dataesr/scientific-paragraphs-categorization huggingface dataset:
-Species distribution modelling (SDM) We modelled favourable habitats for birds and butterflies in the three study sites using SDM (Figure  2 , step 2. 1). A resolution of 50m was used to meet the needs of the SEA of land-use planning. A buffer zone of 10km around each of the study sites (i. e. local scale) was used for the SDM prediction to limit any border edge effects and to increase the number of species that could be modelled and evaluated. Indeed, species with less than 15 data points for the calibration (threshold explain above,  Støa et al. , 2019)  and/or less than 10 data for performance evaluation (threshold defined by expert opinion) were not modelling. Biodiversity data used for the SDM came from the databases decribed above at the national scale, according to the results of section 2.
3 (Table  2 ). These data were separated into two independent datasets that allow for robust validations with independent data  (Matutini et al. , 2021). National without local data were used for model calibration and local data were used only for model performance evaluation. Therefore, the SINP database, which is only available at local scale, was not used for model calibration. For model calibration, pseudo-absences were generated with two methods. First, methods to generate pseudo-absences in the individual databases for all their data (i. e. GBIF, Vigie Nature and National databases). For databases with a protocol for sampling (i. e. Vigie Nature) to optimize species detection (day and year periods), the absence points were defined as all the points without the observed species. For databases without such sampling protocols for all their data (i.
e. GBIF and National databases), pseudo-absence data were generated with the target-group (TG) approach, which infers the sampling bias from the aggregated occurrences of (TG) species, i. e. the respective taxonomic groups  (Ponder et al. , 2001; Anderson, 2003; Phillips et al. , 2009). Second, a method to mix the presence data in the GBIF and National databases with the absence from Vigie Nature (named mixed databases) was applied  (Hermoso et al. , 2015a). Three types of environmental variables used for SDM: biogeographic, human occupancy and pollution and fragmentation (Appendix A. 2). SDM were calibrate by Random Forest down-sample  (Valavi et al. , 2021a)  which according to  Valavi et al. (2021b)  is among the best performing models for presence-only data. Although the Random Forest is not very sensitive to the non-independence of the variables and over-parametrization  (Matsuki et al.
, 2016; Srisa-An, 2021) , in order to be parsimonious, the collinear variables were removed  (Pearson >0. 7, Appendix A. 2, Brun et al. , 2020). Thirty bootstraps were performed for each SDM  (Guisan et al. , 2017)  using a calibration for 70% of the data at the national scale outside of local scale. The thirty Random Forest bootstraps were combined with mean to provide an ensemble prediction of habitat suitability for all species. The performance evaluation of the models was done using the Boyce's index (CBI), the most suitable metric for model in presence-only  (Boyce et al. , 2002; Leroy et al. , 2018) , with local data (i. e. All databases combining SINP, GBIF and Vigie Nature). Dubos et al. (2022)  reveal the CBI turns out to be misleading in some cases, thus we used a threshold of 0.
-Systematic conservation planning (SCP) tool Priority conservation areas in the three territories were analysed from SDMs for each database source in using a SCP tool (Figure  2 , step 3. 1). To meet the needs of SEAs, the study site was restricted to administrative boundaries with a buffer zone of 1km to maintain coherence between administrative entities and a resolution of 50m. The SEAs biodiversity conservation strategies aim is to establish priorities for the whole territory and all the cells have the same cost value of 1. The objective was a maximum coverage objective that seeks to maximize the number of features, i. e. the SDMs  (Church et al. , 1996). The features were the predictions of SDM with a Boyce's index > 0. 3 to limit the influence of poor models. The priority conservation areas decisions were between 0 and 1.
To obtain a priority gradient, we cumulated Ferrier importance scores  (Ferrier et al. , 2000)  from nine targets of the total amount of each feature (from 0. 1 to 0. 9 every 0. 1). The package "prioritizr" was used  (Hanson et al. , 2021)  with the open-source solver SYMPHONY  (Kim et al. , 2023).
