Genetic structure of the Marseilles cat population: is there really a strong founder effect ?

HAL Id: hal-00894036

https://hal.archives-ouvertes.fr/hal-00894036

Submitted on 1 Jan 1994

HAL is a multi-disciplinary open access

archive for the deposit and dissemination of

sci-entific research documents, whether they are

pub-lished or not. The documents may come from

teaching and research institutions in France or

abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est

destinée au dépôt et à la diffusion de documents

scientifiques de niveau recherche, publiés ou non,

émanant des établissements d’enseignement et de

recherche français ou étrangers, des laboratoires

publics ou privés.

Genetic structure of the Marseilles cat population: is

there really a strong founder effect ?

M Ruiz-Garcia

To cite this version:

Original

article

Genetic

structure

of

the Marseilles

cat

population:

is there

_really

a

strong

founder effect ?

M

Ruiz-Garcia

1

1

Instituto de genetica, Ureiversidad de Los Andes,

calle 18 Carrera 1E,

Bogota DC,

Colombia;

C

igeem

avd virgen Montserrat, 207, se!to _{primera, Barcelona,} 080!6,

Spain

(Received

4

_February

_1992;

_accepted

21 December

₁₉₉₃₎

Summary - In a

_{previous study}

on the Marseilles cat

_population

it was concluded that

the small cat colonies were

_subject

to a _strong founder effect. A more detailed

_study

with

the

Gg

T

and

Fg

T

(genetic diversity)

statistics and with a

_spatial

autocorrelation

analysis

shows

_that,

for the a

_(non-agouti)

and

t

b

(blotched)

genes, there is neither

significant

heterogeneity

nor

_spatial

autocorrelation. This is

_probably

due to an

_appreciable

_gene

flow

_throughout

Marseilles

_(although

a uniform selection _{pressure in} favour of these alleles

cannot be

_totally

ruled

_out).

The 0

_(orange)

allele does not show

_spatial

autocorrelation

either,

but it does show

significant heterogeneity,

which could have been caused

by

the late introduction of this allele into the

population, coming

from

populations

with low 0

frequencies

in a

_sporadic

and

irregular

way

(although

the influence

of diversifying

selection cannot be

_completely

ruled

_out).

Only

this allele 0

might

be influenced

by

a _strong founder effect as stated

_{previously. However,}

the a and

t

b

data do not _support the

hypothesis

of a

strong founder effect in these cat colonies.

cat

_/

_genetic structure

_/

founder effect / gene flow

/

spatial autocorrelation

Résumé - Structure _génétique de la _population des chats marseillais : y a-t-il réellement un fort effet fondateur ? Dans une étude

précédente

sur la

population

des chats

marseillais,

il avait été conclu que les petites colonies de chats étaient soumises à un

fort effet fondateur.

Une étude

_{plus détaillée,}

à l’aide des _statistiques

_G

_ST

et FST

(diversité

génétique)

et d’une

analyse

d’autocorrélation

_spatiale,

a montré _{que, pour} les allèles a

(non

agouti)

et

t

b

_(tigré),

il n’existe ni

_{hétérogénéité significative}

ni autocorrélation

spatiale.

Ceci est

_probablement

dû au

_flux

_important de

gènes

dans toute l’étendue de Marseille

(bien

qu’on

ne _{puisse pas} totalement écarter une _pression

_uniforme

de sélection

en faveur

de ces

_allèles).

L’allèle 0

_(orange)

ne _{montre pas} non

_plus

d’autocorrélation

spatiale,

mais

il

présente

une

hétérogénéité significative,

_{qui pourrait} bien avoir été

_produite

par l’arrivée

*

tardive de cet allèle dans la

population,

provenant de manière

_sporadique

et

_{irrégulière}

de

populations

à

faibles fréquences

de 0

_(quoique

_{l’influence}

d’une sélection

_{diversifccatrice}

ne _{puisse pas} être

complètement

exclue).

Seul ce

_gène

0 pourrait être soumis à une

forte in

f

t

uence

de

l’effet fondateur. Cependant

les données relatives aux allèles a et

t

b

b

ne

_confirment

_pas

_{l’influence}

d’un

_{important effet fondateur}

dans ces colonies de chats marseillais.

chat

_/

structure _génétique

_/

effet fondateur

_/

flux _{génique /} autocorrélation _spatiale

INTRODUCTION

Dreux

₍₁₉₇₅₎

_analysed

the

_genetic

_composition

of the Marseilles cat

_population.

Having

studied the distribution of the allele

_frequencies

for 3 coat colour _genes

(0 (orange),

a

(non-agouti), t

b

(blotched))

_among a series of small cat colonies

throughout

this French town, he concluded with the

_following

statements: &dquo;... A

certain number of small semi-wild cat colonies have been observed and it is found that

they

are

_relatively

isolated from one

_another;

the

great

differences between the _gene

_frequencies

_among the colonies are attributed to the influence of a

_strong

founder

_{effect...&dquo;;}

&dquo;... The _gene

_frequencies

are _very variable and

_certainly

show

an

_important

influence of founder effect at the moment of constitution of these isolated colonies...&dquo;.

