Section 9Resolving Taxonomic Uncertainties &

Defining Management Units

The taxonomic status of, and relationships amongmany taxa are unresolved.

In conservation, many erroneous decisions may result if the taxonomic status of populations isnot correctly assigned, such as:

Unrecognized endangered species may be allowedto become extinct.

Incorrectly diagnosed species may be hybridizedwith other species, resulting in reduced reproductive fitness.

Resources may be wasted on abundant species, or hybrid populations.

Populations that could be used to improve the fitness of inbred populations may be overlooked.

Endangered species may be denied legal protectionwhile populations of common species, or hybridsbetween species, may be granted protection.

Recent molecular studies among sea turtlescompared the Kemp’s ridley turtleKemp’s ridley turtle (Lepidochelyskempi) and the similar olive ridley turtleolive ridley turtle (L. olivacea)and supported recognition of Kemp’s ridley turtleas a valid species.

Studies of the genetics of minke whalesminke whales (Balaenoptera acutorostrata) have led investigatorsto advocate that the Northern and SouthernHemisphere populations be treated as two distinctspecies (Hoelzel and Dover, 1991).

Similar conclusions were reached based on molecular studies of sympatric populations ofkiller whaleskiller whales (Orcinus orca).

This case is particularly interesting because itsuggests that observed differences in behaviorin sympartric populations, so called “resourcepolymorphisms”, may be genetically based(Hoelzel 1998).

Proper identification and determination ofevolutionary relationships can preventhybridization, and sometimes genetic extinctionof “look-alike” species.

Case of the Extinct Dusky Sea Side SparrowCase of the Extinct Dusky Sea Side Sparrow

1872, a melanistic form ofseaside sparrow was discoveredin Brevard co. FL and describedas a distinct species:Ammodramus nigrescens.

1960s the population (now asubspecies) was in severe decline due to habitat alterationsand the Dusky Seaside Sparrow was placed on theU.S. Endangered Species List.

Currently, about 9 subspecies recognized with more or less abutting ranges along the species coastal-marsh habitat from New England tosouth Texas.

1980, the few remainingbirds (all males) were brought into captivity and mated to individualsfrom a Gulf Coast population.

The objective was to produce F1 hybrids and thenbackcross progeny (the latter carrying primarilydusky nuclear genes) for eventual reintroduction.

The breeding program was not successful and thusdiscontinued.

Avis and Nelson (1989) assayed mtDNA haplotypesfrom 40 seaside sparrows representing 7 namedsubspecies and the last available dusky male, whichdied in captivity in 1987.

1

2

3

4

5

6

78

9

10

11

Most commonAtlantic haplotypeand haplotype ofDusky seaside sparrow

Gulf CoastGulf Coasthaplotypeshaplotypes

Atlantic CoastAtlantic Coasthaplotypeshaplotypes

Thus, the traditional taxonomy for seasidesparrows, from which conservation prioritieswere derived, probably had been a misleadingguide to evolutionary relationships in this complexfor two reasons:

1. in failure to recognize the fundamental phylogenetic dichotomy between Atlantic & Gulf populations.

2. in taxonomic emphasis on distinctions withinboth coastal regions that appear evolutionarily minorcompared to the between-region genetic differences.

Captive breeding of gazelles and dik-diks thatwere supposedly of the samespecies ha sometimes producedinfertile offspring.

Subsequent cytogenetic analyses revealed that theparents were of different species.

Dealing specifically with dik-diks, Benirschke &Kumamoto (1991) noted that not only wereindividuals of different species bred togetherin captivity, but also hybrids of Kirk’sKirk’s (Madoquakirkii) and Guenther’sGuenther’s (M. rhyncotragus) dik-dikswere found in 300 collections.

These authors concluded that a cytogenetic analysis should be mandatory prior to captivebreeding populations are established to eliminateunnecessary hybridization and reduced fertility.

Resolving TaxonomicResolving TaxonomicUncertaintiesUncertainties

Phylogenetic trees are used to resolve taxonomicuncertainties.

A phylogenetic tree is composed of lines calledbranches that intersect and terminate at nodes.

The nodes at the tips of the branches representthe taxa that exist today and that we can actually examine.

The internal nodes represent ancestral taxa,whose properties we can only infer from theexisting data.

