Plant phenological variation related to temperature in Norway during the period 1928-1977
First flowering was observed in some native herbaceous and woody plants in Norway at latitudes of ∼58°N to nearly 71°N from 1928 to 1977. For woody plants, the timing for first bud burst was also often observed. Generally, there were highly significant correlations (0.1% level) between the timing of nearly all spring-early summer observations in plants and gridded mean monthly temperatures for the various phenophases (up to 65% of the variance was accounted for, less so for the autumn phenophases). Analyses by a low pass Gaussian smoothing technique showed early phenophases in the warm period of the early 1930s, delayed phases for most sites and species in colder periods in the early 1940s, mid-1950s, late 1960s and also towards the end of the study period in the late 1970s, all in approximately 10- to 12-year cycles. The study thus starts in a relatively early (warm) period and ends towards a late (cooler) period, resulting in mainly weak linear trends in phenophases throughout the total period. The end of the observation period in 1977 also predates the strongly increasing "earliness" in phenology of plants in most Norwegian lowland areas due to global warming. The strong altitudinal and latitudinal variations in Norway, however, do cause regional differences in trends. The study showed a tendency towards earlier spring phenophases all along the western coast from south to north in the country. On the other hand, the northeasternmost site and also the more continental sites in the southeast showed tendencies to weak trends for later phenophases during the 50 years of these field observations.
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Available from: Hans C. Pedersen
- "The OPG in spring is related to weather conditions such as snow-cover and temperature (Wielgolaski et al. 2011; Odland 2011). Variation in the timing of plant growth can possibly affect recruitment of juveniles through its effect on maternal nutrition and prey availability (Steen et al. 1988a; Moss and Watson 1984; Erikstad and Spidso 1982). "
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ABSTRACT: Recruitment of juveniles is important for the size of the next year's breeding population in many bird species. Climate variability and predation may affect recruitment rates, and when these factors are spatially correlated, recruitment rates in spatially separated popula-tions of a species may be synchronized. We used produc-tion data from an extensive survey of Willow Ptarmigan from 2000 to 2011 to investigate spatial synchrony in recruitment of juveniles within and among mountain region populations. In addition, we assessed the effects of preda-tion and large—as well as local—scale climate on recruitment of juveniles. Recruitment was synchronized both within and among mountain regions, but the mean spatial correlation was strongest among mountain regions. This may be caused by small-scale factors such as preda-tion or habitat structure, or be a result of sampling varia-tion, which may be large at small spatial scales. The strong synchrony suggests that populations are subject to similar environmental forces. We used mixed effect models at the survey area and mountain region scales to assess the effect of rodent abundance (a proxy for predation rates) and local and regional climate during the breeding season on the recruitment of juvenile birds. Model selection based on AICc revealed that the most parsimonious models at both spatial scales included positive effects of rodent abundance and the North Atlantic oscillation during May, June and July (NAO MJJ). The NAO MJJ index was positively related to temperature and precipitation during the pre-incubation period; temperature during the incubation period and positive NAO MJJ values accelerate plant growth. A com-parison of the relative effects of NAO MJJ and rodent abundance showed that variation in NAO MJJ had greatest impact on the recruitment of juveniles. This suggests that the climate effect was stronger than the effect of rodent abundance in our study populations. This is in contrast to previous studies on Willow Ptarmigan, but may be explained by the collapse in rodent cycles since the 1990s. If Willow Ptarmigan dynamics in the past were linked to the rodent cycle through a shared predator regime, this link may have been weakened when rodent cycles became more irregular, resulting in a more pronounced effect of envi-ronmental perturbation on the dynamics of ptarmigan. Keywords Spatial synchrony Á NAO Á Recruitment of juveniles Á Ptarmigan Á Temperature Á Precipitation Á Breeding season Á Alternative prey hypothesis Á Local weather Á Breeding success Á Onset of plant growth
Journal of Ornithology 04/2014; 155(4). DOI:10.1007/s10336-014-1072-6 · 1.71 Impact Factor
Available from: Irmgard Krisai-Greilhuber
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ABSTRACT: In terrestrial ecosystems, fungi are the major agents of decomposition processes and nutrient cycling and of plant nutrient uptake. Hence, they have a vital impact on ecosystem processes and the terrestrial carbon cycle. Changes in productivity and phenology of fungal fruit bodies can give clues to changes in fungal activity, but understanding these changes in relation to a changing climate is a pending challenge among ecologists. Here we report on phenological changes in fungal fruiting in Europe over the past four decades. Analyses of 746,297 dated and geo-referenced mushroom records of 486 autumnal fruiting species from Austria, Norway, Switzerland, and the United Kingdom revealed a widening of the annual fruiting season in all countries during the period 1970-2007. The mean annual day of fruiting has become later in all countries. However, the interspecific variation in phenological responses was high. Most species moved toward a later ending of their annual fruiting period, a trend that was particularly strong in the United Kingdom, which may reflect regional variation in climate change and its effects. Fruiting of both saprotrophic and mycorrhizal fungi now continues later in the year, but mycorrhizal fungi generally have a more compressed season than saprotrophs. This difference is probably due to the fruiting of mycorrhizal fungi partly depending on cues from the host plant. Extension of the European fungal fruiting season parallels an extended vegetation season in Europe. Changes in fruiting phenology imply changes in mycelia activity, with implications for ecosystem function.
Proceedings of the National Academy of Sciences 08/2012; 109(36):14488-93. DOI:10.1073/pnas.1200789109 · 9.67 Impact Factor
Available from: Hans Tømmervik
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ABSTRACT: Global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. In Fennoscandia, as well as Northern Europe, the advances of phenological events in spring have been recorded in recent decades. In this study, we analyzed the start of the growing season within five different vegetation regions in Fennoscandia using the 30-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g dataset. We applied a previously developed pixel-specific Normalized Difference Vegetation Index (NDVI) threshold method, adjusted it to the NDVI3g data and analyzed trends within the different regions. Results show a warming trend with an earlier start of the growing season of 11.8 +/- 2.0 days (p < 0.01) for the whole area. However, there are large regional differences, and the warming/trend towards an earlier start of the growing season is most significant in the southern regions (19.3 +/- 4.7 days, p < 0.01 in the southern oceanic region), while the start was stable or modest earlier (two to four days; not significant) in the northern regions. To look for temporal variations in the trends, we divided the 30-year period into three separate decadal time periods. Results show significantly more change/trend towards an earlier start of the growing season in the first period compared to the two last. In the second and third period, the trend towards an earlier start of the growing season slowed down, and in two of the regions, the trend towards an earlier start of the growing season was even reversed during the last decade.
Remote Sensing 09/2013; 5(9):4304-4318. DOI:10.3390/rs5094304 · 3.18 Impact Factor
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