When Imagery and Physical Sampling Work Together: Toward an Integrative Methodology of Deep-Sea Image-Based Megafauna Identification

Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms. The comparison was done on selected taxa (Pisces, Crustacea, Echinoidea, and Asteroidea), which are good candidates for identification from images. Taxonomic identification ranks obtained for the images varied among these taxa (e.g., family/order for fishes, genus for echinoderms). At these ranks, imagery provided a higher taxonomic richness for hard-bottom and complex habitats, partially explained by the poor performance of trawling on these rough substrates. For the same reason, the gamma diversity of Pisces and Crustacea was also higher from images, but no difference was observed for echinoderms. On soft bottoms, physical sampling provided higher alpha and gamma diversity for fishes and crustaceans, but these differences tended to decrease for crustaceans identified to the species/morphospecies level from images. Physical sampling and imagery were selective against some taxa (e.g., according to size or behavior), therefore providing different facets of biodiversity. In addition, specimens collected at a larger scale facilitated megafauna identification from images. Based on this complementary approach, we propose a robust methodology for image-based faunal identification relying on a taxonomic framework, from collaborative work with taxonomists. An original outcome of this collaborative work is the creation of identification keys dedicated specifically to in situ images and which take into account the state of the taxonomic knowledge for the explored sites.

Flora of Ceará, Brazil: Cabombaceae

The present study analyzed taxa of the family Cabombaceae occurring in the state of Ceará. Only Cabomba species was represented, with C. aquatica and C. haynesii recorded in the state. Populations occur in permanent and temporary lentic environments, such as lagoons and shores of lotic systems. This work presents taxon identification keys, morphological descriptions, illustrations, comments and geographic distribution data.

The Taxon Hypothesis Paradigm—On the Unambiguous Detection and Communication of Taxa

Here, we describe the taxon hypothesis (TH) paradigm, which covers the construction, identification, and communication of taxa as datasets. Defining taxa as datasets of individuals and their traits will make taxon identification and most importantly communication of taxa precise and reproducible. This will allow datasets with standardized and atomized traits to be used digitally in identification pipelines and communicated through persistent identifiers. Such datasets are particularly useful in the context of formally undescribed or even physically undiscovered species if data such as sequences from samples of environmental DNA (eDNA) are available. Implementing the TH paradigm will to some extent remove the impediment to hastily discover and formally describe all extant species in that the TH paradigm allows discovery and communication of new species and other taxa also in the absence of formal descriptions. The TH datasets can be connected to a taxonomic backbone providing access to the vast information associated with the tree of life. In parallel to the description of the TH paradigm, we demonstrate how it is implemented in the UNITE digital taxon communication system. UNITE TH datasets include rich data on individuals and their rDNA ITS sequences. These datasets are equipped with digital object identifiers (DOI) that serve to fix their identity in our communication. All datasets are also connected to a GBIF taxonomic backbone. Researchers processing their eDNA samples using UNITE datasets will, thus, be able to publish their findings as taxon occurrences in the GBIF data portal. UNITE species hypothesis (species level THs) datasets are increasingly utilized in taxon identification pipelines and even formally undescribed species can be identified and communicated by using UNITE. The TH paradigm seeks to achieve unambiguous, unique, and traceable communication of taxa and their properties at any level of the tree of life. It offers a rapid way to discover and communicate undescribed species in identification pipelines and data portals before they are lost to the sixth mass extinction.

Notebook: Customizable web forms for recording observations

’Notebook’ is one of the primary data management systems of the Finnish Biodiversity Information Facility (FinBIF). It is a web solution for recording opportunistic as well as sampling-event-based species observations. It is being used for systematic monitoring schemes, various citizen science projects, and platforms for species enthusiasts. Notebook's main software component is LajiForm, which is the engine that renders a given JSON Schema into a web form. LajiForm is a separate, reusable module that is fully independent from other FinBIF systems. Notebook as a whole, includes other features embedded in FinBIF, such as linking users' geographical data to observation documents, spreadsheet document importing and form templates. We will demonstrate how the Notebook system works as a whole and also focus on LajiForm's technical aspects. Fig. 1 All Notebook forms use FinBIF's ontological schema in JSON Schema format. Rendering user-friendly web forms based on a single schema is a difficult task, because the web form should be asking meaningful questions, instead of just rendering the schema fields according to the form description. We want to present questions in an interactive style. For instance, after drawing a geographical location on a map for a potential flying squirrel nesting tree, we would ask "did you see droppings at the nest?", and answering "yes" would update the document to include a flying squirrel taxon identification with fields "breeding" and "record basis" filled in but not rendered to the form. A simpler form engine without a user interface (UI) customization layer would just render the "taxon", "breeding" and "record basis" fields and the user would have no understanding why there are so many fields to answer and how they relate to their work or study. Some forms are complex, e.g., for experienced biology enthusiasts who need a form that is advanced, customizable, and compact. Some forms are simple, e.g., for elementary school children. To tackle these challenges, LajiForm uses a separate schema for UI that allows everything from simple customization like: defining widgets for fields (e.g., date widgets, taxon autocomplete widget, map widget), changing field order or customizing field labels; to more complex customization like transforming the schema object structure, defining conditions when certain fields are shown or if updating a field should have an effect on other fields. defining widgets for fields (e.g., date widgets, taxon autocomplete widget, map widget), changing field order or customizing field labels; to more complex customization like transforming the schema object structure, defining conditions when certain fields are shown or if updating a field should have an effect on other fields. All the functionality is split into a loosely coupled collection of components, which can be either used as standalone components or composed together in order to achieve more advanced customization. The programming philosophy has drawn inspiration from functional programming, which has been helpful in writing isolated, composable functionality. LajiForm is written with the JavaScript framework React. LajiForm is built on top of react-jsonschema-form (RJSF), which is an open source JSON schema web form library provided by Mozilla. RJSF handles only simple customization, but it is very flexible in design and allows us to build extensions with features that are more powerful. Some features and design proposals were submitted to Mozilla – FinBIF is the largest code contributor to RJSF outside of Mozilla, with a dozen pull requests merged.

