Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2025
Sammendrag
We assessed soil organic carbon (SOC) stocks and changes across six upland forest sites with 13replicated plots, spanning bioclimatic regions from the boreonemoral to the northern borealzone. The sites included three ICP Forests Level II plots in older coniferous stands and threelong-term experiments focusing on thinning intensity, tree species effects (Norway spruce, Scotspine, silver birch), and mixtures of Norway spruce and downy birch, the latter two followingclear-cutting. Repeated soil surveys spanned 9–34 years. SOC stocks in the organic LFH horizonranged from 1.4 to 3.6 kg m−2, while total stocks down to 30 cm and 70–100 cm mineral soildepths ranged from 3.0 to 13.5 kg m−2 and 8.5 to 17.5 kg m−2, respectively. Annual SOC stockchanges in the LFH horizon ranged from −106 to 111 g m−2 yr−1, with significant changesobserved in five plots. Total SOC stock changes down to 15, 18 or 20 cm mineral soil depthranged from −77 to 154 g m−2 yr−1, with significant increases detected in two ICP level II plots.Sensitivity analyses supported these findings but highlighted inconsistencies in samplingmethods, hight spatial variability, and limited replicates, affecting estimates in the remaining 11plots.ARTICLE HISTORYReceived 31 March 2025Accepted 8 July 2025KEYWORDSBoreal forest; downy birch;Norway spruce; Scots pine;soil organic carbon; SOC;SOC stock changesIntroductionForest ecosystems are crucial biomes for carbon (C)storage, with boreal forests playing a significant role asa C sink (Pan et al. 2011; Watts et al. 2023). Globally,the soil organic carbon (SOC) pool contains more thanthree times as much C as the atmosphere (Schmidt etal. 2011). From a climate perspective, the importanceof SOC storage is tied to its overall size as well as itspotential as a long-term reservoir. Estimates of SOCstocks in boreal upland forests suggest 3–4 times moreC relative to the aboveground tree biomass (Scharle-mann et al. 2014; Bradshaw and Warkentin 2015). Thebiological stability of SOC is mediated by a broad setof environmental drivers, notably temperature and soilmoisture content (Soucémarianadin et al. 2018).Additionally, microbial communities play an importantrole in both decomposition and accumulation of SOC(Lindahl et al. 2021; Gundale et al. 2024), processesthat are further influenced by forest management prac-tices (Mayer et al. 2020; Jörgensen et al. 2022) and treespecies (Mundra et al. 2022, 2024). The effect of treespecies on SOC stocks may primarily influence the distri-bution of SOC within the soil profile rather than the totalSOC stock (Vesterdal et al. 2013; Kjønaas et al. 2021). Thisdistribution, however, affects the stability of SOC and itsvulnerability to decomposition, consequently impactingthe CO2 flux from the soil (James and Harrison 2016;Cotrufo et al. 2019; Georgiou et al. 2024).Estimated C allocation in Norwegian forests is approxi-mately 21% in vegetation and 79% in soil (Grønlund etal. 2010). SOC stocks in Norwegian forest soils are con-sidered higher compared to those of Sweden andFinland (Olsson et al. 2009; Rantakari et al. 2012; Strandet al. 2016). This disparity may partly stem from varyinginventory methods. However, differences in precipitationand temperature gradients account for approximately68% of the variability in SOC stocks across Nordic forests(Callesen et al. 2003), suggesting that climate factors alsoplay a significant role. On a European scale, coniferousforest soils represent one of the largest and most vulner-able SOC stocks (Lugato et al. 2021). The size and stabilityof the SOC stock may determine the magnitude of© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the AcceptedManuscript in a repository by the author(s) or with their consent.CONTACT O. Janne Kjønaas janne.kjonaas@nibio.noSupplemental data for this article can be accessed online at https://doi.org/10.1080/02827581.2025.2533379.SCANDINAVIAN JOURNAL OF FOREST RESEARCH2025, VOL. 40, NOS. 7–8, 321–356https://doi.org/10.1080/02827581.2025.2533379
Forfattere
Peter ZubkovSammendrag
Nordic boreal forests deliver critical ecosystem services but are increasingly vulnerable to abiotic disturbances, particularly wind and snow damage, potentially intensified by climate change. Climate-resilient forest management requires reliable decision-support tools for proactive risk assessment and post-event damage mapping. This thesis contributes to advancing adaptive abiotic forest disturbance management by integrating high-resolution satellite imagery, numerical weather prediction, tree mechanics, and machine learning techniques. It is composed of three papers. The first paper demonstrated that very high-resolution stereo satellite imagery and photogrammetric digital surface model reconstruction effectively map windthrow, particularly in moderate-to-high-density conifer stands, even under challenging Nordic winter conditions. The second paper proposed a novel mechanistic modeling framework predicting snow-induced stem breakage at the single-tree level, leveraging numerical weather prediction-based snow accumulation data and mechanistic critical snow load computations. The model provides physically interpretable risk assessments using basic tree metrics and predicted snow loads and can be readily integrated into forest management scenario planning. The third paper applied interpretable machine learning to numerical weather prediction data to identify drivers of forest wind damage during catastrophic windstorms driven by atmospheric mountain waves in a complex terrain. The findings underline that it was atmospheric stratification, turbulence, and vertical airflow that primarily controlled forest damage during the investigated event. Forest structure played minimal role, emphasizing the importance of a landscape-scale risk management approach focused on topographic susceptibility to severe mountain wave occurrences. This work makes a small, yet important, contribution to an integrated decision-support framework strengthening forest damage risk prediction and post-event assessment capabilities under climate uncertainty. Improvement priorities include observational validation of the canopy snow accumulation model, generalizing the interpretation of mountain wave-induced damage to other landscapes, and exploring multi-sensor fusion for windthrow detection. Finally, future efforts should be aimed at scaling the framework to a national scope and integrating advanced neural network-driven models for holistic risk management in an uncertain future.
