Planning for optimal conservation of geographical genetic variability within species

1 23

Conservation Genetics ISSN 1566-0621Volume 13Number 4 Conserv Genet (2012) 131085-1093DOI 101007s10592-012-0356-8

Planning for optimal conservation ofgeographical genetic variability withinspecies

Joseacute Alexandre Felizola Diniz-FilhoDayane Borges Melo Guilherme deOliveira Rosane Garcia CollevattiThannya Nascimento Soares et al

1 23

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RESEARCH ARTICLE

Planning for optimal conservation of geographical geneticvariability within species

Jose Alexandre Felizola Diniz-Filho bull Dayane Borges Melo bull Guilherme de Oliveira bull

Rosane Garcia Collevatti bull Thannya Nascimento Soares bull Joao Carlos Nabout bull

Jacqueline de Souza Lima bull Ricardo Dobrovolski bull Lazaro Jose Chaves bull

Ronaldo Veloso Naves bull Rafael Dias Loyola bull Mariana Pires de Campos Telles

Received 15 November 2011 Accepted 14 April 2012 Published online 3 May 2012

Springer Science+Business Media BV 2012

Abstract Systematic Conservation Planning (SCP)

involves a series of steps that should be accomplished to

determine the most cost-effective way to invest in con-

servation action Although SCP has been usually applied at

the species level (or hierarchically higher) it is possible to

use alleles from molecular analyses at the population level

as basic units for analyses Here we demonstrate how SCP

procedures can be used to establish optimum strategies for

in situ and ex situ conservation of a single species using

Dipteryx alata (a Fabaceae tree species widely distributed

and endemics to Brazilian Cerrado) as a case study Data

for the analyses consisted in 52 alleles from eight micro-

satellite loci coded for a total of 644 individual trees

sampled in 25 local populations throughout speciesrsquo geo-

graphic range We found optimal solutions in which seven

local populations are the smallest set of local populations

of D alata that should be conserved to represent the known

genetic diversity Combining these several solutions

allowed estimating the relative importance of the local

populations for conserving all known alleles taking into

account the current land-use patterns in the region A

germplasm collection for this species already exists so we

also used SCP approach to identify the smallest number of

populations that should be further collected in the field to

complement the existing collection showing that only four

local populations should be sampled for optimizing the

species ex situ representation The initial application of the

SCP methods to genetic data showed here can be a useful

starting point for methodological and conceptual

improvements and may be a first important step towards a

comprehensive and balanced quantitative definition of

conservation goals shedding light to new possibilities for

in situ and ex situ designs within species

Keywords Complementarity Conservation planning Optimization Cerrado Hotspot Dipteryx alata

Introduction

The current biodiversity crisis has forced scientists to

develop systematic strategies to effectively achieve con-

servation goals aimed at solving potential conflicts between

conservation and human development (sensu Araujo 2003

J A F Diniz-Filho (amp) R D Loyola

Departamento de Ecologia ICB Universidade Federal de Goias

CxP 131 Goiania GO 74001-970 Brazil

e-mail dinizicbufgbr

D B Melo

Programa de Pos-Graduacao em Agronomia Escola de

Agronomia e Engenharia de Alimentos Universidade Federal de

Goias Goiania GO 74001-970 Brazil

G de Oliveira

Programa DTI Universidade Federal de Goias Campus Jataı

Rod BR-364 KM 192 Jataı GO 75800-970 Brazil

R G Collevatti T N Soares M P C Telles

Departamento de Biologia Geral ICB Universidade Federal de

Goias CxP 131 Goiania GO 74001-970 Brazil

J C Nabout

Unidade de Ciencias Exatas e da Terra Universidade Estadual

de Goias Anapolis GO 75132-400 Brazil

Jacqueline de S Lima R Dobrovolski

Programa de Pos-Graduacao em Ecologia amp Evolucao ICB

Universidade Federal de Goias CxP 131 Goiania GO 74001-

970 Brazil

L J Chaves R V Naves

Escola de Agronomia e Engenharia de Alimentos Universidade

Federal de Goias Goiania GO 74001-970 Brazil

123

Conserv Genet (2012) 131085ndash1093

DOI 101007s10592-012-0356-8

Authors personal copy

Balmford et al 2001) The overall underlying principle of

this strategy lies in the science of systematic conservation

planning (SCP) which involves a series of steps that

should be accomplished to determine the most cost-effec-

tive way to invest in conservation actions (Margules and

Pressey 2000 see also Sarkar and Illoldi-Rangel 2010 for a

recent update) Ultimately SCP allows better planning for

conservation actions and land use at different spatial scales

It has been applied to a series of datasets to test and

improve its methodological details and used in practical

conservation actions in some parts of the world (see

Margules and Sarkar 2007)

The core of SCP is the principle of complementarity

in which a set of sites (among several available ones) are

selected so as to minimize the overall cost of conservation

action (ie the minimum set coverage problem) or to

maximize the level of feature representation given a

limited budget (ie the maximal coverage problem)

(Cabeza and Moilanen 2001) While resolving such

optimization problems using several possible mathemati-

cal and computational methods SCP always maximizes

the dissimilarities among biodiversity features being

considered (ie conservation targets such as species

vegetation types genes see Diniz-Filho and Bini 2011)

More complexity is usually included to these general

problems by adding socio-economical costs to the areas or

minimizing their spatial aggregation (Abbitt et al 2000

Balmford et al 2001 Diniz-Filho et al 2007a Loyola

et al 2009)

Traditionally SCP has been applied to more graspable

conservation goals such as species or vegetation types (see

Brooks et al 2004 Grelle et al 2010 Pressey 2004)

However it is possible to apply the same framework to

solve a series of problems at much lower hierarchical

levels allowing the definition of conservation strategies at

the intra-specific level (see Diniz-Filho and Telles 2006)