AA

BBCCDD

EE

12

1

1

7

123

RRYY

ZZ

Rooted tree whose branch tipsrepresent 5 taxa (A - E) in a clade, with 4 internal nodes(R, X, Y, ZR, X, Y, Z) representingancestral taxa, including theroot (RR).

The numbers on branches indicate thenumber of changes in a particular sequencethat occurred along that branch.

These numbers represent the branch lengths.

AA

BBCCDD

EE

12

1

1

7

123

RRYY

ZZ

Even if exact values are notprovided, the relative lengthsof the branches may be drawnin proportion to the number ofchanges along that branch.

This tree is additive because thedistance between any two nodesequals the sum of the lengths of allbranches between them.

If multiple substitutions have occurred at any site,then additivity will not hold unless distances arecorrected for multiple substitutions.

AA

BBCCDD

EE

12

1

1

7

123

RRYY

ZZ

A tree is said to be rooted ifthere is a particular node -- theroot -- from which a uniquedirectional path leads to eachextant taxon.

In this tree, RR is the rootbecause it is the only internalnode from which all other nodescan be reached by moving forward(toward the tips).

The root is the common ancestor of all taxa in theanalysis.

AA CC

DDBB

AA BB

DDCC

AA CC

BBDD

Unrooted tree, such as these,specify only the relationshipsamong the taxa, and DO NOTDO NOTdefine evolutionary pathways.

For 4 taxa, there are only 3possible unrooted trees.

Once a root is identified, 5different rooted treescan be created for EACHEACH of these unrooted trees, each with a distinctivebranching pattern reflecting a different evolutionaryhistory.

The number of possible trees, both rooted andunrooted, increases dramatically as the number oftaxa increases.

Let s be the number of taxa, the number of possibleunrooted trees is:

(2s - 5)! / [2(2s - 5)! / [2s-3s-3(s-3)!](s-3)!]

the number of possible rooted trees is:

(2s - 3)! / [2(2s - 3)! / [2s-3s-3(s-3)!](s-3)!]

TaxaTaxa unrooted unrooted rooted rooted treestrees treestrees

4 3 158 13,395 135,13510 2,027,025 34,459,42522 1 x 1023 almost a mole

of trees

50 3 x 1074 more trees than atoms inthe universe

Unrooted trees tell us only about phylogeneticrelationships; they tell us nothing about thedirections of evolution -- the order of descent.

Rooted trees tell us about the order of descentfrom the root toward the tips of the tree.

While unrooted trees are always more “correct”in that they don’t imply knowledge that we donot have, they are considerably lessinformative.

Alignment of DNA sequencesAlignment of DNA sequences

A pair of sequences can be aligned by writing one above the other in such a way as to maximize thenumber of residues that match by introducing gapsinto one or the other sequence.

Biologically, these gaps are assumed to representinsertions or deletions that occurred as thesequences diverged from a common ancestor.

If we could insert as many gaps as we chose, wecould align any two random, unrelated sequences sothat all residues either matched perfectly or wereacross from a gap in the other sequence.

Such an alignment would be meaningless!!!

It is necessary to somehow constrain the numberof gaps so that the resulting alignment makesbiological sense.

To do this, a scoring system is used so thatmatching residues get some sort of positive numerical score, and gaps get some sort of negativescore, or gap penaltygap penalty.

An alignment program seeks an arrangement thatmaximizes the net score.

For nucleic acid alignments, matching residuesusually get a score of 1 and mismatches get a scoreof 0.

Gap penalties are typically set by the user andtypically there is a penalty for creating a gapplus an extra penalty for the length of the gap.

Aligning a pair of sequences is not a computationallydifficult process, and a variety of programs existto align sequence pairs.

Multiple alignments are considerably more complex,and only a few programs do a really good job.

CLUSTALX is one of the best tools for creatingmultiple sequence alignments.

An alignment is not an absolute thing.

It is a “best guess” according to some algorithmused by a computer program.

One cannot simply have a program compute analignment and, without further thought, usethat alignment to create a phylogeny.

Distance Based Methods of Tree ConstructionDistance Based Methods of Tree Construction

In these methods, distances are expressed as thefraction of sites that differ between 2 sequencesin a multiple alignment.

It is fairly obvious that a pair of sequences differingat only 10% of their sites are more closely relatedthan a pair differing at 30% of their sites.

