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      Occurrence Cubes: A new way of aggregating heterogeneous species occurrence data

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      Biodiversity Information Science and Standards

      Pensoft Publishers

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          Abstract

          The digital era has brought about an impressive increase in the volume of published species occurrence data. Research infrastructures such as the Global Biodiversity Information Facility (GBIF), the digitization of legacy data, and the use of mobile applications have all played a role in this transition. More data implies, unavoidably, more heterogeneity at multiple levels as a result of the different methods and standards used to collect data. Data standardization and aggregation help to reduce this heterogeneity. Furthermore, intermediate data products that can be used for activities such as mapping, modeling and monitoring improve the repeatability and reproducibility of biodiversity research (Kissling et al. 2017).Occurrences can be defined as events in a three-dimensional space where the dimensions are taxonomic (what), temporal (when) and spatial (where). They are then aggregated into what we coined occurrence cube (Fig. 1).The taxonomic dimension is categorical. Research infrastructures like GBIF use a taxonomic backbone, thus making data aggregation at species level or higher rank relatively easy. The temporal dimension is a continuum and the temporal uncertainty is usually lower than the typical aggregation span, typically a year. Regarding the spatial dimension, occurrences are typically filtered to remove those with too large an uncertainty to fit the grid scheme being used. Meaning that the spatial uncertainty is largely unused. We developed a method to take into account this spatial uncertainty while aggregating data. In particular, we state that an occurrence is spatially representable as a closed plane figure such as a circle, hexagon or square, never as the geometric centre (centroid) of it. As for GBIF occurrence data, the coordinateUncertaintyInMeters is defined as the radius describing the smallest circle containing the whole of the location (see Darwin Core standard). So, spatially speaking, we refer to occurrences as circles, even if the method described below is general.After harvesting the occurrence data and providing a data quality assessment (e.g. removing occurrences without coordinates or with suspicious coordinates) we can assign occurrences to a reference grid such as the European reference grid of the European Environment Agency (EEA) at 1 km scale. In this spatial aggregation we randomly choose a point within the occurrence circle and assign it to the grid cell in which it is contained. We can aggregate further by time (e.g. by year) and taxonomy (e.g. by species), where aggregating means counting how many occurrences are in each specific taxonomic-spatial-temporal unit.The analogy with geometry goes further: the occurrence cube can, as any cube, be projected on an orthogonal plane by aggregating along one of the three dimensions. In particular, projecting the cube on the taxonomic and temporal dimensions can be done by adding up the number of occurrences, or counting the number of occupied cells, thus estimating the area of occupancy.The occurrence cube paradigm has been developed within the Tracking Invasive Alien Species (TrIAS) project (Vanderhoeven et al. 2017) following Open Science and FAIR principles. We created and published occurrence cubes at the species level for Belgium and Italy (Oldoni et al. 2020b) and the occurrence cubes for non-native taxa in Belgium and Europe (Oldoni et al. 2020a).

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          Tracking Invasive Alien Species (TrIAS): Building a data-driven framework to inform policy

          Imagine a future where dynamically, from year to year, we can track the progression of alien species (AS), identify emerging problem species, assess their current and future risk and timely inform policy in a seamless data-driven workflow. One that is built on open science and open data infrastructures. By using international biodiversity standards and facilities, we would ensure interoperability, repeatability and sustainability. This would make the process adaptable to future requirements in an evolving AS policy landscape both locally and internationally. In recent years, Belgium has developed decision support tools to inform invasive alien species (IAS) policy, including information systems, early warning initiatives and risk assessment protocols. However, the current workflows from biodiversity observations to IAS science and policy are slow, not easily repeatable, and their scope is often taxonomically, spatially and temporally limited. This is mainly caused by the diversity of actors involved and the closed, fragmented nature of the sources of these biodiversity data, which leads to considerable knowledge gaps for IAS research and policy. We will leverage expertise and knowledge from nine former and current BELSPO projects and initiatives: Alien Alert, Invaxen, Diars, INPLANBEL, Alien Impact, Ensis, CORDEX.be, Speedy and the Belgian Biodiversity Platform. The project will be built on two components: 1) The establishment of a data mobilization framework for AS data from diverse data sources and 2) the development of data-driven procedures for risk evaluation based on risk modelling, risk mapping and risk assessment. We will use facilities from the Global Biodiversity Information Facility (GBIF), standards from the Biodiversity Information Standards organization (TDWG) and expertise from Lifewatch to create and facilitate a systematic workflow. Alien species data will be gathered from a large set of regional, national and international initiatives, including citizen science with a wide taxonomic scope from marine, terrestrial and freshwater environments. Observation data will be funnelled in repeatable ways to GBIF. In parallel, a Belgian checklist of AS will be established, benefiting from various taxonomic and project-based checklists foreseen for GBIF publication. The combination of the observation data and the checklist will feed indicators for the identification of emerging species; their level of invasion in Belgium; changes in their invasion status and the identification of areas and species of concern that could be impacted upon by bioinvasions. Data-driven risk evaluation of identified emerging species will be supported by niche and climate modelling and consequent risk mapping using critical climatic variables for the current and projected future climate periods at high resolution. The resulting risk maps will complement risk assessments performed with the recently developed Harmonia+ protocol to assess risks posed by emergent species to biodiversity and human, plant, and animal health. The use of open data will ensure that interested stakeholders in Belgium and abroad can make use of the information we generate. The open science ensures everyone is free to adopt and adapt the workflow for different scenarios and regions. The checklist will be used at national level, but will also serve as the Belgian reference for international databases (IUCN - GRIIS, EASIN) and impact assessments (IPBES, SEBI). The workflow will be showcased through GEO BON, the Invasivesnet network and the COST Actions Alien Challenge and ParrotNet. The observations and outcomes of risk evaluations will be used to provide science-based support for the implementation of IAS policies at the regional, federal and EU levels. The publication of Belgian data and checklists on IAS is particularly timely in light of the currently ongoing EU IAS Regulation and its implementation in Belgium. By proving that automated workflows can provide rapid and repeatable production of information, we will open up this technology for other conservation assessments.
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            Author and article information

            Contributors
            Journal
            Biodiversity Information Science and Standards
            BISS
            Pensoft Publishers
            2535-0897
            September 30 2020
            September 30 2020
            : 4
            Article
            10.3897/biss.4.59154
            © 2020

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