea-surface temperature layers
	Variance of temperature	variance	0.1°2005-2012 Guillaumot et al. (2016), derived from
	near sea floor			World Ocean Circulation Experiment
				(2013) sea-surface temperature layers
	Average of surface	practical	0.1°2005-2012 World Ocean Database (2013)
	salinity	salinity scale
	Variance of surface	variance	0.1°2005-2012 World Ocean Database (2013)
	salinity
	Average of salinity near	practical	0.1°2005-2012 Guillaumot et al. (2016), derived from
	sea floor	salinity scale		World Ocean Circulation Experiment
				(2013) sea-surface salinity layers
	Variance of salinity	variance	0.1°2005-2012 Guillaumot et al. (2016), derived from
	near sea floor			World Ocean Circulation Experiment
				(2013) sea-surface salinity layers
	Average of chlorophyll	mg/m 3	0.1°2002-2009 NASA Goddard Space Flight Center,
	concentration in			Ocean Biology Processing Group
	summer			(2014)
	Geomorphology	categorial	1°ATLAS ETOPO2 2014 (Douglass et
				al., 2014)
	Sediments	categorial	1°McCoy (1991), updated by Griffiths
				2014 (unpublished)
	Sea floor slope	index	30 seconds	Smith and Sandwell (1997)
			of arc
	Average of oxygen	mL/L	0.1°2005-2012 Guillaumot et al. (2016), derived from
	concentration near sea			World Ocean Database (2013) sea-
	floor			surface oxygen concentration layers
	Ruggedness	index	30 seconds	Derived from bathymetric layer
			of arc

Table 2 :

 2 PCA applied to the clusterisation of the model predictions; table of correlated variables according to the Dimension 1 of the PCA; VME ecoregionalisation clusters = supplementary variable.

	Ecoregionalisation	Correlation	p value	Ecoregionalisation	Correlation	p value
	model			model
	Sessile	0.9322399	~0	Corals	0.8186677	~0
	Porifera	0.9165757	~0	CCAMLR VME	0.7765180	~0
	Echinodermata	0.9165757	~0	Cnidaria	0.7663308	~0
	Annelida	0.9085115	~0	Asteroidea	0.7648303	~0
	Holothuroidea	0.9051280	~0	Detritivore	0.7295518	~0
	Ascidiacea	0.8853334	~0	Vagile	0.6576453	~0
	Suspension feeder	0.8768481	~0	Arthropoda	0.4634152	~0
	Ophiuroidea	0.8491374	~0	Actiniaria	0.3456607	~0
	Predator	0.8357889	~0	Echinoidea	0.3039301	~0
	Necrophagous	0.8314260	~0	Mollusca	0.2803254	~0

Table 3 :

 3 Quality indices for each ecoregionalisation model: number of ecotypes, number of taxa supporting the modelling process, Sih index of the clustering, number of IndVal bioindicators and number of ecotypes including IndVal bioindicators for each output.

	Group	Ecoregionalisation	Number of	Number	Sih index	Number of	Number of
		model	ecotypes	of taxa		IndVal	clusters
						bioindicators	including
							bioindicators
	Taxa	Actiniaria	4	7	0.7	0	0
		Annelida	3	2	0.84	1	1
		Arthropoda	3	10	0.73	0	0
		Ascidiacea	3	3	0.93	1	1
		Asteroidea	4	30	0.75	3	2
		Cnidaria	3	12	0.73	1	1
		Corals	5	5	0.87	0	0
		Echinodermata	4	52	0.69	8	3
		Echinoidea	3	4	0.69	0	0
		Holothuroidea	3	10	0.78	0	0
		Mollusca	3	10	0.88	0	0
		Ophiuroidea	4	8	0.68	0	0
		Porifera	4	3	0.87	0	0
	Feeding mode Detritivore	3	18	0.85	18	1
		Necrophagous	5	10	0.67	3	1
		Predator	6	37	0.73	29	2
		Suspension feeder	3	21	0.93	4	1
	Motility	Sessile	5	26	0.7	16	3
		Vagile	8	52	0.7	39	2
	Prot. status	CCAMLR VME	8	23	0.77	12	3
	Complete	All Taxa	4	111	0.69	16	3

