According to migration theory and several empirical studies, long-distance migrants are more time-limited during spring migration and should therefore migrate faster in spring than in autumn. Competition for the best breeding sites is supposed to be the main driver, but timing of migration is often also influenced by environmental factors such as food availability and wind conditions.Using GPS tags, we tracked 65 greater white-fronted geese Anser albifrons migrating between western Europe and the Russian Arctic during spring and autumn migration over six different years. Contrary to theory, our birds took considerably longer for spring migration (83 days) than autumn migration (42 days). This difference in duration was mainly determined by time spent at stopovers.Timing and space use during migration suggest that the birds were using different strategies in the two seasons: In spring they spread out in a wide front to acquire extra energy stores in many successive stopover sites (to fuel capital breeding), which is in accordance with previous results that white-fronted geese follow the green wave of spring growth. In autumn they filled up their stores close to the breeding grounds and waited for supportive wind conditions to quickly move to their wintering grounds. Selection for supportive winds was stronger in autumn, when general wind conditions were less favourable than in spring, leading to similar flight speeds in the two seasons. In combination with less stopover time in autumn this led to faster autumn than spring migration.White-fronted geese thus differ from theory that spring migration is faster than autumn migration. We expect our findings of different decision rules between the two migratory seasons to apply more generally, in particular in large birds in which capital breeding is common, and in birds that meet other environmental conditions along their migration route in autumn than in spring.
Many migratory herbivores seem to follow the flush of plant growth during migration in order to acquire the most nutrient-rich plants. This has also been hypothesized for arctic-breeding geese, but so far no test of this so-called green wave hypothesis has been performed at the individual level. During four years, a total of 30 greater white-fronted geese Anser albifrons albifrons was tracked using GPS transmitters, of which 13 yielded complete spring migration tracks. From those birds we defined stopover sites and related the date of arrival at each of these stopovers to temperature sum (growing degree days, GDD), snow cover, accumulated photoperiod and latitude. We found that geese arrived at spring stopovers close to the peak in GDD jerk; the 'jerk' is the third derivative, or the rate of change in acceleration, and GDD jerk maxima therefore represent the highest acceleration of daily temperature per site. Day of snow melt also correlated well with the observed arrival of the geese. Factors not closely related to onset of spring, i.e. accumulated photoperiod and latitude, yielded poorer fits. A comparison with published data revealed that the GDD jerk occurs 1-2 weeks earlier than the onset of spring derived from NDVI, and probably represents the very start of spring growth. Our data therefore suggest that white-fronted geese track the front of the green wave in spring.
The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.
Recent outbreaks of highly pathogenic avian influenza (HPAI) in poultry have raised interest in the interplay between avian influenza (AI) viruses and their wild hosts. Studies linking virus ecology to host ecology are still scarce, particularly for non-duck species. Here, we link capture-resighting data of greater white-fronted geese Anser albifrons albifrons with the AI virus infection data collected during capture in The Netherlands in four consecutive winters. We ask what factors are related to AI virus prevalence and whether there are ecological consequences associated with AI virus infection in staging white-fronted geese. Mean seasonal (low pathogenic) AI virus prevalence ranged between 2.5 and 10.7 per cent, among the highest reported values for non-duck species, and occurred in distinct peaks with near-zero prevalence before and after. Throat samples had a 2.4 times higher detection frequency than cloacal samples. AI virus infection was significantly related to age and body mass in some but not other winters. AI virus infection was not related to resighting probability, nor to maximum distance travelled, which was at least 191 km during the short infectious lifespan of an AI virus. Our results suggest that transmission via the respiratory route could be an important transmission route of AI virus in this species. Nearzero prevalence upon arrival on their wintering grounds, in combination with the epidemic nature of AI virus infections in white-fronted geese, suggests that white-fronted geese are not likely to disperse Asian AI viruses from their Siberian breeding grounds to their European wintering areas.
IntroductionHighly pathogenic avian influenza (HPAI) viruses of subtype H5N8 were re-introduced into the Netherlands by late 2016, after detections in south-east Asia and Russia. This second H5N8 wave resulted in a large number of outbreaks in poultry farms and the deaths of large numbers of wild birds in multiple European countries. Methods: Here we report on the detection of HPAI H5N8 virus in 57 wild birds of 12 species sampled during active (32/5,167) and passive (25/36) surveillance activities, i.e. in healthy and dead animals respectively, in the Netherlands between 8 November 2016 and 31 March 2017. Moreover, we further investigate the experimental approach of wild bird serology as a contributing tool in HPAI outbreak investigations. Results: In contrast to the first H5N8 wave, local virus amplification with associated wild bird mortality has occurred in the Netherlands in 2016/17, with evidence for occasional gene exchange with low pathogenic avian influenza (LPAI) viruses. Discussion: These apparent differences between outbreaks and the continuing detections of HPAI viruses in Europe are a cause of concern. With the current circulation of zoonotic HPAI and LPAI virus strains in Asia, increased understanding of the drivers responsible for the global spread of Asian poultry viruses via wild birds is needed.
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