Céline Clauzel

Claire Godet

Combining spatial modeling tools and biological data for improved multispecies assessment in restoration areas

Keywords: multispecies approach, amphibian, habitat suitability models, landscape graph, functional connectivity, biodiversity offset

Introduction

Urbanization and agricultural intensification lead to a reduction and a fragmentation of natural and seminatural habitats. Wetlands and ponds have particularly been affected, experiencing a global 70% decrease since the beginning of the 20 th century [START_REF] Davidson | How much wetland has the world lost? Long-term and recent trends in global wetland area[END_REF]. This reduction has direct consequences on the viability of animal populations. Among the current mass extinction of biodiversity, amphibian and reptile species are recognized as the most threatened vertebrate group. According to the International Union for Conservation of Nature (IUCN), more than half (59%) of amphibians and 42% of reptiles are declining worldwide. These trends are probably underestimated because a large proportion of species (23%) are "data deficient" and are not integrated into population trend analyses [START_REF] Howard | Amphibians over the edge: silent extinction risk of Data Deficient species[END_REF]. The drivers of this decline are mostly related to human activities, habitat loss and landscape fragmentation, as well as environmental pollutants, infectious diseases and global climate change [START_REF] Hayes | The cause of global amphibian declines: a developmental endocrinologist's perspective[END_REF]. Their vulnerability is notably due to their biphasic life cycle, which induces many movements between aquatic and terrestrial habitats.

Many studies have documented this extinction crisis and proposed conservation actions mostly related to protected area design, sustainable conservation measures, species translocation or habitat restoration/creation [START_REF] Godet | What Conservation Does[END_REF]. Since many amphibians can live in artificial wetlands and ponds, habitat restoration and even the creation of new ponds could be an effective solution to reduce landscape fragmentation. But [START_REF] Grant | Amphibian conservation in the Anthropocene[END_REF] noted that species conservation, especially amphibian conservation, had failed. They advocate an improved complementarity between research and management to increase the effectiveness of conservation actions. For example, in the case of amphibian populations living in fragmented habitats and highly dependent on fluxes between patches [START_REF] Hanski | The metapopulation capacity of a fragmented landscape[END_REF], aquatic habitat restoration should integrate both local-scale factors such as microtopography, water quality or nearby terrestrial habitat and broad-scale factors such as connectivity between habitats [START_REF] Hodgson | Habitat area, quality and connectivity: striking the balance for efficient conservation[END_REF]) to maintain regional connections and, consequently, the viability of populations [START_REF] Briers | Incorporating connectivity into reserve selection procedures[END_REF][START_REF] Moilanen | Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems[END_REF]. If expert opinions and field data could provide information about local-scale factors, then their large-scale generalization would be complex due to the large amount of data required. In addition, landscape managers lack information on where to restore future habitats, how many and how to arrange them, e.g., many habitats close together or habitats scattered to cover a large area [START_REF] Schmidt | Benefits and limits of comparative effectiveness studies in evidence-based conservation[END_REF]. Recently, several studies have attempted to answer these questions by performing dynamic models [START_REF] Scroggie | Optimizing habitat management for amphibians: From simple models to complex decisions[END_REF] or connectivity models [START_REF] Clauzel | Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France[END_REF][START_REF] Jeliazkov | Cross-scale effects of structural and functional connectivity in pond networks on amphibian distribution in agricultural landscapes[END_REF][START_REF] Matos | Landscape connectivity and spatial prioritization in an urbanising world: A network analysis approach for a threatened amphibian[END_REF]. Despite promising results for conservation actions, these studies focused on a single species. Designing a method to target restoration areas depending on regional connectivity for multiple species is still challenging.

We hypothesized that bringing together field expertise (expert opinion and biological data) and spatial modeling tools (prediction and simulation) could fill this gap. Several studies have been published on multispecies assessment of connectivity to prioritize core habitats and corridors [START_REF] Khosravi | Multispecies assessment of core areas and connectivity of desert carnivores in central Iran[END_REF][START_REF] Lechner | Characterising landscape connectivity for conservation planning using a dispersal guild approach[END_REF][START_REF] Meurant | Selecting surrogate species for connectivity conservation[END_REF], to reduce road fragmentation [START_REF] Mimet | Locating wildlife crossings for multispecies connectivity across linear infrastructures[END_REF][START_REF] Santini | A Composite Network Approach for Assessing Multi-Species Connectivity: An Application to Road Defragmentation Prioritisation[END_REF] and to quantify the impacts of land cover changes [START_REF] Sahraoui | A multi-species approach for assessing the impact of landcover changes on landscape connectivity[END_REF]. But to our knowledge, no studies have implemented it on the assessment of habitat restoration, which is a major challenge for biodiversity conservation. We proposed to combine habitat suitability models and landscape graphs to take advantage of their respective strengths and partially overcome their limitations. Graph-based modeling is a widely used method for analyzing connectivity in habitat networks [START_REF] Dale | From graphs to spatial graphs[END_REF][START_REF] Foltête | How ecological networks could benefit from landscape graphs: A response to the paper by Spartaco Gippoliti and Corrado Battisti[END_REF][START_REF] Galpern | Patch-based graphs of landscape connectivity: A guide to construction, analysis and application for conservation[END_REF][START_REF] Minor | A graph-theory framework for evaluating landscape connectivity and conservation planning[END_REF][START_REF] Urban | Graph models of habitat mosaics[END_REF]. Landscape graphs are considered spatially explicit models of ecological networks designed from both environmental and biological data. Similar to any spatial graph [START_REF] Fall | Spatial Graphs: Principles and Applications for Habitat Connectivity[END_REF], landscape graphs constitute a mathematical object on which connectivity metrics can be calculated at different levels [START_REF] Rayfield | Connectivity for conservation: a framework to classify network measures[END_REF]. These metrics provide indicators for prioritizing habitats to preserve, assessing the potential impact of land-use changes and identifying the best locations to restore habitats or corridors [START_REF] Foltête | How ecological networks could benefit from landscape graphs: A response to the paper by Spartaco Gippoliti and Corrado Battisti[END_REF]. However, like any modeling method, landscape graphs also have limitations, especially regarding the reliance of the representation of the actual habitat network [START_REF] Moilanen | On the limitations of graph-theoretic connectivity in spatial ecology and conservation[END_REF]. One solution to improve this limitation is to incorporate biological data in graph modeling through habitat suitability models (HSMs). Based on the concept of ecological niche, a HSM predicts species potential distribution by analyzing and extrapolating the statistical relation between species presence and environmental variables [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF]. The resulting HSM maps allow the identification of the most suitable areas for habitat patches. However, they cannot provide information about the connections between these suitable areas, for which a connectivity analysis is required. Combining HSM and landscape graphs has already been performed using various alternative techniques and input data for a single species [START_REF] Almpanidou | Providing insights on habitat connectivity for male brown bears: A combination of habitat suitability and landscape graph-based models[END_REF][START_REF] Clauzel | Impact assessment of a high-speed railway line on species distribution: Application to the European tree frog (Hyla arborea) in Franche-Comté[END_REF][START_REF] Decout | Integrative approach for landscape-based graph connectivity analysis: a case study with the common frog (Rana temporaria) in human-dominated landscapes[END_REF][START_REF] Duflot | Combining habitat suitability models and spatial graphs for more effective landscape conservation planning: An applied methodological framework and a species case study[END_REF][START_REF] Schank | Population status, connectivity, and conservation action for the endangered Baird's tapir[END_REF][START_REF] Ziólkowska | Potential habitat connectivity of European bison (Bison bonasus) in the Carpathians[END_REF]. In most cases, HSM has been used to define both habitat patches and least-cost paths, thus mixing two different ecological processes, habitat selection and movement, which may be a questionable practice. Radio-telemetry data [START_REF] Ziólkowska | Potential habitat connectivity of European bison (Bison bonasus) in the Carpathians[END_REF] or individual-based simulations [START_REF] Coulon | A stochastic movement simulator improves estimates of landscape connectivity[END_REF] appear relatively more relevant but require complex data that are difficult to collect for several species and over a wide area. Furthermore, GPS data provide information on actual movements but not on alternative routes that could be used in case of disturbance. To overcome these difficulties, we used HSM to delineate optimal habitat patches as well as previous field experimentations combined with expert opinions to calibrate least-cost paths from a resistance landscape map.