Table 4 :

 4 IndVal bioindicator taxa (summarised) of the VME ecoregionalisation ecotypes.

	Taxa	Stat.	p value	Ecotype
	Molgula_pedunculata+Actiniaria_5	1.000	0.001	6
	Molgula_pedunculata+Actiniaria_5+Porifera_3	1.000	0.001	6
	Actiniaria_5+Porifera_3	0.920	0.001	6
	Actiniaria_5	0.874	0.001	6
	Molgula_pedunculata+Porifera_3	0.798	0.001	6
	Porifera_3	0.590	0.032	6
	Molgula_pedunculata	0.529	0.018	6
	Ascidiacea_2	0.618	0.002	7
	Ascidiacea_1+Ascidiacea_2	0.603	0.048	7
	Actiniaria_1+Ascidiacea_2	0.522	0.039	7
	Ascidiacea_1+Porifera_2	0.516	0.038	7
	Asteronix_loveni	0.595	0.045	8

Acknowledgments

We wish to thank the master, the crew and the scientific team of the FV Austral who have collectively contributed to the collection and sorting, and therefore made available for study, the benthic samples of the POKER 2 (2010) cruise. We are grateful to the late Noémie Vasset (MNHN), who has helped access this unique benthic collection. We also wish to thank Patrice Pruvost, manager of the MNHN team involved in the scientific survey of Kerguelen's fisheries, to have supported and encouraged this study. Work at sea was supported by TAAF, the Syndicat des Armateurs Réunionnais de Palangriers Congélateurs (SARPC), the Direction des Pêches Maritimes et de l'Aquaculture, Ministère de l'Agriculture et de l'Alimentation (DPMA), the Réserve Naturelle of TAAF, and the MNHN, Paris.


domain: sdv shs

text:
F Kling-Eveillard

N Hostiou

E Ganis

The effects of PLF on human-animal relationships on farms

Keywords: (6 maxi) human-animal relationship, precision livestock farming, work, sensor, robot, livestock farmer profession

Precision livestock farming induces not only technical and economic changes, but also modifies farmers' work. It affects the nature and frequency of their daily tasks, specifically in relation to animals, and the data available about the animals. It consequently may affect the quality of the human-animal relationship and how farmers perceive their profession. To better understand these effects, a survey was carried out on 25 French farms raising three different species and equipped with different tools: milking robots and heat detectors for dairy cows, automatic feeders for sows, and housing management and automated weighing systems for poultry. Semi-structured interviews with the farmers were conducted. The main results showed that there were diverse motivations behind the farmers' decision to install new equipment: some sought better working conditions, others to improve their technical management, yet others were induced by value chain incentives. Most mentioned that their job had become more technical, and the majority was satisfied. Farmers' interactions with their animals had changed and sometimes decreased, with less time spent in their presence or in direct contact. Digital data enabled a different view of animals, focusing on problematic individuals. Some farmers continued to observe their animals and used specific "relational practices" to facilitate work and reduce animal stress, while others delegated decisions and tasks entirely to their equipment. Nevertheless, some farmers noted limits regarding the place of new technologies on a farm, such as the risk of losing their own autonomy or their ability to observe animals and detect problems.

