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
Eli Knispel Rueness Maria Gulbrandsen Asmyhr Dean Basic Katrine Eldegard Andrew M. Janczak Hans Christian Pedersen Bjørnar Ytrehus Angelika Agdestein Paul Ragnar Berg Anders Bryn Sonya Rita Geange Kjetil Hindar Lars Robert Hole Lawrence R. Kirkendall Anders Nielsen Erlend Birkeland Nilsen Brett Sandercock Eva Bonsak Thorstad Gaute VelleAbstract
Background Since the late 1800s, an unknown number of common pheasants and grey partridges from captive bred stocks have been released in Norwegian nature. The birds are released to be used for training of pointing dogs. The import, keeping and release of gamebirds, as well as the management of release sites, have been largely unregulated. The consequences to biodiversity, animal health and welfare have not been investigated. The Norwegian Environment Agency (NEA) and the Norwegian Food Safety Authority (NFSA) have jointly requested the Norwegian Scientific Committee for Food and Environment (VKM) for a scientific opinion on the release of common pheasants and grey partridges for pointing dog training regarding consequences for biodiversity, animal welfare of the released birds and health of the released birds as well as wild birds to which pathogens may be transmitted. VKM was further asked to suggest risk reducing measures for biodiversity and animal welfare. Methods VKM established a project group with expertise within avian ecology, landscape ecology, population biology, wildlife veterinary medicine and animal welfare. The group conducted systematic literature searches, scrutinized the resulting literature, and supplemented by other relevant articles and reports. In the absence of Norwegian studies, VKM used literature from other countries where common pheasants and grey partridges (and in some cases other gamebirds), are released, as references. The project group applied observation data of common pheasants and grey partridges in Norway for the period 2000-2022, presented by the Norwegian Biodiversity Information Centre (NBIC). In the assessments, VKM assumed that the release of birds will be in the same order of magnitude as in previous years (a few thousand birds annually on a national level). The number of release sites and the density of released birds per site are unknown. Increasing the number and density of birds would also increase the probability of negative effects and the severity of the consequences. VKM assessed the impacts of released common pheasants and grey partridges on competition, predation, hybridization, transmission of disease, herbivory and indirect impacts through interactions with other species (predator abundance and pathogen-mediated competition). VKM also assessed the impact on biodiversity in a 50-year perspective. Furthermore, VKM discusses how the birds’ welfare might be impacted by rearing, transport, release and exposure to pointing dogs. Finally, VKM provides a list of relevant diseases and assessed their potential impact on animal health during transport, rearing and release. Results and conclusions VKMs assessment show that there are several risks to biodiversity, animal health, and animal welfare from the release of captive bred common pheasants and grey partridges in Norway. The risk of increased competition for food, particularly in winter, with birds with similar niches as common pheasants and grey partridges, is low on a national scale and moderat on a local scale. This is particularly so for yellowhammer, Emberiza citronella, a species categorized as vulnerable on the national red list due to its progressive population decline caused by reduced availability of food during winter. There is a moderate risk for predation on invertebrates and negative impacts on flora. Indirectly, activities connected to the release of birds may lead to moderate risks of altered predator abundance and disease-mediated competition. VKM concludes that the ecological impacts will be more severe for redlisted species present within the release areas for common pheasants and grey partridges. Repeated release of common pheasants and grey partridges can lead to high risk of disease transmission to wild birds. .............
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2021
Authors
Anna Palmé Birgitte Lund Elina Kiviharju Heli Fitzgerald Hjörtur Thorbjörnsson Jenny Hagenblad Jens Weibull Kjersti Bakkebø Fjellstad Kristina Bjureke Linn Borgen Nilsen Magnus Göransson Maija Häggblom Marko Hyvärinen Mora Aronsson Virva LyytikäinenAbstract
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Authors
Nina SvartedalAbstract
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Authors
Nina SvartedalAbstract
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Abstract
Distribution modeling methods are used to provide occurrence probability surfaces for modeled targets. While most often used for modeling species, distribution modeling methods can also be applied to vegetation types. However, surfaces provided by distribution modeling need to be transformed into classified wall-to-wall maps of vegetation types to be useful for practical purposes, such as nature management and environmental planning. The paper compares the performance of three methods for assembling predictions for multiple vegetation types, modeled individually, into a wall-to-wall map. The authors used grid-cell based probability surfaces from distribution models of 31 vegetation types to test the three assembly methods. The first, a probability-based method, selected for each grid cell the vegetation type with the highest predicted probability of occurrence in that cell. The second, a performance-based method, assigned the vegetation types, ordered from high to low model performance, to a fraction of the grid cells given by the vegetation type’s prevalence in the study area. The third, a prevalence-based method, differed from the performance-based method by assigning vegetation types in the order from low to high prevalence. Thus the assembly methods worked in two principally different ways: the probability-based method assigned vegetation types to grid cells in a cell-by-cell manner, and both the performance-based method and prevalence-based method assigned them in a type-by-type manner. All methods were evaluated by use of reference data collected in the field, more or less independently of the data used to parameterize the vegetation-type models. Quantity, allocation, and total disagreement, as well as proportional dissimilarity metrics, were used for evaluation of assembly methods. Overlay analysis showed 38.1% agreement between all three assembly methods. The probability-based method had the lowest total disagreement with, and proportional dissimilarity from, the reference datasets, but the differences between the three methods were small. The three assembly methods differed strongly with respect to the distribution of the total disagreement on its quantity and allocation components: the cell-by-cell assignment method strongly favored allocation disagreement and the type-by-type methods strongly favored quantity disagreement. The probability-based method best reproduced the general pattern of variation across the study area, but at the cost of many rare vegetation types, which were left out of the assembled map. By contrast, the prevalence-based and performance-based methods represented vegetation types in accordance with nationwide area statistics. The results show that maps of vegetation types with wall-to-wall coverage can be assembled from individual distribution models with a quality acceptable for indicative purposes, but all the three tested methods currently also have shortcomings. The results also indicate specific points in the methodology for map assembly that may be improved. area frame survey, assembly strategies, distribution modeling, spatial probabilities, vegetation mapping, vegetation types
Abstract
No abstract has been registered
Authors
Peter Horvath Hui Tang Rune Halvorsen Frode Stordal Lena M. Tallaksen Terje Koren Berntsen Anders BrynAbstract
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.
Abstract
Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.