The geographical variation of both phenotypic and genetic

data within species have been known for some time and

used in many cases to understand the evolutionary drivers

of population differentiation (Epperson 2003) In a con-

servation context the conservation of intraspecific varia-

tion has been dominated by the debate among how to

define evolutionary significant units (ESUs) or manage-

ment units (MUs) (eg Fraser and Bernatchez 2001 see

also Diniz-Filho and Telles 2002 for a proposal to define

operational units to be used with continuous variation in

the geographical space) or meta-population dynamic pro-

cesses related to persistence (eg McCarthy et al 2005) It

is important however to generalize this to a more basic

representation of the genetic variability throughout geo-

graphical space and in this context SCP procedures may

be quite useful as well (eg see Diniz-Filho and Bini 2011

Loyola et al 2011 Neel and Cummings 2003)

Here we applied the systematic conservation planning

protocol to define conservation priorities for Dipteryx alata

(Fabaceae) This species is endemic to the Brazilian

Cerrado one of the global Biodiversity Hotspots (Myers

et al 2000) where it is commonly known as the lsquolsquoBarursquorsquo

tree Despite endemics to the biome it is a widely dis-

tributed tree species in the Cerrado found in eutrophic and

drained soils of seasonal savannas The edible nuts are also

important for local economies and are now used for several

natural goods or products (eg Felfili et al 2004) Previous

studies showed a significant amount of spatial genetic

differentiation among and within local populations

(Collevatti et al 2010 Soares et al 2008) and broad-scale

geographical structure is apparently associated with pat-

terns of historical habitat fragmentation in the Cerrado

More specifically we found here optimal solutions

indicating the smallest set of local populations of D alata

that should be conserved to represent the known genetic

diversity of the species in the Cerrado aiming at in situ

conservation of the species We also took into account the

current land-use patterns found in the Cerrado (expressed

as the proportion of natural remnants in a buffer zone

around local populations) to weight final solutions and thus

increase the chance of long-term persistence of these

populations by protecting them from human impacts A


also used SCP in a different analysis to identify

the smallest number of samples that should be collected

in the field to complement the existing collection aiming at

the species ex situ conservation

Methods and materials

Genetic data

Genetic data for D alata consisted of microsatellite

markers analyzed for 25 widely distributed local popula-

tions encompassing most of speciesrsquo geographical range

(Fig 1) A total of 644 individual trees were genotyped for

eight microsatellite loci (see Soares et al 2012 for meth-

odological details) with sample sizes within each locality

ranging from 12 to 32 (15 out of the 25 local populations

had 32 individuals analyzed) Details of sample size pri-

vate alleles and genetic characteristics of each local pop-

ulation are given in Table 1 We also calculated standard

population genetics statistics such as observed and

expected heterozygosity as well as an estimate of FST

obtained from an analysis of variance of allele frequencies

(Weir and Cockerham 1984) to provide an overall

description of genetic population structure of D alata The

genetic analyses were conducted with the software FSTAT

2932 (Goudet 2002)

1086 Conserv Genet (2012) 131085ndash1093

123


Systematic conservation planning

We produced an allele-by-site (local populations) presence-

absence matrix to solve the so-called minimum set coverage

problem in optimization theory (Kirkpatrick 1983) We

defined alleles as our conservation goal and used an algo-

rithm to achieve the initial conservation target of finding the

smallest number of local populations in which all alleles are

represented at least once Even though genetic diversity

would be a best goal from a conservation viewpoint due to its

relationship with inbreeding depression (Charlesworth and

Charlesworth 1987 Frankham 2003) allele presence-

absence is a best choice in this case because D alata popu-

lations are highly genetic differentiated mainly because of

differences in allele composition Further it allows an easy

solution to SCP because alleles can be directly analogous to

species (or other goals at higher organizational level)

Because there are several feasible solutions for this mini-

mization problem we used the selection frequency ie the

frequency at which each local population appears in 100

analyzed solutions as an indicator of site importance for

achieving the conservation target

The SCP problems are solved using several possible

computational and mathematical methods based on exact

optimization algorithms (eg branch-and-bound algorithm

implemented in linear programming) or approximate

algorithms that generate near-optimum solutions (eg

simulated annealing) (see Cabeza and Moilanen 2001

Williams et al 2004) Here we applied the simulated

annealing algorithm which is a computer-intensive search

method (see Possingham et al 2000) to find the smallest

number of local populations that represent all alleles at

least once and that at the same time maximize the amount

of natural habitat remnants around local populations taking

into account broad-scale properties of landscape (see

Segelbacher et al 2010) We obtained this variable for each

local population by analyzing land use data from vegeta-

tion cover maps of the Brazilian biomes at the 1250000

spatial scale We reclassified the maps into natural vege-

tation or anthropogenic land cover classes (this last clas-

sification includes agriculture pasture lands urban areas

and mining regions) and then created 10 km-size buffers

around the geographical position of each population The

proportion of the buffer areas covered by natural vegetation

Fig 1 Geographic location of

the 25 local populations of

Dipteryx alata in Central Brazil

analyzed using SCP methods

based on microsatellite allelic

variation The region shown in

dark tone is still covered by

natural remnants of Cerrado

vegetation

Conserv Genet (2012) 131085ndash1093 1087

123


were then calculated and used as a surrogate for regional

level of habitat conservation (see Fig 1 for the overall

patterns of natural remnants in the region) We also used a

randomization procedure to evaluate if the amount of mean

proportion of natural remnants in the best solution differs

from random solutions with the same number of local

populations

Finally there is a large accessible germplasm collection

for D alata composed by 178 fully growth adult trees

located at the Agronomy School of the Federal University

of Goias Brazil These 178 individuals came from several

locations in the state of Goias and were also genotyped for

the eight microsatellite loci A different analysis was

conducted in which the individuals in the germplasm bank

were included as another lsquolsquolocal populationrsquorsquo We then

repeated the SCP analysis by assuming that this germplasm

bank was already protected (ie we included this

population in the initial and final portfolios when running

the algorithm) In that case the optimization problem was

to find the smallest number of local populations that better

complement the genetic variability already preserved in the

germplasm bank This is thus equivalent to running a gap

analysis in which a single reserve (the germplasm bank) is

already established We also calculated the SCP solutions

by minimizing the geographic distance to Goiania (ie

using this distance as a cost variable) so that overall costs

of sampling and transporting material to the locality where

the germplasm collection is maintained are minimum

Results

After coding the eight microsatellites a total of 52 alleles

were recorded out of which only six were found in a

Table 1 Genetic characterization for 25 populations of D alata based on eight microsatellite loci