Introduction

The human-animal relationship is a major issue in livestock farming, both for the farmer and the animal, and reflects how farmers consider the place of animals within their work [START_REF] Dockès | Farmers' and advisers' representations of animals and animal welfare[END_REF]. Defined as the degree of closeness or distance between an animal and a person [START_REF] Waiblinger | Assessing the Human-animal Relationship in Farmed Species: A Critical Review[END_REF], the human-animal relationship develops over the course of daily interactions on the farm. It consequently is directly impacted by any change in livestock farming conditions, particularly the arrival of sensors, automated machines and new technology, referred to as precision livestock farming [START_REF] Hostiou | Elevage de précision : quelles conséquences pour le travail des éleveurs ? In : Numéro spécial, Quelles innovations pour quels systèmes d'élevage[END_REF]. With precision livestock farming, automated machines take over certain tasks that were previously done by farmers, directly influencing the human-animal relationship [START_REF] Schewe | Diversity in agricultural technology adoption: How are automatic milking systems used and to what end?[END_REF]. Moreover, the production of new, instant and readily accessible data on biological parameters and animal behaviour can influence how farmers perceive their animals and modify their direct observations of their animals [START_REF] Hostiou | Elevage de précision : quelles conséquences pour le travail des éleveurs ? In : Numéro spécial, Quelles innovations pour quels systèmes d'élevage[END_REF]. Lastly, the development of tasks linked on one hand to computers and new technology and, on the other, to equipment and automated machines, can impact how farmers experience and imagine their professions, and their job satisfaction or dissatisfaction [START_REF] Cornou | Automation Systems for Farm Animals: Potential Impacts on the Human-Animal Relationship and on Animal Welfare[END_REF]. However, the new technology does not necessarily create a greater distance between humans and animals; it can enable new relationships to develop [START_REF] Lagneaux | De la traite robotisée au raid d'avatars. Incorporation et virtualisation[END_REF]. Farmer profiles were defined by Dockès and Kling (2007) based on their closeness with their animals. Several authors [START_REF] Butler | The impact of technological change in dairy farming: robotic milking systems and the changing role of the stockperson[END_REF][START_REF] Schewe | Diversity in agricultural technology adoption: How are automatic milking systems used and to what end?[END_REF][START_REF] Désire | L'Elevage de Précision : quels changements dans l'organisation du travail et la gestion de données en élevage ?[END_REF] furthermore have shown a diversity between farmers with regard to the consequences of precision livestock farming on work organization. This article suggests that diversity also exists between farmers with regard to the consequences of precision livestock farming on how farmers perceive their profession, their animals and the human-animal relationship.

Material and methods

Survey method

We were interested in farmers' social representations, defined by [START_REF] Jodelet | Les représentations sociales[END_REF] as "a form of socially formulated and shared knowledge intended for a practical purpose". The study of farmers' representations drew from in-depth, face-to-face, semi-structured interviews. The following topics were addressed: the profession of a livestock farmer (farmer's motivations, place of animals, definition of a "good farmer"); the introduction of precision livestock farming tools (reasons the farmer acquired the equipment, how the transition is carried out); the management of precision livestock farming (use of data, observation tasks, changes in practices related to animals); the human-animal relationship (definition of a good human-animal relationship, challenges and factors behind a good human-animal relationship in livestock farming, relational practices implemented); precision livestock farming and the evolution of the profession (farmer's representations of changes). Once the qualitative interview was completed, a closed questionnaire was given to the farmers in which they could note the extent to which they agreed (6 levels possible, from "strongly disagree" to "strongly agree") to items involving their representations of animals, their profession, precision livestock farming, and what they appreciate about their work. They also were asked to describe their current and past (prior to the introduction of precision livestock farming) relationships with their animals using an ungraduated axis between "very poor relationship" to "very good relationship".