Sammendrag
Presentasjon av status og utfordringer i gjennomføringen av EUs regelverk for LULUCF, EUDR, REDIII og CRCF
Sammendrag
Formålet var å komme fram til anbefalte skogbehandlinger for et tematisk område, dvs lavlandet (< 500 moh) på Østlandet, gjennom en prosess med diskusjon i en gruppe interessenter. Vi har i prosjektet begrenset studieområdet til Fritzøe Skoger med tanke på at anbefalingene skulle kunne anvendes på hele den nevnte landsdelen.
Forfattere
Marius Hauglin Ulrika Jansson Asplund Marie-Claude Jutras-Perreault Olav Skarpaas Victor Strimbu Ken Olaf Storaunet Anne Sverdrup-Thygeson Julius Wold Hans Ole ØrkaSammendrag
Første versjon av tre nasjonale kartlag over naturskog, og metodikken for disse kartlagene er dokumentert i denne rapporten. Prosjektet har også vurdert hvordan NiN3.0-variablene fra skogdynamikkprosjektet kan utledes ved hjelp av heldekkende kart som allerede eksisterer eller som kan utvikles innen rimelig tid. Tilgjengelige datasett, som fjernmålingsdata, SR16 og feltdata fra Landsskogtakseringen ble brukt sammen med maskinlæringsmodeller for å lage følgende tre landsdekkende rasterkartlag: 1) Et binært naturskogkart som viser skog med predikert alder >84 år, 2) et kart med predikert sannsynlighet for naturskog, og 3) et kart som viser predikert grad av naturskogsnærhet. En evaluering av det binære naturskogkartet mot feltflatene i Landsskogtakseringen hadde en samlet nøyaktighet på 76 %. Validering av modellen for naturskogsannsynlighet viste en samlet nøyaktighet på 78 % Validering av modell for naturskogsnærhet ga samlet nøyaktighet på 54 %. Validering av modeller for prediksjon av naturskogsannsynlighet og naturskogsnærhet viser begge at disse gir best prediksjoner i grandominert skog. Prosjektet ble gjennomført med knappe tidsrammer, og de produserte kartene må betraktes som førsteversjoner. En anbefaling fra prosjektet var at metodikken og kartene bør utvikles videre, og en videreføring er allerede i gang. Rapportens referanse: Hauglin, M.,
Forfattere
Svein EilertsenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Statistikk fra Resultatkartleggingen for skogbruk/miljø og Landsskogtakseringen viser at en betydelig andel av sluttavvirkninger gjennomføres før skogen har nådd normal hogstmodenhetsalder (hogstklasse 5). Omfanget av slik tidlig hogst er særlig høyt i granskog på bedre boniteter. Dersom frisk skog avvirkes mens den løpende tilveksten fremdeles er høy vil dette medføre redusert produksjon av virke og lavere karbonopptak på arealene. Hensikten med dette arbeidet var å finne ut hvordan skogforholdene påvirker sannsynligheten for at et bestand avvirkes før det når hogstklasse 5. For å svare på problemstillingen har vi benyttet data fra Landsskogtakseringen og sammenstilt skoglige data registrert ved siste taksering før hogst for prøveflater der det utført snauhogst eller frøtrestillingshogst mellom år 2000 og 2022. Om lag 10 prosent av avvirkningen (areal) i skog som var yngre enn hogstklasse 5 er hogster knyttet til arealbruksendring (utbygging, oppdyrking o.a.). Ved arealbruksendring er det andre hensyn enn best mulig skogøkonomi som utløser beslutningen om hogst. Det står ofte relativt ung skog på de arealene som omdisponeres. Dette driver opp andelen ungskoghogst, men beslutninger gjort med dette som motiv er både vanskelig og kanskje lite relevant å vurdere i et skogfaglig perspektiv. Hovedfokuset i denne rapporten ligger derfor på hogster som gjennomføres på arealer hvor det fortsatt skal drives skogbruk. Vi sammenstiller relevant statistikk som beskriver skogtilstanden på alle avvirkede prøveflater i bartredominert skog uten arealbruksendring, slik den var registrert ved siste taksering før hogst. Vi undersøkte også om bestandsskader bidrar som en viktig årsaksfaktor til hogst. Dette på bakgrunn av at skogsskader og/eller bekymringer rundt skoghelse ble oppgitt som en viktig årsak til skogeiers beslutning om å avvirke i en tidligere undersøkelse. For ung barskog med alder tilsvarende 60-70% av hogstmodenhetsalder vil sannsynligheten for hogst omtrent dobles hvis bestandsskadeomfanget er ca. 15 %, mens for gammel barskog (50% over hogstmodenhetsalder) er denne forskjellen litt mindre. Selv om bestandsskader er en viktig bidragsyter til sannsynligheten for sluttavvirkning for både yngre og eldre skog, er det også en del frisk skog som avvirkes før bestandet kommer i hogstklasse 5. Prøveflatene i landsskogtakseringen takseres ikke hyppig nok til å avdekke sikkert om det har vært bestandsskader forut for en hogst. For å fastslå sikkert hvor stor andel av sluttavvirkningene som har vært utsatt for en forutgående bestandsskade, må andre metoder derfor benyttes.