It also makes sense that the more time has passed since two sequences diverged from a commonancestor, the more the sequences will differ.

Although the latter assumption is reasonable, itis not always true.

It might be untrue because one lineage evolvedfaster than the other.

Even if two lineages evolved at the same rate,the assumption might be untrue because ofmultiple substitutions.

As two sequences diverge from a common ancestor, each nucleotide substitution initially will increase the number of differences betweenthe two lineages.

As those differences accumulate, however, itbecomes increasingly likely that a substitutionwill occur at the same sitethe same site where an earliersubstitution occurred.

While there are statistical corrections used toestimate corrected distances from the number ofobserved differences, differences almost alwaysunderestimate the actual amount of change alonglineages.

The two most popular distance methods, UPGMAUPGMA and Neighbor-JoiningNeighbor-Joining, are both algorithmicmethods -- i.e., they use a specific series ofcalculations to estimate a tree.

The calculations involve manipulations of a distancematrix that is derived from a multiple alignment.

Starting with the multiple alignment, both programscalculate for each pair of taxa the distance, or thefraction of differences, between the two sequencesand write that distance to a matrix.

UPGMAUPGMA:

UPGMA (Unweighted Pair-Group Method withArithmetic Mean) is an example of a clusteringmethod.

We covered this procedure in chapter 13.

UPGMA has built into it an assumption that thetree is additive and that it is ultrametric -- alltaxa are equally distant from a root -- an assumptionthat is very unlikely. For that and other reasons,UPGMA is rarely used today.

Neighbor-Joining (NJ):Neighbor-Joining (NJ):

NJ is similar to UPGMA in that it manipulates adistance matrix, reducing it in size at each step, then reconstructs the tree from that series ofmatrices.

It differs from UPGMA in that it does not constructclusters but directly calculates distances to internalnodes.

From the original matrix, NJ first calculates foreach taxon its net divergence from all other taxa asthe sum of the individual distances from thetaxon.

It then uses the net divergence to calculate a corrected distance matrix.

NJ then finds the pair of taxa with the lowestcorrected distance and calculates the distancefrom each of those taxa to the node that joins them.

A new matrix is then created in which the newnode is substituted for those two taxa.

NJ does not assume that all taxa areequidistant from a root.

NJ is, like parsimony, a minimum-change method,but it does not guarantee finding the tree withthe smallest overall distance.

Indeed, there are cases in which many shortertrees than the NJ exist.

Some authors think that the best use of an NJ tree is as a starting point for a model-basedanalysis such as Maximum-likelihood.

Parsimony:Parsimony:

Parsimony is based on the assumption that themost likely tree is the one that requires the fewestnumber of changes to explain the data.

The basic premise of parsimony is that taxasharing a common characteristic do so becausethey inherited that characteristic from a commonancestor.

When conflict occur, they are explained by reversal, convergence, or parallelism and theseexplanations are gathered under the termhomoplasyhomoplasy.

HomoplasiesHomoplasies are regarded as “extra” steps orhypotheses that are required to explain the data.

Parsimony operates by selecting the tree or treesthat minimize the number of evolutionary steps,including homoplasies, required to explain the data.

Parsimony or minimum change, is the criterion forchoosing the best tree.

For protein or nucleotide sequences, the data arealigned sequences.

Each site in each alignment is a character, and eachcharacter can have a different state in differenttaxa.

Not all characters are useful in constructing aparsimony tree.

Invariant characters, those that have the same state in all taxa, are obviously useless and areignored by parsimony.

Also ignored are characters in which a state occursin only on taxon.

An algorithm is used to determine the minimumnumber of steps necessary for any given tree to beconsistent with the data.

That number is the score for the tree, and the treeor trees with the lowest score are most parsimonious.

The algorithm is used to evaluate a possible treeat eachat each informative site.

Consider a set of 6 taxa, named 1 -- 6.

At some site (character) in the alignment, the states of that character are:

1 = A2 = C3 = A4 = G5 = G6 = C

There are 105 possible unrooted trees of 6 taxa.

We will pick one unrooted tree, but all will be evaluated by the computer.