Choice of species and equipment to study

The aim of the sample was to encompass diverse changes in the relationship between farmers and their animals resulting from the use of precision livestock farming tools. For this, three species were studied (Prim'Holstein dairy cows (DC), gestating sows (GS), and broiler chickens (BC)), all in conventional livestock farming systems. We used the following criteria to choose the precision farming tools to study: i) tools widely used on farms, and ii) tools differing in terms of the impact they have on animals' living conditions and farmers' working conditions. We selected equipment that was either composed exclusively of sensors or was associated with automated machines (Table 1). Heat detectors for dairy cows (DC) thus function only as sensors. Other equipment combine one or more sensors with one or more automated machines: milking robots (DC), individual sow feeding (ESF or Selfi-feeder) for gestating sows (GS), housing management and automated weighing systems for broiler chickens (BC). The introduction of a milking robot (DC) and automated feeding (GS) led to, or wound up associated with, a new way of managing animals. For dairy cows, this involved a transition from two milkings per day in a milking parlour to cows having direct access to a robot to be milked whenever the cows wish. For gestating sows, group housing replaced individual pens. For broiler chickens, housing management systems have existed for some time. The new feature consists in being able to control building parameters from a distance (remote control using, for example, a smartphone) without having to go to the building (to open ventilation hatches, for example).

Sample and identification of farms to survey

The surveys were conducted in Brittany, the leading livestock farming region in France in terms of numbers of farms. The sample was composed of 25 farms broken down by the three species and equipment presented above. The size of the livestock unit was a criteria used to select farms because precision livestock farming accompanies increased herd sizes, which can lead to farmers becoming more detached from their animals. Surveys were therefore conducted for each species in two farm size classes, one above the French average and the other below, without including extremes in either class. The farmers' contact information was provided by field experts. For dairy cows, the person conducting the survey contacted farmers on a list of 200 livestock farmers identified by their heat detection or milking robot equipment. For gestating sows, only a few names were provided and the farmers surveyed themselves provided the names of other farmers. For broiler chickens, an expert provided some twenty names. Few farmers refused to participate in the survey.

Tallying and analysis method

The interviews with the farmers were recorded. First, a monograph was prepared for each interview summarizing the main topics addressed in the survey. The contents of the interviews and the closed questionnaires were then broken down in a tallying grid which served as a support for an analysis of the thematic content. The principal findings are shown below. This analysis then enabled the construction of a concise grid regrouping the most discriminating variables. Lastly, a statistical analysis combining a multiple correspondence analysis (MCA) and an ascending hierarchical classification (ACH) was carried out. The active variables selected concerned the representations of the profession, the animal and the human-animal relationship. Three profiles were thereby identified. The explanatory variables concerning representations and practices involved in precision livestock farming were then compared with the three profiles selected.

Results

The characteristics of the livestock farms and farmers in the sample Table 2 presents the breakdown of the farms surveyed according to the diversity criteria selected.

Table2: breakdown of surveys carried out by species and equipment type The farms in the sample were slightly larger than the average French or Breton farm, and the farmers were slightly younger than the average French or Breton farmer. Of the 25 people surveyed, 7 were women and 18 men.

Representations of the profession, the animal and the human-animal relationship The satisfaction farmers say they find in their work and their definition of what makes a good farmer reflect how they view the place of the animal.

What the farmers appreciate in livestock farming activities can be grouped around three features. Some emphasize the animal, the contact, the work with animals. Others mention instead technical features, whether these be technical aspects of animal management, animal genetics, technical monitoring of production or technology at the service of farmers. Lastly, some note the characteristics of their profession, such as being independent, being their own boss, having a real profession, and pleasant working conditions. Diversity with regard to the place of the animal is also found in the farmers' definition of what makes a good farmer. Some farmers define a good farmer as one who takes good care of his or her animals. However, this can mean two different things depending on the individual: taking good care can mean being attentive to the animals' needs so that they are well, or it can mean ensuring that the animals are productive.

Other notions are mentioned: a good farmer has strong technical skills, achieves good technical or economic results, or combines animal, technical and economic expertise.