Forfattere
Knut ØistadSammendrag
Det er ikke registrert sammendrag
Sammendrag
Environmental research is facing a drastic increase of available high-quality data, not the least due to the eLTER activities. Here simultaneous time series of numerous observables from the atmosphere, soil, streams, lakes and groundwater, etc., and comprising both abiotic and biotic variables will be made available from hundreds of sites. On the one hand quality control of these large data sets becomes a major challenge. On the other hand, though, it opens up completely new options for science as long as some key problems are solved:· How to differentiate between different effects?· How to deal with the filter effects of environmental systems?· How to identify unexpected relationships that a model would not depict?However, environmental sciences still lack a toolbox of approved integrated exploratory data analysis approaches to tackle these challenges in a systematic way. Here we suggest a combination of different methods that proved very efficient both in terms of data quality control and of exploratory data analysis for large sets of time series. Examples will be presented from the AgroScapeLab Quillow (LTER site DE-07-UM, Germany) and the Hurdal ICOS and ICP Forest Level II site (Norway). The Hurdal site is planned to be established as an elTER site as well.Any change of boundary conditions, of input fluxes, emerging invasive species etc. (termed “signal propagation” for short) in environmental systems is subject to filtering effects. A key feature thereof is low-pass filtering. Here we suggest the new Cumulative Periodogram Convexity (CPC) index to quantify the effect size for comparison of various time series. Principal Component Analysis of time series (termed Empirical Orthogonal Function approach in climatology) is suggested as another decisive step. Loadings on single components can be used for assessing the size of single effects on observed time series. Visualization of the communalities and of similarities between different observables and sites in a combination of Self-Organizing Maps and Sammon Mapping allows a rapid survey of some tens to hundreds of time series at a glance, e.g., for quality control. Additional consideration of the CPC index proved a powerful tool for identification of the respective key drivers and of the pathways of signal propagation through environmental systems, comprising both biotic and abiotic observables. Applying machine learning approaches to principal components rather than to the raw data facilitates developing a better understanding of complex interactions in environmental systems. To conclude, we see great potential in a systematic combination of existing approaches deserving to be explored further.
Sammendrag
Litter decomposition is coupled to carbon (C) sequestration through C release to the atmosphere, C transformation and nutrient release to the soil. We investigated if clear-cutting has long-term effects on this vital ecological process and consequently on C dynamics in boreal forests using twelve pairs of previously clear-cut and near-natural forests. Three litterbag experiments were conducted using (I) standardised spruce and bilberry litter, (II) melanised and non-melanised fungal necromass and (III) rooibos and green tea. We found weak and inconsistent effects of harvesting history, that did not depend on litter quality or mesofauna exclusion. Litter quality was more important in explaining net mass remaining for fungal necromass than for aboveground plant litter. Mesofauna exclusion had only marginal effects on initial litter decomposition. Results obtained with the highly standardised Tea Bag Index were not readily comparable to those of the plant litter or fungal necromass and we therefore question its use in this regional context. Further, we show that net mass or C remaining in the litterbags do not correlate consistently with in situ soil respiration. This finding is discussed in relation to previous measurements of soil C fluxes from the same system. In conclusion, we suggest that potential disturbances to the physical environment or the capacity of the decomposer community to facilitate litter decomposition are no longer clearly evident when clear-cut stands approach maturity.