@misc{ShawGafos2015,
  author    = {Shaw, Jason A. and Gafos, Adamantios I.},
  title     = {Stochastic time models of syllable structure},
  series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  number    = {514},
  issn      = {1866-8364},
  doi       = {10.25932/publishup-40981},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-409815},
  pages     = {36},
  year      = {2015},
  abstract  = {Drawing on phonology research within the generative linguistics tradition, stochastic methods, and notions from complex systems, we develop a modelling paradigm linking phonological structure, expressed in terms of syllables, to speech movement data acquired with 3D electromagnetic articulography and X-ray microbeam methods. The essential variable in the models is syllable structure. When mapped to discrete coordination topologies, syllabic organization imposes systematic patterns of variability on the temporal dynamics of speech articulation. We simulated these dynamics under different syllabic parses and evaluated simulations against experimental data from Arabic and English, two languages claimed to parse similar strings of segments into different syllabic structures. Model simulations replicated several key experimental results, including the fallibility of past phonetic heuristics for syllable structure, and exposed the range of conditions under which such heuristics remain valid. More importantly, the modelling approach consistently diagnosed syllable structure proving resilient to multiple sources of variability in experimental data including measurement variability, speaker variability, and contextual variability. Prospects for extensions of our modelling paradigm to acoustic data are also discussed.},
  language  = {en}
}
@misc{MakowiczTiedemannSteeleetal.2016,
  author    = {Makowicz, Amber M. and Tiedemann, Ralph and Steele, Rachel N. and Schlupp, Ingo},
  title     = {Kin recognition in a clonal fish, Poecilia formosa},
  series = {PLoS ONE},
  journal   = {PLoS ONE},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-411329},
  pages     = {20},
  year      = {2016},
  abstract  = {Relatedness strongly influences social behaviors in a wide variety of species. For most species, the highest typical degree of relatedness is between full siblings with 50\% shared genes. However, this is poorly understood in species with unusually high relatedness between individuals: clonal organisms. Although there has been some investigation into clonal invertebrates and yeast, nothing is known about kin selection in clonal vertebrates. We show that a clonal fish, the Amazon molly (Poecilia formosa), can distinguish between different clonal lineages, associating with genetically identical, sister clones, and use multiple sensory modalities. Also, they scale their aggressive behaviors according to the relatedness to other females: they are more aggressive to non-related clones. Our results demonstrate that even in species with very small genetic differences between individuals, kin recognition can be adaptive. Their discriminatory abilities and regulation of costly behaviors provides a powerful example of natural selection in species with limited genetic diversity.},
  language  = {en}
}