We applied this protocol to the simultaneous conservation of eight species to search for the best locations for pond creation, i.e., optimizing connectivity for as many species as possible. The analysis was applied in the Ile-de-France region (France), a highly disturbed region, which is poorly represented in dispersal studies that tend to occur in less disturbed semi-natural habitats [START_REF] Bailey | Integrating amphibian movement studies across scales better informs conservation decisions[END_REF].

Materials & Methods

Study area and studied species

The study area (12,000 km²) is the administrative region of Ile-de-France surrounding the city of Paris (France) (48.6°N; 3.2°E). The landscape is dominated by intensive agriculture (50%), urbanized areas (24.5%) and forests (24%). Water bodies and rivers occupy only 1.5% of the area (IAU, 2019). The Ile-de-France region is home to 12 reptile species and 17 amphibian species [START_REF] Zucca | Panorama de la biodiversité francilienne[END_REF]. The species under study were selected according to several criteria: ponds as one of the habitats, sufficient occurrence data, various optimal habitat types and various dispersal abilities between species. Species occurrence data were extracted from the naturalist database CETTIA, which has included both protocoled and opportunistic data at the regional level since 2012. Eight species with distinct ecological preferences were retained: 7 amphibians (Alytes obstetricans, Bufo bufo, Epidalea calamita, Hyla arborea, Rana temporaria, Triturus cristatus, and Salamandra salamandra) and 1 reptile (Natrix natrix). Literature including naturalist guides [START_REF] Bensettiti | Connaissance et gestion des habitats et des espèces d'intérêt communautaire[END_REF][START_REF] Grand | Cahier d'identification des libellules de France, Belgique, Luxembourg et Suisse. Biotope, Mèze[END_REF][START_REF] Lescure | Atlas des amphibiens et des reptiles de France. Ouvrage collectif de la société herpétologique de France. Biotope, Mèze[END_REF], regional studies [START_REF] Sordello | Trame verte et bleue. Critères nationaux de cohérence. Contribution à la définition du critère sur les espèces[END_REF] and scientific studies [START_REF] Sahraoui | A multi-species approach for assessing the impact of landcover changes on landscape connectivity[END_REF] helped us to identify the ecological characteristics of the species in terms of aquatic and terrestrial habitats, environments favorable or unfavorable to movement and daily and annual movement capacity. Some species are specialized more than others with a preference for either forest ponds or open-area ponds. Some tolerate artificial environments such as quarries or sandpits, while others avoid them (Table 1). Their dispersal abilities vary from 100 m for S. salamandra to 9,000 m for R. temporaria.

Table1 -General species' ecology, freshwater habitat types, number of occurrence data in CETTIA database, daily and dispersal distances for each of the eight species under study, based on information from the literature.

Habitat suitability modeling

For each species, habitat suitability was modeled by linking occurrence records, which varied from 130 points for S. salamandra and 9,608 for B. bufo, and a set of environmental variables. These predictive variables were chosen according to their potential ecological relevance for pond-dwelling species [START_REF] Wilson | The relative contribution of terrain, land cover, and vegetation structure indices to species distribution models[END_REF]. Thirteen variables corresponding to four different data categories were selected (Table 2). Two topographic variables (slope and roughness) were obtained from the digital elevation model of the Ilede-France provided by IGN. Temperatures were extracted from BIOCLIM data derived from MODIS LST reconstructed at a resolution of 250 m pixels [START_REF] Metz | Surface temperatures at the continental scale: Tracking changes with remote sensing at unprecedented detail[END_REF] and precipitations from CHELSEA (climatologies at high resolution for the earth's land surface areas) data [START_REF] Karger | Climatologies at high resolution for the earth's land surface areas[END_REF]. The NDVI (normalized difference vegetation index) was obtained from Landsat 8 satellite images. Landscape and proximity variables described the habitat quality of the pixel neighborhood (availability of daily resources or local disturbances). As the extent of this neighborhood could be different depending on the ecology of the species, three radius buffers were selected to calculate the percentage area of the land cover categories. Landscape and proximity variables were calculated from a landscape map built from the combination of three databases: i) the Ile-de-France's land use produced by the L'Institut Paris Region, ii) the BD TOPO® IGN for linear landscape elements such as roads and rivers and iii) the pond database of the National Society for the Protection of Nature. The resulting landscape map had 17 categories (Appendix A) and was rasterized at a 10 m resolution. Habitat suitability modeling was performed with MaxEnt [START_REF] Phillips | Opening the black box: an opensource release of Maxent[END_REF], an open-source software based on the maximum-entropy approach for modeling species niches and distributions. To avoid potential bias [START_REF] Fourcade | Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias[END_REF][START_REF] Kramer-Schadt | The importance of correcting for sampling bias in MaxEnt species distribution models[END_REF], we analyzed the data set before modeling to check for inconsistencies or outliers. We then removed duplicates in each 10mx10m resolution cell so that there were no overestimates in some places. We maintained the default settings that are recognized to perform as well as the adjusted settings [START_REF] Phillips | Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[END_REF]. Models were replicated ten times, and the average probabilities of predicted presence were retained for further analysis. The robustness of the models was examined using a k-fold cross-validation procedure, wherein species presence points are randomly split into k equal-sized subsets to successively serve as test data, while the remaining k-1 folds are used as training data [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF]. Prediction performance is related to the error rate (misclassification) of the test data, which is measured by the extrinsic omission rate and the area under the curve (AUC) [START_REF] Guisan | Predictive habitat distribution models in ecology[END_REF]. AUC is a threshold-independent standard measure of model accuracy that ranges from 0 to 1. It can be interpreted as the probability that a randomly chosen species presence point will be correctly classified by the model [START_REF] Phillips | Maximum entropy modeling of species geographic distributions[END_REF]. Maxent indicates which variables most contribute to the presence of each species and what the shape the relationship is. As a result, the habitat suitability maps were created by applying each species distribution model to all cells in the study area using a logistic function to obtain a habitat suitability index (HSI) ranging from 0 to 1 [START_REF] Phillips | Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation[END_REF].

Landscape graph construction

From each habitat suitability map, corresponding to the presence probability of each species, a landscape graph, composed of a set of habitat patches (nodes) potentially connected by links, was built at the dispersal scale. Habitat patches were defined as the highest values of habitat suitability [START_REF] Duflot | Combining habitat suitability models and spatial graphs for more effective landscape conservation planning: An applied methodological framework and a species case study[END_REF]. The threshold value used corresponded to the maximum sum of sensitivity and specificity (Max SSS) [START_REF] Liu | Selecting thresholds for the prediction of species occurrence with presence-only data[END_REF] and could be different according to the species. Habitat patches were associated with an attribute representing their potential quality for pond-dwelling species. As the habitat patches derived from the HSI map could include both aquatic and terrestrial environments, the quality was determined by multiplying the area of the patch by its average HSI value.

The links of the graph were defined in a complete topology, i.e., all the links between patches were potentially taken into account. Their impedance was calculated in cost distances based on a resistance map.