-Comparative analysis of SDM and priority conservation areas The influence of database sources on SDM predictions and priority conservation areas were analysed (Figure  2 , step 2. 2 and 3. 2). SDM performance evaluations were analysed between database sources. The SDM prediction and priority conservation areas maps were compared with the Spearman's rank coefficient  (Phillips et al. , 2009)  and the Schoener's D index as a measure of projection overlap  (Schoener, 1968 ) which was calculated with the ENMTool R package  (Warren et al. , 2008; Warren and Dinnage, 2022).
-Species community analysis We assessed the influence of the complete data driven workflow on the composition of species communities, i. e. differences between the original community in the database and the final community used to identify priority conservation areas (Figure  2 , step 4). To do so, we developed a workflow resistance score for each of the methodological filters for all species. A score of 1 is allocated to species observed in the study area that did not cross any of the stepwise filters. A score of 2 is allocated to species with sufficient data to calibrate SDM, i. e > 15 national observations, or evalue the performance of SDM, i. e. > 10 local observations. A score of 3 is allocated to species with sufficient data to calibrate and evaluate the performance of the SDM. A score of 4 is for species that were present in the final analysis as a priority species for conservation planning (i.
e. with the two previous filters and a Boyce's index > 0. 3). In order to assess species composition bias ultimately considered in priority conservation areas, species communities were analysed through traits that can influence species detection (mass, displacement capacity, period activity) and ecological traits (habitats, specialisation) (Appendix A. 3). Missing data were completed with a trait imputation procedure generated using the R package "missForest"  (Stekhoven and Bühlmann, 2012)  by considering evolutionary relationships in the imputation process (see  Carmona et al. , 2021)  using the R script of  Toussaint et al. (2021). Due to the nature of the response variable (i. e. ordinal scoring including four modalities), we used ordinal regression mixed models with cumulative link using the clmm function of "ordinal" R package (Christensen, 2022).
We adapted the link function to the data distribution for Aves and Papilionidae, using the "cauchit" and "logit" links, respectively. Species traits were used as fixed effects, while the random effects selected were the study sites for Aves and the combination for study sites and database sources for Papilionidae. Finally, we evaluated the quality of the full model by comparing to the null model with Akaike's information criterion (AIC)  (Mac Nally et al. , 2018). Thus, our models were structured in the following way: For Aves 𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 ~ 𝛽 0 + 𝛽 𝑖. ′𝑡𝑟𝑎𝑖𝑡′ 𝑖 + 1|𝑆𝑡𝑢𝑑𝑦𝑆𝑖𝑡𝑒𝑠 For Papilionidae 𝑅𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 ~ 𝛽 0 + 𝛽 𝑖. ′𝑡𝑟𝑎𝑖𝑡𝑠′ 𝑖 + 1|𝑆𝑡𝑢𝑑𝑦𝑆𝑖𝑡𝑒𝑠_𝐷𝑎𝑡𝑎𝑏𝑎𝑠𝑒𝑠 Where β is the parameter estimates, i correspond to the variables of 'traits' using in fix effect, 0 is the shift between ordinal class of resistance (i.
-Quantitative description of databases for SDM The use of individual observation databases provided a limited number of observation data for distribution modelling of many species, regardless of the taxonomic group (Table  2 , Appendix B. 1). None of the GBIF and SINP individual databases have more than 50 observations for all nine taxonomic groups, and there are currently no programs for three of the nine taxonomic groups in the Vigie Nature databases. Local databases and, to a lesser extent, regional databases are not equivalent in terms of the amount of data available for different taxa for the three study sites. The difference is particularly pronounced for the SINP databases, with no data available for five taxonomic groups in the T3 study site, whereas in the T1 study site, six taxonomic groups have sufficient data to model over 50% of the species.