If we root this tree at taxon 1, we get the following tree:

AA11

CC66

CC22

AA33

GG44

GG55

1A1A

2C2C

3A3A 4G4G 5G5G 6C6C

Z

X

Y

The algorithm starts at a tip and moves to theinterior node that connects to another tip.

If the two tips have the same state, they assign that state to the node; if they do not, they assignan “or” state.

W

1A1A

2C2C

3A3A 4G4G 5G5G 6C6C

Z

X

Y

Thus, node W is assigned the state A or G, andnode X the state G or C. Node Y connects nodes W and X. Because the states at nodes W and Xboth include G, node Y is assigned the state G.

Node Z is assigned the state C or G as follows:

W

Once the root has been reached, the algorithmproceeds back up from the root toward the tips.

Because node Z does not include the state at thenode that is ancestral to it (taxon 1), its assignmentis arbitrary

1A1A

2C2C

3A3A 4G4G 5G5G 6C6C

C or G

G or C

GA or G

1A1A

2C2C

3A3A 4G4G 5G5G 6C6C

C or G

G or C

GA or G

6C6C3A3A 4G4G 5G5G

1A1A

2C2C

G

GG

G

Assume that it is assigned state G. Node Y is alreadyassigned, so the algorithm moves to node W. NodeW is assigned G because that assignment does notrequire a change from the node that is ancestralto it. Similarly, node X is assigned state G.

Each branch along which thestate changed, indicated bythick branches, is counted.

This tree has 4 changes.

The other possible rootings of the tree are considered in the same way, and if a differentrooting of the tree produces fewer changes, that isthe score for that site.

3A3A 4G4G 5G5G

1A1A

2C2C

G

GG

G

The parsimony program evaluates the tree foreach informative site, then adds up the changesto calculate the minimum number of changes forthat particular tree.

As it works its way through the various possibletrees, the program keeps track of the tree(or trees) with the lowest scores.

Evolutionary ModelsEvolutionary Models:

Sequences diverge from a common ancestor becausemutations occur and some fraction of thosemutations are fixed into the evolving population byselection and by chance, resulting in the substitution of one nucleotide for another atvarious sites.

To reconstruct evolutionary trees, we must makesome assumptions about the substitution processand state those assumptions in the form of a model.

The simplest model is one in which the probabilitiesof any nucleotide changing to any other nucleotideare equal.

To predict the probability that a particularnucleotide at a particular site will change to someother specific nucleotide over some time interval,we need to know the instantaneous rate of changeinstantaneous rate of change.

This simple model has only one parameter and isknown as the Jukes-Cantor model.

If we know there is a GG at some site at t = 0, wecan ask what is the probability that there will stillbe a GG at that site at some time t, and what is theprobability that there will be, for instance, an AA atthat site instead.

These are expressed, respectively, as P(GG)(t) andP(GA)(t). If the substitution rate is per time unit, then:

P(GG)(t) = 1/4 + 3/4e-4 t and P(GA)(t)=1/4-1/ 4e-4 t

Because according the the Juke-Cantor model allsubstitutions are equally likely, a more generalstatement is:

P(ii)(t) = 1/4 + 3/4e-4 t and P(ij)(t)=1/4-1/ 4e-4 t

When t is very close to zero, the probability thatthe site has not changed, P(ii), is very close to 1,while P(ij) the probability that the nucleotide atthat site has changed from i to some other nucleotide, j -- is close to 0.

As time goes on, both probabilities approach 0.25;the time required for that approach depends on.

We can construct a table that shows theinstantaneous rates for each of the possibilitiesfor change at a site as:

Substituted BaseA C G T

A -3 Original base C -3

G -3 T -3

This matrix is commonly called the Q-matrixQ-matrix.

This is not a matrix of probabilities but a matrix ofrates, and the elements in a row sum to 0.

The Jukes-Cantor model is the simplest, but notvery realistic.

We know that not all changes occur at the samerate and a variety of models have been proposedthat allow the specification of different rates.

The most general is one in which each different substitution can occur at a different rate, whichdepends upon the equilibrium frequency of thatnucleotide, symbolized as AA for the equilibriumfrequency of A.

-aA-b G-c t a C b G c T

d A -d A-e G-f T e G f T

Q = g A h C -g A-h C-i T i Tj A k C l G -j A-k C-l G

General Nonreversible Model

All other important models are special cases of this general nonreversible model.

