@article{fdi:010065421,
  title = {{E}lucidating the spatio-temporal dynamics of an emerging wildlife pathogen using approximate {B}ayesian computation},
  author = {{R}ey, {O}. and {F}ourtune, {L}. and {P}az-{V}inas, {I}. and {L}oot, {G}. and {V}eyssiere, {C}. and {R}oche, {B}enjamin and {B}lanchet, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{E}merging pathogens constitute a severe threat for human health and biodiversity. {D}etermining the status (native or non-native) of emerging pathogens, and tracing back their spatio-temporal dynamics, is crucial to understand the eco-evolutionary factors promoting their emergence, to control their spread and mitigate their impacts. {H}owever, tracing back the spatio-temporal dynamics of emerging wildlife pathogens is challenging because (i) they are often neglected until they become sufficiently abundant and pose socio-economical concerns and (ii) their geographical range is often little known. {H}ere, we combined classical population genetics tools and approximate {B}ayesian computation (i.e. {ABC}) to retrace the dynamics of {T}racheliastes polycolpus, a poorly documented pathogenic ectoparasite emerging in {W}estern {E}urope that threatens several freshwater fish species. {O}ur results strongly suggest that populations of {T}. polycolpus in {F}rance emerged from individuals originating from a unique genetic pool that were most likely introduced in the 1920s in central {F}rance. {F}rom this initial population, three waves of colonization occurred into peripheral watersheds within the next two decades. {W}e further demonstrated that populations remained at low densities, and hence undetectable, during 10 years before a major demographic expansion occurred, and before its official detection in {F}rance. {T}hese findings corroborate and expand the few historical records available for this emerging pathogen. {M}ore generally, our study demonstrates how {ABC} can be used to determine the status, reconstruct the colonization history and infer key evolutionary parameters of emerging wildlife pathogens with low data availability, and for which samples from the putative native area are inaccessible.},
  keywords = {approximate {B}ayesian computation ; {B}ayesian clustering ; pathogen ; population genetics ; spatio-temporal dynamic ; {FRANCE}},
  booktitle = {},
  journal = {{M}olecular {E}cology},
  volume = {24},
  numero = {21},
  pages = {5348--5363},
  ISSN = {0962-1083},
  year = {2015},
  DOI = {10.1111/mec.13401},
  URL = {https://www.documentation.ird.fr/hor/fdi:010065421},
}