Population N A Ar Ap Ho He f Habitata

ABMT 32 2375 2223 0 0291 0382 0238 2

AMG 32 2250 1973 0 0293 0310 0055ns 3

AMS 32 4250 3609 1 0349 0476 0267 3

AQMS 31 3375 2817 0 0442 0459 0038ns 1

ARTO 15 2250 2205 0 0381 0372 -0025ns 3

ATO 32 3000 2747 0 0382 0488 0217 1

CAMT 30 3750 3152 2 0278 0466 0405 2

CMS 13 2875 2861 0 0466 0480 0029ns 4

CMT 32 2625 2502 1 0249 0322 0229 1

ENGO 12 2500 2478 0 0408 0474 0139 1

IGO 13 2500 2447 0 0350 0453 0226 1

ISP 32 2375 2212 0 0322 0455 0291 3

JGO 32 2625 2480 0 0638 0599 -0065ns 1

LGO 32 2500 2425 0 0424 0509 0166 1

MAMG 32 2875 2582 0 0448 0514 0128 4

NTO 12 2000 2093 0 0458 0393 -0165ns 3

PCMS 13 2875 2808 0 0169 0345 0509 1

PGO 32 2500 2164 0 0259 0426 0336 3

PMG 32 2375 2103 0 0176 0257 0314 1

PMS 13 3125 3059 0 0215 0410 0476 1

RAGO 32 3250 2865 0 0473 0462 -0024ns 3

RAMT 32 3375 2976 2 0308 0462 0333 3

SMGO 32 2625 2409 0 0354 0336 -0052ns 1

SMS 32 3625 3213 0 0422 0482 0125 2

STGO 12 2875 2823 0 0356 0387 0080ns 1

Overall 644 2835 2609 6 0356 0429 0171 ndash

P [ 005

N sample size A total number of alleles Ar Allelic richness Ap private alleles Ho observed heterozygosity He expected heterozygosity

f inbreeding coefficient ns not significanta 1mdashIsolated adult individuals in pasture or crop field 2mdashisolated adult individuals in remnant of savanna at roadsides 3mdashsavanna remnant

surrounded by pasture 4mdashfragment of savanna


123


unique population Observed heterozygosity was low in all

populations and most populations show deviations from

HardyndashWeinberg equilibrium with f-values significantly

different from zero for most local populations (Table 1) A

significant amount of divergence among these local popu-

lations was also observed (FST = 0267 SD = 0036

P 00001) indicating that several local populations

should be conserved to maintain all genetic diversity

Optimization solutions revealed that a minimum of

seven local populations are necessary to represent all 52

alleles at least once Several solutions for this problem

were reached and after considering a total of 100 minimum

solutions four local populations were extremely important

for achieving this goal (ie they were found in 100 of

solutions and a fiftieth additional one appeared in 95 of

the solutions) Further a total of ten populations had fre-

quencies of selection higher than 0 indicating that the

solutions are not always the same (Fig 2)

There was no significant relationship between selection

frequency and genetic parameters within local populations

except for Ar (r = 0488 P = 0013) but this is someway

tautological because optimization works to maximize

number of alleles (unique in each local population) Even

so it is important to note that these data could be also used

as covariates if one is interested for example in conserv-

ing the populations with higher selection frequency that

have more expected heterozygosity

Given the distribution of alleles in local populations (ie

many rare alleles are found in one or two populations

only) the mean amount of proportional remaining habitat

around the seven local populations selected (equal to 022)

was not significantly different from a 1000 random sam-

ples with same size (032 plusmn 014 P = 0265) However in

none of these random samples were the total number of

alleles represented (the mean number of alleles represented

in random samples of seven populations is 385 plusmn 33)

(Fig 3) This supports the idea that although human

actions cannot be minimized because of data constraints

random solutions are much less efficient than the optimized

one as expected

For the alternative analysis for an ex situ SCP strategy

samples of only four additional local populations would be

necessary to optimally complement the genetic variability

currently represented in the germplasm bank (Fig 4) Only

these four populations have maximum frequency of

selection (ie because only they appeared in all solutions)

Minimizing distances to Goiania did not change this

solution in this particular case because of the rare variants

found in the state of Mato Grosso are unique and conse-

quently they have maximum frequencies of selection (ie

appear in all solutions)