From a previously constructed landscape map, each category was classified according to its resistance to species movement, using the ecological literature and local experts' opinions (see Appendix A for all the resistance values for each species). Habitat patches, i.e., the highest habitat suitability values, were assigned a resistance of 1. The favorable landscape elements were assigned a low value (10), neutral elements a value of 100, unfavorable elements a value of 1,000, and barrier elements the highest value (10,000). This logarithmic scale was chosen based on several studies [START_REF] Clauzel | Impact assessment of a high-speed railway line on species distribution: Application to the European tree frog (Hyla arborea) in Franche-Comté[END_REF][START_REF] Verbeylen | Does matrix resistance influence Red squirrel (Sciurus vulgaris L. 1758) distribution in an urban landscape?[END_REF] that have shown the importance of contrasting resistance values between favorable and unfavorable elements to explain the presence of species. These resistance values were then submitted to several experts in herpetology, who allowed the values to be refined according to the local characteristics of the landscape and/or the species. Only the links with distances shorter than the dispersal capacity of the species were retained. Each resulting graph thus represented the potential ecological network of a species, taking into account the environmental variables that best explain its presence in the Ile-de-France region.

Patch addition process

The graphs constructed in the previous step were used to identify the best locations for pond creation. We applied the method developed by [START_REF] Clauzel | Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France[END_REF] and [START_REF] Mimet | Locating wildlife crossings for multispecies connectivity across linear infrastructures[END_REF]. The method consists of (1) calculating a global metric quantifying the initial connectivity of the network, (2) applying a sampling grid of a given resolution to the study area and then (3) evaluating the potential contribution of each centroid of cells by quantifying the increase in the connectivity index provided by the addition of a new node and of new potential links from this node to other existing nodes (Fig. 1). After testing all the cells, the algorithm validated the one for which patch addition produced the largest increase in the metric value.

The global connectivity metric to be maximized was the Equivalent Connectivity (EC) index, defined as the "amount of reachable habitat" for a species at the landscape scale [START_REF] Saura | Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000)[END_REF]:

*
where n is the number of nodes, ai is the attribute of node i, aj is the attribute of node j and Pij * is the maximum probability of all potential paths between nodes i and j. Pij is determined by an exponential function such that:

∝
where dij is the least-cost distance between these nodes and α is a constant expressing the intensity of the decrease in the probability of movement (p) with distance (d). As d corresponded to the dispersal distance, the setting of the metric was specific to each species.

Running these calculations (graph construction and patch addition process) over the whole region was very time consuming and not necessarily ecologically relevant. We therefore choose to limit the connectivity analysis to a 150 km² subpart for the demonstration. The patch addition process was applied on a sampling grid of 15,093 points evenly spaced 100 m apart.

The landscape graph construction and the patch addition method were performed using Graphab 2.4 software [START_REF] Foltête | A software tool dedicated to the modelling of landscape networks[END_REF]; see http://thema.univ-fcomte.en/productions/graphab/).

From single-species to multispecies connectivity improvement

The protocol described above was used to calculate the connectivity gain Gki from each location tested k and for each species i. A multivariate analysis was then performed to identify the best locations for creating a new pond with a multispecies approach, i.e., optimizing connectivity for as many species as possible. The values of Gki were grouped in a table and processed in a principal component analysis (PCA). To obtain a sufficiently satisfactory approximation, we used eigenvalues to determine the number of principal components to be retained [START_REF] Kaiser | A Note on Guttman's Lower Bound for the Number of Common Factors[END_REF] and the graph of eigenvalues (scree plot). The number of axes was determined by the point beyond which the remaining eigenvalues were all relatively small and of comparable size [START_REF] Jollife | Principal Component Analysis[END_REF][START_REF] Peres-Neto | How Many Principal Components? Stopping Rules for Determining the Number of Non-Trivial Axes Revisited[END_REF].

We also performed cluster analysis on the observations, i.e., for each of the 15,093 cells of the grid to group together points that had similar values of Gki using the k-means classification algorithm. From an ecological point of view, this allows to identify which species could benefit at the same time from the creation of a same pond. The appropriate number of clusters was determined by the elbow method. It consisted of plotting the explained variance as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use. A Kruskal-Wallis test was then used to study the variations in the species abundance between the different clusters. Subsequent post hoc pairwise Wilcoxon rank sum test was carried out to reveal the differences between each pair of clusters regarding the values of Gki of each species.

Results

Habitat suitability models (HSM) and significant environmental variables

For each species, the variables were selected from a collinearity study (<70%) and the contribution of the variables in an HSM for each species, including all environmental variables. A 10-fold cross-validation procedure indicated good models for our studied eight species (see Appendix B, AUC > 0.7). In most cases, "distance to the nearest ponds" and "water surface area" were logically the environmental variables that contributed most to the best predictive model (Appendix C). Distance to the nearest pond was negatively correlated with the presence of T. cristatus (an average contribution of 69.5%), H. arborea (44.6%), A. obstetricans and R. temporaria (34.4 and 33%, respectively), E. calamita (22%), N. natrix and B. bufo (15% and 12%). Proportion of the water surface was positively correlated with the presence of B. bufo (51%), N. natrix (36.5%), R. temporaria (31%), S. salamandra (24%), H. arborea (21%), A. obstetricans (16%), and T. cristatus (13%). Some variables were significant only for a few species depending on their ecological preferences, such as the proportion of forests (S. salamandra 30.5%, R. temporaria 27.7%, B. bufo 18.6%, and N. natrix 16.9%) or the proportion of artificial environments (A. obstetricians 24.2% and E. calamita 14.8%). The first variable was positively correlated with species presence (the greater the proportion of forest, the higher the probability was), while the response of the second variable had a Gaussian form; i.e., only the moderate values were positive (see Appendix C). For all the landscape composition variables, the most significant buffer radius was 500 m for the species except for A. obstetricans and T. cristatus (200 m). Temperatures were significant only for E. calamita (32.1%), for which the probability of presence was a maximum temperature between 12°C and 13.6°C. High annual amounts of precipitation (< 700 mm) were correlated with the presence of S. salamandra (17.4%).

Given the different responses of the variables between species, the maps of the probability of presence were very different (Appendix D). The proportion of the Ile-de-France region occupied by habitat patches varied according to the species, between 0.55% for S. salamandra and 15.30% for R. temporaria. The combination of the eight habitat binary maps (Fig. 1) highlighted that 68% of the region had no potential habitat (mainly urban areas and intensive agricultural land), and 14% was habitat for only one species. In addition, 13% of the region (1617 km²) was habitat for 2 to 4 species and 7% (846 km²) for more than 4 species. Only 0.002% (0.24 km²) was habitat for all eight species. These areas had a high density of water bodies (ponds, rivers, and wetlands), located either in a few forest massifs (Fig. 1a) or along river valleys such as the southeastern Seine valley (Fig. 1b).

Ecological network modeling

From these HSM maps, we selected a subpart of the region based on two criteria: i) the area must contain optimal habitat for the eight species, ii) habitat must be fragmented so that the creation of a new habitat is of ecological interest. A complete-topology graph was built for each species by keeping only those links whose distance was less than the dispersal distance. Connectivity evaluated by the Equivalent Connectivity metric varied greatly between species (Table 3 and Appendix E). The ecological networks of B. bufo, R. temporaria and to a lesser extent N. natrix had the highest connectivity due to the large area of potential habitat. The ecological networks of T. cristatus, H. arborea, E. calamita and A. obstetricans had seven times less connectivity than those of the other species. Their ecological networks contained fewer habitat patches and were more fragmented, including that of A. obstetricans. S. salamandra was the only species that had extremely low connectivity, as the 16 potential habitat patches were almost all disconnected.

