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.
2022
Authors
Lise GrøvaAbstract
No abstract has been registered
Abstract
Population densities of several cervid species have increased in recent decades in North America and Europe, and cervids frequently eat and damage agricultural crops. Competition and depletion of natural food resources are the main mechanisms for the density-dependent decline in vital rates of large herbivores. The extent to which access to agricultural crops can buffer density effects in cervid populations, however, is unknown. Agricultural grasslands cover more than a third of the European agricultural area, and red deer (Cervus elaphus) use these grasslands in many European countries. Over the past few decades, such grasslands have been subject to management intensification (with renewal and fertilization) in some areas and abandonment (no longer being harvested) in other areas. We used generalized linear mixed-effects models to examine the development of body masses of red deer in Norway during a period of population density increase in 16 local management units with different availability of cultivated grasslands (0.87–6.44%) in a region with active management of grasslands (Tingvoll, n = 5,780, 2000–2019) and a region with ongoing abandonment (Hitra, n = 10,598, 2007–2020). There was a consistent decline in the body mass of red deer linked to increased population density in both regions. A higher proportion of agricultural grassland was linked to higher body mass and lower density effects in both sexes and across all age classes. There is a link between body mass, survival, and reproduction. Therefore, the buffering of density effects of access to agricultural crops will fuel cervid population growth and lead to less natural regulation of abundance, making it more difficult to control dense cervid populations by harvesting.
Authors
Erling MeisingsetAbstract
No abstract has been registered
Authors
Kjersti Holt HanssenAbstract
No abstract has been registered
Authors
Kjersti Holt HanssenAbstract
No abstract has been registered
Authors
Marian Schönauer Robert Prinz Kari Väätäinen Rasmus Astrup Dariusz Pszenny Harri Lindeman Dirk JaegerAbstract
Milder winters and extended wetter periods in spring and autumn limit the amount of time available for carrying out ground-based forest operations on soils with satisfactory bearing capacity. Thus, damage to soil in form of compaction and displacement is reported to be becoming more widespread. The prediction of trafficability has become one of the most central issues in planning of mechanized harvesting operations. The work presented looks at methods to model field measured spatio-temporal variations of soil moisture content (SMC, [%vol]) – a crucial factor for soil strength and thus trafficability. We incorporated large-scaled maps of soil characteristics, high-resolution topographic information – depth-to-water (DTW) and topographic wetness index – and openly available temporal soil moisture retrievals provided by the NASA Soil Moisture Active Passive mission. Time-series measurements of SMC were captured at six study sites across Europe. These data were then used to develop linear models, a generalized additive model, and the machine learning algorithms Random Forest (RF) and eXtreme Gradient Boosting (XGB). The models were trained on a randomly selected 10% subset of the dataset. Predictions of SMC made with RF and XGB attained the highest R2 values of 0.49 and 0.51, respectively, calculated on the remaining 90% test set. This corresponds to a major increase in predictive performance, compared to basic DTW maps (R2 = 0.022). Accordingly, the quality for predicting wet soils was increased by 49% when XGB was applied (Matthews correlation coefficient = 0.45). We demonstrated how open access data can be used to clearly improve the prediction of SMC and enable adequate trafficability mappings with high spatial and temporal resolution. Spatio-temporal modelling could contribute to sustainable forest management.
Authors
Inger Sundheim FløistadAbstract
No abstract has been registered
Authors
Inger Sundheim FløistadAbstract
No abstract has been registered
Authors
Toby Marthews Holger Lange Alberto Martinez-de la Torre Richard J. Ellis Sarah E. Chadburn Martin G. de KauweAbstract
The role of soil in current climate models is reviewed and discussed, with a focus on developments over the last two decades. Soil modeling may be divided into three major parts: simulation of soil hydrological dynamics, soil biogeochemistry and the soil thermal environment. Each of these three major parts is summarized with a brief description of current best practice and developments. Specific issues and modifications relevant to four extreme environments are highlighted: drylands, tropical moist and wet forests, cold regions, and peatlands and wetlands. Finally, current advances in the areas of hyperresolution and coupled model environments are discussed, which we see as the two leading edges of current soil model development.
Abstract
The three-dimensional structure of forest canopies is essential for light use efficiency, photosynthesis and thus carbon sequestration. Therefore, high-quality characterization of canopy structure is critical to improving our carbon cycle estimates by Earth system models and better understanding disturbance impacts on carbon sequestration in forested ecosystems. In this context, a widely used observable is the Leaf Area Density (LAD) and its integral over the vertical dimension, the Leaf Area Index (LAI). A multitude of methods exists to determine LAD and LAI in a forest stand. In this contribution, we use a mature Norway spruce forest surrounding an ICOS flux tower at Hurdal site (NO-Hur) to investigate LAD and LAI with six different methods: field campaigns using (1) the Plant Canopy Analyzer LAI-2000; (2) the LaiPen LP 110; (3) Digital Hemispheric Photography at a set of plots within the area; (4) a Lidar drone flight covering the footprint area of the tower; (5) an airborne Lidar campaign, and (6) a satellite LAI product (MODIS). The horizontal spatial structure of LAI values is investigated using marked point process statistics. Intercomparison of the methods focusses not only on biases and root mean squared errors, but also on the spatial patterns observed, quantifying to which extent a simple bias correction between the methods is sufficient to make the different approaches match to each other.