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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.

2020

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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

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Young forest stands and clearcuts in the boreal forest created by modern forestry practices along with meadows of abandoned summer farms may contribute as feeding areas for beef cattle. The patchy distribution and varying quality and diversity of forage on such unimproved lands may affect cattle productivity. Weight gain of 336 beef cows and 270 calves free-ranging during three summer grazing seasons was monitored in boreal forests of southeastern Norway, stocked at either high (0.16 cows ha-1) and low (0.04 cows ha-1) stocking densities. We used linear mixed effect models for assessing intrinsic correlates of weight gain in cows and calves in the two areas. Habitat use and home range size of a subsample of 53 cows were monitored by using GPS collars programmed to log locations at 5 min. intervals during the grazing season. Additional extrinsic correlates of weight gain for the subsampled cows using a linear mixed model were also tested. Average weight gain of beef cows grazing at the low stocking density was positive among cows of early maturing breeds (represented by Hereford) gaining 24 ± 2.8 kg ( ± SE), while cows of late maturing breeds (mainly represented by Charolais) had an average weight loss of 9 ± 8.4 kg. The average weight gain was negative for beef cows of both early (Herefords) and late maturing breeds (mainly represented by Charolais but also Limousin and Simmental) at the high stocking density. Within both breed groups, there was a negative relationship between breed-specific average weight of cows at turnout and weight gain during the grazing period, while a prolonged grazing period was slightly positively related to weight gain. There was no relationship between weight gain and home range size and proportion of grazing habitat for the 53 cows fitted with GPS collars. Higher weight gains in calves of the low compared to the high stocking density area was found. However, there was no breed effect of weight gain in calves. Across study areas, spring-born suckler calves gained more weight than autumn-born calves (92 ± 1.7 kg vs. 65 ± 4.4 kg). Also, there were higher weight gains for springborn bull-calves than spring-born heifers (100 ± 2.4 kg vs. 94 ± 2.2 kg). Overall, the results indicate that it is possible to achieve acceptable weight gains for cattle grazing coniferous forest by finding breeds suitable for these extensive areas and stocking at moderate densities.

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The Norwegian sheep industry is based on utilization of “free” rangeland pasture resources. Use of mountain pastures is dominating, with about two million sheep grazing these pastures during summer. Regional challenges related to e.g., loss of sheep to large carnivores make farmers think differently. The Norwegian coastline is among the longest globally and is scattered with islets and islands. Alone along the coast of Nordland county, it is estimated more than 14,000 islands. Use of islands for summer pasture is an alternative but there is a limited knowledge about such a management system. In this study, we examined lambs' average daily gain on island pastures at the coast of Norway. In total 230 lambs on three islands (Sandvær, Sjonøya, and Buøya), with varying pasture quality and stocking rate, for 3 years (2012, 2013, and 2014). At Sandvær as much as 92% of the island was characterized as high nutritional value while at Sjonøya and Buøya only 15%, was characterized high nutritional value. We found an average daily lamb growth rate of 0.320 kg d−1. Lambs on Sandvær had a higher daily gain (P < 0.05) than those on Sjonøya and Buøya, and lambs' average daily gain was significantly lower (P < 0.05) in 2013 compared to 2012 and 2014. We conclude that with a dynamic and adaptive management strategy there is a potential to utilize islands for sheep grazing during summer.

2019

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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.

2018

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The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.