With regard to the representation of the profession, some of the farmers interviewed demonstrated satisfaction with --or were even passionate about --their work, while others dwelled on the difficulties involved, and reflected a loss of motivation. The farmers were questioned about what they thought the human-animal relationship encompassed. It was difficult for most of them to answer this question for two main reasons. One is that they were unfamiliar with the term, the other is that the subject involved a very personal dimension that is not usually discussed in livestock farming. Four farmers thus considered that they did not have a relationship with their animals on the farm (3 BC and 1 GS). It was easier for the farmers to speak about their view of a good human-animal relationship. Most frequently, they mentioned the animal's welfare, and some spoke of an absence of fear in relation to people, or even a mutual sense of confidence between the farmer and the animals. For some farmers, good production levels reflected a satisfying human-animal relationship. For the majority, a good humananimal relationship renders it possible to work more easily with the animals, regardless of the species. At the same time, they also mention farmers' well-being, and good livestock farming conditions with equipment. Three profiles emerge from the statistical analysis of variables involving the representation of the profession, the animal and the human-animal relationship. Profile A is characterized by a negative image of the profession, experienced as not very rewarding. Farmers with this profile consider that one cannot talk about the humananimal relationship on their farm, and do not enjoy either touching or talking to their animals. These five farmers are all men, working with all three species (2 GS, 2 DC, 1 BC). Profile B is characterized by a rather positive image of the profession, which they consider rewarding. Independence, a diversity of tasks and technical features are the characteristics which satisfy them most. They associate a good human-animal relationship with animals' welfare. Thirteen farmers correspond to this profile, 10 men and 3 women, split between the 3 species (3 GS, 4 DC, 6 BC), notably including nearly all of the broiler chicken farmers in the sample (6 out of 7). Profile C is characterized by the central place occupied by animals. The animals are the main source of job satisfaction for these farmers. They associate a good human-animal relationship with the animals' absence of fear, revealing through this response their feelings for the animal itself. They enjoy touching and observing the animals and say that animals have a memory more often than farmers from the other two profiles.

Among these 7 farmers, there are 3 men and 4 women, grouping gestating sow and dairy cow farmers (3 GS, 4 DC).

Satisfaction and new practices under precision livestock farming For many farmers, setting up precision farming tools on their farms was an expression of their desire to work differently: to improve working conditions with robots (work comfort, reduced drudgery, free themselves from the constraint of milking, ...) or to improve their technique and performance with sensors (better identify cows in heat, better adapt feed rations to animals' needs, ...). In addition to these motives, some farmers were encouraged to invest in new technologies by economic and regulatory incentives. We mentioned in the preceding section a shift to group housing for sows, which is both a consequence of automated feed distribution and required by European regulations. On broiler chicken farms, farmers receive a bonus if they provide an accurate estimation of the weight of the birds in a batch. This has encouraged the installation of automated weighing systems, as they can provide data on a greater number of chickens than if the birds are weighed manually. Nearly all of the farmers surveyed (save for one) expressed satisfaction about working with the new technology. They highlight that work is easier and the equipment allows them more control over animal management, particularly with the provision of data. They furthermore consider that precision livestock farming will prove to be indispensible for farms in the future. Mastering new technologies appears to be a new job skill in a profession which has become more technical. The modern image given to the profession deeply pleased many of the farmers surveyed, who felt less left behind in relation to other professions. A few farmers (4 sow or dairy with milking robot) describe a profession which is in closer contact with animals and state feeling "more like a farmer" in livestock farming conditions where they themselves and their animals are less restricted in their activities. According to the farmers interviewed, the improved working conditions and connectivity of the farming profession renders it more attractive to younger generations. A few, however, expressed some reservations about the tools, which cannot do everything, and noted the importance of also trusting a farmer's eye and gut feelings.

The farmers describe a profession that has not fundamentally changed but which involves new tasks and new daily schedules. They spend more time in front of the computer. They also esteem that they spend either more time or less time with their animals compared to before the installation of the equipment. For many, "observing" animals includes both direct observations, for example by moving among a herd of cows in a shed, and looking at digital data about the animals on their computer. Furthermore, when questioned about what they thought of as a "good animal", some spoke of the "invisible animal" which does not trigger alerts because it poses no problems.