Improving single-species connectivity

The patch addition process was applied on a sampling grid of 15,093 points evenly spaced 100 m apart to cover the entire 150 km² regional subpart. The connectivity gain provided by the best locations for the creation of a new pond was very low overall (< 2%), except for T. cristatus, for which the gain reached 38.4% and to a lesser extent for S. salamandra (8.6%).

Improving multispecies connectivity

From the PCA analysis, the first two components explained 45% of the total variance (Table 4). The species that were best represented by these components were B. bufo (80%), R. temporaria (65%), H. arborea, A. obstetricans and E. calamita (50%). Only S. salamandra was not well represented by the first two components (Fig. 2b). The first component hierarchized the new pond locations for five species (R. temporaria, B. bufo, N. natrix, E. calamita, and H. arborea) and the second axis for two species (A. obstetricans and T. cristatus). The nearly orthogonal arrangement of several species in the PCA (Fig. 2a) indicated that there was no spatial correspondence in the best locations, and no compromise could be found to account for the eight species. We classified the 15,093 locations for the new ponds into 4 groups using the k-means classification algorithm (Fig. 3) and spatially projected these clusters on the study area (Fig. 4). The connectivity gain varied significantly between the clusters for each species (Appendix F). The percentage increase in the EC was the highest in cluster 2 for most species, except for B. bufo and R. temporaria, which had their highest EC value in cluster 3. The landscape of cluster 2 was dominated by forests (43%) but also contained urban green spaces, artificial areas and cultures (approximately 15% each). The landscape of cluster 3 was dominated by agriculture (49%), followed by forests (28%) and artificial areas (12%). Cluster 1, characterized by a strong dominance (89%) of artificial surfaces and urban green spaces (9%), seemed to have the lowest EC increase values for all species, whereas cluster 4 (containing both artificial areas at 39% and forests at 34%) had average values for B. bufo, R. temporaria, N. natrix and E. calamita.

Discussion

Our approach for restoring habitats prioritized areas based on their contribution to multispecies regional connectivity. Targeted addition of habitat within an ecological network can increase habitat density in deficient areas and reconnect network sub-parts. This approach is therefore promising to guide conservation actions and "no net loss" biodiversity measures, especially the final stage of "offset" in the mitigation hierarchy [START_REF] Ermgassen | The ecological outcomes of biodiversity offsets under "no net loss" policies: A global review[END_REF]. Indeed, depending on the structure of the network, it may sometimes be more ecologically relevant to create habitat at a strategic location in the network, rather than necessarily near the habitat destroyed by the project [START_REF] Bergès | Environmental mitigation hierarchy and biodiversity offsets revisited through habitat connectivity modelling[END_REF]. However, managers currently lack information on the contribution of biodiversity offset areas to regional connectivity [START_REF] Moilanen | Fifteen operationally important decisions in the planning of biodiversity offsets[END_REF]. This involved taking into account the spatio-temporal nesting of ecological processes to propose effective and relevant conservation actions. Among the many studies on amphibian movement that have been published [START_REF] Pittman | Movement ecology of amphibians: A missing component for understanding population declines[END_REF][START_REF] Smith | Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations?[END_REF], most are focused on a single scale and life stage (habitat use, migration or dispersal), and very few have tried to integrate multiple spatial scales [START_REF] Bailey | Integrating amphibian movement studies across scales better informs conservation decisions[END_REF]. The modeling of ecological networks based on graph theory included both daily movements, through the definition of the patch quality, and dispersal events in maximizing the connectivity metric. Furthermore, the same protocol can be applied to other scales depending on the ecological process (e.g., seasonal migrations, range shifts related to climate change) we want to support through habitat restoration. Here, we focused on connectivity at the dispersal scale, using the global metric EC, which integrates both the amount of habitat and the degree of connections between habitat patches. Selecting this metric to optimize allowed us to take into account both the gain in local connectivity through additional habitat area and in regional connectivity through connections at the dispersal scale. Habitat restoration in areas with high connectivity gains could therefore enhance existing local populations and promote colonization of new habitats [START_REF] Bailey | Integrating amphibian movement studies across scales better informs conservation decisions[END_REF]. However, connectivity values should be analyzed in relation to the network configuration to recommend the most appropriate measures for the conservation of the species under study. Indeed, low gains in connectivity may correspond to radically different connectivity situations. The first situation related to very well-connected ecological networks with a high density of habitat patches and connections, such as that for B. bufo, R.temporaria or E. calamita. In this case, adding a new pond would only marginally improve an already high level of regional connectivity. In contrast, another connectivity situation may also correspond to a highly fragmented ecological network, either due to a low density of habitat (e.g., S. salamandra) or due to an unfavorable landscape matrix separating main habitat areas (e.g., the central part of the networks of H. arborea and N. natrix). In these two situations, adding a unique pond is not enough to significantly improve regional connectivity. Nevertheless, in the latter case, adding several ponds cumulatively, as in [START_REF] Clauzel | Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France[END_REF], could reconnect the two main subparts of the network and thus greatly improve connectivity. The proposed protocol can therefore not only provide indications on where to restore a habitat but also on how many habitats in order to significantly improve connectivity, or to restore the original state of connectivity destroyed by a development project.

A major challenge for modeling is to build models that are realistic and robust enough to propose relevant conservation actions. This point is crucial and involves the integration of species observation data (presence, abundance, genetic diversity, etc.) into modeling. However, a recent review [START_REF] Foltête | Coupling landscape graph modeling and biological data: a review[END_REF] showed that among the large number of graph-based connectivity studies, very few articles used biological data in combination with landscape graphs. This scenario is probably related to the difficulty in obtaining relevant data, especially when studies involve multiple species and a large area. For example, in [START_REF] Clauzel | Integrating regional-scale connectivity in habitat restoration: An application for amphibian conservation in eastern France[END_REF], the ecological network of H. arborea was modeled from occurrence data to retain only occupied ponds as the graph nodes and not all potential habitats, as it is often the case in graph-based connectivity studies. However, this study was applied to a region of 4,000 km² and focused on a single species. Recently, [START_REF] Jeliazkov | Cross-scale effects of structural and functional connectivity in pond networks on amphibian distribution in agricultural landscapes[END_REF] integrated occurrence data for three amphibian species for an area of 430 km 2 and a set of environmental variables to provide information about structural and functional connectivity on several scales but without providing management recommendations. In our framework, occurrence data of each species was confronted with environmental variables to predict habitat suitability and to delineate optimal habitat patches. This part of the results is interesting in itself for biodiversity conservation because it allows managers to detect -and preserve -shared habitats or conversely speciesspecific habitats. The large differences in the potential distribution between species could be explained by the degree of habitat specialization. R. temporaria, which is a ubiquitous species, had the widest predicted distribution, with 15.30% of the region classified as potential habitat. Conversely, S. Salamandra, a more specialized species with low movement abilities, had the lowest predicted distribution (0.55%), but the small number of observations could have limited the relevance of the model.

