@misc{JeltschBontePe'eretal.2013,
  author    = {Jeltsch, Florian and Bonte, Dries and Pe'er, Guy and Reineking, Bj{\"o}rn and Leimgruber, Peter and Balkenhol, Niko and Schr{\"o}der-Esselbach, Boris and Buchmann, Carsten M. and M{\"u}ller, Thomas and Blaum, Niels and Zurell, Damaris and B{\"o}hning-Gaese, Katrin and Wiegand, Thorsten and Eccard, Jana and Hofer, Heribert and Reeg, Jette and Eggers, Ute and Bauer, Silke},
  title     = {Integrating movement ecology with biodiversity research},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-401177},
  pages     = {13},
  year      = {2013},
  abstract  = {Movement of organisms is one of the key mechanisms shaping biodiversity, e.g. the distribution of genes, individuals and species in space and time. Recent technological and conceptual advances have improved our ability to assess the causes and consequences of individual movement, and led to the emergence of the new field of 'movement ecology'. Here, we outline how movement ecology can contribute to the broad field of biodiversity research, i.e. the study of processes and patterns of life among and across different scales, from genes to ecosystems, and we propose a conceptual framework linking these hitherto largely separated fields of research. Our framework builds on the concept of movement ecology for individuals, and demonstrates its importance for linking individual organismal movement with biodiversity. First, organismal movements can provide 'mobile links' between habitats or ecosystems, thereby connecting resources, genes, and processes among otherwise separate locations. Understanding these mobile links and their impact on biodiversity will be facilitated by movement ecology, because mobile links can be created by different modes of movement (i.e., foraging, dispersal, migration) that relate to different spatiotemporal scales and have differential effects on biodiversity. Second, organismal movements can also mediate coexistence in communities, through 'equalizing' and 'stabilizing' mechanisms. This novel integrated framework provides a conceptual starting point for a better understanding of biodiversity dynamics in light of individual movement and space-use behavior across spatiotemporal scales. By illustrating this framework with examples, we argue that the integration of movement ecology and biodiversity research will also enhance our ability to conserve diversity at the genetic, species, and ecosystem levels.},
  language  = {en}
}
@article{JeltschBontePeeretal.2013,
  author    = {Jeltsch, Florian and Bonte, Dries and Peer, Guy and Reineking, Bj{\"o}rn and Leimgruber, Peter and Balkenhol, Niko and Schr{\"o}der-Esselbach, Boris and Buchmann, Carsten M. and M{\"u}ller, Thomas and Blaum, Niels and Zurell, Damaris and B{\"o}hning-Gaese, Katrin and Wiegand, Thorsten and Eccard, Jana and Hofer, Heribert and Reeg, Jette and Eggers, Ute and Bauer, Silke},
  title     = {Integrating movement ecology with biodiversity research - exploring new avenues to address spatiotemporal biodiversity dynamics},
  doi       = {10.1186/2051-3933-1-6},
  year      = {2013},
  language  = {en}
}
@article{GrimmRevillaBergeretal.2005,
  author    = {Grimm, Volker and Revilla, Eloy and Berger, Uta and Jeltsch, Florian and Mooij, Wolf M. and Railsback, Steven Floyd and Thulke, Hans-Hermann and Weiner, Jacob and Wiegand, Thorsten and DeAngelis, Donald L.},
  title     = {Pattern-oriented modeling of agend-based complex systems : lessons from ecology},
  year      = {2005},
  abstract  = {Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity},
  language  = {en}
}