Discussion

Here we showed that it is possible to design optimal con-

servation strategies for the conservation of genetic diver-

sity under the framework of SCP Moreover alternative

applications of SCP for in situ and ex situ conservation

strategies were done based on the same dataset although

using distinct weights revealing their distinct purposes

We understand that these ex situ and in situ strategies can

Fig 2 Frequency of solutions

from SCP maximizing the total

number of alleles represented

minimizing the number of local

populations and maximizing the

amount of natural remnants

This count if based on

combination of several solutions

with seven local populations

that we found for optimizing the

in situ conservation Local

populations with frequencies

tending to 100 tend to be

irreplaceable so that losing

them will not allow one to

achieve the conservation goal


123


be implemented together so as to maximize the effective-

ness of conservation planning In our case study dealing

with the genetic variability of D alata natural populations

we showed that it is possible to represent all known alleles

at least once by strategically conserving (in situ) seven

local populations distributed in the Cerrado biome

Because of the analytical constraints imposed by the dis-

tribution of alleles in different local population and the

predetermined targets trying to define a solution that

minimizes the human impacts of these local populations

(ie by selecting local population in regions with more

natural cover remnants) did not change the selected pop-

ulations although it may help to define priorities among

the populations studied (eg planning a schedule for con-

servation interventions) Further assuming that the germ-

plasm bank is a fully protected lsquolsquopopulationrsquorsquo we also

showed that even fewer local populations need to be

sampled to represent the known genetic variability of the

species aiming at its ex situ conservation

Systematic conservation planning is usually applied to

species conservation building conservation priority

regions based on several goals with different conservation

targets (Sarkar and Illoldi-Rangel 2010) This initial

application with genetic data represents a step further to the

theoretical ideas originally presented by Diniz-Filho and

Telles (2006) who proposed how different algorithms

could be used to represent genetic diversity under distinct

spatial population structures The analysis shown here for

D alata indeed suggests that systematic conservation

planning can be a quite useful and objective tool to guide

actions for within-species conservation although of course

further improvements may be important and more work is

needed to expand the framework

A first important issue to be discussed is that our

application is focused on neutral molecular markers which

represents the overall variability of the species that have

been accumulated throughout its evolutionary history Of

course this does not necessarily reflect adaptive traits that

must be important for population persistence or the traits

(ie fruit production and quality) that may be important for

000 015 030 045 060 075

Mean proportion of natural remnants

25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution

Fig 3 Relationship between number of alleles represented and

proportion of natural remnants in the conserved populations under a

null model in which the seven local populations are randomly

sampled from the pool of 25 and combined in a reserve network The

optimum solution obtained by SCP is highlighted

Fig 4 Local populations

defined by SCP necessary to

better complement the

germplasm collection in the exsitu conservation program The

four local populations are

connected by arrows to Goiania

the locality where the

germplasm of D alata is

maintained


123


economic use of these species Our application is more

focused on overall conservation of variability within spe-

cies someway equivalent in this sense to definition of

evolutionary significant units (although this is usually

based on explicitly historical data obtained by mtDNA or

cpDNAmdashbut see Fraser and Bernatchez 2001 for a more

general definition) Our main purpose here was to show the

possibility of developing a systematic conservation plan

based on genetic data of course if adaptive data is avail-

able the same procedures can be applied (perhaps with

more sophisticated targets related to the persistence of local

populations or its economic use) Also other targets can be

added to allow improving chances of population persis-

tence and this could be coupled with the analyses per-

formed here

Another limited aspect of our analyses is that genetic

data is based on a sample of local populations and this

does not ensure that all genetic variability for the species

was actually sampled There are two different issues mixed

here First because of the relatively small sample sizes

within local populations it is not possible to ensure that all

genetic diversity (ie all alleles) have been sampled and

adding more rare alleles can change the frequencies of

selection reported here This is a general problem in SCP

when applied to species data when it is assumed that all

species found in the alternative sites or localities are known

(usually because these analyses are based on maps of

extents of occurrence although it is well known that these

maps have important omission and commission errorsmdashsee

Lemes et al 2011 for a recent discussion) In our case with

genetic data this problem could be solved in the future by

using simulations to generate stochastic distributions of

alleles within populations and applying SCP to the simu-

lated data Alternatively other algorithms could be devel-

oped to deal directly with allele frequencies giving less

weight to rare alleles or targeting not representation of

alleles only but also their combinations in genotypic fre-

quencies (ie taking into account how and where they

appear in heterozygosity or homozygosity) as well which

could also be better linked with population persistence

Second and perhaps more important although we

sampled D alata along most of its geographic range and

analyzed a relatively large number of localities it is quite

probable that sampling more populations will lead to dis-

covery of new (rare) alleles that should deserve attention

Even though there was a significant and positive autocor-

relation for most allele frequencies and overall variability

(see Soares et al 2008) rare alleles are by definition ran-

domly distributed in geographical space So we are actu-

ally showing only how the known genetic variability can be

optimally represented Even so we believe that our pro-

cedure is geographical representative because of the

underlying spatial structure in data

As pointed out above the purpose of our SCP was to

represent the estimated genetic variability in microsatellite

neutral markers we trust that our samples captured most of

the genetic diversity using this surrogate When comparing

the local populations analyzed here this can be better

viewed as a model II design for statistical analyses in

which local populations are actually samples of an

unknown number of local populations widely distributed

throughout speciesrsquo range Although it is not certain that

these local samples represent all genetic diversity infor-

mation about autocorrelation structure can be used to

establish if a local population not sampled and analyzed

can be effective in conserving genetic diversity (see Diniz-

Filho and Telles 2002) Applying a Mantel correlogram to

genetic distances revealed that correlogram intercept is

about 300ndash350 km with Mantelrsquos correlation in the first

distance class equal to 0324 (P 001 with 1000 per-

mutations) (see Diniz-Filho and Telles 2002 Epperson

2003) Thus although correlation is not too high (most

likely because of the rare alleles) if a non-sampled local

population is within this autocorrelation range of about

300ndash350 km to one of the local population considered

important in our SCP it quite probably holds a similar set

of alleles Even rare alleles could be found in this unknown

population if gene flow is effective at these small geo-

graphic distances (ie many private alleles could be

actually not private if closer populations are sampled)

Consequently it is safe to assume that this new unknown

local population would be effective to achieve the con-

servation goals at least in terms of the more common

alleles (which tend to determine the spatial patterns) If

opportunities to conserve this new local population actually

arise they should be prioritized even in the absence of

genetic information about it The same reasoning is valid if

lsquolsquoBarursquorsquo trees are found in protected areas close to a pop-

ulation assigned here with high selection frequency which

will increase the conservation value of this protected area

(because this reserve will also probably represent the

genetic diversity of the target species beyond its other

potential benefits)

