Greening of Svalbard.

Svalbard, located between 76°30'N and 80°50'N, is among the regions in the world with the most rapid temperature increase. We processed a cloud-free time-series of MODIS-NDVI for Svalbard. The dataset is interpolated to daily data during the 2000-2022 period with 232 m pixel resolution. The onset of growth, with a clear phenological definition, has been mapped each year. Then the integrated NDVI from the onset (O) of growth each year to the time of average (2000-2022) peak (P) of growth (OP NDVI) have been calculated. OP NDVI has previously shown high correlation with field-based tundra productivity. Daily mean temperature data from 11 meteorological stations are compared with the NDVI data. The OP NDVI values show very high and significant correlation with growing degree days computed from onset to time of peak of growth for all the meteorological stations used. On average for the entire Svalbard, the year 2016 first had the highest greening (OP NDVI values) recorded since the year 2000, then the greening in 2018 surpassed 2016, then 2020 surpassed 2018, and finally 2022 was the year with the overall highest greening by far for the whole 2000-2022 period. This shows a rapid recent greening of Svalbard very strongly linked to temperature increase, although there are regional differences: the eastern parts of Svalbard show the largest variability between years, most likely due to variability in the timing of sea-ice break-up in adjacent areas. Finally, we find that areas dominated by manured moss-tundra in the polar desert zone require new methodologies, as moss does not share the seasonal NDVI dynamics of tundra communities.

Moose body mass variation revisited: disentangling effects of environmental conditions and genetics.

Large-scale geographical variation in phenotypic traits within species is often correlated to local environmental conditions and population density. Such phenotypic variation has recently been shown to also be influenced by genetic structuring of populations. In ungulates, large-scale geographical variation in phenotypic traits, such as body mass, has been related to environmental conditions and population density, but little is known about the genetic influences. Research on the genetic structure of moose suggests two distinct genetic lineages in Norway, structured along a north-south gradient. This corresponds with many environmental gradients, thus genetic structuring provides an additional factor affecting geographical phenotypic variation in Norwegian moose. We investigated if genetic structure explained geographical variation in body mass in Norwegian moose while accounting for environmental conditions, age and sex, and if it captured some of the variance in body mass that previously was attributed to environmental factors. Genetic structuring of moose was the most important variable in explaining the geographic variation in body mass within age and sex classes. Several environmental variables also had strong explanatory power, related to habitat diversity, environmental seasonality and winter harshness. The results suggest that environmental conditions, landscape characteristics, and genetic structure should be evaluated together when explaining large-scale patterns in phenotypic characters or life history traits. However, to better understand the role of genetic and environmental effects on phenotypic traits in moose, an extended individual-based study of variation in fitness-related characters is needed, preferably in an area of convergence between different genetic lineages.

The consequences of climate-driven stop-over sites changes on migration schedules and fitness of Arctic geese.

1. How climatic changes affect migratory birds remains difficult to predict because birds use multiple sites in a highly interdependent manner. A better understanding of how conditions along the flyway affect migration and ultimately fitness is of paramount interest. 2. Therefore, we developed a stochastic dynamic model to generate spatially and temporally explicit predictions of stop-over site use. For each site, we varied energy expenditure, onset of spring, intake rate and day-to-day stochasticity independently. We parameterized the model for the migration of pink-footed goose Anser brachyrhynchus from its wintering grounds in Western Europe to its breeding grounds on Arctic Svalbard. 3. Model results suggested that the birds follow a risk-averse strategy by avoiding sites with comparatively high energy expenditure or stochasticity levels in favour of sites with highly predictable food supply and low expenditure. Furthermore, the onset of spring on the stop-over sites had the most pronounced effect on staging times while intake rates had surprisingly little effect. 4. Subsequently, using empirical data, we tested whether observed changes in the onset of spring along the flyway explain the observed changes in migration schedules of pink-footed geese from 1990 to 2004. Model predictions generally agreed well with empirically observed migration patterns, with geese leaving the wintering grounds earlier while considerably extending their staging times in Norway.

Variability of the start of the growing season in Fennoscandia, 1982-2002.

Fennoscandia is characterized by a large degree of climatic diversity. Vegetation phenology may respond differently to climate change according to the climatic gradients within the region. To map the annual and spatial variability of the start of the growing season (SOS) in Fennoscandia, the twice-monthly GIMMS-NDVI satellite dataset was used. The data set has an 8 x 8 km(2) spatial resolution and covers the period from 1982 to 2002. The mapping was done by applying pixel-specific threshold values to the NDVI data. These threshold values were determined form surface phenology data on birch (Betula sp.). Then, we produced NDVI based maps of SOS for each of the 21 years. Finally, the time differences between the SOS and the last day of snow cover, as well as dates of passing different temperatures, were analyzed for 21 meteorological stations. The analyses showed that 1985 was the most extreme year in terms of late SOS. In terms of early SOS, the year 1990 was by far the most extreme. Locally, the SOS has an average range of 1 month between the earliest and latest recorded SOS, with a trend towards a bigger range in the oceanic parts. The results indicate that a 1 degrees C increase in spring temperatures in general corresponds to an advancement of 5-6 days in SOS. However, there is a clear trend according to the degree of oceanity, with a 1 degrees C increase in the most oceanic parts corresponding roughly to 7-9 days earlier SOS, compared to less than 5 days earlier in the continental parts.

Environmental phenology and geographical gradients in moose body mass.