The morning routine illustrates the diversity of practices between farmers. Only 5 farmers (4 of which women) say they start their day by first looking at the animals, while all of the others begin by looking at the computer and the daily alerts before going to see the animals. A range of practices also was identified with regard to delegating a task or a decision to a tool. Some farmers verify the data provided by a sensor. For example, broiler chicken farmers weigh several chickens manually in addition to the automatic weighing system, and dairy cow farmers visually verify that the cow designated by a detector as being in heat is showing the associated signs before calling the inseminator. The others delegate all responsibility to the equipment. In certain situations, farmers equipped with milking robots continue to manually carry out certain tasks, for example leading a heifer to the robot and attaching the teats in order to accustom the animal to the machine. The equipment can induce new kinds of contacts with the animals when first set up or when new animals arrive. On dairy cow and gestating sow farms, the arrival of new animals appears to be a key period when opportunities exist for the famer to establish contact with his or her animals and implement habituation strategies (apple juice to tame gilts in quarantine, for example).

Farmer profiles in relation to the profession and the animal also differ with regard to precision livestock farming. Profile A and B farmers distinguish themselves from profile C farmers in their responses concerning precision livestock farming. They most often claim to appreciate working in a modern profession, and consult their computers first thing in the morning before going to see their animals. In contrast, profile C farmers feel that they know their animals better since installing the equipment (milking robots for cows and automated feeders for sows housed in groups). They also say that the humananimal relationship is better. They implement strategies to familiarize the animals with humans and the equipment to facilitate their work.

Discussion

The study showed that the farmers entertain a fairly positive image of precision livestock farming. Some of the farmers surveyed mention a deterioration in the humananimal relationship, such as cited in certain studies [START_REF] Boivin | Hommes et animaux d'élevage au travail : vers une approche pluridisciplinaire des pratiques relationnelles[END_REF][START_REF] Cornou | Automation Systems for Farm Animals: Potential Impacts on the Human-Animal Relationship and on Animal Welfare[END_REF]. However, most are much more positive and say that precision livestock farming, sometimes associated with new farming conditions, has not degraded but has rather contributed to an improved relationship.

The study enabled diverse profiles to be identified which appear to be fairly generic due to their similarity with those identified in a previous study [START_REF] Dockès | Farmers' and advisers' representations of animals and animal welfare[END_REF] on closeness with animals. Precision livestock farming responds to two areas of interest to farmers: animals and technology.

The study covered a small number of farmers and the findings would benefit from being validated on a larger sample. It would be particularly interesting to survey farmers who had encountered difficulties, for example, farmers who installed a milking robot and did not keep it. Also of interest would be complementary studies on several parameters that could influence changes in farmers' work in relation to animals and equipment like sensors and robots, such as gender (in our sample, women are proportionally more numerous in profile C, focused on the animals), farm size, or farm work force composition (the human-animal relationship is the result of interactions between animals and several people). These parameters can have an influence on both the work and the relationship with animals, and on interest in new technologies.

A comprehensive approach to the human-animal relationship would be multidisciplinary and would combine zootechnical (animal welfare and performance), ethological, ergonomic and sociological approaches [START_REF] Boivin | Hommes et animaux d'élevage au travail : vers une approche pluridisciplinaire des pratiques relationnelles[END_REF]. Such an approach was not possible within this study but is planned for a new project.

Conclusion and perspectives

Our findings showed broad satisfaction with precision livestock farming among the farmers surveyed. The farmers consider that their work had become easier with sensors and automated machines, and that they had greater control. The majority did not see a deterioration in the human-animal relationship. Instead, they describe a situation in which there are fewer constraints on both themselves and the animals.