We believe that the most important issue raised by our

analysis is the possibility of applying the methods of sys-

tematic conservation planning to cope with within-species

genetic variation and based on distinct approaches for in situ

and ex situ strategies Although many improvements can be

made to deal with more complex aspects of genetic data (such

as dealing with allele frequencies rather than with allele

presence-absence doing independent analyses for the distinct

loci and dealing with within-population variability using

genotypes as targets adding more adaptive markers or

including more socio-economic costs of conservation action)

we believe that the analyses presented here are a good starting

point for planning for the conservation of genetic diversity in a


123


more systematic and objective way The most important idea

is that different plans can be developed for dealing with in situ

and ex situ situations

In general we understand that for in situ conservation the

applications match those frequently used with other conser-

vation goals usually species in which development-conser-

vation conflicts are solved (or at least minimized) by selecting

sites (in this case local populations) that have less human

impacts Of course depending on the distribution of goals and

defined targets this minimization will not produce a lot of

changes (for example if irreplaceable sites are found in

regions with higher levels of human occupation) Even so

another possibility is to relax the targets so that some of these

targets can be lost if the cost of the total solution becomes too

high This could be done more effectively by also incorpo-

rating more complex targets based on allele frequency or

doing particular combinations of targets within populations

However this is difficult to do in practice because we are

usually not aware of the biological or ecological meaning of

the variable used to constraint the selection of sites or popu-

lations In our case here with D alata for example it is dif-

ficult to establish which is the minimum amount of natural

remnants within the buffer that will ensure the persistence of

local populations We know that individual trees can persist in

disturbed environments such as highly disturbed pastures

because the tree furnishes shadows and food for cattle but

these isolated trees have their fitness widely reduced because

of lack of pollinators and dispersers so populations will col-

lapse in short ecological times The existence of some private

alleles (see Table 1) suggests that some level of isolation

already exists among these local populations and this may be

also related to broad scale habitat fragmentation in Cerrado

(see Telles et al 2007 Diniz-Filho et al 2007b) If a next step

determining how this isolation reduces fitness at more local

scales is thus very important for establishing how the cost

variable can be a surrogate for such demographic processes

within local populations This can be done for example by

testing departures from HW equilibrium (ie see Table 1) of

local populations with high selection frequencies in the final

solution If these crucial local populations which will tend to

be characterised by rare or private alleles because of how

algorithm achieve the conservation goal are dominated by

rare allele homozygous individuals this may indicate whether

the conservation of these local populations is only going to be

effective in the short-term and should be augmented with

active management As pointed out above further develop-

ments in SCP procedures that deals with genotypes and not

only alleles as conservation targets may be effective in dealing

with this situation

On the other hand for ex situ conservation it is also

possible to derive effective conservation plans based on

different approaches Here we lsquolsquofixedrsquorsquo an additional pop-

ulation preserved in the germplasm bank that is maintained

and which ensures long-term persistence of the existing

genetic variability Doing this revealed that only four

additional samples are necessary to optimally represent all

the variability Other solutions could be used for instance

by minimizing the distance from each local population to

the germplasm bank (this would reduce transport costs for

example) In our case as discussed above the distinct

solutions did not change a lot the final solutions because of

the distribution of alleles in the populations

An overall principle in producing conservation plans based

on genetic data would also resemble reactive and pro-active

conservation strategies at broader scales (Brooks et al 2006)

