Erik Kusch

• Kusch, Erik & Vinton, Anna (2023). A Novel Simulation Framework for Validation of Ecological Network Inference. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.08.05.552122.
• Kusch, Erik & Ordonez, Alejandro (2023). Ecological Network Resilience & Extinction Proxies - Updating Projections of Ecological Networks. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.08.02.551629.
• Kusch, Erik; Bimler, Malyon; Lutz, James A. & Ordonez, Alejandro (2023). Ecological network inference is not consistent across scales or approaches. bioRxiv. ISSN 2692-8205. doi: 10.1101/2023.07.13.548816.
• Ávila-Thieme, M. Isidora; Kusch, Erik; Corcoran, Derek; Castillo, Simón P.; Valdovinos, Fernanda S. & Navarrete, Sergio A [Show all 7 contributors for this article] (2023). NetworkExtinction: An R package to simulate extinction propagation and rewiring potential in ecological networks. Methods in Ecology and Evolution. ISSN 2041-210X. doi: 10.1111/2041-210X.14126.
• Bernard, Connor; Santos, Gabriel Silva; Deere, Jacques A.; Rodriguez- Caro, Roberto; Capdevila, Pol & Kusch, Erik [Show all 9 contributors for this article] (2023). MOSAIC - A Unified Trait Database to Complement Structured Population Models. Scientific Data. ISSN 2052-4463. doi: 10.1038/s41597-023-02070-w.
• Lundgren, Erick J.; Ramp, Daniel; Middleton, Owen S.; Wooster, Eamonn I. F.; Kusch, Erik & Mills, Mystyn [Show all 11 contributors for this article] (2022). A novel trophic cascade between cougars and feral donkeys shapes desert wetlands. Journal of Animal Ecology. ISSN 0021-8790. doi: 10.1111/1365-2656.13766.
• Kusch, Erik; Davy, Richard & Seddon, Alistair William Robin (2022). Vegetation-memory effects and their association with vegetation resilience in global drylands. Journal of Ecology. ISSN 0022-0477. 110(7), p. 1561–1574. doi: 10.1111/1365-2745.13891.
• Kusch, Erik & Davy, Richard (2022). KrigR-a tool for downloading and statistically downscaling climate reanalysis data. Environmental Research Letters. ISSN 1748-9326. 17(2). doi: 10.1088/1748-9326/ac48b3.
• Davy, Richard & Kusch, Erik (2021). Reconciling high resolution climate datasets using KrigR. Environmental Research Letters. ISSN 1748-9326. 16. doi: 10.1088/1748-9326/ac39bf.

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• Chala, Desalegn ; Kusch, Erik; Andrew, Carrie; Torma, Michal; Johaadien, Rukaya Sarah & Kehel, Zakaria [Show all 9 contributors for this article] (2024). BioDT Hackathon: Second End-Users Workshop, January 2024. Show summary

  Three-day hackathon titled Harnessing Digital Twinning for Sustainable Agriculture: Predictive Characterisation and Conservation of Crop Wild Relatives, takes place from January 23rd to 25th, 2024 in Oslo, Norway. The event, promises to unravel the immense potential of digital twinning in revolutionizing global agricultural practices.

• Islam, Sharif; Koivula, Hanna; Endresen, Dag; Kusch, Erik; Schigel, Dmitry & Addink, Wouter (2023). Data Standards and Interoperability Challenges for Biodiversity Digital Twin: A novel and transformative approach to biodiversity research and application. Biodiversity Information Science and Standards (BISS). ISSN 2535-0897. 7. doi: 10.3897/biss.7.112373. Show summary

