Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2024
Abstract
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Abstract
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Authors
Marie-Christin Wimmler Nadezhda Nadezhdina Hannah Bowen Susana Alvarado-Barrientos Teresa David Gabriela Fontenla-Razzetto Britt Kniesel Holger Lange Roman Mathias Link Yang Liu Jorge López-Portillo Clara Pinto Junbin Zhao Alejandra G. VovidesAbstract
1. Sap flow measurements are fundamental to understanding water use in trees and could aid in predicting climate change effects on forest function. Deriving knowledge from such measurements requires empirical calibrations and upscaling methods to translate thermometric recordings to tree water use. Here, we developed a user-friendly open-source application, the Sap Flow Analyzer (SFA), which estimates sap flow rates and tree water use from the heat field deformation (HFD) instruments. 2. The SFA incorporates four key features to ensure maximum accuracy and reproducibility of sap flow estimates: diagnosis diagrams to assess data patterns visually, regression models implemented to increase accuracy when estimating K (the main HFD parameter), three approaches to upscale sap flow rates to whole-tree water use and visualization of the input parameters' uncertainty. Thirteen participants were given three raw datasets and assigned data processing tasks using the SFA user guide, from estimating sapwood depth to scaling sap flow rates to whole-tree water use to assess the reproducibility and applicability of the SFA. 3. Participants' results were reasonably consistent and independent of their background in using the SFA, R, or HFD method. The results showed lower variability for high flow rates (SD: mean 1% vs. 10%). K estimates and sapwood depth differentiation were the primary sources of variability, which in turn was mainly caused by the user's chosen scaling method. 4. The SFA provides an easy way to visualize and process sap flow and tree water use data from HFD measurements. It is the first free and open software tool for HFD users. The ability to trace analysis steps ensures reproducibility, increasing transparency and consistency in data processing. Developing tools such as the SFA and masked trials are essential for more precise workflows and improved quality and comparability of HFD sap flow datasets.
Abstract
1. Root and butt rot caused by pathogenic fungi in the genera Heterobasidion and Armillaria is a pressing issue in managed Norway spruce forests. The disease results in financial losses for the forest owners and reduces the volume of wood that can be used in long-lived products. Pathogenic wood decay fungi spread either with the aid of airborne spores or via mycelial growth among neighbouring trees, the latter leading to clustering (tendency of decayed trees to be in close proximity relative to their neighbouring trees) of decay-affected trees in forests. Understanding the spatial patterns of the decay-affected trees at the forest stand level is vital for designing management strategies to address this problem. 2. We examined decay clustering in 273 clear-cut Norway spruce stands in Norway using harvester-recorded data on spatial occurrence of decayed and healthy Norway spruce trees. We tested clustering using three global-cluster tests that account for population density and distribution, evaluating clustering without identifying specific cluster locations. 3. The proportions of clustered and non-clustered stands differed depending on the statistical test used for clustering assessment, resulting in overall agreement of 32.8% for clustered and 36.9% for non-clustered. Clustered stands exhibited a median cluster distance (maximum distance between the decay-affected trees within a cluster) of 12 m (Inter-Quantile Range, IQR, 6–20 m) and a median of 6 (IQR 3–16) nearest neighbour trees (number of decayed trees forming a cluster), estimates comparable with prior studies focused on assessment of trees infected by mycelial spread of the same fungal individual. The decay incidence in the clustered stands was 16.24%, while the non-clustered stands had a butt-rot incidence of 20.97%. In clustered stands the average number of trees per hectare was higher (693) than in non-clustered stands (553). 4. Synthesis and applications: Our study demonstrates that Norway spruce stands display a diverse range of spatial patterns of butt rotted trees. We found that higher densities of Norway spruce trees probably facilitate the vegetative spread of pathogenic wood decay fungi, leading to clustering of decay-affected trees. To disrupt the spread of decay fungi between tree generations, precision planting of trees other than Norway spruce around infested stumps of prior generation trees has been recommended by earlier studies. We discussed the potential of using harvester-derived geoposition data for butt-rotted trees upon planning and execution of forest regeneration.
Abstract
PixSim is a flexible, open-source forest growth simulator designed to operate at the pixel level of high-resolution, wall-to-wall forest resource maps generated through remote sensing approaches. PixSim addresses the need to adapt forest growth simulators to the data produced by modern remote sensing-based forest inventories, rather than relying on stand-level averages from traditional field-based inventories. By operating at the pixel level, PixSim captures intra-stand variability in high-resolution forest resource maps, which is often overlooked by stand-level simulators. This capability aligns with the current focus on precision forestry, aimed at improving management decisions with localized data and small-scale management. Implemented in the R programming language, PixSim features minimal package dependencies, provides flexibility and scalability, and has been optimized for high-resolution, large-scale simulations, ensuring efficient computation. The simulator’s flexibility and open-source nature support the incorporation of management modules and the inclusion of climate change scenarios in simulations.
