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Section 9 Resolving Taxonomic Uncertainties & Defining Management Units. The taxonomic status of, and relationships among many taxa are unresolved. In conservation, many erroneous decisions may result if the taxonomic status of populations is not correctly assigned, such as:.
Resolving Taxonomic Uncertainties &
Defining Management Units
The taxonomic status of, and relationships among
many taxa are unresolved.
In conservation, many erroneous decisions may
result if the taxonomic status of populations is
not correctly assigned, such as:
to become extinct.
Incorrectly diagnosed species may be hybridized
with other species, resulting in reduced
Resources may be wasted on abundant species,
or hybrid populations.
fitness of inbred populations may be overlooked.
Endangered species may be denied legal protection
while populations of common species, or hybrids
between species, may be granted protection.
compared the Kemp’s ridley turtle (Lepidochelys
kempi) and the similar olive ridley turtle (L. olivacea)
and supported recognition of Kemp’s ridley turtle
as a valid species.
Studies of the genetics of minke whales
(Balaenoptera acutorostrata) have led investigators
to advocate that the Northern and Southern
Hemisphere populations be treated as two distinct
species (Hoelzel and Dover, 1991).
reached based on
molecular studies of
sympatric populations of
This case is particularly interesting because it
suggests that observed differences in behavior
in sympartric populations, so called “resource
polymorphisms”, may be genetically based
evolutionary relationships can prevent
hybridization, and sometimes genetic extinction
of “look-alike” species.
1872, a melanistic form of
seaside sparrow was discovered
in Brevard co. FL and described
as a distinct species:
1960s the population (now a
subspecies) was in severe
decline due to habitat alterations
and the Dusky Seaside Sparrow was placed on the
U.S. Endangered Species List.
with more or less abutting ranges
along the species coastal-marsh
habitat from New England to
1980, the few remaining
birds (all males) were
brought into captivity
and mated to individuals
from a Gulf Coast population.
backcross progeny (the latter carrying primarily
dusky nuclear genes) for eventual reintroduction.
The breeding program was not successful and thus
Avis and Nelson (1989) assayed mtDNA haplotypes
from 40 seaside sparrows representing 7 named
subspecies and the last available dusky male, which
died in captivity in 1987.
and haplotype of
sparrows, from which conservation priorities
were derived, probably had been a misleading
guide to evolutionary relationships in this complex
for two reasons:
in failure to recognize the fundamental
phylogenetic dichotomy between Atlantic & Gulf
2. in taxonomic emphasis on distinctions within
both coastal regions that appear evolutionarily minor
compared to the between-region genetic differences.
gazelles and dik-diks that
were supposedly of the same
species ha sometimes produced
analyses revealed that the
parents were of different
Kumamoto (1991) noted that not only were
individuals of different species bred together
in captivity, but also hybrids of Kirk’s (Madoqua
kirkii) and Guenther’s (M. rhyncotragus) dik-diks
were found in 300 collections.
These authors concluded that a cytogenetic
analysis should be mandatory prior to captive
breeding populations are established to eliminate
unnecessary hybridization and reduced fertility.
A phylogenetic tree is composed of lines called
branches that intersect and terminate at nodes.
The nodes at the tips of the branches represent
the taxa that exist today and that we can
The internal nodes represent ancestral taxa,
whose properties we can only infer from the
represent 5 taxa (A - E) in a
clade, with 4 internal nodes
(R, X, Y, Z) representing
ancestral taxa, including the
The numbers on branches indicate the
number of changes in a particular sequence
that occurred along that branch.
These numbers represent the branch lengths.
provided, the relative lengths
of the branches may be drawn
in proportion to the number of
changes along that branch.
This tree is additive because the
distance between any two nodes
equals the sum of the lengths of all
branches between them.
If multiple substitutions have occurred at any site,
then additivity will not hold unless distances are
corrected for multiple substitutions.
there is a particular node -- the
root -- from which a unique
directional path leads to each
In this tree, R is the root
because it is the only internal
node from which all other nodes
can be reached by moving forward
(toward the tips).
