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Connectivity of Marine Protected Areas in South-Western Indian Ocean: Using population genetics of reef fish to contribute to the design of MPAs network. - Delphine MUTHS & Jérôme BOURJEA -. Connectivity of Marine Protected Areas in South-Western Indian Ocean. - PowerPoint PPT Presentation

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  • - Delphine MUTHS & Jrme BOURJEA -Connectivity of Marine Protected Areas in South-Western Indian Ocean:

    Using population genetics of reef fish to contribute to the design of MPAs network

  • A MPA is an area delimited in sea, which aims to protect biodiversity in a sustainable perspective

    BUT, for a real efficiency, MPAs need to be integrated in a dense network of representative, individually well-managed and connected MPAs (Mora et al., 2006 - Science )

    How could we estimate the scale of connectivity in marine environment? 26 actual MPAs in the SWIO

  • Using population genetics to estimate efficient connectivity:

  • Marine habitats are often thought as a well-mixed environment with no boundaries. In fact genetic data help to show that dispersal is often very limited:Kinlan & Gaines,2007 Ecology:Literature compilation based on genetic data Which is the scale of connectivity for reef fish in the SWIO?

  • 1/ Elaborate a regional strategy of sampling and use population genetics methodology

    2/ Better understand actual exchanges between populations and help to localize the most isolated areas

    3/ Precise colonisation history of the SWIO

    4/ Contribute to the design of the most efficient MPAs network in the SWIOStrategy of our project CAMP:

  • - Myripristis berndti - Epinephelus merra - Lutjanus kasmira 2 genetic markers: mitochondrial DNA sequencing & microsatellite genotyping 3 species of fishing interestStrategy of our project:

  • - Myripristis berndti - Epinephelus merra - Lutjanus kasmira 2 genetic markers: mitochondrial DNA sequencing & microsatellite genotyping 3 species of fishing interestPreliminary results:Is there similar connectivity pattern and/or isolated areas between these 3 species?

  • We could extract informations from: the morphology of the networkExample of haplotype network : 1 black point = 1 haplotype = 1 unique sequence

    1 grey bar = 1 mutation

    1 color = 1 site the frequency of the different haplotypes the geographical representation of each haplotype

  • Haplotype network which indicated: A star-like pattern, typical of a population in extension (in agreement with a significant DTajima) A main central haplotype = a unique colonisation event Haplotypes homogeneously represented between sitesHd = 0.781 = 0.0027DTajima = -2.4**Results for Lutjanus kasmira:

  • Results for Myripristis berndti:A more complex haplotype network with: A divergent branch indicating connection with Pacific fish, that is long-distance connectivity Haplotypes homogeneously represented between sitesHd = 0.886 = 0.0035DTajima = -1.8**

  • Results for Epinephelus merra:Even more complicated network which indicated: A few well-represented haplotypes = ancestral polymorphism retention, giving evidence of multiple colonisation events A significant DTajima = population still in extension Haplotypes homogeneously reparted between sitesHd = 0.762 = 0.0021DTajima = -2.1**

  • For the 3 species: Different stories : more recent expansion for L. kasmira, more complex for E. merra

    A diversity globaly shared between the different sites Comparison of the results for the 3 species An other genetic indicator is the calculation of Fst, that is fixation index between pair of sites

  • Fst = Fixation index between populationsMore Fst 1, more populations are differentiatedFst Results for the 3 species:

  • Continue the sampling for the CAMP project in the other SWIO sites

    Develop the 2nd genetic marker, i.e. microsatellite, for the 3 species: obtain the best mapping of exchanges between populations

    Integrate all the genetic results in a global ecological background (oceanic currents, biogeographical boundaries, )

    Communicate synthetic results (cartography) to assessment & authorities: Help to understand the MPAs connections and isolated areas

    Preliminary results give interesting perspectives:

  • - Thanks for your attention -Many thanks to:

    people who help us for fish sampling,

    - to our main partners: Rserve Naturelle Marine de La Runion France Marine Park of Mohli Comoros Seychelles Fishing Authority Seychelles SA Institute of Aquatic Biology South Africa Mauritius Oceanographic Institute Mauritius (in progress)