_However,

a more detailed

_study

of the distribution of these

gene

frequencies

among Marseilles cat

colonies,

through

some

_genetic

differentiation

statistics and

_by

means of a

_spatial

autocorrelation

_{analysis applied}

to these 3 _genes

and to the

_expected

_{heterozygosity,}

seems to show that the Dreux

₍₁₉₇₅₎

conclusion is not

_entirely

_justified.

Moreover,

this

_study

_gives

us an

_{interesting opportunity}

to

_study

the

_genetic

structure of the cat colonies within a town at a

_{microgeographical}

_level,

which

will no doubt reflect the interaction of the size of the

_population,

the gene

flow,

the

reproductive

systems

and the human interferences in this

_species

_(Eanes

and

Koehn,

1978;

Gaines and

Whittam,

1980;

Patton and

_{Feder, 1981;}

_Chesser,

_1983;

Gyllensten,

1985;

Kennedy

et

_al,

_1987).

MATERIALS AND METHODS

Dreux

₍₁₉₇₅₎

showed a _map of Marseilles

_{(fig 1),}

where he situated 9 cat colonies studied from a

_genetic

_viewpoint.

The sizes of these small colonies _range from 8 to

72 cats with a mean of 19.88 cats.

_Together

with this _map, the _gene

_frequencies

for

0,

a

and t

b

alleles in these cat colonies are summarized. Genic

_{diversity analysis}

A

_genic

_diversity

_analysis

_(Nei,

_1973,

₁₉₇₅₎

has been

_applied

to the 3 alleles above

to the gene

diversity

in the total

population),

R

ST

(interpopulation

gene

diversity

relative to the

_{intrapopulation}

_gene

_diversity),

Dm

_(absolute

_{interpopulational}

_gene

diversity).

The

Wright’s F

ST

(1951, 1965)

has also been calculated. If there are

_only

2 alleles at a

_{locus, G}

_ST

is identical to

_F

_ST

_{(Nei, 1973)}

as is the case in this

_study.

I have calculated

_FS

_T

=

Fs

T

-

(1/2N

t

)

(Workman

and

Niswander,

1970),

which is

the estimate of

_genetic

_{heterogeneity}

between

_populations

corrected for

_sampling

error, where

N

t

is the total

sample

size.

Fh

is

directly

related to the

chi-squared

statistic

_X

₂

₌

_2N

_t

_FS

_T

_{(K -}

₁₎

with

_{(l! - 1)(s - 1)}

_degrees

of

freedom,

where s is

the number of

populations

studied and k is the number of

alleles

for the locus.

Moreover,

if

_sample

sizes are of different

_magnitudes,

the

_following

_expression

_may

be used:

x

2

=

[E2N

i

p2 - pE2Ni ! pi!/p(1- p)

(Snedecor

and

Irwin,

1933),

where

N

i

and pi are the

sample

size and the _gene

frequency

in

_population

_i,

and _p is the mean

gene

frequency

over all colonies. To determine the

_possible

differences introduced

by

the

_genetic

_{heterogeneity}

between the 3 loci

studied,

a Fisher-Snedecor F test

(Workman

and

Niswander,

1970)

was carried out.

Theoretical _gene flow

The _gene flow

_(Nm,

the _average number of

_{immigrants entering}

an _average deme

in one

_generation)

was calculated

following

the

_expression:

Nm =

[(1/ F!T) -

1]/4

(Wright,

1943,

1965)

This

_equality

is an estimate based on an infinite island

model,

where the effects of

migration

and

_genetic

drift are balanced in a subdivided

_population.

These results

are similar to those

_{produced by}

a 2-dimensional

_{stepping-stone}

model

(Crow

and

Aoki,

1984)

although they

underestimate Nm for a one-dimensional

_{stepping-stone}

model

_(Slatkin,

_1985a;

Trexler,

1988).

I have also obtained estimates of gene flow for

an n-dimensional island model

(Nm a

=

[(11G

ST

) - 1]14oz,

where _a =

[n/{n -1}j

2

and n is the number of

_{populations analyzed}

_(Slatkin,

_1985b)).

Study

of the

_expected

_{heterozygosity}

An

_{important concept}

to determine the

_possible

existence of founder effect is the

study

of the mean

_{expected heterozygosity}

of the 3 loci

_throughout

the diverse

cat colonies

_{(Nei, 1978).}

To determine the

possible

differences between the mean

values of

_{heterozygosity}

_among all

_compared

_pairs

of

_colonies,

the Student’s t-test was used. To determine if there are

_significant

differences _among all

_expected

heterozygosity

means as a

single

set, 2 statistical methods have been

applied:

an Anova and a Kruskal-Wallis H test with corrections

_{(non-parametric}

variance

analysis) .

Phenetic

_analyses

To

_study

the

_genetic

_{relationships}

between these cat

_colonies,

2

_genetic

distances

were

_employed

with

_clearly

differentiated

_properties

_{(Prevosti (1974)}

distance and

with the UPGMA

_algorithm

_(Sneath

and

Sokal,

1973).

