Jon Olav Vik

Norwegian University of Life Sciences (UMB), Ås, Akershus Fylke, Norway

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Publications (26)239.97 Total impact

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    ABSTRACT: Additive genetic variance (VA ) and total genetic variance (VG ) are core concepts in biomedical, evolutionary and production-biology genetics. What determines the large variation in reported VA /VG ratios from line-cross experiments is not well understood. Here we report how the VA /VG ratio, and thus the ratio between narrow and broad sense heritability (h(2) /H(2) ), varies as a function of the regulatory architecture underlying genotype-to-phenotype (GP) maps. We studied five dynamic models (of the cAMP pathway, the glycolysis, the circadian rhythms, the cell cycle, and heart cell dynamics). We assumed genetic variation to be reflected in model parameters and extracted phenotypes summarizing the system dynamics. Even when imposing purely linear genotype to parameter maps and no environmental variation, we observed quite low VA /VG ratios. In particular, systems with positive feedback and cyclic dynamics gave more non-monotone genotype-phenotype maps and much lower VA /VG ratios than those without. The results show that some regulatory architectures consistently maintain a transparent genotype-to-phenotype relationship, whereas other architectures generate more subtle patterns. Our approach can be used to elucidate these relationships across a whole range of biological systems in a systematic fashion.
    PLoS Computational Biology 05/2013; 9(5):e1003053. · 4.87 Impact Factor
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    ABSTRACT: The genotype-phenotype map (GP map) concept applies to any time point in the ontogeny of a living system. It is the outcome of very complex dynamics that include environmental effects, and bridging the genotype-phenotype gap is synonymous with understanding these dynamics. The context for this understanding is physiology, and the disciplinary goals of physiology do indeed demand the physiological community to seek this understanding. We claim that this task is beyond reach without use of mathematical models that bind together genetic and phenotypic data in a causally cohesive way. We provide illustrations of such causally cohesive genotype-phenotype models where the phenotypes span from gene expression profiles to development of whole organs. Bridging the genotype-phenotype gap also demands that large-scale biological ("omics") data and associated bioinformatics resources be more effectively integrated with computational physiology than what is currently the case. A third major element is the need for developing a phenomics technology way beyond current state of the art, and we advocate the establishment of a Human Phenome Program solidly grounded on biophysically based mathematical descriptions of human physiology.
    The Journal of Physiology 02/2013; · 4.38 Impact Factor
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    VPH 2012; 09/2012
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    ABSTRACT: Polymorphisms identified in genome-wide association studies of human traits rarely explain more than a small proportion of the heritable variation, and improving this situation within the current paradigm appears daunting. Given a well-validated dynamic model of a complex physiological trait, a substantial part of the underlying genetic variation must manifest as variation in model parameters. These parameters are themselves phenotypic traits. By linking whole-cell phenotypic variation to genetic variation in a computational model of a single heart cell, incorporating genotype-to-parameter maps, we show that genome-wide association studies on parameters reveal much more genetic variation than when using higher-level cellular phenotypes. The results suggest that letting such studies be guided by computational physiology may facilitate a causal understanding of the genotype-to-phenotype map of complex traits, with strong implications for the development of phenomics technology.
    PLoS Computational Biology 04/2012; 8(4):e1002459. · 4.87 Impact Factor
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    ABSTRACT: In quantitative genetics, the degree of resemblance between parents and offspring is described in terms of the additive variance (V(A)) relative to genetic (V(G)) and phenotypic (V(P)) variance. For populations with extreme allele frequencies, high V(A)/V(G) can be explained without considering properties of the genotype-phenotype (GP) map. We show that randomly generated GP maps in populations with intermediate allele frequencies generate far lower V(A)/V(G) values than empirically observed. The main reason is that order-breaking behaviour is ubiquitous in random GP maps. Rearrangement of genotypic values to introduce order-preservation for one or more loci causes a dramatic increase in V(A)/V(G). This suggests the existence of order-preserving design principles in the regulatory machinery underlying GP maps. We illustrate this feature by showing how the ubiquitously observed monotonicity of dose-response relationships gives much higher V(A)/V(G) values than a unimodal dose-response relationship in simple gene network models.