  The Biodiversity Digital Twin (BioDT) project (2022-2025) aims to create prototypes that integrate various data sets, models, and expert domain knowledge enabling prediction capabilities and decision-making support for critical issues in biodiversity dynamics. While digital twin concepts have been applied in industries for continuous monitoring of physical phenomena, their application in biodiversity and environmental sciences presents novel challenges (Bauer et al. 2021, de Koning et al. 2023). In addition, successfully developing digital twins for biodiversity requires addressing interoperability challenges in data standards. BioDT is developing prototype digital twins based on use cases that span various data complexities, from point occurrence data to bioacoustics, covering nationwide forest states to specific communities and individual species. The project relies on FAIR principles (Findable, Accessible, Interoperable, and Reusable) and FAIR enabling resources like standards and vocabularies (Schultes et al. 2020) to enable the exchange, sharing, and reuse of biodiversity information, fostering collaboration among participating research infrastructures (DiSSCo, eLTER, GBIF, and LifeWatch) and data providers. It also involves creating a harmonised abstraction layer using Persistent Identifiers (PID) and FAIR Digital Object (FDO) records, alongside semantic mapping and crosswalk techniques to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). Governance and engagement with research infrastructure stakeholders play crucial roles in this regard, with a focus on aligning technical and data standards discussions. In addition to data, models and workflows are key elements in BioDT. Models in the BioDT context are formal representations of problems or processes, implemented through equations, algorithms, or a combination of both, which can be executed by machine entities. The current twin prototypes are considering both statistical and mechanistic models, introducing significant variations in (1) data requirements, (2) modelling approaches and philosophy, and (3) model output. The BioDT consortium will develop guidelines and protocols for how to describe these models, what metadata to include, and how they will interact with the diverse datasets. While discussions on this topic exist within the broader context of biodiversity and ecological sciences (Jeltsch et al. 2013, Fer et al. 2020), the BioDT project is strongly committed to finding a solution within its scope. In the twinning context, data and models need to be executed within a computing infrastructure and also need to adhere to FAIR principles. Software within BioDT includes a suite of tools that facilitate data acquisition, storage, processing, and analysis. While some of these tools already exist, the challenge lies in integrating them within the digital twinning framework. One approach to achieving integration is through workflow representation, encompassing standardised procedures and protocols that guide the acquisition, packaging, processing, and analysis of data. The project is exploring Research Object Crate (RO-Crate) implementation for this (Soiland-Reyes et al. 2022). Implementing workflows can ensure reproducibility, scalability, and transparency in research practices, enabling scientists to validate and replicate findings. The BioDT project offers a novel and transformative approach to biodiversity research and application. By leveraging collaborative research infrastructures and adhering to data standards, BioDT aims to harness the power of data, software, supercomputers, models, and expertise to provide new insights. The foundation provided by the data standards, including those of Biodiversity Information Standards (TDWG), is crucial in realising the full potential of digital twins, facilitating the seamless integration of diverse data sources and combinations with models.

• Kusch, Erik & Endresen, Dag (2023). BioDT task 4.3 kickoff workshop. Show summary

  The BioDT task 4.3 kickoff was organized at the GBIF Europe and Central Asia regional nodes meeting in Warsaw 17th and 18th of May 2023.

• Kusch, Erik (2023). Workshop 1. Accessing, handling, and referencing open biodiversity data using the Global Biodiversity Information Facility (GBIF). Show summary

  In the age of changes and threat to the ecosphere at macroecological scales, big data has crystalized as a go-to tool for ecological research. To facilitate the creation of, access to, and referencing of contributors to such big data repositories, the Global Biodiversity Information Facility (GBIF) has established data streams that make readily available a wealth of biodiversity data. Navigating and accessing such large data sets and ensuring fair use and accreditation of observations can seem daunting. In this workshop, we will introduce GBIF, show how to navigate its data portal, demonstrate data streams to obtain and handle data programmatically, and communicate accreditation procedures.