In Kimura’s two-parameter model, transitionsoccur at one rate, , and transversions occur at adifferent rate, ..

Kimura’s Two-Parameter ModelKimura’s Two-Parameter Model

-a-2b b a bb -a-2b b a

Q = a b -a-2b bb a b -a-2b

In the General Time-ReversibleGeneral Time-Reversible (GTRGTR) model, thereare 6 different rates. Time-reversible modelsassume that the overall instantaneous rate ofchange from base i to base j is the same as frombase j to base i.

-aA-b G-c t a C b G c T

a A -a A-d G-e T d G e T

Q = b A d C -b A-d C-f T f Tc A e C f G -c A-e C-f G

When these evolutionary models are used to reconstruct trees, one may either assign specificvalues to those rates, or estimate the values fromthe data.

These models implicitly assume that the rates arethe same at all sites.

It is also possible to include rate variation across sites in the models.

Maximum LikelihoodMaximum Likelihood

Maximum likelihood (ML) tries to infer an evolutionary tree by finding that tree thatmaximizes the probability of observing the data.

For sequences, the data is the alignment ofnucleotides or amino acids.

1 TCAAAAATGGCTTTATTCGCTTAATGCCGTTA2 TCCGTGATGGATTTATTTCTGCAATGCCTGTC3 TTCGTGATGGATTTATTGCTGGTATGCCAGTC4 TTCGTGACGGGTTTATCTCGGCAATGCCGGTC

We begin with an evolutionary model that gives theinstantaneous rates at which each of the 4 possiblenucleotides changes to each of the other 3 possiblenucleotides and a hypothetical tree of some topologyand with branches of some length.

There are three possible unrooted trees for 4 taxa,one of which looks like the following for the site in red:

C

G

T

TX Y

If the model is time-reversible, we can root thetree at any node. One possible rooted tree is:

G C T T

Y

X

G C T T

T

A

We do not know the nucleotides at nodes X and Y,but since there are four possibilities for X and four for Y, there are 16 possible scenarios thatmight lead to the previous tree, one of which is:

G C T T

T

A

The probability of this scenariois the probability of observing anA at the root (PA), which might be1/4 or might be the overall frequencyof A, depending on the model, time and theprobability of each change along the branchesleading to the tips.

The probability of changing from an A at the rootto a G at the tip is calculated from theinstantaneous rate matrix in the chosen model andthe length of the branch from A to G and is PAG.

G C T T

T

A

The probability of this tree is:Ptree=PA x PAG x PAC x PAT x PTT x PTT

Because there are 16 such scenarios, the probabilitiesof each of the scenarios must be determined toobtain the probability of the tree as follows:

Ptree = Ptree1 + Ptree2 + . . . . + Ptree 16

This is the probability for that tree for observing thedata at one site, the site marked in red.

The probability of observing all of the data at allof the sites is the product of the probabilities foreach of the sites i from 1 to N as:

N

Ptree = Pi

i=1

Because these numbers are often too small formost computers to handle, and because it iscomputationally easier, the probability (orlikelihood) of a tree for each site i is usuallyexpressed as a log likelihood, lnLi, and the loglikelihood of the tree is the sum of the loglikelihoods for each of the sites as follows:

N

lnLtree = lnLi

i=1

The term lnLtree is the log likelihood of observingthe alignment under the chosen evolutionary modelgiven that particular tree with its branching orderand branch lengths.

ML programs seek the tree with the largest loglikelihood.

Bayesian Analysis:Bayesian Analysis:

Bayesian inference is based on the notion ofposterior probabilitiesposterior probabilities: probabilities that areestimated, based on some model (prior expectations),after learning something about the data.

For example, if you are tossing coins, your model might be that 90% are true coins and 10% are coinsthat are biased to turn up heads 80% of the time.

Suppose you are blindfolded and asked to pick a coinat random; then you are asked “What is the What is the probability that this coin is a biased coinprobability that this coin is a biased coin?”

Having nothing more to go on than your model that 90% of the coins are true, your obvious answer is 0.1.

If, however, you are allowed to toss the coin youchose 10 times and then are asked the probabilitythat it is biased, you would revise your estimatebased on your model of the expected distribution ofoutcomes from true and biased coins, and yourexpectations of the initial proportion of true coins.

