Speco aeet meeting coimbra_2015_poster_ javier morente lopez
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
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).
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
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
Gredos 0,7217 0,3044
Guadarrama 0,5915 0,2994
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
Flow+Mountain + RF 16
Mountain + RF 19
(Intercept) 18,2090 1,1589
F2 -2,0606 1,6185
F3 -1,7802 1,6540
F4 -2,6018 1,6685
F5 -2,7672 1,6556
Gredos -5,6403 1,5506
Guadarrama -3,7035 1,4946
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
Population (Intercept) 0,0004
Mother:Population (Intercept) 1,2690
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).
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
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
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
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
•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.
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.