    - to our financial supports: WIOMSA (MASMA Grant)POCT-OI (Europe, France, La Runion) TCO

    Good afternoonIm Delphine MuthsI work here in Reunion island at IfremerAnd Im here today to talk about the Connectivity of Marine Protected Areas in South-Western Indian Ocean:And more particularly on the Use of population genetics of reef fish to contribute to the design of MPAs networkAs you know, a Marine Protected Area is an area delimited in sea, which aims to protect biodiversity in a sustainable perspective BUT for a real efficiency, MPAs need to be integrated in a dense network of representative individually well-managed and connected MPAs

    Here are represented most of the 26 MPAs in the SWIO

    So our question is : How could we estimate the scale of connectivity in marine environment?

    One way to estimate the efficient connectivity is the use of population genetics:Indeed, more population are isolated from each other and more the genetic signature existing between those populations will be differentOn the other side, more the population will be connected and more the genetic differentiation will decrease until a situation of total mixing, called panmixia (= no genetic differentiation).

    Generally speaking, Marine habitats are often thought as a well-mixed environment with no boundaries. In fact dispersal is often much more limited: Thus we could see on this graph based on a literature compilation made by Kinlan & Gaines in 2007 on genetic data that dispersion is restricted under the scale of a km for seaweeds, from 1m to a thousand of kilometer for invertebrates and 1 to 1000 km for fish

    Thus, genetic data allow to know the exact scale of connectivityIn the present case, we will focus on reef fish in the SWIO

    To answer this question, the idea of the project is to design a regional strategy of sampling, and analyze them with population genetics methodologyIn the way of better understanding actual

    In a more practical way, the project is based on 3 species of fishing interest. They all have a pelagic larval phase of 30/50 days-long

    We plan to use at least 2 genetic markers

    We hope to sample 13 sampling sites At that time of the project, I could present you preliminary results obtained on the 3 species but with only one marker, and for 6 sitesThe aim is to view if we can observe similar pattern of connectivity or in the opposite similar isolated sitesOne classic way to represent mtDNA information is network of haplotypesWith haplotype, that is unique sequence, represented by black spots

    we could extract information from the morphology of the network itself, from the frequency of the different haplotypes and from the geographical representation of each haplotype

    If we look at the data obtained for L.kasmira,We see a classic star-like pattern with A main central haplotypeThat is typical for a population in expansion, resulting from 1 unique event of colonisation

    Most of the haplotypes seems homogeneously distributed between sites

    The situation for Myrybristis berndti is more complicated with a divergent branch here which indicates connection with the pacific haplotypes

    The network obtained with epinephelus sequences is even more complicatedWith a few of well-represented haplotypes which means that the epinephelus present in the SWIO came from multiple colonisation events.If we sum up we have three species with three different histories. A more recent expansion for Lkasmira and a more complicated story of colonisation for E.merra. The diversity seem to be globally shared between the populations

    To go deeply, An other genetic indicator is the calculation of Fst, that is the fixation index between pair of sites, mainly based on differences in haplotype frequencies

    Results obtained on Lkasmira show that, even if there is some differences (positive value), they are no significant values,That means that on the basis of this genetic marker, there is enough connectivity between sites to homogenize the genetic pool of LkasmiraThe situation is quite the same for MberndtiOn the opposite,We can see with E merra that the differentiation between Reunion islands and some of the other sites is significant. That means that for this species and this marker there is not enough connectivity to homogenise the genetic pool

    For the three species, the highest values of differentiation always include Reunion sites, logically as it is the most distant sites: pattern of isolation by distanceOn the other hand and in a context of MPAs design, the fact that Mayotte is always much more similar than Reunion than from the other closer sites is really interesting.

    The information given by a 2nd marker will permit to confirm or not the similarity between those sites. Even if this results are only preliminary (only one genetic marker, not all the samples) and in the state, they could not permit to deal with the design of MPA network, it helps to view how genetic data could help in conservation programs

    Bmol: information given from mitochondrial sequences are only a partial view and the development of the 2nd markers will help gratly to design the best map of exchanges

    I would profit of this opportunity to thank all the people who help us in this project, for the sampling but also our partners and our financial supportAlso thank you for listening me.