From the

_dendrogram

it

can be _seen, as a

_preliminary

_step,

whether the

_neighbouring

colonies are clustered

randomly.

Principal

coordinates

_analysis

To know the

_possible

_{genetic relationships}

_among these cat colonies in the _space,

a

_principal

coordinates

_analysis

_{(PCA) (Gower, 1966)}

was carried out with the

Prevosti

_genetic

distance matrix. A minimum

_length

_spanning

tree

_(MST)

was

superimposed

to detect local distortions between

_pairs

of

_populations

_{(Rohlf, 1970).}

Mantel test

An

_analysis

of correlation matrices

_(with

_linear,

_power,

_exponential

and

_logarithmic

curves)

between

_geographic

distances

_{(in metres)}

and

_genetic

distances between

the cat colonies was

_computed

with the normalized Mantel test

_{(Mantel, 1967).}

A Monte-Carlo

_simulation,

with 2 000 random

_permutations

of these matrices was

applied

to determine the

_significance

of these results.

Spatial

autocorrelation

_analysis

A

technique

that offers more

_potential

to understand the

_{possible spatial}

relation-ships

among these cat colonies is

spatial

autocorrelation

analysis

(SAA).

An SAA

tests whether the observed value of a _gene

frequency

at one

locality

is

dependent

on values of the same variable at

_neighbouring

localities

_(Sokal

and

Oden,

1978a).

Positive results of SAA indicate that _gene

_frequencies

at

_neighbouring

colonies are

similar,

while

_negative

SAA results show marked differences between

_adjacent

pairs

when we

study

the

_meaning

of SAA at the first distance class

_(Sokal

and

_Menozzi,

1982).

In the

_present

_work,

the Moran’s 1 index

_{(Moran, 1950)}

was used. To _carry out this

spatial

analysis

2 different distance classes

_(DCs)

were used. In the first

analysis,

I defined 3

_DCs,

where each

_particular

DC was chosen in order to allocate

an

_equal

number of

_{colony pairs}

to each DC. In the second

_analysis,

I defined 5 DC with a constant size. Both

_analyses

indicate whether a

_change

in some

_spatial

parameter

can affect the results. These indices were

plotted against

the

geographic

distances to

_produce

_{correlograms.}

For these

spatial

analyses,

the

0,

a,

t

b

alleles

and the

expected

heterozygosity

were used. A matrix of

_binary

connection was used

in the _way described

_by

Sokal and Oden

_{(1978b) (with}

human blood _groups in

_Eire)

and Trexler

_(1988).

This was due to the fact that we do not know the

_history

of

migrations

among these cat colonies and because we consider that the _gene flow

be-tween the colonies

_(caused

_by

the

relationship

between man and

cat)

could

happen

in _any direction and

_possibly

not

_depending

on the

_proximity

of the colonies. For a

single

autocorrelation coefficient for all the colonies studied

simultaneously,

point

pairs

were

weighted

as the inverse square of their

_separation

distance. To determine

statistical

_significance

for autocorrelation

_{coefficients,}

the Bonferroni

_procedure

was

used

_{(Oden, 1984).}

The

_application

of G

ST

and

_F

_sT

statistics needs the

_designation

of

_populations,

_{subpopulation}

or

_colony,

which is often

_arbitrary

_(Ennos,

_1985;

Bos

et

_al,

_1986).

In

_addition,

the border between these units or the size of the units

does not need a definition of

_{subpopulation}

or

_colony,

and is

_independent

of the

spatial

scale level of the structure we want to

_analyse.

RESULTS

Genetic difFerentiation and gene flow

The

_genetic

differentiation and _gene flow statistics for the three

_0,

a,

t

6

alleles are summarized in table I. As we can see, the

intercolony

gene differentiation

exhibited

_by

a

(FS

T

=

0.0183)

and t

b

(FS’

T

=

0.048)

is small. In other

words,

one

_colony

has on _{average 98.2} and

95.2%

of the total

_genic

_diversity

found in the

total cat

_population

of Marseilles for the a

and t

b

alleles, respectively.

The a

and t

b

allele

frequencies

do not show

_{significant heterogeneity}

between the Marseilles cat

colonies. In

_contrast,

0 shows a more

_important

_gene

_frequency

differentiation than

the a

and t

b

alleles

_(Fh

=

0.2015).

Moreover,

this 0

gene

frequency

differentiation is

_significant

₍

_X

₂

=

_72.14,

8

df,

P _<

0.001).

As the F-tests

demonstrate, t

b

does not exhibit

_{significantly}

more

_genetic

_{heterogeneity}

than a

_(F

_[6

_,

_S]

= 1.27

NS),

but

O does exhibit

_{significantly}

more

heterogeneity

than a

and t

b

(F!g,B!

=

_11.93,

P < 0.001 and

_F

_[8

_,

_6]

=

_9.34,

P _<

_0.01,

respectively).