    Journal of Evolutionary Biology 08/2011; 24(10):2269-79. · 3.48 Impact Factor
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    ABSTRACT: Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.
    BMC Systems Biology 06/2011; 5:90. · 2.98 Impact Factor
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    ABSTRACT: Understanding the causal chain from genotypic to phenotypic variation is a tremendous challenge with huge implications for personalized medicine. Here we argue that linking computational physiology to genetic concepts, methodology, and data provides a new framework for this endeavor. We exemplify this causally cohesive genotype-phenotype (cGP) modeling approach using a detailed mathematical model of a heart cell. In silico genetic variation is mapped to parametric variation, which propagates through the physiological model to generate multivariate phenotypes for the action potential and calcium transient under regular pacing, and ion currents under voltage clamping. The resulting genotype-to-phenotype map is characterized using standard quantitative genetic methods and novel applications of high-dimensional data analysis. These analyses reveal many well-known genetic phenomena like intralocus dominance, interlocus epistasis, and varying degrees of phenotypic correlation. In particular, we observe penetrance features such as the masking/release of genetic variation, so that without any change in the regulatory anatomy of the model, traits may appear monogenic, oligogenic, or polygenic depending on which genotypic variation is actually present in the data. The results suggest that a cGP modeling approach may pave the way for a computational physiological genomics capable of generating biological insight about the genotype-phenotype relation in ways that statistical-genetic approaches cannot.
    Frontiers in Physiology 01/2011; 2:106.
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    ABSTRACT: Animals selecting habitats often have to consider many factors, e.g., food and cover for safety. However, each habitat type often lacks an adequate mixture of these factors. Analyses of habitat selection using resource selection functions (RSFs) for animal radiotelemetry data typically ignore trade-offs, and the fact that these may change during an animal's daily foraging and resting rhythm on a short-term basis. This may lead to changes in the relative use of habitat types if availability differs among individual home ranges, called functional responses in habitat selection. Here, we identify such functional responses and their underlying behavioral mechanisms by estimating RSFs through mixed-effects logistic regression of telemetry data on 62 female red deer (Cervus elaphus) in Norway. Habitat selection changed with time of day and activity, suggesting a trade-off in habitat selection related to forage quantity or quality vs. shelter. Red deer frequently used pastures offering abundant forage and little canopy cover during nighttime when actively foraging, while spending much of their time in forested habitats with less forage but more cover during daytime when they are more often inactive. Selection for pastures was higher when availability was low and decreased with increasing availability. Moreover, we show for the first time that in the real world with forest habitats also containing some forage, there was both increasing selection of pastures (i.e., not proportional use) and reduced time spent in pastures (i.e., not constant time use) with lowered availability of pastures within the home range. Our study demonstrates that landscape-level habitat composition modifies the trade-off between food and cover for large herbivorous mammals. Consequently, landscapes are likely to differ in their vulnerability to crop damage and threat to biodiversity from grazing.
    Ecology 04/2009; 90(3):699-710. · 5.18 Impact Factor