Finally, the major contribution of our paper is the multispecies assessment of restoration areas. In multispecies connectivity studies, the aggregation of the results between species is still challenging [START_REF] Meurant | Selecting surrogate species for connectivity conservation[END_REF]. Most of them analyzed the results on a species-by-species basis and eventually combined them by simple spatial overlay. The sum or average of the connectivity values obtained per species provides an overall diagnosis on high connectivity areas but with a risk of giving more weight to species that already have a well-connected network. By normalizing the values beforehand, more weight is given to species with rare habitats, which may be consistent with biodiversity conservation objectives [START_REF] Sahraoui | A multi-species approach for assessing the impact of landcover changes on landscape connectivity[END_REF]. However, in any case, knowing precisely which species is more concerned is made impossible by these combinations. Here, we combined the results with a multivariate statistical analysis to potentially identify different clusters, i.e., sets of points where connectivity gains are close for some species. This method allowed to identify more robustly the restoration areas that were simultaneously strategic for species that do not have the same ecological network or movement capabilities. If our results revealed that no area seemed to benefit all species, the landscapes dominated by forests and located near agricultural land (cluster 2) were the most strategic area for improving multispecific connectivity (at least for 5 species). Secondly, areas dominated by agriculture appeared to be more suitable for habitat restoration for B. bufo and R. temporaria (cluster 3).

Conclusions

By coupling biological data, habitat suitability models and spatial graphs, the described method in this paper appears to be useful for targeting habitat restoration areas that would simultaneously benefit several species. Integrating functional connectivity into this research increases the probability of colonization of the new habitat as well as potentially reconnecting isolated habitats and thereby improving the robustness and the resilience of the ecological network. The value of this method goes beyond the creation of ponds and can be applied to any type of habitat that can be the subject of restoration measures or a complete creation. The results provide insights into central issues of biodiversity offset and habitat restoration [START_REF] Moilanen | Fifteen operationally important decisions in the planning of biodiversity offsets[END_REF]: where and how many new habitats should be created to optimize connectivity for multiple species with various movement abilities and within different habitats.

Fig. 1 :

 1 Fig. 1: Combination of the eight habitat binary maps extracted from the habitat suitability models. The colors indicate the number of concerned species by habitat patches.

Fig 2 .

 2 Fig 2. -A) The eight species projected onto the planes formed by the first two components of the PCA; B) histogram of the contribution of each species to the first two components.

Fig. 3 .

 3 Fig. 3. The locations for new ponds were projected in the factorial planes formed by the first two components of the PCA (a). The colors of the points were related to the four different clusters attributed by the k-means method (b): cluster 1 (artificial areas) ; cluster 2 (forest edges) ; cluster 3 (mainly agriculture) ; cluster 4 (artificial areas and forest cores).

Fig. 4 -

 4 Fig. 4 -Land cover map of the patch addition area (a) and spatial projection of the four clusters grouping the 15,093 points according to the connectivity gain for the eight species (b).

General species' ecology Preferred freshwater habitat type Species Number of observations in CETTIA database Maximum dispersal distance Daily movement

	Can use different kind of ponds, including large bodies of water	Ponds and all water surfaces	Bufo bufo	9,608	2,500 m	200 m
	Dependent on a network of varied types of ponds	Ponds	Hyla arborea	372	4,000 m	500 m
	Dependent on low-density forest, shrublands, and grasslands	Ponds in or near the forest	Natrix natrix Rana temporaria	750 1,038	3,000 m 9,000 m	200 m 200 m
						4

Table 2 .

 2 Description and sources of the 13 environmental variables used for modeling habitat suitability of each species

	Category	Variable	Resolution Data source
				Landsat 8 (layers band 5 et band 4)
		NDVI	30 m	NDVI = (NIR -R)/(NIR+R)
	Bioclimatic	Temperatures	250 m	August 2019 BIOCLIM data derived from MODIS LST
		Precipitations	1 km	Chelsea (Climatologies at high resolution for the earth's land surface areas)
	Topographic	Slope Roughness	25 m 25 m	MNT MNT
	Landscape	Forest	10 m	Landscape map
	composition	Grassland	10 m	Landscape map
	with a buffer	Quarry	10 m	Landscape map
	size of 50 m;	Water	10 m	Landscape map
	200 m; 500 m	Artificial areas	10 m	Landscape map
		Distance to the nearest road	10 m	Landscape map
	Proximity	Distance to the nearest river	10 m	Landscape map
		Distance to the nearest pond	10 m	Landscape map

Table 3 -

 3 Initial Equivalent Connectivity (EC) metric (expressed in m²) and the minimum, average and maximum values of the rate of change (%) in the EC provided by the addition of a new pond in the network of each species.

	Rate of change (%)

Table 4 .

 4 Eigenvalue and amount of variance accounted for by each component of the PCA.

	Component	Eigenvalue	% of variance	Cumulative %
	1	2.4412414	30.515517	30.51552
	2	1.1277741	14.097176	44.61269
	3	1.0391739	12.989674	57.60237
	4	0.9782465	12.228082	69.83045
	5	0.9220738	11.525923	81.35637
	6	0.7297676	9.122094	90.47847
	7	0.4675788	5.844735	96.32320
	8	0.2941439	3.676799	100.00000

Acknowledgements

The authors thank Carole Gaber (funded by ARP-Astrance in 2018-2019) for her preliminary work on pond-dwelling species and advice. This study was funded by the Ile-de-France Region (TRAMARE project 2019-2020). Computations were performed on the TGIR Huma-num server (https://www.huma-num.fr). The manuscript was improved by comments from the two anonymous reviewers.

Appendices

Appendix A: Resistance values assigned to each category of the landscape map for each species, based on information from the literature. Appendix B: Results of the average accuracy of the predictive models for the 8 species using Maxent. AUC = area under the curves of relative operational characteristics. The results of the threshold "maximum test sensitivity plus specificity" = max SSS and the extrinsic omission rate (proportion of test localities not included in the prediction) are given for each of the 10 random data partitions. For all 8 species, the binomial test was highly significant for all partitions (p < 0.001, one-sided


Sébastien Mateo
email: sebastien.mateo@chu-lyon.fr.

Une procédure pour conduire avec succès une revue de littérature selon la méthode PRISMA A procedure for a successful literature review accordingly to the PRISMA statement

Keywords: Flow diagram, Evidence-based practice, Method, Literature review, Summary

Introduction: A (systematic) literature review is based on the PRISMA recommendations, the 4 steps of which are detailed in the flow chart. To date, there is no detailed description of this 4-steps procedure using simple and free tools. This pedagogical article aims at presenting this procedure.

Material: Zotero bibliography management software (5.0.84) and Microsoft® Excel (16.16.18).

Method:

The first step describes how to import into Zotero and Microsoft® Excel.

During the second step, duplicates are identified and then deleted. The third step is to identify eligible articles based on the reading of the title and abstract. Finally, the fourth step reports the eligibility of articles for inclusion in the journal based on the entire reading of the manuscript. A check for the absence of errors is presented for each step of the process.

Discussion: The procedure follows the formulation of the research question and precedes the assessment of the quality of evidence of the selected articles and the formulation, of recommendations.

Conclusion:

The procedure detailed here is simple and should help limit the risk of error during a process that often remains conducted alone in the context of end-ofstudy papers.

Résumé :

Introduction : La réalisation d'une revue (systématique) de la littérature repose sur les recommandations PRISMA dont les quatre étapes sont détaillées dans le diagramme de flux. A ce jour, il n'existe pas de procédure qui détaille ces quatre étapes et qui repose sur l'utilisation d'outils simples et gratuits. Cet article pédagogique vise à présenter cette procédure.

Matériel : Le logiciel de gestion de bibliographie Zotero (5.0.84) et le tableur Microsoft® Excel (16.16.18).

Méthode : La première étape décrit l'importation des références dans Zotero puis dans Excel. La seconde, permet d'identifier les doublons, puis de les supprimer. La troisième porte sur l'identification des articles éligibles d'après la lecture du titre et du résumé. La quatrième présente l'éligibilité des articles pour leur inclusion dans la revue sur la base de la lecture entière du manuscrit. Une vérification de l'absence d'erreurs est présentée pour chaque étape du processus.