At the regional scale, the number of observations per species is highly variable among territories for the GBIF databases and is more similar for the Vigie Nature databases. The use of combined databases (i. e. All databases) increased the total number of species that can be studied. At the local scale, the proportion of species in each suitability class showed only small changes, while at the national scale of France, it allowed a significant gain in species with suitable data (Table  2 ). Indeed, on a national scale, the GBIF and Vigie Nature databases are complementary with each other. For example, the GBIF has few Chiroptera data, which is complemented by Vigie Nature data, and vice versa for Amphibian data. At the national scale, aggregation of the databases seem to provide the most suitable setup (databases and scale) for SDM analysis.
-SDM and priority conservation areas analysis The evaluation of SDM revealed differences among the database sources; none of which produced more than 87% of satisfactory models for the two studied taxonomic groups and some had less than 20% of satisfactory models (Table  3 , Appendix B. 2). Use of the GBIF data led to a higher proportion of well-evaluated SDM, ranging between 48 and 79% of satisfactory models for the species in the two taxonomic groups. GBIF data are also more suitable than Vigie Nature data, they produced between 11% and 37% more satisfactory models than the latter database (Table  3 ). Nevertheless, between 4% and 9% of species provide well-evaluated models from Vigie Nature and poorly evaluated by GBIF database. The combination of National databases (i. e. GBIF and Vigie Nature) decreased the performance of SDMs with GBIF data, but still yield better results than SDM based on the Vigie Nature database.
The substitution in individual databases of pseudo-absences for the absences from Vigie Nature (i. e. mixed databases) reduced the performance of models based on GBIF, but increased the performance of the combined National databases (Figure  3 , Appendix B. 2). Regardless of the database used, our analyses revealed significant differences between territories (Table  3 ). No SDMs for butterflies in T2 could be evaluated due to insufficient local data. For the T3 study site, over 50% of the Papilionidae and Aves models perform poorly, whereas for the T1 study site poor models occur in less than 50% of the evaluations. Although important differences in model performance between the database sources used for SDM showed a high degree of overlap, as indicated by Shoener's D index with values above 0.
8, the ranking of habitat suitability was highly variable. This was in particularly the case for the GBIF and Vigie Nature databases that had median spearman's rank coefficient values between 0. 2 and 0. 5 and a very wide distribution (Appendix B. 3). The substitution of pseudo-absence data in GBIF and National databases by absence data from Vigie Nature, showed a similar situation (Appendix B. 4). For priority conservation areas, whatever the individual databases used, the overlaps with Schoener's D index were above 0. 72 and similar in each territory. Nevertheless, Sperman's rank coefficients showed a greater difference in prioritization ranks in particular between GBIF and Vigie Nature and for the T3 study site (Table  4 ). Between maps of priority conservation areas, we observed similarities in overlap, despite a significant difference in the hierarchy of areas to be prioritized (Figure  4 , Appendix B.
-Species community analysis The distribution of workflow resistance scores showed that only 30% and 42% respectively of Aves and Papilionidae species were integrated in priority conservation areas maps. Among species not integrated, the workflow steps filtering the most species concern the amount of suitable data for model evaluation followed by the quality of the models (Figure  5 ). The analysis of the species community composition observed in each of the three study sites in comparison with the species community integrated in priority conservation areas revealed significant differences. For Aves communities, the differences concern an under-representation of nocturnal species, large species with high dispersal capacity, and species of swamp habitats and deciduous forests in relation to the observed species community in the databases for the three study sites.
-Discussion The absence of recommendations for the use of available databases that differ in terms of their quality and pertinence by public authorities or other institutions remains a serious problem for local conservation planning  (Clare et al. 2019). The goal of this study was to test the suitability of different database sources that can be used by public stakeholders to map priorities for biodiversity stakes in SEAs and SCP. We found that the compilation of databases at the national scale is the most suitable procedure to apply SDM to a large number of species. For Aves and Papilionidae, the GBIF database provided the highest proportion of well-assessed SDM. We detected a significant overlap in species distributions in different database sources despite significant variability in the order of habitat suitability and similar spatial predictions for priority conservation areas.