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    ABSTRACT: The population cycles of rodents at northern latitudes have puzzled people for centuries, and their impact is manifest throughout the alpine ecosystem. Climate change is known to be able to drive animal population dynamics between stable and cyclic phases, and has been suggested to cause the recent changes in cyclic dynamics of rodents and their predators. But although predator-rodent interactions are commonly argued to be the cause of the Fennoscandian rodent cycles, the role of the environment in the modulation of such dynamics is often poorly understood in natural systems. Hence, quantitative links between climate-driven processes and rodent dynamics have so far been lacking. Here we show that winter weather and snow conditions, together with density dependence in the net population growth rate, account for the observed population dynamics of the rodent community dominated by lemmings (Lemmus lemmus) in an alpine Norwegian core habitat between 1970 and 1997, and predict the observed absence of rodent peak years after 1994. These local rodent dynamics are coherent with alpine bird dynamics both locally and over all of southern Norway, consistent with the influence of large-scale fluctuations in winter conditions. The relationship between commonly available meteorological data and snow conditions indicates that changes in temperature and humidity, and thus conditions in the subnivean space, seem to markedly affect the dynamics of alpine rodents and their linked groups. The pattern of less regular rodent peaks, and corresponding changes in the overall dynamics of the alpine ecosystem, thus seems likely to prevail over a growing area under projected climate change.
    Nature 12/2008; 456(7218):93-7. · 38.60 Impact Factor
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    ABSTRACT: Many species of fungi produce ephemeral autumnal fruiting bodies to spread and multiply. Despite their attraction for mushroom pickers and their economic importance, little is known about the phenology of fruiting bodies. Using approximately 34,500 dated herbarium records we analyzed changes in the autumnal fruiting date of mushrooms in Norway over the period 1940-2006. We show that the time of fruiting has changed considerably over this time period, with an average delay in fruiting since 1980 of 12.9 days. The changes differ strongly between species and groups of species. Early-fruiting species have experienced a stronger delay than late fruiters, resulting in a more compressed fruiting season. There is also a geographic trend of earlier fruiting in the northern and more continental parts of Norway than in more southern and oceanic parts. Incorporating monthly precipitation and temperature variables into the analyses provides indications that increasing temperatures during autumn and winter months bring about significant delay of fruiting both in the same year and in the subsequent year. The recent changes in autumnal mushroom phenology coincide with the extension of the growing season caused by global climate change and are likely to continue under the current climate change scenario.
    Proceedings of the National Academy of Sciences 04/2008; 105(10):3811-4. · 9.74 Impact Factor
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    ABSTRACT: Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of non-stationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables.
    Oecologia 04/2008; 156(2):287-304. · 3.01 Impact Factor
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    ABSTRACT: We analysed the effects of Quercus crispula acorn abundance on the density dependence of the large Japanese wood mouse Apodemus speciosus using time series data (1992-2007). The data were obtained in a forest in northern Hokkaido, Japan, by live-trapping rodents and directly counting acorns on the ground. Acorn abundance in one year clearly influenced the abundance of wood mice in the following year in all models examined based on the Gompertz and Ricker model; in addition, the abundance of wood mice had effects on the population. Acorn abundance influenced the strength of density dependence (intraspecific competition) of the wood mouse population. When the abundance of acorns was high, density dependence was relaxed, and as a result the equilibrium density at which the population growth rate decreased to zero became higher. Those effects of acorn abundance were regarded as a nonlinear perturbation effect (sensu Royama 1992). The nonlinearity of density dependence was also detected; higher densities had stronger effects on population growth rates.
    Population Ecology 01/2008; · 1.92 Impact Factor
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    ABSTRACT: Both's comment questions our suggestion that the advanced spring arrival time of long-distance migratory birds in Scandinavia and the Mediterranean may reflect a climate-driven evolutionary change. We present additional arguments to support our hypothesis but underscore the importance of additional studies involving direct tests of evolutionary change.
    Science 02/2007; 315(5812). · 31.03 Impact Factor