The _mean value obtained

for the 3 alleles shows a

significant

FS

T

value

(see

table

I),

but if the 0 allele

is

_excluded,

the mean value for the a

and t

b

alleles

(FS

T

=

0.033)

is

clearly

not

significant.

For the estimations of the _gene

_flow,

I found a similar situation. I obtained

high

theoretical estimates of Nm for the a and

’

t

l

alleles

(Nm’

= 13.4 and

4.9,

respectively),

but the Nm value for 0

_(A!m’

=

0.99)

_was

very small.

So,

as a first

step,

we can observe how the 0 _gene

_might

seem

_strongly

affected

_by

an

_important

founder

_effect,

but the

_homogeneity

of the a

and t

6

_genes does not

_support

this

Expected

heterozygosity

Table II shows the

_expected

_{heterozygosity}

for the 9 colonies

_analyzed.

The

comparisons

of the

_expected

mean

heterozygosity

between all

_pairs

of colonies

using

the Student’s t-test are summarized in table III.

_Only

one

_comparison

out

of the 36

_possible

combinations reached

_{significance.}

The Anova

_applied

to the

expected

mean

heterozygosity

set did not show

_{significant heterogeneity}

_{(table IV),}

as confirmed

_by

the Kruskal-Wallis H-test

(H’

=

4.82,

8

df,

0.70 < P <

_0.80).

Thus,

the founder effect does not seem to

_strongly

influence

the

_present

results for

_{heterozygosity.}

All the colonies show similar levels of

_{heterozygosity,}

even those

with _very small

_samples

_(n

= 19.88 cats for the 9 colonies and n = 13.77

_cats,

excluding

the E

_colony

_(n

= 72

cats)).

Phenetic and

_principal

coordinates

_analyses

A first

_graphic

_{approximation}

on the

spatial genetic relationships

between the

genetic

distances does not exhibit _any

_special

trend to cluster the

_neighbouring

colonies

_{(fig 2).}

_{Nevertheless,}

the UPGMA

_{phenetic analyses}

with the Prevosti and the Cavalli-Sforza and Edwards distances show certain different

_{relationships}

between the colonies. The PCA with the

_graphic

matrix MST

superimposed

also shows the same

tendency

(fig 3).

This means that there seems to exist a

_stronger

tendency

for

_neighbouring

colonies to _group

_together.

This occurs for both

_genetic

Mantel test

Other

approaches

to understand the

_{spatial relationships}

among these colonies were

the correlations obtained between

_geographic

and

_genetic

distance matrices

_using

the Mantel test. There are no

significant

associations between both

_types

of matrices

in either case. In the case of the Prevosti

_distance,

all correlations are

_negative.

For

this

distance,

the

_geographic

_separation

_{negatively explains}

between 4.38 and

8.23%

of the

_genetic

_{heterogeneity}

found

_(according

to the different mathematical

_models).

For the Cavalli-Sforza and Edwards

_distance,

the correlations are

_positive,

but not

significant

(between

3.35 and

9.12%

of the

_genetic

_{heterogeneity).}

Spatial

autocorrelation

_analysis

The most

_{powerful methodological technique}

used to

_explain

the

_spatial

relation-ships

between these colonies is the

_spatial

autocorrelation. The

_application

of the Moran’s index as a

single

coefficient for all colonies

simultaneously

for the 3 alleles

studied did not show _any

defined in table

_V,

neither the allele nor the

expected heterozygosity

showed

sig-nificant individual

_spatial

autocorrelation coefficients. The 4 overall

_correlograms

for

_0,

a

and t

b

alleles and for the

_expected

_{heterozygosity}

were also

_{non-significant.}

The _average

_correlogram

for the _{3 genes} studied did not show _any

_spatial

trend

(&mdash;0.259,

-0.008,

-0.125).

With 5 distance

_classes,

_only

one coefficient out of the 20 1 values was

significant.

The 4 overall

correlograms

for

0,

_a,

t

b

and

_expected

heterozygosity

were not

_significant.

The _average

_correlogram

for the 3 alleles did not

show _any

_spatial

trend

_(-0.208,

_-0.293,

_{0.222, -0.233,}

_-0.012).

_Globally,

_spatial

autocorrelation does not seem to exist for any of these 3 alleles or for the

_expected

heterozygosity.

In a

_large

number of

_correlograms

there seems to exist a

disposi-tion to

_{’crazy quilt’ resembling}

that

_generated

_by

_Royaltey

et al

_(1975).

Most of the

_correlograms

show random fluctuations between

_positive

and

_negative

values without a clear

_tendency

to offer

_{significantly}

more

_positive

I values at a short dis-tance

_compared

with those observed at

_longer

distance. This poor autocorrelation

DISCUSSION

Possible causes of

_genetic

_{heterogeneity}

and

_spatial

_patterns

Sokal and Oden

_(1978b)

showed that 2 different

_concepts

must be

_{distinguished}

to

explain

the differentiation of a

_genetic

variable distributed over a

_geographic

area:

statistical

_{heterogeneity}

and

_geographic

_patterns.