The farmers have room to manoeuvre in how they use the equipment; this can be seen particularly in the degree to which tasks are delegated to the equipment, which can be partial or total. Farmers motivated by animals find in precision livestock farming benefits related to animals, while those who are not much motivated by their profession or animals find technical benefits detached from the animals. New livestock farming conditions such as open buildings instead of individual stalls are providing opportunities to work with animals differently and better. Some farmers implement practices to familiarize animals with people to facilitate later human interventions when getting the animals accustomed to the equipment and over their entire life cycles. The effects of the development of precision livestock farming on the general public's image of livestock farming was not addressed in this research but also merit being examined in detail as society's image of livestock farming is a decisive factor in the evolution of livestock farms.

Table 1 :

 1 characteristics of the equipment studied

	Species		Gestating sows	Dairy cows			Broiler chickens BC
			GS	DC
	Equipment	ESF, Selfi-feeder	Milking	Heat	Housing mgt.	Automatic
					robot	detectors	system	weighing
										system
	Main functions	Sensor: provides	Sensor:		Sensor:		Sensor: provides	Sensor: provides
	of	the	data	on	provides data	provides data	data on building	data on daily
	equipment		consumption	on	the	on	the	environment	growth	of
			Automaton: feed	quantity and	animals'	parameters and	chickens
			distribution	quality of milk	activity:	animals'	water	Automaton:
					produced,	suspicion of	and	feed	automatically
					frequency of	heat		consumption	weighs a sample
					milking, etc.			Automaton:	of chickens
					Automaton:			regulates building
					milking				equipment
									(ventilation
									hatches...)
	Changes	in	The	farmer's	The farmer no	The	farmer	The farmer no	The farmer can
	terms	of	presence is no	long	sees	can decide to	longer	moves	decide either to
	farmer-animal	longer associated	his/her		either	no	among his or her	transmit the data
	interactions		with meals	animals and	longer		animals	to	to the PO (and in
			The farmer moves	no	longer	observe his or	operate heating	this case, no
			among	the	touches them	her cows, and	equipment	longer touch the
			animals	for	2 times a day	directly call	The	farmer	animals) or to
			interventions	The	farmer	the		moves among his	verify	by
			(vaccinations,	may need to	inseminator as	or her animals to	manually
			ultrasounds, ...)	move among	soon as the	remove	dead	weighing a few
					the herd up to	sensor sends	birds, and to	animals.
					the	robot,	an alert, or to	make occasional
					push certain	visually verify	repairs.
					animals, put	before making
					down straw,	the call
					observe, etc.

Acknowledgements

This study was co-funded by GIS Elevage Demain, RMT (a mixed technology network), Travail en élevage and RMT Bien-être animal, supported by MAAF.


Aurelie Canaguier

Véronique Jorge

Vanina Guérin

Odile Rogier

Isabelle Le Clainche

Aurélie Chauveau

Elodie Marquand

Aurélie Berard

Marie-Christine Le Paslier

Catherine Bastien

BSA-Seq : An efficient tool to characterize loci involved in the Poplar

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

1 2 3 4 5 0 2 0 4 0 6 0 •  •  •  •  •  •   • • • • https://
• • • • • • • •


Nicolas Brichet

Llorenç Cabrera Bosquet
email: llorenc.cabrera-bosquet@inra.fr

Olivier Turc

Claude Welcker

Francois Tardieu

Cabrera-Bosquet

An image-based automated pipeline for maize ear and silk detection in a highthroughput phenotyping platform