Discussion : Cette procédure fait suite à la formulation de la question de recherche et précède la synthèse et l'évaluation de la qualité des articles sélectionnés ainsi que la formulation de recommandations.

Conclusion

Introduction

La kinésithérapie de rééducation basée sur les preuves consiste à prendre et mettre en oeuvre des décisions thérapeutiques (i.e., fixer des objectifs et mettre en place les moyens thérapeutiques correspondants). La kinésithérapie basée sur les preuves repose sur un triptyque qui s'appuie sur les objectifs et valeurs du patient, l'expérience personnelle (clinique) du thérapeute et les données de la science [START_REF] Guyatt | Evidence based medicine: A new approach to teaching the practice of medicine[END_REF]. La rééducation s'appuie sur l'expérience du thérapeute et les consensus d'experts qui proposent des recommandations pour la kinésithérapie en l'absence d'études [START_REF] Has | Etat des lieux: Niveau de preuve et gradation des recommandations de bonne pratique[END_REF].

Lorsque des études sont disponibles, le kinésithérapeute qui s'intéresse à l'efficacité d'une technique peut se concentrer sur les résultats provenant d'essais contrôlés randomisés. Ce design d'étude est considéré comme un niveau preuves scientifiques élevé [START_REF] Has | Etat des lieux: Niveau de preuve et gradation des recommandations de bonne pratique[END_REF]. Dans un contexte où le nombre d'étude augmente exponentiellement chaque année [START_REF] Has | Etat des lieux: Niveau de preuve et gradation des recommandations de bonne pratique[END_REF][START_REF] Delafontaine | Chapitre 4. Bases pratiques pour le mémoire d'initiation à la recherche de diplôme d'Etat de masseur-kinésithérapeute[END_REF][START_REF] Bruyneel | Concepts de base en méthodologie de la recherche clinique dans le domaine de la physiothérapie[END_REF], la synthèse des articles qui portent sur le sujet concerné est nécessaire pour pratiquer une kinésithérapie basée sur les preuves (e.g, pour choisir une technique qui a fait la preuve de son efficacité). Cette synthèse peut être réalisée par une revue de littérature (possiblement systématique) plutôt qu'une revue exploratoire [START_REF] Munn | Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach[END_REF], en particulier si la technique est identifiée et si ses indications sont bien ciblées. Cependant, bien que les revues diffèrent selon le type (e.g., exploratoire ou systématique), toutes formulent une question de recherche, puis identifient, sélectionnent et synthétisent les études en lien avec cette question et présentent, si possible, les résultats sous forme de tableaux [START_REF] Pham | A scoping review of scoping reviews: advancing the approach and enhancing the consistency[END_REF].

La procédure de la revue (systématique) de la littérature est décrite dans les recommandations PRISMA « Preferred Reporting Item for Systematic Review and Meta-Analysis ». Celles-ci détaillent en particulier les étapes d'identification et de sélection des articles [START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF][START_REF] Gedda | Traduction française des lignes directrices PRISMA pour l'écriture et la lecture des revues systématiques et des méta-analyses[END_REF]. Le diagramme de flux de PRISMA résume ces quatre phases :

I identification des références [START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF][START_REF] Gedda | Traduction française des lignes directrices PRISMA pour l'écriture et la lecture des revues systématiques et des méta-analyses[END_REF] procédure Zotero complétée ». En téléchargeant ces fichiers, le lecteur a la possibilité de réaliser la procédure à mesure de son avancée dans la lecture de cet article.

Procédure

Les prérequis nécessaires à la réalisation de la revue (systématique) de littérature sont la formulation de la question de recherche [START_REF] Thomas | Chapter 2: Determining the scope of the review and the questions it will address[END_REF] et l'équation de recherche [START_REF] Delafontaine | Chapitre 4. Bases pratiques pour le mémoire d'initiation à la recherche de diplôme d'Etat de masseur-kinésithérapeute[END_REF][START_REF] Lafon | Chapitre 5. Réussir sa revue de littérature[END_REF][START_REF] Pallot | Chapitre 8. Formuler une question de recherche[END_REF]. Comme cet article ce concentre sur les quatre étapes décrites dans PRISMA [START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF], la procédure débute à l'interrogation des bases de données, en prenant l'exemple de l'article publié par Mateo et 1B). Si le nombre total de références identifiées dépasse 200, l'exportation vers Zotero doit être répétée pour chaque page (Figure 1C). Enfin, reporter le nombre de références identifiées dans la cellule correspondante du diagramme de flux, pour compléter l'étape d'identification (Figure 1D). Dans le cas d'une recherche systématique, l'exportation des références 1 Le nombre de références peut varier si la plage temporelle de recherche diffère de celle indiquée et si l'option « sort by Most recent » n'est pas activée (« Best match » serait dans ce cas sélectionnée).

se fait vers les sous-collections correspondantes (e.g., « 1.2. base de donnée B » jusqu'à « 1.n. base de donnée n ») de la collection « 1.identification » de Zotero.

A ce stade, les documents issus des autres bases de données sont utilisés. Dans le cas de l'interrogation de cinq bases de données (PubMed, PEDro, Web of Science, Scopus, et CENTRAL), l'exportation des références identifiées est réalisée vers les 1E) ; la collection 1.1 est utilisée pour l'exemple précédent. Reporter alors le nombre total de références identifiées dans la cellule correspondante du diagramme de flux (Figure 1F).

sous-collections Zotero de « 1.2. […] » à « 1.6. […] (Figure

Vérification de l'étape d'éligibilité de l'article en texte intégral

Répéter la procédure décrite au paragraphe 3.

Remplissage du diagramme de flux

Pour résumer ces quatre étapes, compléter le diagramme de flux sur la base du nombre de références dans les différentes collections et sous-collections de Zotero (Figure 5).

Discussion

Les réalisées dans chaque groupe, la durée par séance, la fréquence de répétition des séances par semaine, la durée totale de l'intervention [START_REF] Mateo | Inconclusive efficacy of intervention on upper limb function after tetraplegia: a systematic review and meta-analysis[END_REF]. Il est possible de réaliser un ou plusieurs tableaux pour aider à la lecture de cette synthèse. La retranscription des tableaux dans la partie 'Résultats' de la revue peut se faire au moyen de statistiques descriptives, comme par exemple le nombre total de participants, le délai moyen ± écart-type depuis la survenue de la lésion au moment de l'inclusion de l'étude ainsi que le nombre d'études qui ont porté sur une intervention similaire, la durée totale moyenne ± écart-type des interventions [START_REF] Higgins | Cochrane Handbook for Systematic Reviews of Interventions version 6.0[END_REF].

Les étapes d'identification, de sélection, d'éligibilité des articles puis d'inclusion détaillées dans cet article permettent le travail de synthèse et ce, quel que soit le type de revue de littérature effectué (revue exploratoire « scoping review » ou systématique) [START_REF] Munn | Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach[END_REF].

Lorsque la revue vise à évaluer l'efficacité d'une technique particulière pour déterminer son possible usage lors de la rééducation kinésithérapique, la revue systématique est utilisée [START_REF] Munn | Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach[END_REF]. La synthèse des études incluses est alors complétée par une analyse méthodologique à la recherche de biais, par exemple au moyen de l'échelle de PEDro [START_REF] Maher | Reliability of the PEDro Scale for Rating Quality of Randomized Controlled Trials[END_REF] ou de l'outil d'évaluation du risque de biais de Cochrane (Risk of Bias Tool) [START_REF] Higgins | Chapter 8: Assessing risk of bias in a randomized trial[END_REF]. Cochrane propose une échelle des risques de biais ( faible, modéré ou élevé) et distingue les biais qui dépendent (i) du processus de randomisation, (ii) d'une déviation par rapport à l'intervention telle qu'elle avait été développée avant d'être conduite comme les biais dus aux données manquantes, à la mesure du critère de jugement, (iii) de la sélection des résultats rapportés [START_REF] Higgins | Chapter 8: Assessing risk of bias in a randomized trial[END_REF][START_REF] Pezel | Chapitre 22. Analyse critique des études thérapeutiques en kinésithérapie et physiothérapie[END_REF].