Statistical

_{heterogeneity}

can be

studied

_by

different mathematical

_techniques

_(Anova,

_homogeneity

_x-square

_test,

etc)

while the

_geographic

_patterns

can be

_{analyzed using}

a

_spatial

autocorrelation

analysis.

Statistical

_{heterogeneity}

and

_patterns

are

mutually independent

of each

other. For this reason, we can

_analyze

the 3

possible

and

_logical

combinations

(Sokal

and

_Oden,

_1978b):

A

_{Significant heterogeneity}

and

significant spatial

patterns:

1)

migration

between

neighbouring populations;

2)

founder effects with the establishment of new demes

_by

relatively

close

_founders;

₃₎

selective

agents

in response to environmental

_gradients

or

patterned patches;

and

4)

_{systematic migration.}

B

_{Significant heterogeneity}

and absence

_of

spatial

patterns:

1)

genetic drift;

2)

founder effects with the founders

_coming

with near

_{equiprobability}

from entire array of colonies over the range of the

population;

and

3)

selective

agents

and/or

unpatterned patches.

C

_Homogeneity

_{of means}

and absence

_{of pattern}

_{(population’s}

_poor

_genetic

sub-structuring): 1)

high

gene flow at random within the entire

_study

area;

2)

uniform

selective pressures within entire

_study

area

(Ayala

et

al,

1971;

Hebert,

1974).

With these

_premises

and

_taking

into account the

_global

results for the 3 genes

studied,

we would find ourselves in case B.

_Therefore,

we would have 3

_possible

causes to

_explain

the gene distribution we have observed. The second cause would be

in accordance with Dreux’s

₍₁₉₇₅₎

statements, ie

_frequent

founder effects with the

same

_probability

over the _range of the

population.

In other

studies,

this

explanation

has also been useful to

_explain

the

_genetic

structure of other

_organisms

_(Sokal

and

Oden,

1978b;

Waser,

1987;

Lopez-Alonso

and

Pascual-Requera,

1989).

However,

if

we

_analyze

each of these _genes

_separately

and the

_expected

_average

_{heterozygosity,}

we observe that the situation

_changes.

The a

and t

b

_genes show neither

_significant

statistical

_{heterogeneity}

nor

spatial

autocorrelation. The same

happens

with the

expected

mean

_{heterozygosity.}

In _contrast, the 0 _gene shows

_significant

statistical

heterogeneity,

but no

_spatial

autocorrelation.

_Thus,

the individualized

_analysis

seems to dismiss this second cause as the

global explaining

factor of the allele

distributions observed. The

_genetic

drift and the founder effects with the same

demographic

parameters

affect the 3 genes studied in the same _way and should

have the same effect on the whole _genome. At least for the a

and t

b

alleles and for

expected

mean

_{heterozygosity,}

case C above seems to be more

_{acceptable. So,}

the 2

foreground

agents

would be:

_a)

intense _gene flow without

_following

fixed

routes;

and

b)

uniform selective _pressure. It is difficult to

_distinguish

which of the 2

_hypotheses

is more

likely.

Moreover,

the 2

hypotheses

are not

_mutually

exclusive and could be

acting simultaneously.

from the urban effect

_(Todd,

1969, 1977, 1978;

Clark, 1975,

1976).

This selective

cause could

_{have induced}

the

_homogeneity

of means found and the absence of

autocorrelation for those 2 alleles in the cat colonies of Marseilles. On a small

scale,

the heterotic effect

_(Bulmer,

_1973;

Bush et

al,

1987)

for these genes should

promote

spatial homogeneity.

However,

there are

examples

of other towns where the urban selective effect

_might

be at least as intense as in Marseilles

(eg,

_Barcelona,

Palma in

_Majorca,

Murcia in

_Spain,

Rimini in

_Italy,

Buenos Aires in

_Argentina,

and Jerusalem and Tel-Aviv in

_Israel;

_Ruiz-Garcia,

_1991,

_1993ab)

and where the

a

_and,

_especially,

the t

b

alleles show a

_strong

_significant

statistical

_{heterogeneity.}

These

_examples

make us doubt the existence of a uniform selective _pressure within the urban environment or of a heterotic effect

(or,

at

_least,

other

evolutionary

agents

are

_superimposed

on

_them).

It would be

_strange

if this

_happened

in the

city

of Marseilles and not in other

_intensely

urban towns. From a selective

point

of

_view,

the 0 _gene could be submitted to some

diversifying

selective

_agent

over

heterogeneous patches unpatterned

in the _space.

_{Nevertheless,}

there does not seem to be sufficient microenvironmental differences

_(at

least

they

are _very difficult to

imagine

in this

_case)

between these different areas of

_Marseilles,

which _may have some selective influence on this _gene.

All this taken into

_account,

a neutral

_point

of view could be taken to

_explain

the different

_{genetic heterogeneity}

shown for each _gene. It is

_possible

that each _gene studied in this work was introduced into Marseilles at different historical moments

and with different

_ecological

and

demographic

parameters

(effective

population

sizes

(Ne),

migration

rates _per

_generation

_(m),

number of colonists

_(K),

and extinction

rates _per

_generation

_(eo)).