When dealing with in situ conservation a pro-active strategy

is being applied minimizing human impacts in selected

regions to increase persistence in natural habitats Perhaps for

ex situ conservation the opposite strategymdasha reactive

approachmdashshould be established by selecting regions with

higher levels of human impacts (in this case by inverting the

direction of cost vector) Doing this would ensure that genetic

variability found in regions with higher impacts that may

have reduced fitness will be prioritized for sampling and

seeding in the germplasm collection

Thus despite the simplicity of the approach adopted here

and the problems and caveats pointed out above we believe

that this initial application of the systematic conservation

planning theory to genetic data can be a useful starting point

for methodological and conceptual improvements Improving

our knowledge on the ecological meaning of the cost variable

used in the prioritization process may be a first important step

towards a comprehensive and quantitative balance definition

of goals and will allow establishing threshold for acceptable

loss of conservation targets within species In general dealing

with more complex genetic data can be important for increase

the range of applications and explicitly incorporating eco-

nomic use of the species can shed more light in new possi-

bilities for in situ and ex situ designs

Acknowledgments We thank two anonymous reviewers for sug-

gestions that improved initial version of this manuscript Our research

program integrating macroecology and molecular ecology of plants

and the DTI fellowship to GO has been continuously supported by

several grants and fellowships to the research network GENPAC

(Geographical Genetics and Regional Planning for natural resources

in Brazilian Cerrado) from CNPqMCTCAPES (projects

5647172010-0 and 5636242010-8) and by the lsquolsquoNucleo de Excel-

encia em Genetica e Conservacao de Especies do CerradorsquorsquomdashGECER

(PRONEXFAPEGCNPq CP 07-2009) Field work has been sup-

ported by Systema Naturae Consultoria Ambiental LTDA Work by

JAFD-F MPCT LJC RGC and RDL have been contin-

uously supported by productivity fellowships from CNPq and work

by DBM and JSS by fellowships by CAPES

References

Abbitt RJF Scott JM Wilcove DS (2000) The geography of

vulnerability incorporating species geography and human


123


development patterns into conservation planning Biol Conserv

96169ndash175 doi101016S0006-3207(00)00064-1

Araujo MB (2003) The coincidence of people and biodiversity in

Europe Glob Ecol Biogeogr 125ndash12 doi101046j1466-822X

200300314x

Balmford A Moore JL Brooks T Burgess N Hansen LA Williams

P Rahbek C (2001) Conservation conflicts across Africa

Science 2912616ndash2619 doi101126science29155132616

Brooks T da Fonseca GAB Rodrigues ASL (2004) Species data and

conservation planning Conserv Biol 181682ndash1688 doi

101111j1523-1739200400457x

Brooks TM Mittermeier RA Fonseca GAB Da Gerlach J Hoffmann

M Lamoreux JF Mittermeier CG Pilgrim JD Rodrigues ASL

(2006) Global biodiversity conservation priorities Science

31358ndash61 doi101126science1127609

Cabeza M Moilanen A (2001) Design of reserve networks and the

persistence of biodiversity Trends Ecol Evol 16242ndash248 doi

101016S0169-5347(01)02125-5

Charlesworth D Charlesworth B (1987) Inbreeding depression and its

evolutionary consequences Annu Rev Ecol Syst 18237ndash268

doi101146annureves18110187001321

Collevatti RG Lima JS Soares TN Telles MPDC (2010) Spatial

genetic structure and life history traits in Cerrado tree species

inferences for conservation Nat Conservacao 854ndash59 doi

104322natcon00801008

Diniz-Filho JAF Bini LM (2011) Geographical patterns in biodiver-

sity towards an integration of concepts and methods from genes

to species diversity Nat Conservacao 9(2)179ndash187 doi

104322natcon2011023

Diniz-Filho JAF Telles MPC (2002) Spatial autocorrelation analysis

and the identification of operational units for conservation in

continuous populations Conserv Biol 16924ndash935 doi101046

j1523-1739200200295x

Diniz-Filho JAF Telles MPC (2006) Optimization procedures for

establishing reserve networks for biodiversity conservation

taking into account population genetic structure Genet Mol

Biol 29207ndash214

Diniz-Filho JAF Bini LM Pinto MP Rangel TFLVB Carvalho P

Vieira SL Bastos RP (2007a) Conservation biogeography of

Anurans in Brazilian Cerrado Biodivers Conserv 16997ndash1008

doi101007s10531-006-9010-4

Diniz-Filho JAF Nabout JC Bini LM Soares TN Telles MPC

DeMarco P Collevatti RG (2007b) Niche modeling and

landscape genetics of Caryocar brasiliense (lsquolsquoPequirsquorsquo tree

Caryocaraceae) in Brazilian Cerrado an integrative approach

for evaluating central-peripheral population patterns Tree Genet

Genomes 5617ndash627 doi101007s11295-009-0214-0

Epperson BK (2003) Geographical genetics Princeton University

Press Princeton

Felfili JM Ribeiro JF Borges Filho HC Vale AT (2004) Potencial

economico da biodiversidade do Cerrado estadio atual e

possibilidades de manejo sustentavel dos recursos da flora In

Aguiar LMS Camargo AJA (eds) Cerrado ecologia e caracter-

izacao Embrapa Informacao Tecnologica Brasılia pp 17ndash40

Frankham R (2003) Genetics and conservation biology C R Biol

32622ndash29 doi101016S1631-0691(03)00023-4

Fraser DJ Bernatchez L (2001) Adaptive evolutionary conservation

towards a unified concept for defining conservation units Mol Ecol

102741ndash2752 doi101046j1365-294X2001t01-1-01411x

Goudet J (2002) FSTAT a program to estimate and test gene

diversities and fixation indices (version 2932) Available from

httpwwwunilchizeasoftwaresfstathtml

Grelle CEV Lorini ML Pinto MP (2010) Reserve selection based on

vegetation in the Brazilian Atlantic Forest Nat Conservacao

846ndash53 doi104322natcon00801007

Kirkpatrick JB (1983) An iterative method for establishing priorities

for the selection of nature reserves an example from Tasmania

Biol Conserv 25127ndash134 doi1010160006-3207(83)90056-3

Lemes P Faleiro FAMV Tessarolo G Loyola RD (2011) Refinando

dados espaciais para conservacao da biodiversidade Nat Con-

servacao 9240ndash243 doi104322natcon2011032

Loyola RD Oliveira-Santos LGR Almeida-Neto M Nogueira DM

Kubota U Diniz-Filho JAF Lewinsohn TM (2009) Integrating

economic costs and biological traits into global conservation

priorities for carnivores Plos One 4(8)e6807 doi101371

journalpone0006807

Loyola RD Eizirik E Machado RB Aguiar LMS Brito D Grelle

CEV (2011) Toward innovative integrated approaches for the

conservation of mammals Nat Conservacao 91ndash6 doi104322

natcon2011001

Margules CR Pressey RL (2000) Systematic conservation planning

Nature 405243ndash253 doi10103835012251

Margules CR Sarkar S (2007) Systematic conservation planning

Cambridge University Press Cambridge

McCarthy MA Thompson CJ Possingham HP (2005) Theory for

designing nature reserves for single species Am Nat

165250ndash257 doi0003-0147200516502-4046

Myers N Mittermeier RA Mittermeier CG Fonseca GAB Kents J

(2000) Biodiversity hotspots for conservation priorities Nature

403853ndash858 doi10103835002501

Neel MC Cummings MP (2003) Effectiveness of conservation targets

in capturing genetic diversity Conserv Biol 17219ndash229 doi

101046j1523-1739200301352x

Possingham H Ball I Andelman S (2000) Mathematical methods for

identifying representative reserve networks In Ferson S

Burgman M (eds) Quantitative methods for conservation biol-

ogy Springer New York pp 291ndash305

Pressey RL (2004) Conservation planning and biodiversity assem-

bling the best data for the job Conserv Biol 181677ndash1681 doi

101111j1523-1739200400434x

Sarkar S Illoldi-Rangel P (2010) Systematic conservation planning an

updated protocol Nat Conservacao 819ndash26 doi104322natcon

00801003

Segelbacher G Cushman SA Epperson BK Fortin M-J Francois O

Hardy OJ Holderegger R Taberlet P Waits LP Manel S (2010)

Applications of landscape genetics in conservation biology

concepts and challenges Conserv Genet 11375ndash385 doi

101007s10592-009-0044-5

Soares TN Chaves LJ Telles MPD Diniz-Filho JAF Resende LV

(2008) Spatial distribution of intrapopulational genetic variabil-

ity in Dipteryx alata Pesqui Agropecu Bras 431151ndash1158

Soares TN Melo DB Resende LV Vianello RP Chaves LJ

Collevatti RG Telles MPC (2012) Development of microsatel-

lite markers for the Neotropical tree species Dipteryx alata(Fabaceae) Am J Bot 99e72ndashe73