The probability you estimate after observing theoutcomes -- the posterior probabilitythe posterior probability -- shouldbe a better estimate than the 0.1 probabilityyou estimated with no knowledge.

Suppose you observe the following results of yourcoin tosses: HHTHHTTHHHHHTHHTTHHH.

We will use X to symbolize that result.

The probability of that result given that the coinis true -- symbolized P[X|True] where | means“given that” is:

P[X|True] = 0.5P[X|True] = 0.51010 = 9.76 X 10 = 9.76 X 10-4-4..

The probability of that result given a biased coin is:

P[X|Biased] = 0.8P[X|Biased] = 0.877 X 0.2 X 0.233 = 1.6 X 10 = 1.6 X 10-3-3..

The posterior probability that the coin is biased isgiven by the Bayes formula as:

P[X|Biased] =P[X|Biased] x P[Biased]

(P[X|Biased] x P[Biased]) + (P[X|True] x P[True])

P[X|Biased] = 1.67 x 10-3 X 0.1

(1.67 x 10-3 X 0.1) + (9.76 x 10-4 X 0.9)

Thus, P[Biased|X] = 0.13P[Biased|X] = 0.13 and your estimate of theprobability that this is a biased coin has increasedfrom 0.1 to 0.13 based on your observation ofresults.

Bayesian analysis of phylogenies is similar to MLin that the used postulates a model of evolution and the program searches for the best trees thatare consistent with both the model and with thedata (the alignment).

It differs somewhat from ML in that while MLseeks the tree that maximizes the probability ofobserving the data given that tree, Bayesiananalysis seeks the tree that maximizes theprobability of the tree given the data and the modelfor evolution.

In essence, this rescales likelihood to true probabilities in that the sum of the probabilitiesover all trees is 1.0 under the Bayesian approach,which in turn permits using ordinary probabilitytheory to analyze the data.

Like Parsimony and ML, the Bayesian method ischaracter-based and is applied to each site alongthe alignment.

T. elegans

T. tatei

T. pallidior

T. pallidior

T. pusilla

T. sponsoria

T. cinderella

T. venustusT. sponsoriaT. cinderella

T. pusillaT. macruraGracilinanus

1.097

10098

0.78576891

1.099

100100

1.093

100100

1.0999399

1.0689387

1.0<508799

1.097

100100

1.06399

100

1.09197

100

1.05797

100

0.955588

100

BayesianMaximum likelihoodunweighted parsimonyNJ-Kimura 2 parameter

T. pallidior

Evolutionary Significant Units (ESUs)Evolutionary Significant Units (ESUs)Management Units (MUs) andManagement Units (MUs) and

Although genetics has assumed an important rolein conservation biology, genetic surveys ofmanaged species are far from routine and thereis a perception that genetic analyses are of moresignificance to long-term than short-term needsand thus, are of lower priority than demographicanalysis.

Why are the theory and practice so far apart?

Moritz suggests that it is because the relevanceof genetic analyses to practical issues inwildlife management have not been adequatelyexplained and demonstrated.

mtDNA is a powerful tool in evolutionary biologybecause:--rapid rate of base substitutions--effectively haploid and maternal inheritance

reduces Ne and increases sensitivity togenetic drift.

--ease of isolation and manipulation.

mtDNA can produce results of considerablepracticle importance, but the conservation goalsmust be clearly defined first and the analysesdesigned to fit the goals.

It is important to distinguish between:

1.1. Gene ConservationGene Conservation -- the use of genetic information to measure and manage geneticdiversity for its own sake.

2.2. Molecular EcologyMolecular Ecology -- genetic analyses as acomplement to ecological studies of demography.

In many respects, molecular ecology is morestraight forward and is of more use to wildlifemanagers faced with short-term managementpriorities.

Gene Conservation: Measuring & ManagingGene Conservation: Measuring & ManagingGenetic DiversityGenetic Diversity

With few noticeable exceptions, such as translocations, managing genetic diversity in so faras it relates to conserving evolutionary potential, ismore relevant to long-term planning and policythan to short-term management of threatenedpopulations.

mtDNA has been used in 3 ways in this contextmtDNA has been used in 3 ways in this context:

1. To measure genetic variation within populations,especially ones thought to have declinedrecently.

2. Identifying evolutionary divergent sets ofpopulations, including the resolution ofEvolutionary Significant UnitsEvolutionary Significant Units.