_Moreover,

these different

_migrant

_genes could have been introduced

following

different models. For

_instance,

Slatkin

₍₁₉₇₇₎

defined

4

_population

structures in terms of the source of the

_migrant

individuals and in

terms of the way in which new colonies were established. The 2 models of the

source of

_migrant

individuals are:

a)

Model I.

_Migrants

move from an external source

with a constant _gene

_frequency

to an infinite number of local

colonies;

b)

Model II.

Migrants

are drawn at random from within a finite _array of subdivided

populations.

For these 2

_models,

there are 2 different _{ways in} which colonists

_might

be chosen to

found new colonies:

a)

_migrant

_pool,

where new colonists

(K)

are a random

_sample

from the entire

_{population; and,}

_b)

_{propagule pool,}

where the new colonies are

founded

_by

_choosing

colonists at random from a

single

randomly

chosen

colony.

If

the 3 _genes studied were introduced at different historical moments with different

demographic

parameters,

different sources of

_migrants

and different _{ways in} which

colonists were

chosen,

we should

_expect

different

F

ST

values for each _gene studied

(Wade,

and

_McCauley,

_1988).

With all this in

_mind,

0 is the

_unique

allele that could be influenced

_by

a

_strong

founder effect in the Marseilles cat

_{population. Nevertheless,}

the a and

t

b

data do

not

_support

this

_strong

influence.

_Only

in the case that the 0 allele is neutral and that the a

and t

b

alleles are under uniform selective _pressure, should the Dreux

(1975)

conclusion

_(importance

of the founder

_effect)

be certain. Gene flow and

_{heterozygosity}

Trexler

₍₁₉₈₈₎

showed that if Nm > 1

_(in

an infinite island

model)

or Nm > 4

dif-ferentiation between

populations

balanced for

_migration

and gene drift.

According

to the infinite island

model,

if 1 < Nm <

_0.5,

the

_genetic

differentiation between

populations.

is smaLL but

_important

in a

stepping-stone. model:-.4f::-Nm

<--4.5,

the

populations

are

_largely

unconnected under _any model of gene flow. The Nm values

for a

and t

b

(Nm’

= 13.4 and

4.9,

respectively)

are

higher

than 1

(and

even

_4).

On the

_contrary,

for the 0 gene

(Nm’

=

0.99)

the

gene flow would be

considerably

smaller. As we can observe from the absence of

spatial

autocorrelation and from

the absence of

_significant

correlation between

genetic

and

_geographic

distances with

the Mantel test, we find a situation very similar to an island model where the effect

of

_geographical

distance seems

_{non-significant}

(Cavalli-Sforza

and

Bodmer,

1981).

The

_analysis

of the

_expected

mean

heterozygosity

seems to confirm this model.

The absence of autocorrelation and the

_homogeneity

of the means confirm that

stochastic _processes are not

_{extraordinarily important}

as

evolutionary

_agents

_among

the cat colonies studied in Marseilles

_(even

_though

_{the average} size of the

_samples

is

_only

19

_{individuals).}

The same has been observed for other animals

(Grant,

1980;

Kennedy

et

_al,

₁₉₈₇₎

but

_they

differ from what has been observed in other mammals

_(Patton,

1972; Penney

and

Zimmerman,

1976).

As can be

observed,

the

average levels of

heterozygosity

of these 3 genes are

_high,

and as has been

proved

by

other

studies,

high

gene flow maintains

high

levels of

heterozygosity

(Wheeler

and

Guries, 1982;

Waples,

1987; Ruiz-Garcia,

1991)

confirming

the

_probable

great

importance

of _gene flow in this model. Even

_though

the sizes of the colonies could

be

_small,

the fact that cat litters are

strongly dispersed, spreading

out from their

original colony

(either

as a _consequence of the intrinsic characteristics of their

reproductive

behaviour,

or direct human

action)

and the

subsequent

integration

into other

reproduction

units favours the maintenance of

_high

mean

_{heterozygosity}

values. The same was determined for

Thomomys

bottae

(Patton

and

Feder,

1981).

Nevertheless,

we do not

know

whether the _gene flow occurs at the time of

_colony

formation or between colonies that have been

_present

for a

_long

time.

ACKNOWLEDGMENTS

For different reasons, my infinite thanks to P Dreux

_{(Paris, France),}

A

Sanjuan

(Vigo,

Spain),

AT

_Lloyd

_{(Dublin, Ireland),}

KK Klein

_{(Minnesota, USA),}

R Robinson

_(London,

UK)

and A Prevosti

_(Barcelona,

_Spain).

My

thanks also to BH Zimmermann

_(Michigan,

USA)

for

help

with the

_English

_syntax of this manuscript, and,

especially

to Diana Alvarez

(Bogota

DC,

Colombia)

for her valuable assistance.