Telles MPC Diniz-Filho JAF Bastos RP Soares TN Guimaraes

LDH Lima LP (2007) Landscape genetics of Physalaemuscuvieri in Brazilian Cerrado correspondence between population

structure and levels of human occupation and habitat loss Biol

Conserv 13937ndash46 doi101016jbiocon200706003

Weir BS Cockerham CC (1984) Estimating F-statistics for the

analysis of population structure Evolution 381358ndash1370 doi

1023072408641

Williams JC Revelle CS Levin SA (2004) Using mathematical

optimization models to design nature reserves Front Ecol Environ

298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123


1 23

Your article is protected by copyright and

all rights are held exclusively by Springer

Science+Business Media BV This e-offprint

is for personal use only and shall not be self-

archived in electronic repositories If you

wish to self-archive your work please use the

accepted authorrsquos version for posting to your

own website or your institutionrsquos repository

You may further deposit the accepted authorrsquos

version on a funderrsquos repository at a funderrsquos

request provided it is not made publicly

available until 12 months after publication

RESEARCH ARTICLE










































Introduction









D B Melo




G de Oliveira






J C Nabout






970 Brazil




123


DOI 101007s10592-012-0356-8






























et al 2009)
























































Genetic data

















2932 (Goudet 2002)


123





















































vegetation


123








populations






















Results





ABMT 32 2375 2223 0 0291 0382 0238 2

AMG 32 2250 1973 0 0293 0310 0055ns 3

AMS 32 4250 3609 1 0349 0476 0267 3

AQMS 31 3375 2817 0 0442 0459 0038ns 1

ARTO 15 2250 2205 0 0381 0372 -0025ns 3

ATO 32 3000 2747 0 0382 0488 0217 1

CAMT 30 3750 3152 2 0278 0466 0405 2

CMS 13 2875 2861 0 0466 0480 0029ns 4

CMT 32 2625 2502 1 0249 0322 0229 1

ENGO 12 2500 2478 0 0408 0474 0139 1

IGO 13 2500 2447 0 0350 0453 0226 1

ISP 32 2375 2212 0 0322 0455 0291 3

JGO 32 2625 2480 0 0638 0599 -0065ns 1

LGO 32 2500 2425 0 0424 0509 0166 1

MAMG 32 2875 2582 0 0448 0514 0128 4

NTO 12 2000 2093 0 0458 0393 -0165ns 3

PCMS 13 2875 2808 0 0169 0345 0509 1

PGO 32 2500 2164 0 0259 0426 0336 3

PMG 32 2375 2103 0 0176 0257 0314 1

PMS 13 3125 3059 0 0215 0410 0476 1

RAGO 32 3250 2865 0 0473 0462 -0024ns 3

RAMT 32 3375 2976 2 0308 0462 0333 3

SMGO 32 2625 2409 0 0354 0336 -0052ns 1

SMS 32 3625 3213 0 0422 0482 0125 2

STGO 12 2875 2823 0 0356 0387 0080ns 1

Overall 644 2835 2609 6 0356 0429 0171 ndash

P [ 005





123









































one as expected












Discussion

























123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123


RESEARCH ARTICLE










































Introduction









D B Melo




G de Oliveira






J C Nabout






970 Brazil




123


DOI 101007s10592-012-0356-8






























et al 2009)
























































Genetic data

















2932 (Goudet 2002)


123





















































vegetation


123








populations






















Results





ABMT 32 2375 2223 0 0291 0382 0238 2

AMG 32 2250 1973 0 0293 0310 0055ns 3

AMS 32 4250 3609 1 0349 0476 0267 3

AQMS 31 3375 2817 0 0442 0459 0038ns 1

ARTO 15 2250 2205 0 0381 0372 -0025ns 3

ATO 32 3000 2747 0 0382 0488 0217 1

CAMT 30 3750 3152 2 0278 0466 0405 2

CMS 13 2875 2861 0 0466 0480 0029ns 4

CMT 32 2625 2502 1 0249 0322 0229 1

ENGO 12 2500 2478 0 0408 0474 0139 1

IGO 13 2500 2447 0 0350 0453 0226 1

ISP 32 2375 2212 0 0322 0455 0291 3

JGO 32 2625 2480 0 0638 0599 -0065ns 1

LGO 32 2500 2425 0 0424 0509 0166 1

MAMG 32 2875 2582 0 0448 0514 0128 4

NTO 12 2000 2093 0 0458 0393 -0165ns 3

PCMS 13 2875 2808 0 0169 0345 0509 1

PGO 32 2500 2164 0 0259 0426 0336 3

PMG 32 2375 2103 0 0176 0257 0314 1

PMS 13 3125 3059 0 0215 0410 0476 1

RAGO 32 3250 2865 0 0473 0462 -0024ns 3

RAMT 32 3375 2976 2 0308 0462 0333 3

SMGO 32 2625 2409 0 0354 0336 -0052ns 1

SMS 32 3625 3213 0 0422 0482 0125 2

STGO 12 2875 2823 0 0356 0387 0080ns 1

Overall 644 2835 2609 6 0356 0429 0171 ndash

P [ 005





123









































one as expected












Discussion

























123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123






























et al 2009)
























































Genetic data

















2932 (Goudet 2002)


