Lasse Keetz

• Keetz, Lasse Torben; Lieungh, Eva; Karimi-Asli, Kaveh; Geange, Sonya Rita; Gelati, Emiliano & Tang, Hui [Vis alle 24 forfattere av denne artikkelen] (2023). Climate–ecosystem modelling made easy: The Land Sites Platform. Global Change Biology. ISSN 1354-1013. 29(15), s. 4440–4452. doi: 10.1111/gcb.16808. Fulltekst i vitenarkiv Vis sammendrag

  Dynamic Global Vegetation Models (DGVMs) provide a state-of-the-art process-based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single-site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version-controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site-level simulations. We provide input data for 20 established geo-ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines.

• Naas, Adam Eindride; Halvorsen, Rune; Horvath, Peter; Wollan, Anders Kvalvåg; Bratli, Harald & Brynildsrud, Katrine Marie [Vis alle 16 forfattere av denne artikkelen] (2023). What explains inconsistencies in field-based ecosystem mapping? Applied Vegetation Science. ISSN 1402-2001. 26(1). doi: 10.1111/avsc.12715. Fulltekst i vitenarkiv Vis sammendrag

  Questions Field-based ecosystem mapping is prone to observer bias, typically resulting in a mismatch between maps made by different mappers, that is, inconsistency. Experimental studies testing the influence of site, mapping scale, and differences in experience level on inconsistency in field-based ecosystem mapping are lacking. Here, we study how inconsistencies in field-based ecosystem maps depend on these factors. Location Iškoras and Guollemuorsuolu, northeastern Norway, and Landsvik and Lygra, western Norway. Methods In a balanced experiment, four sites were field-mapped wall-to-wall to scales 1:5000 and 1:20,000 by 12 mappers, representing three experience levels. Thematic inconsistency was calculated by overlay analysis of map pairs from the same site, mapped to the same scale. We tested for significant differences between sites, scales, and experience-level groups. Principal components analysis was used in an analysis of additional map inconsistencies and their relationships with site, scale and differences in experience level and time consumption were analysed with redundancy analysis. Results On average, thematic inconsistency was 51%. The most important predictor for thematic inconsistency, and for all map inconsistencies, was site. Scale and its interaction with site predicted map inconsistencies, but only the latter were important for thematic inconsistency. The only experience-level group that differed significantly from the mean thematic inconsistency was that of the most experienced mappers, with nine percentage points. Experience had no significant effect on map inconsistency as a whole. Conclusion Thematic inconsistency was high for all but the dominant thematic units, with potentially adverse consequences for mapping ecosystems that are fragmented or have low coverage. Interactions between site and mapping system properties are considered the main reasons why no relationships between scale and thematic inconsistency were observed. More controlled experiments are needed to quantify the effect of other factors on inconsistency in field-based mapping.

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• Lieungh, Eva; Keetz, Lasse Torben & Melteig, Elina (2023). Alle har gratis tilgang til en av verdens mest komplekse vitenskapelige modeller. Nå blir den enklere å bruke. [Internett]. Forskning.no.
• Lieungh, Eva; Yilmaz, Yeliz; Tang, Hui; Keetz, Lasse Torben; Olsen, Siri Lie & Gya, Ragnhild [Vis alle 11 forfattere av denne artikkelen] (2023). Complementary field and modelling experiments: alpine vegetation under climate warming.
• Lieungh, Eva; Melteig, Elina & Keetz, Lasse Torben (2023). Nå er det lettere å bli klimaforsker. [Internett]. titan.no.
• Bryn, Anders; Brynildsrud, Katrine Marie; Horvath, Peter; Keetz, Lasse Torben; Lieungh, Eva & Naas, Adam Eindride [Vis alle 9 forfattere av denne artikkelen] (2023). “Ground-truth" from field-based ecosystem mapping.
• Karimi-Asli, Kaveh; Keetz, Lasse Torben; Lieungh, Eva; Yilmaz, Yeliz A.; Tang, Hui & Torma, Michal [Vis alle 7 forfattere av denne artikkelen] (2022). NorESMhub/noresm-land-sites-platform: Release v1.0.0.
• Bryn, Anders; Dalen, Thea Grobstok; Finne, Eirik Aasmo; Heiberg, Hanne; Keetz, Lasse Torben & Nilsen, Irene Brox [Vis alle 30 forfattere av denne artikkelen] (2021). Natur i endring - samspillet mellom klima og økosystemene.
• Keetz, Lasse Torben; Bryn, Anders; Horvath, Peter; Skarpaas, Olav; Tallaksen, Lena M. & Zliobaite, Indre (2021). Using machine learning to model the distribution of Vegetation Types across Norway.

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