The root is the common ancestor of all taxa in the
specify only the relationships
among the taxa, and DO NOT
define evolutionary pathways.
For 4 taxa, there are only 3
possible unrooted trees.
Once a root is identified, 5
different rooted trees
can be created for EACH of
these unrooted trees, each with a distinctive
branching pattern reflecting a different evolutionary
unrooted, increases dramatically as the number of
Let s be the number of taxa, the number of possible
unrooted trees is:
(2s - 5)! / [2s-3(s-3)!]
the number of possible rooted trees is:
(2s - 3)! / [2s-3(s-3)!]
4 3 15
8 13,395 135,135
10 2,027,025 34,459,425
22 1 x 1023almost a mole
50 3 x 1074more trees
than atoms in
relationships; they tell us nothing about the
directions of evolution -- the order of descent.
Rooted trees tell us about the order of descent
from the root toward the tips of the tree.
While unrooted trees are always more “correct”
in that they don’t imply knowledge that we do
not have, they are considerably less
A pair of sequences can be aligned by writing one
above the other in such a way as to maximize the
number of residues that match by introducing gaps
into one or the other sequence.
Biologically, these gaps are assumed to represent
insertions or deletions that occurred as the
sequences diverged from a common ancestor.
could align any two random, unrelated sequences so
that all residues either matched perfectly or were
across from a gap in the other sequence.
Such an alignment would be meaningless!!!
It is necessary to somehow constrain the number
of gaps so that the resulting alignment makes
matching residues get some sort of positive
numerical score, and gaps get some sort of negative
score, or gap penalty.
An alignment program seeks an arrangement that
maximizes the net score.
For nucleic acid alignments, matching residues
usually get a score of 1 and mismatches get a score
typically there is a penalty for creating a gap
plus an extra penalty for the length of the gap.
Aligning a pair of sequences is not a computationally
difficult process, and a variety of programs exist
to 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 creating
multiple sequence alignments.
It is a “best guess” according to some algorithm
used by a computer program.
One cannot simply have a program compute an
alignment and, without further thought, use
that alignment to create a phylogeny.
In these methods, distances are expressed as the
fraction of sites that differ between 2 sequences
in a multiple alignment.
It is fairly obvious that a pair of sequences differing
at only 10% of their sites are more closely related
than a pair differing at 30% of their sites.
It also makes sense that the more time has passed
since two sequences diverged from a common
ancestor, the more the sequences will differ.
is not always true.
It might be untrue because one lineage evolved
faster than the other.
Even if two lineages evolved at the same rate,
the assumption might be untrue because of
ancestor, each nucleotide substitution initially
will increase the number of differences between
the two lineages.
As those differences accumulate, however, it
becomes increasingly likely that a substitution
will occur at the same site where an earlier
estimate corrected distances from the number of
observed differences, differences almost always
underestimate the actual amount of change along
The two most popular distance methods, UPGMA
and Neighbor-Joining, are both algorithmic
methods -- i.e., they use a specific series of
calculations to estimate a tree.
matrix that is derived from a multiple alignment.
Starting with the multiple alignment, both programs
calculate for each pair of taxa the distance, or the
fraction of differences, between the two sequences
and write that distance to a matrix.
UPGMA (Unweighted Pair-Group Method with
Arithmetic Mean) is an example of a clustering
We covered this procedure in chapter 13.
UPGMA has built into it an assumption that the
tree is additive and that it is ultrametric -- all
taxa are equally distant from a root -- an assumption
that is very unlikely. For that and other reasons,
UPGMA is rarely used today.
NJ is similar to UPGMA in that it manipulates a
distance matrix, reducing it in size at each step,
then reconstructs the tree from that series of
It differs from UPGMA in that it does not construct
clusters but directly calculates distances to internal
each taxon its net divergence from all other taxa as
the sum of the individual distances from the
It then uses the net divergence to calculate a
corrected distance matrix.