Authors
Annika M. Felton Hilde Karine Wam Zbigniew Borowski Aksel Granhus Laura Juvany Juho Matala Markus Melin Märtha Wallgren Anders MårellAbstract
Climate change causes far-reaching disruption in nature, where tolerance thresholds already have been exceeded for some plants and animals. In the short term, deer may respond to climate through individual physiological and behavioral responses. Over time, individual responses can aggregate to the population level and ultimately lead to evolutionary adaptations. We systematically reviewed the literature (published 2000–2022) to summarize the effect of temperature, rainfall, snow, combined measures (e.g., the North Atlantic Oscillation), and extreme events, on deer species inhabiting boreal and temperate forests in terms of their physiology, spatial use, and population dynamics. We targeted deer species that inhabit relevant biomes in North America, Europe, and Asia: moose, roe deer, wapiti, red deer, sika deer, fallow deer, white-tailed deer, mule deer, caribou, and reindeer. Our review (218 papers) shows that many deer populations will likely benefit in part from warmer winters, but hotter and drier summers may exceed their physiological tolerances. We found support for deer expressing both morphological, physiological, and behavioral plasticity in response to climate variability. For example, some deer species can limit the effects of harsh weather conditions by modifying habitat use and daily activity patterns, while the physiological responses of female deer can lead to long-lasting effects on population dynamics. We identified 20 patterns, among which some illustrate antagonistic pathways, suggesting that detrimental effects will cancel out some of the benefits of climate change. Our findings highlight the influence of local variables (e.g., population density and predation) on how deer will respond to climatic conditions. We identified several knowledge gaps, such as studies regarding the potential impact on these animals of extreme weather events, snow type, and wetter autumns. The patterns we have identified in this literature review should help managers understand how populations of deer may be affected by regionally projected futures regarding temperature, rainfall, and snow.
Authors
Annika M. Felton Hilde Karine Wam Zbigniew Borowski Aksel Granhus Laura Juvany Canovas Juho Matala Markus Melin Märtha Wallgren Anders MårellAbstract
Climate change causes far-reaching disruption in nature, where tolerance thresholds already have been exceeded for some plants and animals. In the short-term, deer may respond to climate through individual physiological and behavioral responses. Over time, individual responses can aggregate to the population level and ultimately lead to evolutionary adaptations. Because responses by deer to climate change may take many paths - both positive and negative - it is generally difficult to predict outcomes. Here we take the first step to understanding these complexities by systematically synthesizing the literature (published 2000-2022) regarding direct effects of temperature, rainfall and snow on deer inhabiting boreal and temperate regions of the northern hemisphere. Our review (based on N= 219 papers) shows that while many deer populations will likely benefit from warmer winters, hotter and drier summers may exceed their physiological tolerances, causing northwards shifts in distributional ranges. We found support for deer expressing both phenotypic and behavioral plasticity in response to climate variability at different temporal and spatial scales. We identified 20 general patterns, among which some illustrate antagonistic pathways, suggesting that detrimental effects will cancel out some of the benefits of climate change. Our findings highlight the importance of local variables for any predictions of future responses by a given deer population. We identified several knowledge gaps, such as studies regarding the potential impact on these animals of extreme weather events, snow type and wetter autumns.
Abstract
No abstract has been registered
Abstract
Lingonberry (Vaccinium vitis-idaea L.) grows in a range of nature types in the boreal zone, and understanding factors affecting the abundance of the plant, as well as mapping its spatial distribution, is important. The abundance of the species can be an indicator of ecosystem changes, and lingonberry can also be a source for commercial utilisation of berry resources. Using country-wide data from 6404 field plots of the Norwegian national forest inventory (NFI), we modelled the relationship between lingonberry cover and airborne laser scanning (ALS) and satellite metrics and bioclimatic variables describing the forest structure, terrain, soil properties and climate using a generalised mixed-effects model with a quasipoisson distribution. The validation carried out with an independent set of 2124 NFI plots indicated no obvious bias in predictions. The most important predictors were found to be interactions between dominant tree species, stand basal area and latitude, as well as the reflectance in the near-infrared band from Sentinel-2 satellite imagery, the dominant height based on the ALS variable and the long-term mean summer (June–August) temperature. The results provide an indicator of the effects of global warming, as well as the possibility of giving forest management prescriptions that favour lingonberry and locating the most abundant lingonberry sites in Norwegian forests.
2023
Abstract
Parametric modeling of downwelling longwave irradiance under all-sky conditions (LW↓) typically involves “correcting” a clear- (or non-overcast) sky model estimate using solar-irradiance-based proxies of cloud cover in lieu of actual cloud cover given uncertainties and measurement challenges of the latter. While such approaches are deemed sound, their application in time and space is inherently limited. We report on a correction model free of solar irradiance-derived cloud proxies that is applicable at the true daily (24 hr) and global scales. The new “cloud-free” correction model demonstrates superior performance in a range of environments relative to existing cloud-free modeling approaches and to corrections based on solar-derived cloudiness proxies. Literature-based performance benchmarking indicates a performance that is often comparable to—and in some cases superior to—performances yielded by conventional parametric modeling approaches employing locally or regionally calibrated parameters, as well as to performances of satellite-based algorithms.