3. to assess conservation value of populations orareas from an evolutionary or phylogeneticperspective.

1.1. Genetic Variability within Populations:Genetic Variability within Populations:

A common aim of quantifying mtDNA variation withinpopulations is to test for the loss of genomicvariability, perhaps as a consequence of reductionin population size.

This will have conservation significance if the loss ofvariation translates to reduced individual fitness.

This is a weak application of mtDNA because of thelack of any theoretical or empirical evidence for astrong correlation between mtDNA diversity anddiversity in the nuclear genome.

For example, low mtDNA diversity has beenreported in rapidly expanding species such as northern elephant seals and parthenogenetic gekoswhereas moderate to high mtDNA diversity hasbeen observed in declining species subjected to intense harvesting such as coconut crabs,humpback whales or in species otherwise suggestedto be inbred.

Low mtDNA diversity is correlated with lownuclear gene diversity is some case but not others.

These observations indicate that putting management priorities on the basis of withinpopulation mtDNA diversity is inappropriate.

2.2. mtDNA and the identification of EvolutionarymtDNA and the identification of Evolutionarydistinct populations.distinct populations.

A prerequisite for managing biodiversity is theidentification of populations with independent evolutionary histories.

Such groupings are variously referred to as species,subspecies, or evolutionary significant units (ESUs).

Following from the Rio Biodiversity Convention,genetically divergent populations increasingly arebeing recognized as appropriate units for conservation, regardless of their taxonomic status.

mtDNA phylogenies can provide unique insights intopopulation history and can suggest hypotheses aboutthe boundaries of genetically divergent groups (i.e., cryptic species).

However, mtDNA must be used in conjunction withnuclear markers to identify evolutionary distinctpopulations for conservation because given thelower effective number of genes or greaterdispersal by males than females, mtDNA candiverge while nuclear genes do not.

This is exemplified by the ring species Ensatinaeschscholtzii.

xan esch oreg pic plat cro klau

Allozyme Group AAllozymeGroup B

mtDNA= evolutinary entities

Simplified mtDNA phylogeny from differentsubspecies of the salamander ring speciesE. schscholtzii overlain with major allozyme groups.

Recognition of ESUs:Recognition of ESUs:

The concept of an evolutionary significant unit (ESU),a set of populations with a distinct, long-termevolutionary history, as a focus of conservationeffort fits well with the goal of recognizing andmaintaining biodiversity.

However, the criteria for defining an ESU remainsto be established.

It has been suggested that thresholds rangefrom any population that “contributes substantiallyto the overall genetic diversity of the species andis reproductively isolated” to “populations showingphylogenetic distinctiveness of alleles acrossmultiple loci.”

The question that plagues the approach is “Howmuch difference is enough?”

There is no theoretical or empirical justificationfor setting an amount of sequence divergence beyond which a set of populations is recognized asan ESU, although comparisons to divergences withinan among related species may provide an empiricalyardstick.

One approach to defining an ESU is to considerthe geographic distribution of alleles in relationshipto their phylogeny, the rationale being that gene flow must be restricted a long period(2 - 4 Ne generations) to create phylogeographicstructuring of alleles.

This suggests a qualitative criterion -- ESUs shouldshow complete monophyly of mtDNA alleles --thereby avoiding the quantitative question of “How much is enough?”.

However, this criterion may be to stringent giventhat well characterized species with paraphyleticmtDNA lineages have been documented.

A less stringent criterion would be significant,but not necessarily absolute, phylogeneticseparation of haplotypes between populations.

As already stressed, it is important to seekcorroborating evidence from nuclear loci and Aviseand Ball (1990) suggest that ESUs should exhibitcongruent phylogenetic structure with other genes.

However, alleles of nuclear genes are expected totake substantially longer to show phylogeneticsorting between populations or species because ofthe larger effective population size and slowerneutral mutation rate.

3.3. Defining evolutionary conservation value ofDefining evolutionary conservation value ofpopulations or areas:populations or areas:

An extension of the use of mtDNA variation torecognize ESUs is to explicitly define conservationvalue from an evolutionary perspective.

It has been proposed that phylogenetic uniquenessshould be considered in prioritizing species for management and this concept has been modified totake account of evolutionary distance and isparticularly well suited to molecular data.