123





















































vegetation


123








populations






















Results





ABMT 32 2375 2223 0 0291 0382 0238 2

AMG 32 2250 1973 0 0293 0310 0055ns 3

AMS 32 4250 3609 1 0349 0476 0267 3

AQMS 31 3375 2817 0 0442 0459 0038ns 1

ARTO 15 2250 2205 0 0381 0372 -0025ns 3

ATO 32 3000 2747 0 0382 0488 0217 1

CAMT 30 3750 3152 2 0278 0466 0405 2

CMS 13 2875 2861 0 0466 0480 0029ns 4

CMT 32 2625 2502 1 0249 0322 0229 1

ENGO 12 2500 2478 0 0408 0474 0139 1

IGO 13 2500 2447 0 0350 0453 0226 1

ISP 32 2375 2212 0 0322 0455 0291 3

JGO 32 2625 2480 0 0638 0599 -0065ns 1

LGO 32 2500 2425 0 0424 0509 0166 1

MAMG 32 2875 2582 0 0448 0514 0128 4

NTO 12 2000 2093 0 0458 0393 -0165ns 3

PCMS 13 2875 2808 0 0169 0345 0509 1

PGO 32 2500 2164 0 0259 0426 0336 3

PMG 32 2375 2103 0 0176 0257 0314 1

PMS 13 3125 3059 0 0215 0410 0476 1

RAGO 32 3250 2865 0 0473 0462 -0024ns 3

RAMT 32 3375 2976 2 0308 0462 0333 3

SMGO 32 2625 2409 0 0354 0336 -0052ns 1

SMS 32 3625 3213 0 0422 0482 0125 2

STGO 12 2875 2823 0 0356 0387 0080ns 1

Overall 644 2835 2609 6 0356 0429 0171 ndash

P [ 005





123









































one as expected












Discussion

























123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123





















































vegetation


123








populations






















Results





ABMT 32 2375 2223 0 0291 0382 0238 2

AMG 32 2250 1973 0 0293 0310 0055ns 3

AMS 32 4250 3609 1 0349 0476 0267 3

AQMS 31 3375 2817 0 0442 0459 0038ns 1

ARTO 15 2250 2205 0 0381 0372 -0025ns 3

ATO 32 3000 2747 0 0382 0488 0217 1

CAMT 30 3750 3152 2 0278 0466 0405 2

CMS 13 2875 2861 0 0466 0480 0029ns 4

CMT 32 2625 2502 1 0249 0322 0229 1

ENGO 12 2500 2478 0 0408 0474 0139 1

IGO 13 2500 2447 0 0350 0453 0226 1

ISP 32 2375 2212 0 0322 0455 0291 3

JGO 32 2625 2480 0 0638 0599 -0065ns 1

LGO 32 2500 2425 0 0424 0509 0166 1

MAMG 32 2875 2582 0 0448 0514 0128 4

NTO 12 2000 2093 0 0458 0393 -0165ns 3

PCMS 13 2875 2808 0 0169 0345 0509 1

PGO 32 2500 2164 0 0259 0426 0336 3

PMG 32 2375 2103 0 0176 0257 0314 1

PMS 13 3125 3059 0 0215 0410 0476 1

RAGO 32 3250 2865 0 0473 0462 -0024ns 3

RAMT 32 3375 2976 2 0308 0462 0333 3

SMGO 32 2625 2409 0 0354 0336 -0052ns 1

SMS 32 3625 3213 0 0422 0482 0125 2

STGO 12 2875 2823 0 0356 0387 0080ns 1

Overall 644 2835 2609 6 0356 0429 0171 ndash

P [ 005





123









































one as expected












Discussion

























123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123








populations






















Results





ABMT 32 2375 2223 0 0291 0382 0238 2

AMG 32 2250 1973 0 0293 0310 0055ns 3

AMS 32 4250 3609 1 0349 0476 0267 3

AQMS 31 3375 2817 0 0442 0459 0038ns 1

ARTO 15 2250 2205 0 0381 0372 -0025ns 3

ATO 32 3000 2747 0 0382 0488 0217 1

CAMT 30 3750 3152 2 0278 0466 0405 2

CMS 13 2875 2861 0 0466 0480 0029ns 4

CMT 32 2625 2502 1 0249 0322 0229 1

ENGO 12 2500 2478 0 0408 0474 0139 1

IGO 13 2500 2447 0 0350 0453 0226 1

ISP 32 2375 2212 0 0322 0455 0291 3

JGO 32 2625 2480 0 0638 0599 -0065ns 1

LGO 32 2500 2425 0 0424 0509 0166 1

MAMG 32 2875 2582 0 0448 0514 0128 4

NTO 12 2000 2093 0 0458 0393 -0165ns 3

PCMS 13 2875 2808 0 0169 0345 0509 1

PGO 32 2500 2164 0 0259 0426 0336 3

PMG 32 2375 2103 0 0176 0257 0314 1

PMS 13 3125 3059 0 0215 0410 0476 1

RAGO 32 3250 2865 0 0473 0462 -0024ns 3

RAMT 32 3375 2976 2 0308 0462 0333 3

SMGO 32 2625 2409 0 0354 0336 -0052ns 1

SMS 32 3625 3213 0 0422 0482 0125 2

STGO 12 2875 2823 0 0356 0387 0080ns 1

Overall 644 2835 2609 6 0356 0429 0171 ndash

P [ 005





123









































one as expected












Discussion

























123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123









































one as expected












Discussion

























123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123










































000 015 030 045 060 075


25

35

45

55N

umbe

r of

alle

les

repr

esen

ted

Best solution














maintained


123
















formed here














































































potential benefits)















123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


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References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123


















































with this situation























































References




123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123



96169ndash175 doi101016S0006-3207(00)00064-1



200300314x






101111j1523-1739200400457x







101016S0169-5347(01)02125-5



doi101146annureves18110187001321




104322natcon00801008




104322natcon2011023




j1523-1739200200295x




Biol 29207ndash214




doi101007s10531-006-9010-4








Press Princeton







32622ndash29 doi101016S1631-0691(03)00023-4



102741ndash2752 doi101046j1365-294X2001t01-1-01411x

















journalpone0006807




natcon2011001


Nature 405243ndash253 doi10103835012251





165250ndash257 doi0003-0147200516502-4046



403853ndash858 doi10103835002501



101046j1523-1739200301352x







101111j1523-1739200400434x



00801003





101007s10592-009-0044-5













1023072408641



298ndash105 doi1018901540-9295(2004)002[0098UMOMTD]2

0CO2


123


Planning for optimal conservation of geographical genetic variability within species

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Transcript of Planning for optimal conservation of geographical genetic variability within species