Bibliographic Details
| Title: |
A periodic Markov model to formalize animal migration on a network |
| Authors: |
Andrea Kölzsch, Erik Kleyheeg, Helmut Kruckenberg, Michael Kaatz, Bernd Blasius |
| Source: |
Royal Society Open Science, Vol 5, Iss 6 (2018) |
| Publisher Information: |
The Royal Society, 2018. |
| Publication Year: |
2018 |
| Collection: |
LCC:Science |
| Subject Terms: |
spatial migration network, graph theory, periodic markov process, white stork, greater white-fronted goose, Science |
| Description: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2054-5703 |
| Relation: |
https://doaj.org/toc/2054-5703 |
| DOI: |
10.1098/rsos.180438 |
| Access URL: |
https://doaj.org/article/e4976bafaa7f417aba409d1bb5f53375 |
| Accession Number: |
edsdoj.4976bafaa7f417aba409d1bb5f53375 |
| Database: |
Directory of Open Access Journals |