An exciting application of mtDNA phylogeographyis to define geographic regions within whichmultiple species have genetically unique populationsor ESUs; moving from species to communitygenetics.

This involves testing for congruence ofphylogeographic patterns among species to definegeographic regions within which a substantial proportion of species have had evolutionary historiesseparate from their respective conspecifics.

For example, analysis of mtDNA diversity in birdsand skinks endemic to the wet tropical rainforestsof north-eastern Australia have revealed ageographically congruent genetic break on eitherside of a dry corridor.

The significance of this for conservation isobvious -- regions with a high proportion of ESUsshould be accorded high conservation priorityeven if they do not have an array of endemicspecies as recognized by conventional methods.

This discussion on conservation “value” skirtssome basic philosophical and ethical issues:

What do we mean by the “s” in ESU?

Can we justify ranking species according to a measure of molecular divergence?

We can only measure evolutionary significance orvalue in terms of past history, the proportion of aspecies total genetic diversity represented by aparticular set of populations.

We cannot, however, predict which, if any, of theseunits will diversify to produce future biodiversity.

Therefore, in the face of these inescapableuncertainties, we must be very clear about the nature of the advice we are providing when wediscuss conservation priorities from a molecularevolutionary perspective.

Molecular Ecology:Molecular Ecology:

This second general area of application usesgenetics as a tool for ecologists, in particular:

1. to define the appropriate geographic scale for monitoring and managing.

2. to provide a means for identifying the originof individuals in migratory species.

3. to test for dramatic changes in populationsize and connectedness.

In general, these applications are conceptuallysimpler and much more relevant to short-termmanagement issues than are those related to geneconservation.

1.1. Defining Management Units:Defining Management Units:

A great deal of effort is spent on monitoringpopulations as part of the species recoveryprocess. Yet, too often, little consideration isgiven to the appropriate geographic scale formonitoring or management.

An exception is with fisheries, where it has longbeen recognized that species typically consist ofmultiple stocks that respond independently toharvesting and management.

A simple but powerful and practical application ofgenetics is to define such Management UnitsManagement Units (MUsMUs)or stocksstocks, the logic being that populations thatexchange so few migrants as to be geneticallydistinct will also be demographically independent.

In contrast to ESUs, MUs are defined by significantdivergence in allele frequencies, regardless of thephylogeny of the alleles because allele frequencieswill respond to population isolation more rapidlythan phylogenetic patterns.

mtDNA is especially useful for detecting boundariesbetween MUs because it is usually more prone togenetic drift than nuclear loci; meaning that agreater proportion of the variation is distributedbetween populations.

So long as variation exists, differences betweenpopulations will be more readily detected withmtDNA than with nuclear genes, an importantconsideration when sample sizes are limited asis often the case with threatened species.

2.2. Identification & Use of Genetic Tags:Identification & Use of Genetic Tags:

A practical and exciting use of genetics for short-term management is to provide a source ofnaturally occurring genetic tags, genetic variantsthat individually or in combination diagnosedifferent MUs.

Genetic tags are indelible, present in all membersof a population at all ages, and can be used to determine the source(s) of animals in harvest,international commerce, or areas subjected toimpacts or management.

Genetic tags are particularly useful for migratoryspecies where impacts in one area (e.g., feedingground) can affect one or more distant MUs.

Genetic tags are most effective where thevariation within areas is low relative to thatbetween areas, with the ideal situation being fixedgenetic differences.

Where MUs are characterized by differences inallele frequencies of shared alleles, maximumlikelihood methods can be used to estimate thecontribution of various MUs to a sample ofindividuals taken from a particular feeding ground,migratory route, or commercial harvest.

Another problem faced by wildlife managers isassessing the degree to which populations are connected by migration and are changing in size.

Estimating these parameters via ecological studiesis an important but, very difficult and expensiveexercise, prompting a search for indirect methodsbased on patterns of genetic variation.

At the same time, there has been rapid developmentof methods for using information on alleledistributions and relationships to infer long-termmigration rates and trends in Ne.

Although these new statistical tools can provideinsight into the long-term behavior of populations,it is not clear that they can produce informationrelevant to short-term management, especiallywhere populations are fluctuating in size and/orconnectedness as is often the case in conservation studies.