Intraspecific body mass in ungulates has often been shown to increase with latitude. The biological basis for such latitudinal gradients is, however, poorly known. Here we examined whether satellite-derived indices of environmental phenology, based on the normalised difference vegetation index (NDVI), as well as variables derived from meteorological stations, altitude, and population density, can explain latitudinal gradients and regional variation in body mass of Norwegian moose. The best model gave a considerably better fit than latitude alone, and included all explanatory environmental variables. Accordingly, heavy moose were found in areas with short and intense summers that were followed by long, cold winters, at low altitude relative to the tree-limit, and with low population density relative to the available plant biomass. This relationship was stronger for yearlings than for calves, except for the effect of population density. This indicates that differences in the characteristics of the vegetation quality and environmental phenology, as well as winter harshness and population density, are important factors that shape both the latitudinal and other geographical gradients in moose body mass.

Adaptive contrast enhancement of two-dimensional electrophoretic protein gel images facilitates visualization, orientation and alignment.

2-DE is a powerful technique to discriminate post-translationally modified protein isoforms. However, all steps of 2-DE preparation and gel-staining may introduce unwanted artefacts, including inconsistent variation of background intensity over the entire 2-DE gel image. Background intensity variations limit the accuracy of gel orientation, overlay alignment and spot detection methods. We present a compact and efficient denoising algorithm that adaptively enhances the image contrast and then, through thresholding and median filtering, removes the gray-scale range covering the background. Applicability of the algorithm is demonstrated on immunoblots, isotope-labeled gels, and protein-stained gels. Validation is performed in contexts of (i) automatic gel orientation based on Hough transformation, (ii) overlay alignment based on cross correlation and (iii) spot detection. In gel stains with low background variability, e.g. Sypro Ruby, denoising will lower the spot detection sensitivity. In gel regions with high background levels denoising enhances spot detection. We propose that the denoising algorithm prepares images with high background for further automatic analysis, without requiring manual input on a gel-to-gel basis.

Population characteristics predict responses in moose body mass to temporal variation in the environment.

1. A general problem in population ecology is to predict under which conditions stochastic variation in the environment has the stronger effect on ecological processes. By analysing temporal variation in a fitness-related trait, body mass, in 21 Norwegian moose Alces alces (L.) populations, we examined whether the influence of temporal variation in different environmental variables were related to different parameters that were assumed to reflect important characteristics of the fundamental niche space of the moose. 2. Body mass during autumn was positively related to early access to fresh vegetation in spring, and to variables reflecting slow phenological development (low June temperature, a long spring with a slow plant progression during spring). In contrast, variables related to food quantity and winter conditions had only a minor influence on temporal variation in body mass. 3. The magnitude of the effects of environmental variation on body mass was larger in populations with small mean body mass or living at higher densities than in populations with large-sized individuals or living at lower densities. 4. These results indicate that the strongest influence of environmental stochasticity on moose body mass occurs towards the borders of the fundamental niche space, and suggests that populations living under good environmental conditions are partly buffered against fluctuations in environmental conditions.

The length of growing season and adult sex ratio affect sexual size dimorphism in moose.

While factors affecting body growth have been extensively studied, very little is known about the factors likely to affect the sexual size dimorphism (SSD) in polygynous mammals. Based on the carcass mass of 24420 male and female moose recorded in 14 Norwegian populations, we examine three hypotheses to explain geographical variation in SSD. First, SSD is expected to decrease when the relative density of animals (for a given habitat quality) increases, because resource limitation at high population densities is assumed to affect body growth of males more than females. Second, because males are selected to invest in growth more than females, environmental seasonality and related improvement of the forage quality during the short and intense growing season are expected to increase SSD. Third, by decreasing the proportion of adult males in the population, resulting in start of rutting earlier in life, hunting may decrease the SSD by increasing the reproductive cost of young males. We found that males grew faster and for a longer time of their life than did females and thus were heavier (-24%) when they reached adulthood. Sexual size dimorphism was independent of density but was higher in areas with short growing seasons. The low SSD in populations with largely adult female-biased sex ratios (males per female) shows that male body growth decreases with a decreasing proportion of adult males in the population. Our results indicate that geographical variation in moose SSD is influenced by divergent responses in the sexes to ecological factors affecting body growth.

Correlation analysis of two-dimensional gel electrophoretic protein patterns and biological variables.

BACKGROUND: Two-dimensional gel electrophoresis (2DE) is a powerful technique to examine post-translational modifications of complexly modulated proteins. Currently, spot detection is a necessary step to assess relations between spots and biological variables. This often proves time consuming and difficult when working with non-perfect gels. We developed an analysis technique to measure correlation between 2DE images and biological variables on a pixel by pixel basis. After image alignment and normalization, the biological parameters and pixel values are replaced by their specific rank. These rank adjusted images and parameters are then put into a standard linear Pearson correlation and further tested for significance and variance. RESULTS: We validated this technique on a set of simulated 2DE images, which revealed also correct working under the presence of normalization factors. This was followed by an analysis of p53 2DE immunoblots from cancer cells, known to have unique signaling networks. Since p53 is altered through these signaling networks, we expected to find correlations between the cancer type (acute lymphoblastic leukemia and acute myeloid leukemia) and the p53 profiles. A second correlation analysis revealed a more complex relation between the differentiation stage in acute myeloid leukemia and p53 protein isoforms. CONCLUSION: The presented analysis method measures relations between 2DE images and external variables without requiring spot detection, thereby enabling the exploration of biosignatures of complex signaling networks in biological systems.