Sensor:
Argos Doppler Shift

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Name
Argos Doppler Shift
External ID
argos-doppler-shift
Is Location Sensor
true

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2016 - 20182
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search.filters.applied.f.scientificName: Anser albifrons ×

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Now showing 1 - 2 of 2
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    Data package
    Data from: Towards a new understanding of migration timing: slower spring than autumn migration in geese reflects different decision rules for stopover use and departure
    (2016-02-25) Kölzsch, Andrea; Kruckenberg, Helmut; Glazov, Peter; Müskens, Gerhard J.D.M.; Wikelski, Martin
    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.
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    Data package
    Data from: A periodic Markov model to formalise animal migration on a network [white-fronted goose data]
    (2018-06-13) Kruckenberg, Helmut; Müskens, Gerhard J.D.M.; Ebbinge, Barwolt S.
    NOTE: A portion of these same individuals and data are also published with doi 10.5441/001/1.31c2v92f. Regular, long-distance migrations of thousands of animal species have consequences for the ecosystems that they visit, modifying trophic interactions and transporting many non-pathogenic and pathogenic organisms. The spatial structure and dynamic properties of animal migrations and population flyways largely determine those trophic and transport effects, but are yet poorly studied. As a basis, we propose a periodic Markov model on the spatial migration network of breeding, stopover and wintering sites to formally describe the process of animal migration on the population level. From seasonally changing transition rates we derived stable, seasonal densities of animals at the network nodes. We parametrized the model with high-quality GPS and satellite telemetry tracks of white storks (Ciconia ciconia) and greater white-fronted geese (Anser a. albifrons). Topological and network flow properties of the two derived networks conform to migration properties like seasonally changing connectivity and shared, directed movement. Thus, the model realistically describes the migration movement of complete populations and can become an important tool to study the effects of climate and habitat change and pathogen spread on migratory animals. Furthermore, the property of periodically changing transition rates makes it a new type of complex model and we need to understand its dynamic properties.