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    ABSTRACT: The effects of acorn (Quercus crispula) abundance on the population dynamics of three rodent species (Apodemus speciosus, A. argenteus, and Clethrionomys rufocanus) were analyzed using time series data (1992–2006). The data were obtained in a forest in northern Hokkaido, Japan, by live trapping rodents and directly counting acorns on the ground. Apodemus speciosus generally increased in abundance following acorn masting. However, the clear effect of acorn abundance was not detected for the other two rodent species. Acorns of Q. crispula contain tannins, which potentially have detrimental effects on herbivores. Apodemus speciosus may reduce the damage caused by acorn tannins with tannin-binding salivary proteins and tannaseproducing bacteria, whereas such physiological tolerance to tannins is not known in the other two rodent species. The differences in the effects of acorns between the three species may be due to differences in their physiological tolerance to tannins.
    Population Ecology 01/2007; · 1.92 Impact Factor
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    ABSTRACT: Long-term data from standardized monitoring programmes at bird observatories are becoming increasingly available. These data are frequently used for detecting changes in the timing of bird migration that may relate to recent climate change. We present an overview of problematic issues in the analysis of these data, and review approaches to and methods for characterizing bird migration phenology and its change over time. Methods are illustrated and briefly compared using autumn data on garden warbler Sylvia borin from a standardized mist-netting programme at Lista bird observatory, southern Norway. Bird migration phenology is usually characterized rather coarsely using a small number of sample statistics such as mean, median and selected quantiles. We present 2 alternative approaches. Smoothing methods describe the within-season pattern in the data at an arbitrary level of detail, while fitting a parametric seasonal distribution curve offers a coarse description of migration phenology relatively robust to sampling effects. Various methods for analyzing linear trends in the timing of bird migration are reviewed and discussed. Exploratory studies using long-term data gathered at bird observatories can yield more detailed insight into the phenomenon of bird migration and how phenologies relate to climate. Methodological advances are needed, particularly in order to better characterize the shape of phenological distributions and separate between sampling effects and 'true' phenology.
    Climate Research. 01/2007; 35(1-2):59-77.
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    Journal of Plankton Research - J PLANKTON RES. 01/2007; 29(10):903-911.
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    ABSTRACT: Several bird species have advanced the timing of their spring migration in response to recent climate change. European short-distance migrants, wintering in temperate areas, have been assumed to be more affected by change in the European climate than long-distance migrants wintering in the tropics. However, we show that long-distance migrants have advanced their spring arrival in Scandinavia more than short-distance migrants. By analyzing a long-term data set from southern Italy, we show that long-distance migrants also pass through the Mediterranean region earlier. We argue that this may reflect a climate-driven evolutionary change in the timing of spring migration.
    Science 07/2006; 312(5782):1959-61. · 31.20 Impact Factor
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    ABSTRACT: We explored the effects of regime shifts (drastic changes usually observed in marine ecosystems, corresponding to climatic variability) in a terrestrial system focusing on a key event that occurred in 1976-77. We used data on the gray-sided vole Clethrionomys rufocanus (Sundevall, 1846) from 89 time series covering 31 yr (1962–1992), recorded in Asahikawa, Hokkaido, Japan, where both cyclic and non-cyclic populations occur. Wavelet analyses demonstrated a clear shift of dynamic patterns in the mid-1970s, presumably resulting from the Aleutian Low Pressure (as measured by the Aleutian Low Pressure Index). The vole populations exhibited erratic fluctuations until the mid- 1970s, and then changed their pattern to cyclic fluctuations at a 4 yr interval. The structure of density dependence changed during the regime shift. Although the strength of direct density dependence was similar, delayed density dependence became stronger after the shift. Altogether these findings suggest that changing climate may affect the ecological interactions among voles, predators and resources.
    01/2006;
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    ABSTRACT: Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.
    Trends in Ecology & Evolution 10/2005; 20(9):503-10. · 15.39 Impact Factor

Publication Stats

907 Citations
239.97 Total Impact Points

Institutions

  • 2012
    • Norwegian University of Life Sciences (UMB)
      • Centre for Integrative Genetics (Cigene)
      Ås, Akershus Fylke, Norway
  • 2004–2008
    • University of Oslo
      • • Department of Biosciences
      • • Centre for Ecological and Evolutionary Synthesis
      Oslo, Oslo, Norway
  • 2006
    • Lund University
      Lund, Skåne, Sweden