Peter Horvath

• Horvath, Peter; Tang, Hui; Halvorsen, Rune; Stordal, Frode; Tallaksen, Lena M.; Berntsen, Terje Koren & Bryn, Anders (2021). Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models. Biogeosciences.  ISSN 1726-4170.  18, s 95- 112 . doi: 10.5194/bg-18-95-2021 Fulltekst i vitenarkiv. Vis sammendrag

  Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.

• Simensen, Trond; Horvath, Peter; Vollering, Julien; Erikstad, Lars; Halvorsen, Rune & Bryn, Anders (2020). Composite landscape predictors improve distribution models of ecosystem types. Diversity and Distributions: A journal of biological invasions and biodiversity.  ISSN 1366-9516.  26(8), s 928- 943 . doi: 10.1111/ddi.13060 Fulltekst i vitenarkiv. Vis sammendrag

  Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

• Horvath, Peter; Halvorsen, Rune; Stordal, Frode; Tallaksen, Lena Merete; Tang, Hui & Bryn, Anders (2019). Distribution modelling of vegetation types based on area frame survey data. Applied Vegetation Science.  ISSN 1402-2001.  22(4), s 547- 560 . doi: 10.1111/avsc.12451 Vis sammendrag

  Aim: Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location: Mainland Norway, covering ca. 324,000 km2. Methods: We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

• Eriksen, Eva Lieungh; Ullerud, Heidrun Asgeirsdatter; Halvorsen, Rune; Aune, Sigrun; Bratli, Harald; Horvath, Peter; Volden, Inger Kristine; Wollan, Anders Kvalvåg & Bryn, Anders (2018). Point of view: error estimation in field assignment of land-cover types. Phytocoenologia.  ISSN 0340-269X.  49(2), s 135- 148 . doi: 10.1127/phyto/2018/0293 Fulltekst i vitenarkiv. Vis sammendrag

  Questions: Substantial variation between observers has been found when comparing parallel land-cover maps, but how can we know which map is better? What magnitude of error and inter-observer variation is expected when assigning land-cover types and is this affected by the hierarchical level of the type system, observer characteristics, and ecosystem properties? Study area: Hvaler, south-east Norway. Methods: Eleven observers assigned mapping units to 120 stratified random points. At each observation point, the observers first assigned a mapping unit to the point independently. The group then decided on a ‘true’ reference mapping unit for that point. The reference was used to estimate total error. ‘Ecological distance’ to the reference was calculated to grade the errors. Results: Individual observers frequently assigned different mapping units to the same point. Deviating assignments were often ecologically close to the reference. Total error, as percentage of assignments that deviated from the reference, was 35.0% and 16.4% for low and high hierarchical levels of the land-covertype system, respectively. The corresponding figures for inter-observer variation were 42.8% and 19.4%, respectively. Observer bias was found. Particularly high error rates were found for land-cover types characterised by human disturbance. Conclusions: Access to a ‘true’ mapping unit for each observation point enabled estimation of error in addition to the inter-observer variation typically estimated by the standard pairwise comparisons method for maps and observers. Three major sources of error in the assignment of land-cover types were observed: dependence on system complexity represented by the hierarchical level of the land-cover-type system, dependence on the experience and personal characteristics of the observers, and dependence on properties of the mapped ecosystem. The results support the necessity of focusing on quality in land-cover mapping, among commissioners, practitioners and other end users.

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• Halvorsen, Rune; Wollan, Anders Kvalvåg; Bryn, Anders; Bratli, Harald & Horvath, Peter (2021). Naturtypekart etter NiN for området omkring Veia (Nedre Eiker og Øvre Eiker, Buskerud). UiO Naturhistorisk museum Rapport. 100.
• Bryn, Anders & Horvath, Peter (2020). Kartlegging av NiN naturtyper i målestokk 1:5000 rundt flux-tårnet og på Hansbunuten, Finse (Vestland). UiO Naturhistorisk museum Rapport. 096. Fulltekst i vitenarkiv.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). Climate JupyterLab as an interactive tool in Galaxy.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). FATES on GALAXY facilitates ecologist and climate modeler collaboration.
• Fouilloux, Anne Claire; Tang, Hui; Lieungh, Eva; Geange, Sonya Rita; Horvath, Peter & Bryn, Anders (2020). Functionally Assembled Terrestrial Ecosystem Simulator (FATES) with Community Land Model in Galaxy.
• Halvorsen, Rune; Bryn, Anders; Bratli, Harald & Horvath, Peter (2020). Naturtypekart etter NiN for et område omkring Unsetsætra (Biri, Gjøvik, Oppland). UiO Naturhistorisk museum Rapport. 094. Fulltekst i vitenarkiv.
• Bryn, Anders; Volden, Inger Kristine; Horvath, Peter; Torma, Michal & Stordal, Frode (2019). Hvor raskt stiger tre- og skoggrensene i Norge? Folkeforskning i praksis..
• Ellefsen, Halvor Weider; Barton, David Nicholas; Ardilla, Pedro; Blumentrath, Stefan; Horvath, Peter; Aamlid, Helene; Betina, Jayne Elizabeth; Kross, Anna & Maris, Mand (2019). MOT EN BLÅGRØNN EIENDOMS-UTVIKLING? «Stresstesting» av Blågrønn Faktor på utvalgte case studier i Bærum Kommune.
• Horvath, Peter; Nilsen, Anne-Barbi & Bryn, Anders (2019). Oppsett og tilrettelegging av QGIS for NiN naturtypekartlegging. UiO Naturhistorisk museum Rapport. 83. Vis sammendrag