Finally, we showed that the composition of the species community used for priority conservation areas in three study sites were clearly not representative of the observed species communities in the original database (in terms of ecological traits). Open-access biodiversity data provide a valuable source of information for conservation and land-use planning stakeholders; they contain vital information on species locations compared to expert knowledge and unshared datasets that are inaccessible to most users  (Sousa-Baena et al. , 2014; Meyer et al. , 2015). Through their use and careful application of SDM, they contribute to the estimation of relative habitat suitability in a given study site  (Baker et al. , 2021). Our study showed however that SDM for a large majority of species observed locally requires their compilation on a national scale.
Local and regional data are not suitable for model calibration but remain important for assessing the suitability of models in a local context. Indeed, this performance evaluation step is one of the most restrictive filters in the workflow we proposed, as evidenced by SDM's for butterflies in the T2 study site, where no species could be evaluted. The spatial extent of data collection can influence distribution modelling  (Meyer, 2007) , and our study emphasises this importance for SDM and the use of data available on national scale in France. Different types of databases are constructed in different way -with opportunistic data collection or, in some cases, as part of a scientific monitoring scheme -allowing the use of as complementary data sources  (Beck et al. , 2013; Shirey et al. , 2021).
The three individual databases examined in this study are indeed complementary in that when they are combined they provide a suitable source for modelling the distribution of many species. Nevertheless, some groups commonly have a low number of data in such bases, e. g. Insecta  (Troudet et al. , 2017). The study of data with a fine spatial grain, as required for SEA and SCP  (Guisan et al. , 2013) , reveals information gaps for more taxonomic groups at the global scale, e. g. Amphibians and Mammals  (Witté and Touroult, 2017). The construction of an overall database at national scale is therefore the most appropriate way to have suitable data for SDM of different taxonomic groups. SDM of Aves and Papilionidae species clearly revealed differences between the databases used for modelling, with differential impacts on the identification of conservation priorities.
Indeed, species distribution overlap is high between database sources as indicated by the Schoener's D index, i. e. regardless of the data source species, are predicted in similar environments  (Warren et al. , 2008). However, Spearman's ranking of habitat suitability between data sources was highly variable, indicative that species' responses to environments are highly variable, as are the location of favourable habitats  (Warren et al. , 2008). Although the use of presence-absence data is advocated for SDM  (Guillera-Arroita et al. , 2015; Valavi et al. , 2021b; Dubos et al. , 2022) , we showed that opportunist data from GBIF provided a greater number of well-assessed models at the local scale. Models using opportunist data with a target-group approach to generate pseudo-absences provides a sufficient quality of information on species distribution  (Phillips et al.
, 2009; Barber et al. , 2022)  and can be correctly used in SCP  (Sofaer et al. , 2019; Baker et al. , 2021). The lack of data at the regional scale, whatever the database, does not allow us to explain a better fit of models using GBIF data. Evaluating the models with a large proportion of opportunist data could however bias the evaluation, but only independent data were used, which provides robust validation of SDM  (Matutini et al. , 2021). Moreover, in contrast to  Hermoso et al. (2015a) , we found that mixing presence-only data with absence data increased the number of misjudged models. In addition to the use of the ROC curve (AUC) as a model evaluation metric by  Hermoso et al. (2015a) , the different results can be explained by different sampling biases between the two data types  (Baker et al.
, 2022; Barber et al. , 2022). Finally, the GBIF data seem to be more adapted to model the distribution of a large number of species. The notion of "garbage in -garbage out" emphasises the critical importance of the quality of data  (Sanders and Saxe, 2017) , nevertheless, the examination of data suitability for conservation planning remains rare. In addition to the above issues our study revealed the importance of attention that should be paid to the representativeness of the species communities used in the models compared with the actual species communities observed in the study sites. This is particularly important in the light of the finding that there are marked differences between conservation priorities when different database sources are employed. Indeed, the number and composition of species in the community used can influence conservation priorities.