L'étape suivante est la synthèse des effets des interventions, puis la formulation de recommandations basées sur la qualité des preuves, l'approche GRADE [START_REF] Guyatt | GRADE: an emerging consensus on rating quality of evidence and strength of recommendations[END_REF].

L'évaluation de la qualité de la preuve est élevée, modérée, faible ou très faible. La présence de risques de biais méthodologiques ou de publication, l'inconsistance des résultats, la mesure indirecte d'un résultat, l'imprécision peuvent amener à baisser le jugement sur la qualité des preuves. A l'inverse, la qualité de la preuve peut être augmentée si la taille de l'effet de l'intervention est importante et si la dose-réponse est favorable (par exemple, si une seule séance apporte de larges bénéfices au patient) [START_REF] Guyatt | GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables[END_REF]. L'approche GRADE se termine avec la formulation d'une recommandation faible ou forte pour ou contre l'utilisation de la technique [START_REF] Guyatt | GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables[END_REF].

Ainsi, le kinésithérapeute peut choisir d'utiliser ou non une technique particulière dans un contexte donné (e.g., pathologie du patient), en fonction de la qualité de la preuve et de la recommandation.

Conclusion

Cet article pédagogique documente une procédure qui repose sur l'utilisation de Zotero, un logiciel de gestion de bibliographie gratuit, ainsi que sur celle d'Excel, un outil largement répandu et utilisé. Il détaille un processus très souvent rapidement traité, alors qu'il s'agit d'une étape cruciale pour obtenir le matériel nécessaire à la réalisation d'une revue de littérature.

Conflits d'intérêts

L'auteur ne déclare aucun conflit d'intérêt.

Financement

Aucun financement n'a été reçu dans le cadre de ce travail. Légende : Dans Zotero, (

Références Titres et Légendes des figures

:

  La procédure présentée est simple. Elle devrait aider à limiter le risque d'erreurs dans un processus qui reste souvent conduit seul, dans le cadre d'un mémoire de fin d'étude. Mots clés : Diagramme de flux ; Kinésithérapie basée sur les preuves ; Méthode ; Revue de littérature ; Synthèse Niveau de preuve : NA

3. 2 .3. 3 .3. 5 .3. 6 .

 2356 figure pas dans son titre et son résumé, il convient de conserver cet article en cas de

5 .

 5 Afficher les 10 études éligibles et incluses dans la feuille Excel « 6. Éligibilité » en utilisant la fonction « filtrer » (procédé similaire à celui détaillé Figure 4.1C), puis les copier pour les coller dans la colonne A de la feuille « 7. Éligibilité vérification » (procédé similaire à celui détaillé Figure 4.2F). Dans la collection Zotero « 7. Inclusion après éligibilité texte entier », exporter la bibliographie des 10 études incluses vers le presse-papier (similaire à la Figure 4.2E). Puis, coller le contenu du presse-papier dans la colonne C de la feuille Excel « 7. Éligibilité vérification » (similaire à la Figure 4.2F). A ce moment, vérifier ligne par ligne la correspondance des articles des colonnes A et C (Figure 4.2F). Enfin, compléter la cellule correspondante du diagramme de flux (Figure 3.2F).

Figure 1 .

 1 Figure 1. Les étapes de l'identification : A. Interrogation de la base de données (ici PubMed, https://www.ncbi.nlm.nih.gov/pubmed); B. Importation vers Zotero ; C. Vérification du succès de l'opération ; E. Remplissage du diagramme de flux PRISMA pour Mateo et al., 2019 ; E. Répétition de la procédure pour la poursuite de l'exemple ; Remplissage du diagramme de flux pour les cinq bases de données de l'exemple. Légende : Dans Zotero, vérifier que la collection « 1.1. PubMed […] » est sélectionnée. Avec le navigateur internet, dans PubMed: (1) Renseigner l'équation de recherche indiquée sur bas de la figure 1A ; (2) S'assurer que l'option « most recent » est sélectionnée ; (3.1 et 3.2). Modifier l'affichage du nombre de références par page (100 par page) qui doit être supérieur au nombre total de références identifiées (« Search results » = 76) pour exporter l'ensemble des références en une fois ; (4) après s'être assuré que l'ensemble des références est affiché sur 1 page ; (5) cliquer sur le dossier dans la barre « save to zotero (PubMed) ». (6) Une fenêtre s'ouvre et cliquer « select all » pour sélectionner l'ensemble des références de la page puis ; (7) « OK » pour exporter ; (8) Dans la sous-collection Zotero « 1.1. PubMed […] » ; (9) vérifier que les 76 références ont été importées et ; (10) reporter ce nombre dans le cadre correspondant du diagramme de flux. Répéter l'opération avec les références identifiées dans l'exemple : (11) en sélectionnant la sous-collection « 1.2. Base de données A (e.g., PubMed) ; (12) en notant le nombre de documents sélectionnés et ; (13) en le reportant dans le diagramme de flux. Répéter cette opération pour les quatre autres bases de données (PEDro, Web of Science, Scopus et CENTRAL).

Figure 2 . 1 .

 21 Figure 2.1. Partie 1 de l'identification des doublons. A. Préparation de l'identification des doublons dans Zotero ; B. Création de la bibliographie dans Zotero ; C. Exportation de la bibliographie vers Excel pour chaque base de données interrogée.

Figure 3 . 2 .« 3 .

 323 Figure 3.2. Partie 2 de la suppression des doublons. D. Exportation des références sans doublons de Zotero vers Excel ; E. Vérification de l'absence d'erreur lors de la suppression des doublons ; F. remplissage du diagramme de flux. Légende : Poursuivre dans la collection Zotero « 3. id tot ref sans doublons » ; (8) sélectionner l'ensemble des références sans doublons (touches CTL+A) ; puis réaliser 1 clic droit de la souris ; et (9) dans la fenêtre qui s'ouvre, sélectionner l'option « créer une bibliographie à partir des documents » ; (10) conserver le même style de citation que précédemment « American Psychological Association 7th » ; (11) le mode de création « bibliographie » ; et (12) la méthode de création « copier dans le presse-papier » ; et (13) valider avec « OK ». Dans la feuille Excel, « 3. Id ref tot sans doublons » (14) coller le contenu du presse-papier dans la colonne C. (15). Vérifier la correspondance des références ligne par ligne attestant l'absence d'erreur lors de la suppression des doublons. (16) Compléter le

Figure 4 . 1 .

 41 Partie 1 de la sélection sur la base du titre et du résumé. A. Préparation de la sélection dans Excel ; B. Sélection des articles dans Excel ; C. Affichage des références sélectionnées dans Excel. Légende : (1). Coller le contenu du presse-papier où a été exportée la bibliographie de la collection Zotero « 3. Id tot ref sans doublons » dans la colonne A de la feuille Excel « 4. Sélection [Screening] » ; (2). Débuter le processus de sélection en attribuant pour chaque référence le chiffre « 1 » en regard d'un critère d'exclusion (colonnes B, C, D ou E) ou d'un critère d'inclusion (colonne F). (3). Sélection terminée. (4). Pour afficher uniquement les références incluses, (5) sélectionner la colonne F ; puis (6) dans l'onglet « données », (7) sélectionner « filtrer », (8) cliquer sur l'onglet filtrer apparaissant dans l'en-tête de la colonne F ; (9) et dans la fenêtre qui apparaît, ne laisser coché que le chiffre « 1 » ; (10) seules les références incluses sont affichées dans la colonne A.