REFERENCES

Ayala FJ,

Powell

JR, Dobzhansky

T

₍₁₉₇₁₎

_Polymorphism

in continental and island

populations

of

_Drosophila

williston,i. Proc Nat Acad Sci USA 68, 2480-2483

Bos _M, Harmens

_{H, Vrieling}

K

₍₁₉₈₆₎

Gene flow in

_plantago.

I. Gene flow and

neighbor-hood size in P lanceolata.

Heredity 56,

43-54

Bulmer MG

₍₁₉₇₃₎

_{Geographical uniformity}

of protein

polymorphisms.

Nature

_(Lond)

241, 199-200

Bush

_RM,

Smouse PE,

Ledig

FT

₍₁₉₈₇₎

The fitness consequences of

multiple-locus

heterozygosity:

the

relationship

between

heterozygosity

and

_growth

rate in

_pitch

pine

Cavalli-Sforza LL, Bodmer WF

₍₁₉₈₁₎

Gen!tica de las

_poblaciones

hurrcanas. 942 _p

Ediciones

_Omega,

Barcelona

Cav!lli-Sforza’LL!

Edwards AWF’

₍₁₉₆₇₎

_{Phylogenetic analysis:}

models and estimation

procedures.

Evolution 21, 550-570

Chesser RK

₍₁₉₈₃₎

Genetic

_variability

within and _among

_populations

of the black-tailed

prairie

dog.

Evolution

_37,

320-331

Clark JM

₍₁₉₇₅₎

The effects of selection and human

preference

on coat colour gene

frequencies

in urban cats.

_Heredity

35, 195-210

Clark JM

₍₁₉₇₆₎

Variations in coat colour gene

frequencies

and selection in cats of

Scotland. Genetica _46, 401-412

Crow

_JF,

Aoki K

₍₁₉₈₄₎

_Group

selection for a

_polygenic

behavioural trait:

_estimating

the

degree

of

_population

subdivision. Proc Natl Acad Sci USA

_81,

6073-6077

Dreux P

₍₁₉₇₅₎

_G6n6tique

de

population

des chats

domestiques

de Marseille

(Bouches-du-Rh6ne,

France).

Ann G6n6t Sel Anim 7, 23-33

Eanes WF, Koehn RK

₍₁₉₇₈₎

An

analysis

of

_genetic

structure in the monarch

butterfly,

Danaus

_plevipus

L. Evolution 32, 784-797

Ennos RA

₍₁₉₈₅₎

The _mating system and _genetic structure in a

_perennial

_grass,

_Cynosurus

cristatus L.

_Heredity

55, 121-126

Gaines

MS,

Whittam TS

₍₁₉₈₀₎

Genetic

changes

in

_fluctuating

vole

populations:

selective

vs non-selective forces. Genetics 96, 767-778

Gower JC

₍₁₉₆₆₎

Some distance proporties of latent root and vector methods used in multivariate

_analysis.

Biometrika _53, 325-338

Grant V

₍₁₉₈₀₎

Gene flow and the

_{homogeneity of species populations.}

Biol

Zb199,

157-169

Gyllensten

U

₍₁₉₈₅₎

_{Temporal allozyme frequency changes}

in

_{density fluctuating}

popula-tions of willow grouse

(Lagopus

lagopus

L).

Evolution

_39,

115-121

Hebert PDN

₍₁₉₇₄₎

_{Enzyme variability}

in natural

_populations

of

_Daphnia

_magna. I.

Po-pulation

structure in East

_Anglia.

Evolution _28, 546-556

Kennedy

PK,

Kennedy

ML, Beck ML

₍₁₉₈₇₎

Genetic

variability

in white-tailed deer

(Odocoileus virginianus)

and its

_relationship

to environmental _parameters and herd

origin

(Cervidae).

Genetica 74, 189-201

Lopez-Alonso

D,

Pascual-Reguera

L

₍₁₉₈₉₎

_Population

structure and _pattern of

_geographic

variation in Muscari comosum

_along

_{its range of} distribution. Genetica _78, 39-49

Mantel NA

₍₁₉₆₇₎

The detection of disease

clustering

and a

_{generalized regression}

approach.

Cancer Res 27, 209-220

Moran PAP

₍₁₉₅₀₎

Notes on continuous stochastic

phenomena.

Biometrika 37, 17-23 Nei M

₍₁₉₇₃₎

_Analysis

of _gene

_diversity

in subdivided

_populations.

Proc Natl Acad Sci

USA 70, 3321-3323

Nei M

₍₁₉₇₅₎

Molecular

Population

Genetics and Evolution. North-Holland Press, Ams-terdam

Nei M

₍₁₉₇₈₎

Estimation of average

heterozygosity

and genetic distance from a small

number of individuals. Genetics 89, 583-590

Oden N

₍₁₉₈₄₎

_Assessing

the

_significance

of a

spatial correlogram. Geogr

Anal _16, 1-16

Patton JL

₍₁₉₇₂₎

Patterns of

_geographic

variation in

_karyotype

in the

_{pocket gophers.}

Thomomys

bottae

_(Eydoux

and

_Gervais).