Our study presents a workflow (Figure  2 ) for identifying biodiversity stakes using a data-driven approach from open-access database sources. Land-use planning stakeholders can use this workflow as a step towards the rationalization of data in order to reduce the biases mentioned above. The confrontation of the limits of such a workflow with the needs of stakeholders could illustrate how to precisely target new sources of database that should be collected according to the suitability of current databases for priority groups. Hermoso et al. (2015b)  revealed that evaluation models using a new collection of field data does not necessarily reduce the problems of model uncertainty. However, other databases can be examined by stakeholders as well as other monitoring schemes (e. g. "PopAmphibien" for Reptilian and Amphibian populations in France http://lashf.
org/popamphibien-2/) or negotiate the use of databases that are not yet shared. An important issue is thus the integration of stakeholders in the workflow we propose, and their appropriation of the procedure. This could be done by a form of participatory modelling  (Lagabrielle et al. , 2010; Lees et al. , 2021) , where stakeholders are consulted for issues and choices such as the species to be examined. In such participatory modelling it is important to avoid arbitrary choices that are neither reproducible nor representative of local diversity, but rather the result of administrative or political interest. Finally, it is currently recommended to use these tools to elaborate a more holistic approach to SCP  (Cadotte and Tucker, 2018).
-Perspectives: operational implementation by SEA stakeholders Spatially exhaustive and ecologically representative priority conservation areas are crucial for the elaboration of SEAs that aim to limit artificialization impacts as early as possible in the planning process. Empirical observations are major sources of information on biodiversity that are still rarely used by SEAs. The collection of open-access databases for SEA territories provides important but incomplete knowledge on species occurrence. Furthermore, their use is particularly interesting to help strategically direct inventory campaigns (especially for under sampled taxa and areas). These additional data would clearly improve the assessment of the SDMs suitability in local territories. What is also interesting here for stakeholders is that the process of filtering species and attributing them a score allows us to identify different groups of species in terms of their needs for additional data in order to undertake SDM.
The influence of database sources on the identification of priority conservation areas reveals the importance of examining their suitability. Thus, it is necessary to be prudent in the interpretation of biodiversity maps. The integration of local experts may help limit any misjudgements in the workflow procedure. A data-driven approach that considers as many species as possible requires a large amount of data, biases the species communities considered and does not highlight species of particular interest as their threats and regulatory protections. It is therefore necessary to rationalize this approach, by integrating the needs and issues of local SEA stakeholders. A. 3 -Species traits of Aves and Papilionidae. Missing traits were imputed by considering evolutionary relationships following the process of  Carmona et al.
B. 1 -Description of observation databases (Local SINP, GBIF, Vigie Nature) available nine taxonomic groups in the three study sites (T1: Lodévois-Larzac, T2: Brocéliande, T3: La Rochelle) for "local" (buffer zone 10 km), "Regional" and "France" (continental France) scales. Statistics: "nSp" is the number of species with at least three observations on the study site within a 10 km buffer; "pSp<15obs", "pSp15. 50obs" and "pSp>50obs" are the percentage of species with <15, 15 to 50 and >50 observations respectively and "-" represents no data available. B. B. 2 -SDM evaluation of Aves and Papilionidae taxonomic groups for (A) three individual database sources and (B) between SDM using different pseudo-absence sources. Each graphic is a pair of databases. Each point is an individual species that is listed in both databases, some species are assessed only in one database (missing).
A Boyce's index threshold of 0. 3 is used to assess the proportion of satisfactory and unsatisfactory models conjointly for each of the two database sources. The blue quadrant contains species whose SDMs are evaluated in a satisfactory manner in both data bases (Boyce index > 0. 3 for both databases) ; the red quadrant contains the species for which SDMs are evaluated in an unsatisfactory manner in both databases (Boyce index < 0. 3 for both databases). In the top-left quadrant and the bottom-right quadrants, SDM model evaluation was satisfactory for only one database (on the in y and x axes respectively).
Data, scripts, code, and supplementary information availability Observation data from SINP and Vigie nature databases are not available for confidentiality reasons, but their link and the request process are detailed in Appendix A1. The data of GBIF are available online: https://doi. org/10. 15468/dl. ry6uw7. The data, R scripts, outputs of steps 1. 2 to 4 of figure 2 are available online: https://doi. org/10. 5281/zenodo. 7883973.