We provide a quantitative description of the French national herbarium vascular plants collection dataset. Held at the Muséum national d'histoire naturelle, Paris, it currently comprises records for 5,400,000 specimens, representing 90% of the estimated total of specimens. Ninety nine percent of the specimen entries are linked to one or more images and 16% have field-collecting information available. This major botanical collection represents the results of over three centuries of exploration and study. The sources of the collection are global, with a strong representation for France, including overseas territories, and former French colonies. The compilation of this dataset was made possible through numerous national and international projects, the most important of which was linked to the renovation of the herbarium building. The vascular plant collection is actively expanding today, hence the continuous growth exhibited by the dataset, which can be fully accessed through the GBIF portal or the MNHN database portal (available at: https://science.mnhn.fr/institution/mnhn/collection/p/item/search/form). This dataset is a major source of data for systematics, global plants macroecological studies or conservation assessments.
The Myriapoda and Onychophora collection dataset inventories the occurrence records of the collection of myriapods and onychophorans in the Muséum national d’Histoire naturelle, Paris. The dataset currently consists of 202 lots of onychophorans, representing all of those present, and almost ten thousand (9 795) lots of myriapods, representing 33 to 40% of the MNHN Myriapoda collection. This collection, which is of key historic importance, represents the results of two centuries of myriapod and onychophoran studies. The sources of the collection are worldwide, with a high representation for metropolitan France for the myriapods. None of the occurrences are yet georeferenced. Access to the dataset via the data portals of the MNHN and the GBIF has been made possible through the e-ReColNat project (ANR-11-INBS-0004).The Myriapoda and Onychophora collection of MNHN is actively expanding, hence both the collection and dataset are in continuous growth. The dataset can be accessed through the portals of GBIF at http://www.gbif.org/dataset/3287044c-8c48-4ad6-81d4-4908071bc8db and the MNHN at http://science.mnhn.fr/institution/mnhn/collection/my/item/search/form.
Curbing biodiversity loss and its impact on ecosystem services, resilience and Nature's Contributions to People is one of the main challenges of our generation (IPBES, 2019b, 2019a; Secretariat of the United Nations Convention on Biological Diversity, 2020). A global baseline assessment of the threat status of all of biodiversity is crucial to monitor the progress of conservation policies worldwide (Mace & al., 2000; Secretariat of the United Nations Convention on Biological Diversity, 2021) and target priority areas for conservation (Walker & al., 2021). However, the magnitude of the task seems insurmountable, as even listing the organisms already known to science is a challenge (Nic Lughadha & al., 2016; Borsch & al., 2020; Govaerts & al., 2021). A new approach is needed to overcome this stumbling block and scale-up the assessment of extinction risk. Here we show that analyses of natural history mega-datasets using artificial intelligence allows us to predict a baseline conservation status for all vascular plants and identify target areas for conservation corresponding to hotspots optimally capturing different aspects of biodiversity. We illustrate the strong potential of AI-based methods to reliably predict extinction risk on a global scale. Our approach not only retrieved recognized biodiversity hotspots but identified new areas that may guide future global conservation action (Myers & al., 2000; Brooks & al., 2006). To further work in this area and guide the targets of the post-2020 biodiversity framework (Díaz & al., 2020a; Secretariat of the United Nations Convention on Biological Diversity, 2020; Mair & al., 2021), it will be necessary to accelerate the acquisition of fundamental data and allow inclusion of social and economic factors (Possingham & Wilson, 2005).
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