Evolution _26, 574-586

Patton

JL,

Feder JH

₍₁₉₈₁₎

_{Microspatial genetic heterogeneity}

in

_{pocket gophers:}

non-random

_breeding

and drift. Evolution 35, 912-920

Penney

DF, Zimmerman EG

₍₁₉₇₆₎

Genic

_divergence

and local

_population

differentiation

by

random drift in the

_{pocket gopher}

_genus

_Geomys.

Evolution 30, 473-483

Rohlf FJ

₍₁₉₇₀₎

Adaptive

hierarchical

_clustering

schemes.

_{Systematic Zool 19,}

58-82

Royaltey HH,

Astrachan

E,

Sokal RR

₍₁₉₇₅₎

Tests for patterns in

_geographic

variation.

Geogr

Anal 7, 369-395

Ruiz-Garcia M

₍₁₉₉₁₎

M6s sobre la Gen6tica de

poblaciones

de Felis catus en la costa

MediterrAnea

_Espanola:

Un analisis de la Estructura Gen6tica de las

poblaciones

naturales de _gatos. Evol Biol

_5,

227-283

Ruiz-Garcia M

_(1993a)

_Analysis

of the evolution and

_genetic

diversity

within and between Balearic and Iberian cat

_populations.

J Hered 84, 173-180

Ruiz-Garcia M

_(1993b)

Genetic structure and Evolution of different cat

_populations

_(Felis

catus)

in

_{Spain, Italy, Argentina}

and Israel at

_{microgeographical}

level:

_history

and

time

_and/or

_ecological

and social factors

_and/or

current

_population

size? Evol Biol 7

(in

press)

Slatkin M

₍₁₉₇₇₎

Gene flow and

_genetic

drift in a _species

_subject

to

frequent

local

extinction. Theor

Popul

Biol 12, 253-262

Slatkin M

_(1985a)

Rare alleles as indicators of _gene flow. Evolution _39, 53-65

Slatkin M

_(1985b)

Gene flow in natural

_populations.

Ann Rev Ecol

_Syst

16, 393-430

Sneath

PH,

Sokal RR

₍₁₉₇₃₎

Nvmerical ta!onomy. Ed _Freeman, San Francisco

Snedecor

_G,

Irwin MR

₍₁₉₃₃₎

On the

_chi-square

test for

_homogeneity.

lowa State J Science

8, 75-81

Sokal

RR,

Menozzi P

₍₁₉₈₂₎

_Spatial autocorrelation of HLA

_frequencies

in

_Europe

_support

demic diffusion of

early

farmers. Am Nat _{119, 1-17}

Sokal

RR,

Oden NL

_(1978a)

_Spatial

autocorrelation in

_biology.

I.

_Methodology.

Biol J

Linn Soc

_10,

199-228

Sokal

_RR,

Oden NL

_(1978b)

_Spatial

autocorrelation in

_biology.

II. Some

biological

implications

and four

_applications

of

_evolutionary

and

_ecological

interest. Biol J Linn Soc

_10,

229-249

Todd NB

₍₁₉₆₉₎

Cat gene

frequencies

in

_Chicago

and other

_populations

of the United States. J Hered 60, 273-277

Todd NB

₍₁₉₇₇₎

Cats and Commerce. Sci Amer 237, 100-107

Todd NB

₍₇₉₇₈₎

An

_ecological,

behavioural

_genetic

model for the domestication of the

cat. Carnivore 1, 52-60

Trexler JC

₍₁₉₈₈₎

Hierarchical

organization

of

genetic

variation in the Sailfin

_Molly.

Poecilia

_latipinna

_{(Pisces: Poeciliidae).}

Evolution 42, 1006-1017

Wade

MJ, McCauley

DE

₍₁₉₈₈₎

Extinction and recolonization: their effects on the

genetic

differentiation of local

_populations.

Evolution 42, 995-1005

Waples

RS

₍₁₉₈₇₎

A

_{multispecies approach}

to the

_analysis

of gene flow in marine shore

fishes. Evolution 41, 385-400

Waser NM

₍₁₉₈₇₎

_{Spatial genetic heterogeneity}

in a

_population

of the montane

_perennial

plant Delphinium

nelsonii.

_{Heredity 58,}

249-256

Wheeler NC, Guries RP

₍₁₉₈₂₎

Population

structure,

genic diversity

and

morphological

differentiation in Pinus contorta

_Dougl.

Can J For P 595-606

Workman PL, Niswander JD

₍₁₉₇₀₎

_Population

studies on south western Indian tribes.

II. Local

_genetic

differentiation in the

Papago.

Amer J Hum Genet 22, 24-49

Wright

S

₍₁₉₄₃₎

Isolation

_by

distance. Genetics 28, 114-138

Wright

S

₍₁₉₅₁₎

The

genetical

structure of

populations.

Ann

_Eugen

15, 323-354

Wright

S

₍₁₉₆₅₎

The _{interpretation} of

_population

structure

_by

F-statistics with

_special