@article{Gschweng2012Multi-25871,
  year={2012},
  doi={10.1111/j.1365-2664.2012.02170.x},
  title={Multi-temporal distribution modelling with satellite tracking data : predicting responses of a Long-distance migrant to changing environmental conditions},
  number={4},
  volume={49},
  issn={0021-8901},
  journal={Journal of Applied Ecology},
  pages={803--813},
  author={Gschweng, Marion and Kalko, Elisabeth K. V. and Berthold, Peter and Fiedler, Wolfgang and Fahr, Jakob}
}

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    <dc:creator>Berthold, Peter</dc:creator>
    <dc:creator>Fiedler, Wolfgang</dc:creator>
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    <dc:creator>Kalko, Elisabeth K. V.</dc:creator>
    <dc:contributor>Gschweng, Marion</dc:contributor>
    <dcterms:abstract xml:lang="eng">1. Despite the wealth of data available from satellite tracking (ST) studies, such data have rarely been used to model species distributions. Using a novel method, we show how to exploit satellite data to analyse whether and how a migratory species responds to fluctuating environmental conditions in its wintering area. This is particularly crucial for establishing comprehensive conservation measures for rare species in areas that are threatened by increasing land use and climate change.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;2. We use ST data of Eleonora’s falcon Falco eleonorae, a long-distance migratory raptor that winters in Madagascar, and assess the performance of static species distribution models (SDM) as well as multi-temporal models. ST data were derived from seven falcons tracked during three consecutive wintering periods and for a total of 2410 bearings, of which 512 locations were used in SDMs. We employed environmental predictors (climate, topography and land cover) with a spatial resolution of 30 arc seconds (c. 1 km2) to match rigorously filtered ST data with an accuracy of ≤1 km.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;3. We first created a model with low temporal but high spatial resolution (half-year). To predict suitable habitat for each month of the wintering season, we took advantage of the high temporal resolution inherent in ST data and employed temporally corresponding remote sensing data [Normalized Difference Vegetation Index (NDVI) 10-day composites] together with other variables to create monthly models.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;4. We show that ST data are suited to build robust and transferable SDMs despite a low number of tracked individuals. Multi-temporal SMDs further revealed seasonal responses of the study species to changing environmental conditions in its wintering area.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;5. Synthesis and applications. We present a transferable approach to predict the potential distribution of organisms as well as their dynamic response to changing environmental conditions. Future conservation management plans could include the prediction of a species’ reaction to changing land-use practices or climate change based on the methodology proposed here. This would provide an early warning system for the decline of populations wintering in remote areas that underlie strong climatic fluctuations.</dcterms:abstract>
    <dc:creator>Fahr, Jakob</dc:creator>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-01-15T08:43:32Z</dc:date>
    <dcterms:bibliographicCitation>Journal of Applied Ecology ; 49 (2012), 4. - S. 803-813</dcterms:bibliographicCitation>
    <dc:contributor>Kalko, Elisabeth K. V.</dc:contributor>
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    <dcterms:title>Multi-temporal distribution modelling with satellite tracking data : predicting responses of a Long-distance migrant to changing environmental conditions</dcterms:title>
    <dc:contributor>Fahr, Jakob</dc:contributor>
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    <dc:contributor>Fiedler, Wolfgang</dc:contributor>
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