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Chickpea is a temperate, cool season legume often experiences increasing high temperature stress with advancing stages of crop growth and results in significant yield reduction. Temperature of 35 ºC and above are said to be critical in differentiating heat-tolerant and heat-sensitive genotypes in chickpea under field conditions. In the present study, forty nine chickpea genotypes comprising released varieties and advance breeding lines from central zone were used and phenotyped in order to understand correlated response of morpho-physiological and biochemical traits with yield under variable natural environmental condition in respect to temperature. The genotypes were sown during three different late sown conditions viz., first week of November 2015, first week of December 2015 and first week of January 2016. Amongst the various morphological parameters, canopy temperature depression (CTD) and amongst the various biochemical parameters, activity of enzyme ascorbate peroxidase (APX) was found to be significantly correlated with yield in all the three different durations of sowing. On the basis of mean performance of yield/plot recently released variety Phule G 08108 was found to be less fluctuating genotype in response to heat stress than others and exhibited high total leaf chlorophyll, high CTD, low thermal stress index, high relative leaf water content and high APX content under all growing periods. The results reveal that high total leaf chlorophyll, high CTD, high RLWC with low TSI, and/ or enzymatic activities like APX can effectively be used to identify genotypes performing better under heat stress conditions.

Water deficit strongly impacts silk growth and silk emergence in maize (Zea mays L.), which in turn determines the final number of ovaries developing grains (Turc et al. 2016, Oury et al. 2016). However, phenotyping silk growth and silk expansion is difficult at throughput needed for genetic analyses. We have developed an image-based automated pipeline for maize ear and silk detection in a high-throughput phenotyping platform. The first step consists of selecting the best whole plant side images containing maximum information for each plant and day as that containing the most leaves and whole stem, based on top view images. In the second step, the best side images are segmented and skeletonized, and potential ear positions are determined based on changes in stem widths. The x, y, z ear position identified in this way serves to pilot the movement of a mobile camera able to take a detailed picture taken at 30 cm from the ear, with the final aim of determining silk emergence and silk growth duration. These methods were tested at the PhenoArch plant phenotyping platform (www6.montpellier.inra.fr/lepse/M3P) in a panel of 300 maize hybrids. First results showed that in >80% of cases, ears were successfully detected before silking and duration of silk expansion significantly correlated with visual scores. The image pipeline presented here opens up the way for large-scale genetic analyses of control of reproductive growth to changes in environmental conditions in reproductive structures.


Rémi Cardinael

Tiphaine Chevallier

Aurélie Cambou

Camille Béral

Bernard Barthès

Christian Dupraz

Ernest Kouakoua

Claire Chenu
email: claire.chenu@inra.fr

Camille Beral

Bernard Barthes

Increased soil organic carbon stocks under agroforestry: A survey of six different sites in France

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction: Agroforestry systems are land use management systems in which trees are grown in combination with crops or pasture in the same field. In silvoarable systems, trees are intercropped with arable crops, and in silvopastoral systems trees are combined with pasture for livestock. These systems may produce forage and timber as well as providing ecosystem services such as climate change mitigation. Carbon (C) is stored in the aboveground and belowground biomass of the trees, and the transfer of organic matter from the trees to the soil can increase soil organic carbon (SOC) stocks. Few studies have assessed the impact of agroforestry systems on carbon storage in soils in temperate climates, as most have been undertaken in tropical regions.

Methods: This study assessed five silvoarable systems and one silvopastoral system in France. All sites had an agroforestry system with an adjacent, purely agricultural control plot. The land use management in the inter-rows in the agroforestry systems and in the control plots were identical. The age of the study sites ranged from 6 to 41 years after tree planting. Depending on the type of soil, the sampling depth ranged from 20 to 100 cm and SOC stocks were assessed using equivalent soil masses. The aboveground biomass of the trees was also measured at all sites.

Results: In the silvoarable systems, the mean organic carbon stock accumulation rate in the soil was 0.24 (0.09-0.46) Mg C ha-1 yr-1 at a depth of 30 cm and 0.65 (0.004-1.85) Mg C ha-1 yr-1 in the tree biomass. Increased SOC stocks were also found in deeper soil layers at two silvoarable sites. Young plantations stored additional SOC but mainly in the soil under the rows of trees, possibly as a result of the herbaceous vegetation growing in the rows. At the silvopastoral site, the SOC stock was significantly greater at a depth of 30-50 cm than in the control. Overall, this study showed the potential of agroforestry systems to store C in both soil and biomass in temperate regions.