  Metodene for feltbasert kartlegging av naturtyper i Norge er i utvikling. Kartlegging baserer seg nå i hovedsak på bruk av digitale plattformer med tilrettelagt programvare. Siden lanseringen av Natur i Norge (NiN 2.0) i 2015 har det vært behov for opplæring i digital kartlegging av naturtyper i felt. Til bruk i undervisningen ved Universitetet i Oslo har vi tilrettelagt QGIS (versjon 3.2) for feltbasert kartlegging av NiN-naturtyper. Denne rapporten viser oppsettet og gir veiledning i bruken slik at andre kan benytte seg av QGIS i sin undervisning eller opplæring av ansatte. Oppsettet kan også brukes til kartlegging for andre formål, f.eks. til forskningsformål eller arealundersøkelser. Det brukertilpassede oppsettet ligger fritt tilgjengelig på GitHub.

• Bryn, Anders; Torma, Michal; Horvath, Peter & Volden, Inger Kristine (2018). Natur i endring (mobil app for iOS og Android, tilgjengelig på norsk og engelsk).
• Bryn, Anders; Torma, Michal; Volden, Inger Kristine & Horvath, Peter (red.) (2018). www.naturiendring.no.
• Bryn, Anders; Volden, Inger Kristine; Horvath, Peter & Torma, Michal (2018). Folkeforskning: Norge gror igjen - hva skjer i naturen og hvordan påvirkes ferdsel og opplevelser?. Vis sammendrag

  Skoggrensa i Norge er på vei oppover. I et forsøk på å bevisstgjøre folk på endringene som skjer i naturen, har Naturhistorisk museum i Oslo og Den Norske Turistforening utviklet folkeforskningsprosjektet Natur i endring. Skjelettet i prosjektet er en app, som gir turgåere anledning til å registrere de øverste skogene og trærne. Appen gir folk en plattform som de selv kan utforske det norske landskapet ut ifra. Deltakerne må løfte blikket på jakt etter høytliggende trær og skoger. Samtidig vil de forhåpentligvis reflektere over hvorfor disse trærne befinner seg akkurat der. Allerede da har de tatt inn over seg noe essensielt, at naturen påvirkes av oss mennesker. Da blir det kanskje lettere å forstå hvorfor klimaendringene ikke bare smelter is på Nordpolen, men at de er med på å endre det norske fjellandskapet i dag. Prosjektet har mottatt finansiell støtte fra Sparebankstiftelsen DNB.

• Haugen, Marianne Nilsen & Horvath, Peter (2018, 15. mai). Nå kan også du bli «klimaforsker».  Aftenposten.
• Horvath, Peter; Halvorsen, Rune; Stordal, Frode; Tang, Hui & Bryn, Anders (2018). Distribution models of vegetation types in Norway.
• Skarpaas, Olav; Bryn, Anders; Torma, Michal; Horvath, Peter & Volden, Inger Kristine (2018). Folkeforskning med mobil-app: tregrenser og naturmangfold.
• Barton, David Nicholas; Hauglin, Espen Aukrust; Horvath, Peter & Ellefsen, Halvor Weider (2017). Blue-Green Factor (BGF) mapping in QGIS.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017, 17. februar). Høyest til fjells i landet.  Gudbrandsdølen Dagningen.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017, 06. februar). Norsk høyderekord for liten bjørk.  Varden.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017, 07. februar). Norsk rekord for lita bjørk..  Nationen.
• Bryn, Anders; Horvath, Peter & Volden, Inger Kristine (2017, 08. februar). Norsk rekord for liten bjørk.  Tønsberg Blad.
• Bryn, Anders; Potthoff, Kerstin; Horvath, Peter; Volden, Inger Kristine; Tang, Hui; Berntsen, Terje Koren & Stordal, Frode (2017). Greening and browning: 100 years of tree- and forest line dynamics..
• Bryn, Anders; Volden, Inger Kristine; Horvath, Peter & Stordal, Frode (2017). Skogen er på fjelltur. Aftenposten (morgenutg. : trykt utg.).  ISSN 0804-3116.  s 12- 13
• Horvath, Peter; Barton, David Nicholas; Hauglin, Espen Aukrust & Ellefsen, Halvor Weider (2017). Blue-Green Factor (BGF) mapping in QGIS. User Guide and Documentation. NINA rapport. 1445. Vis sammendrag

  The blue-green factor (BGF) is a rapid assessment tool to help quantify minimum municipal requirements for surface water management, vegetation qualities and biodiversity in outdoor areas of property developments. BGF-QGIS makes it possible to calculate the blue-green factor for larger areas than in the original Excel-based methodology, by taking advantage of remote sensing data, GIS datasets, and CAD-based design proposals. It also provides a flexible platform for adjusting the scoring of blue-green qualities to suit specific local conditions and priorities. Norway, blue-green infrastructure, urban ecosystem accounting, Norge, blågrønn infrastruktur, urbant økosystemregnskap

• Horvath, Peter; Tang, Hui; Stordal, Frode & Bryn, Anders (2017). Terrestrial vegetation ecological climatology.
• Volden, Inger Kristine; Horvath, Peter & Bryn, Anders (2017). Ei historie om Lærdalsskogen.. Sogn Avis.  s 12- 12

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