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Introduction Methods
Results & Discussion
Effects of gene flow on local adaptation: the role of marginal populations
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Speco aeet meeting coimbra_2015_poster_ javier morente lopez

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Speco aeet meeting coimbra_2015_poster_ javier morente lopez

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Speco aeet meeting coimbra_2015_poster_ javier morente lopez

  1. 1. Introduction Methods Results & Discussion Effects of gene flow on local adaptation: the role of marginal populations Morente-López, J., García-Fernández, A., Lara-Romero, C., Rubio, M.L., Ruiz, R., Sánchez, A., Iriondo, J.M. Small edge populations can be affected by processes of inbreeding and genetic drift. Gene flow from large populations at the center of the range can cause different effects on edge populations at the range limit. It may cause a maladaptative flow of non-local genes and an increase of genetic variation. In a similar way, gene flow between small edge populations may provide a beneficial contribution of similar adapted genes and an increase in genetic variability (Sexton el. al. 2011). But these ideas are not easily tested empirically. In this study we tested the effects of different kinds of gene flow on the germination capacity of seeds of populations at the ecological range edge. Thus, we assessed the presence of inbreeding depression in edge populations. Are marginal populations genetically impoverished and maladapted?... … or are they able to adapt locally to the existing limiting environmental conditions? What is the effect of gene flow between populations subject to contrasting limiting environmental conditions? Questions Conclusions Universidad Rey Juan Carlos (URJC), Área de Biodiversidad y Conservación. C/ Tulipán s.n. Madrid, Spain. Este trabajo ha sido parcialmente financiado por el proyecto AdAptA (CGL2012-33528). Population selection: We modeled the habitat of the species to classify the species distribution in optimal and suboptimal areas. 3 central populations (optimal areas) and 6 marginal populations (suboptimal areas) distributed in three different mountains were selected attending to the ecological range of the species. Post Hoc (Tukey): Significant differences between F1/F4 and F1/F5, although a strong gene flow x mountain interaction was found (variation between mountains). Dependent Variable: Germination Random Factors (RF): (Montaña|Poblacion/Madre) MODEL´s ∆ AIC´s Flow*Mountain + RF − R2 : 0,11 Flow + RF 17 Flow + Montaña + RF 21 Intercept + RF 24 Muntain + RF 27 Fixed effects: Random effects: Estimate Std. Std.Dev. (Intercept) -0,4527 0,2778 Mother: Population (Intercept) 0,8046 Gene Flow Gredos 0,7468 F2 0,4820 0,1501 Guadarrama 0,2121 F3 0,4684 0,1520 Population (Intercept) 0,3226 F4 0,5335 0,1474 Gredos 0,3230 F5 0,9103 0,1509 Guadarrama 0,3226 Mountain Gredos 0,7217 0,3044 Guadarrama 0,5915 0,2994 Gene Flow: Mountain F2:Gredos -0,5968 0,1869 F3:Gredos -0,5345 0,1881 F4:Gredos -0,4915 0,1873 F5:Gredos -0,8157 0,1880 F2:Guadarrama -0,3520 0,1806 F3:Guadarrama -0,4076 0,1859 F4:Guadarrama -0,3178 0,1836 F5:Guadarrama -0,9680 0,1850 Dependent Variable: Weighted Mean Germination Time Random Factors (RF): (Montaña|Poblacion/Madre) MODEL´s ∆ AIC´s Flow*Mountain + RF − R2 : 0,10 Flow+Mountain + RF 16 Mountain + RF 19 Fixed effects: Estimate Std. (Intercept) 18,2090 1,1589 Gene Flow F2 -2,0606 1,6185 F3 -1,7802 1,6540 F4 -2,6018 1,6685 F5 -2,7672 1,6556 Mountain Gredos -5,6403 1,5506 Guadarrama -3,7035 1,4946 Gene Flow:Mountain F2:Gredos 3,1081 2,0889 F3:Gredos 3,1668 2,1053 F4:Gredos 3,6949 2,1470 F5:Gredos 1,9786 2,1253 F2:Guadarrama -1,0733 2,0465 F3:Guadarrama 0,4365 2,1170 F4:Guadarrama 2,6147 2,1242 F5:Guadarrama 1,4240 2,1032 Random effects: Std.Dev. Population (Intercept) 0,0004 Gredos 0,0004 Guadarrama 0,0002 Mother:Population (Intercept) 1,2690 Gredos 3,1901 Guadarrama 1,7239 We used Silene cililata Pourr. (Caryophillaceae), a chamaephytic cushion plant that grows in alpine cryophylic pastures of the Mediterranean mountains. Gen flow simulations: In the summer of 2014 we artificially cross-pollinated mother plants from 6 marginal populations using pollen from the same population (F1), from marginal and central populations of the same mountain (F2 and F3) and from marginal and central populations of another mountain (F4 and F5). Seed germination: In 2015, 12.000 seeds obtained from the controlled pollination were placed individually in 48-well plates and incubated in growth chambers for two months at 20:15ºC, day:night temperatures and with a 16:8 h light/dark period. Seeds had previously been subject to cold-humid stratification for two months. Germination was recorded every two or three days. Data analysis: We calculated final germination (G) and weighted mean germination time (WMGT). We then performed General Linear Mixed Models for these two dependent variables and classified them according to the Akaike information criterion (AIC). We included type of gene flow and mountain as fixed factors and population and mother plant both nested in mountain as a random factor to control the effects of other non-measured local factors. The best model selected by AIC for final germination included type of gene flow and mountain as explanatory variables. Random effects had a great influence on the variance principally caused by the effect of mother plant. There was a significant effect of type of gene flow on germination although it differed between mountains. Gene flow from other mountains and especially from central populations increased the levels of seed germination. This suggests that this type of gene flow facilitated an increase of genetic diversity and promoted a decrease of inbreeding depression. Nevertheless, this pattern was not consistent among mountains. This possibly indicates different responses of marginal populations. The responses of WMGT to type of gene flow were different depending on the mountains. The best model selected by AIC for WMGT also included type of gene flow and mountain. Similarly to what observed in Germination, random effects had a great influence on the variance principally caused by the effect of mother plant. •Type of gene flow had a significant effect on germination and weighted mean germination time, although within-population variability of individuals (Mother plants) had the greatest effect on the variance explained. The gene flow from distant populations, especially from the central part of the range seemed to improve these parameters of relative fitness. •More variables that give us information at different life stages should be considered to improve the study and deepen our understanding of the processes that are occurring. •Field work under natural conditions is needed to assess the presence of processes of local adaptation. In this line, we are carrying out an “in situ” seed sowing experiment with 24.000 seeds replicating the same types of gene flow. Acknowledgements: We thank all people that help us in greenhouse work, specially Sandra Gomez Perez, Sergio Eleazar García and Pablo Tabares. We also want to thank the Consejería Superior de Medio Ambiente de Madrid and the Servicio Territorial de Medio Ambiente de Ávila and Salamanca for the corresponding permits for field work.

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