• Halbritter Rechsteiner, Aud Helen; Grainger, Matthew; Telford, Richard James; Kusch, Erik; Nilsen, Erlend Birkeland & Andrew, Carrie [Show all 9 contributors for this article] (2023). Living Norway Course on Open, Reproducible and Transparent Science in Ecology. Show summary

  Universities, journals, and funding bodies increasingly demand open and reproducible research practices across the scientific community. Research data needs to be FAIR (findable, accessible, interoperable, and reusable), workflows need to be reproducible, and science needs to be transparent. This aims to improve the efficiency and quality of research and thus to increase the credibility of science. This course offers hands-on training on methods and technologies to make research more open, reproducible and transparent. The course is centred around the life-cycle of data from planning, managing, collecting, curating, analysing, publishing, storing, sharing and reusing data. It is aimed at PhD students (will be prioritised) as well as early-career researchers in ecology, who produce their own data (i.e. collect data in the field/lab), use data from others (i.e. databases) or both.

• Islam, Sharif; Koivula, Hanna; Endresen, Dag; Kusch, Erik; Schigel, Dmitry & Addink, Wouter (2023). Data Standards and Interoperability Challenges for Biodiversity Digital Twin: A novel and transformative approach to biodiversity research and application. Show summary

  The Biodiversity Digital Twin (BioDT) project (2022-2025) aims to create prototypes that integrate various data sets, models, and expert domain knowledge enabling prediction capabilities and decision-making support for critical issues in biodiversity dynamics. While digital twin concepts have been applied in industries for continuous monitoring of physical phenomena, their application in biodiversity and environmental sciences presents novel challenges (Bauer et al. 2021, de Koning et al. 2023). In addition, successfully developing digital twins for biodiversity requires addressing interoperability challenges in data standards. BioDT is developing prototype digital twins based on use cases that span various data complexities, from point occurrence data to bioacoustics, covering nationwide forest states to specific communities and individual species. The project relies on FAIR principles (Findable, Accessible, Interoperable, and Reusable) and FAIR enabling resources like standards and vocabularies (Schultes et al. 2020) to enable the exchange, sharing, and reuse of biodiversity information, fostering collaboration among participating research infrastructures (DiSSCo, eLTER, GBIF, and LifeWatch) and data providers. It also involves creating a harmonised abstraction layer using Persistent Identifiers (PID) and FAIR Digital Object (FDO) records, alongside semantic mapping and crosswalk techniques to provide machine-actionable metadata (Schultes and Wittenburg 2019, Schwardmann 2020). Governance and engagement with research infrastructure stakeholders play crucial roles in this regard, with a focus on aligning technical and data standards discussions. In addition to data, models and workflows are key elements in BioDT. Models in the BioDT context are formal representations of problems or processes, implemented through equations, algorithms, or a combination of both, which can be executed by machine entities. The current twin prototypes are considering both statistical and mechanistic models, introducing significant variations in (1) data requirements, (2) modelling approaches and philosophy, and (3) model output. The BioDT consortium will develop guidelines and protocols for how to describe these models, what metadata to include, and how they will interact with the diverse datasets. While discussions on this topic exist within the broader context of biodiversity and ecological sciences (Jeltsch et al. 2013, Fer et al. 2020), the BioDT project is strongly committed to finding a solution within its scope. In the twinning context, data and models need to be executed within a computing infrastructure and also need to adhere to FAIR principles. Software within BioDT includes a suite of tools that facilitate data acquisition, storage, processing, and analysis. While some of these tools already exist, the challenge lies in integrating them within the digital twinning framework. One approach to achieving integration is through workflow representation, encompassing standardised procedures and protocols that guide the acquisition, packaging, processing, and analysis of data. The project is exploring Research Object Crate (RO-Crate) implementation for this (Soiland-Reyes et al. 2022). Implementing workflows can ensure reproducibility, scalability, and transparency in research practices, enabling scientists to validate and replicate findings. The BioDT project offers a novel and transformative approach to biodiversity research and application. By leveraging collaborative research infrastructures and adhering to data standards, BioDT aims to harness the power of data, software, supercomputers, models, and expertise to provide new insights. The foundation provided by the data standards, including those of Biodiversity Information Standards (TDWG), is crucial in realising the full potential of digital twins, facilitating the seamless integration of diverse data sources and combinations with models.

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