Figure 4 . 2 .

 42 Figure 4.2. Partie 2 de la sélection sur la base du titre et du résumé et vérification. D. Sélection des références dans Zotero ; E. Préparation de la vérification dans Zotero ; F. Vérification de l'absence d'erreurs lors de la sélection dans Excel ; G. Remplissage du diagramme de flux. (11) Depuis la collection Zotero « 3. Id ref tot sans doublons », faire glisser les références vers (12) les sous-collections « 4.1 à 4.4 critère exclusion 1 à 4 » en fonction du critère d'exclusion rencontré ou dans la collection « 5. Inclusion après sélection sur titre et résumé. » s'il n'y a pas de critère d'exclusion. Pour préparer la vérification de la sélection, (13) dans la collection Zotero « 5. Inclusion après sélection sur titre et résumé. », (14) sélectionner l'ensemble des références et, avec un clic droit de la souris, (15) créer une bibliographie (16) en utilisant le style de citation « American Psychological Association 7 th », (17) le mode de création « bibliographie » et la méthode de création « copier dans le presse-papier » puis (18) en validant par « OK ». (19) Dans la feuille Excel « 5. sélection vérification », coller le contenu du presse-papier (i.e., la bibliographie exportée) dans la colonne C. (20) Dans cette même feuille Excel, coller à la colonne A le contenu de la colonne A de la feuille Excel « 4. Sélection [screening] » (voir étape 10). (21). Vérifier ligne par ligne que les articles des colonnes A et C correspondent. (22) compléter le

identification : Exportation des références vers Zotero.

	al., (2019), et en se limitant à la
	interrogation de PubMed pour cette étape d'identification et d'import des références
	[12].
	3.1. Étape d'L'équation de recherche saisie dans PubMed est la suivante : (mots-clés utilisés
	dans Mateo et al., 2019) : « ''tetraplegia'' OR ''quadriplegia'' AND
	''rehabilitation'' AND ''upper extremity'' AND ''clinical trial'' NOT ''surgery''
	NOT ''tendon transfer'' NOT ''nerve transfer'' NOT ''implanted electrodes'' NOT
	''review'' » [12] (Figure 1A). La recherche, réalisée sans limitation de date de début
	et jusqu'au 12 décembre 2019, a identifié 76 références. 1 Avant de réaliser l'import
	de ces références vers la sous-collection « 1.1 base de donnée A » (ici PubMed)
	contenue dans la collection « 1. Identification » de Zotero, il convient de vérifier
	que la page Internet consultée affiche bien l'ensemble des références (e.g., ici
	affichage par 100 références). Après cette vérification, l'ensemble des références est
	sélectionné, puis exporté (Figure

  » dans la colonne B, à l'aide du code couleur précédemment utilisé ; soit (22) en sélectionnant les références colonne C et en procédant automatiquement avec (23) la fonction « supprimer les doublons » (onglet « données » de Excel) ; (24) choisir l'option « continuer avec la sélection en cours » et (25) sans que soit coché « Ma liste contient des en-têtes » puis (26) cliquer sur « OK » pour terminer l'action.

	cette opération pour les sous collections « 1.3. […] » à « 1.6. […] ». Poursuivre
	dans (1) la sous-collection Zotero « 1.2 base de données A (e.g., PubMed) ; (4)
	sélectionner l'ensemble des références (touches CTL+A), cliquer sur le bouton droit
	de la souris et, dans la fenêtre qui apparaît (5), sélectionner l'option « créer une
	bibliographie à partir des documents » ; (6) dans la nouvelle fenêtre qui s'ouvre
	sélectionner le style « American Psychological Association 7th » (ce style est choisi
	ici pour faciliter le classement par ordre alphabétique par le nom du premier auteur) Figure 3.1. Partie 1 de la suppression des doublons. A. Préparer la suppression des
	puis ; (7) choisir le mode de création « bibliographie », (8) la méthode de création doublons dans Zotero ; B. Préparation de la suppression des doublons dans Excel ;
	« copier dans le presse-papier » et (9) terminer l'opération en cliquant sur « OK ». C. Suppression des doublons dans Zotero.
	Dans la feuille Excel intitulée « 1. Id ref par base de données », (10) ajouter dans la Légende : Dans Zotero, (1) sélectionner la collection « 2. id. tot ref avec doublons »
	colonne A l'en-tête « 1.2. base de données A (e.g., PubMed) » correspondant à celui (2) sélectionner l'ensemble des références (touches CTL+A) ; (3) les faire glisser
	utilisé dans la sous-collection Zotero et ; (11) coller le contenu du presse papier (i.e.,
	la bibliographie générée) dans la colonne A de la feuille intitulée « 1. Id ref par base
	de données » puis attribuer une couleur à l'ensemble de cette bibliographie (e.g.,
	rouge). Puis répéter ces opérations (i.e., en-tête, bibliographie et mise en couleur)
	pour les 4 autres bases de données (i.e., 1.3 à 1.6, avec le code couleur différent,
	ici : orange, vert, jaune et bleu).
	Figure 2.2. Partie 2 de l'identification des doublons. D. Classement des références
	exportées par ordre alphabétique du premier auteur (style APA 7ème édition) ; E.
	Dans Zotero, vérification de l'absence d'erreurs et visualisation des doublons ; F.
	Identification des doublons dans Excel (méthode manuelle et automatique).
	Dans Excel, (12) sélectionner l'ensemble des références et en-têtes de la colonne A
	de cette feuille. (13) Copier les références dans la colonnes A de la feuille intitulée
	« 2. id. tot ref avec doublons » puis supprimer les cinq en-têtes (n=5, pour chaque
	base de données). (14) Classer par ordre alphabétique du premier auteur ; (15) en
	continuant avec la sélection en cours dans la fenêtre qui s'ouvre. (16). Le nombre
	total de références avec doublon apparaît lorsque l'intégralité des références est
	sélectionnée (n=155). (17) Copier les références classées de la colonne A et les
	1) sélectionner la sous-collection « 1.2 base de données A coller dans la colonne C de cette feuille Excel. Dans la collection Zotero, « 2. id tot
	(e.g., PubMed) » qui contient les références constituant la bibliographie ; (2) ref avec doublons », (18) vérifier que le nombre de références affichée est identique
	sélectionner l'ensemble des références (touches CTL+A) et ; (3) faire glisser les
	références sélectionnées dans la collection « 2.id tot ref avec doublon ». Répéter

(n=155) ; (19). Le classement par titre permet de (20) visualiser les doublons. Poursuivre dans la feuille Excel « 2.id. tot ref avec doublons » soit (21) en identifiant manuellement chaque doublon de la colonne A en indiquant le chiffre « 1 dans la collection Zotero « 3.id tot ref sans doublons. ». Dans Excel, (4) copier les références sans doublons de la colonne C de la feuille Excel « 2. id tot ref avec doublons » et les coller dans la colonne A de la feuille Excel « 3. id tot ref sans doublons ». Dans la collection Zotero « 3. id tot sans doublons », (5) sélectionner un doublon (en se basant sur Excel) avec un clic droit ; (6) dans la fenêtre qui s'ouvre, sélectionner l'option « retirer le document de la collection » et (7) valider avec « OK ». Répéter l'opération (5), (

6

) et

[START_REF] Moher | Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement[END_REF]

pour chaque doublon identifié.