Towards Improved Molecular Identification Tools in Fine Fescue (Festuca L., Poaceae) Turfgrasses: Nuclear Genome Size, Ploidy, and Chloroplast Genome Sequencing

Fine fescues (Festuca L., Poaceae) are turfgrass species that perform well in low-input environments. Based on morphological characteristics, the most commonly-utilized fine fescues are divided into five taxa: three are subspecies within F. rubra L. and the remaining two are treated as species within the F. ovina L. complex. Morphologically, these five taxa are very similar, both identification and classification of fine fescues remain challenging. In an effort to develop identification methods for fescues, we used flow cytometry to estimate genome size, ploidy level, and sequenced the chloroplast genome of all five taxa. Fine fescue chloroplast genome sizes ranged from 133,331 to 133,841 bp and contained 113 to 114 genes. Phylogenetic relationship reconstruction using whole chloroplast genome sequences agreed with previous work based on morphology. Comparative genomics suggested unique repeat signatures for each fine fescue taxon that could potentially be used for marker development for taxon identification.

Using Crowd-curation to Improve Taxon Annotations on the Wikimedia Infrastructure

Here we present how two independent infrastructures, Wikimedia and iNaturalist, can be jointly leveraged to improve content on both platforms. iNaturalist.org began as a Master's final project in 2008 and grew to a globally used app to help identify biodiversity. The community behind iNaturalist consists of citizen scientists, who record a species existence through photos or sound recordings. The Wikimedia Foundation provides a spectrum of resources, of which Wikipedia is the most famous sibling. Other siblings we address here are Wikimedia Commons and Wikidata. Commons is the platform where open licensed media can be shared. Wikidata is the linked knowledge graph, where public data can be stored as structured data. Basically, data goes to Wikidata, images and recordings go to Commons and text goes to Wikipedia. Initially, both Commons and Wikidata served primarily the approximately 300 language versions of Wikipedia. However, nowadays both Commons and Wikidata are also being used as is, that is in other contexts than Wikipedia. Although iNaturalist and the Wikimedia family of repositories thrive as independent infrastructures and are thus maintained independently from each other, they can be mutually beneficial to each other. Content created by the iNaturalist community can be very valuable to the Wikimedia community. Firstly, observations in the form of photos and sound recordings can be stored on iNaturalist, with an open and compatible license that can provide valuable illustrations and structured knowledge on biodiversity. Wikipedia articles can be enriched with already approximately 1.2M photos from observations. Furthermore, the assertions made by the iNaturalist community can act as references in various Wikidata claims or Wikipedia articles. In many cases in Wikipedia, we have to rely on personal annotations by the picture taker, who stores it on Wikimedia’s multimedia Commons. iNaturalist provides images of organisms with stronger - peer-reviewed - assertions on the subject in the picture. When a cat is called a cat in Wikipedia or Commons, it is the iNaturalist community that either approves or rejects that claim. In the opposite direction, iNaturalist relies on knowledge described in Wikipedia. It includes Wikipedia articles about taxa on its website. Images can also be uploaded into iNaturalist from Commons. It is therefore possible to add images of museum specimens into iNaturalist to assist with taxon identification. Other citizen science apps do exist. iNaturalist, however, is particularly interesting due to the feature that it allows its users to select from a selection of licenses, of which some are compatible with the licenses upon which content from the Wikimedia family is available. Wikidata uses a CC0 - public - license for its data, Wikipedia is available under a CC-BY-SA and Commons content uses a selection of Creative Commons licenses (Table 1). iNaturalist is also a particular good fit with Wikimedia, because both have a global and multilingual scope. This is a great example of how platforms can support each other's missions by simple policy decisions, such as open licencing, that underpin interoperability.

Comparative morphological and molecular taxonomic study of six Achaeta species (Clitellata: Enchytraeidae) with the description of a new Achaeta species from Kőszeg Mountains, Hungary

Five Achaeta species (A. affinis, A. bohemica sensu stricto, A. camerani, A. cf. danica, A. unibulba) and a new species, described here as A. tothi sp. n., were found during the investigation of the enchytraeid fauna of western Hungary (Őrség National Park and Kőszeg Mts.). Comparative morphological and molecular taxonomic investigations were performed with several individuals representing these six Achaeta species. A detailed description of the new species is given, and we also present some additional morphological data and photos about the other five Achaeta species. Such information could have importance in subsequent taxonomic studies and will aid the discrimination among the members of genus Achaeta. Furthermore, the obtained sequences could serve as references in forthcoming studies applying DNA-based taxon identification.