NJ then finds the pair of taxa with the lowest
corrected distance and calculates the distance
from each of those taxa to the node that joins
node is substituted for those two taxa.
NJ does not assume that all taxa are
equidistant from a root.
NJ is, like parsimony, a minimum-change method,
but it does not guarantee finding the tree with
the smallest overall distance.
trees than the NJ exist.
Some authors think that the best use of an NJ
tree is as a starting point for a model-based
analysis such as Maximum-likelihood.
Parsimony is based on the assumption that the
most likely tree is the one that requires the fewest
number of changes to explain the data.
The basic premise of parsimony is that taxa
sharing a common characteristic do so because
they inherited that characteristic from a common
reversal, convergence, or parallelism and these
explanations are gathered under the term
Homoplasies are regarded as “extra” steps or
hypotheses that are required to explain the data.
Parsimony operates by selecting the tree or trees
that minimize the number of evolutionary steps,
including homoplasies, required to explain the data.
choosing the best tree.
For protein or nucleotide sequences, the data are
Each site in each alignment is a character, and each
character can have a different state in different
Not all characters are useful in constructing a
state in all taxa, are obviously useless and are
ignored by parsimony.
Also ignored are characters in which a state occurs
in only on taxon.
An algorithm is used to determine the minimum
number of steps necessary for any given tree to be
consistent with the data.
That number is the score for the tree, and the tree
or trees with the lowest score are most parsimonious.
at 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 = A
2 = C
3 = A
4 = G
5 = G
6 = C
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
The algorithm starts at a tip and moves to the
interior 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 assign
an “or” state.
Thus, node W is assigned the state A or G, and
node X the state G or C. Node Y connects nodes
W and X. Because the states at nodes W and X
both include G, node Y is assigned the state G.
Node Z is assigned the state C or G as follows:
G or C
A or G
C or G
Once the root has been reached, the algorithm
proceeds back up from the root toward the tips.
Because node Z does not include the state at the
node that is ancestral to it (taxon 1), its assignment
G or C
A or G
C or G
Assume that it is assigned state G. Node Y is already
assigned, so the algorithm moves to node W. Node
W is assigned G because that assignment does not
require a change from the node that is ancestral
to it. Similarly, node X is assigned state G.
state changed, indicated by
thick branches, is counted.
This tree has 4 changes.
The other possible rootings of the tree are
considered in the same way, and if a different
rooting of the tree produces fewer changes, that is
the score for that site.
each informative site, then adds up the changes
to calculate the minimum number of changes for
that particular tree.
As it works its way through the various possible
trees, the program keeps track of the tree
(or trees) with the lowest scores.
Sequences diverge from a common ancestor because
mutations occur and some fraction of those
mutations are fixed into the evolving population by
selection and by chance, resulting in the
substitution of one nucleotide for another at
To reconstruct evolutionary trees, we must make
some assumptions about the substitution process
and state those assumptions in the form of a model.
of any nucleotide changing to any other nucleotide
To predict the probability that a particular
nucleotide at a particular site will change to some
other specific nucleotide over some time interval,
we need to know the instantaneous rate of change.
This simple model has only one parameter and isknown as the Jukes-Cantor model.
can ask what is the probability that there will still
be a G at that site at some time t, and what is the
probability that there will be, for instance, an A at
that site instead.
These are expressed, respectively, as P(GG)(t) and
P(GA)(t). If the substitution rate is per time
P(GG)(t) = 1/4 + 3/4e-4 t and P(GA)(t)=1/4-1/ 4e-4 t
substitutions are equally likely, a more general
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 that
the site has not changed, P(ii), is very close to 1,
while P(ij) the probability that the nucleotide at
that site has changed from i to some other
nucleotide, j -- is close to 0.
the time required for that approach depends on
We can construct a table that shows the
instantaneous rates for each of the possibilities
for change at a site as:
A C G T
Original base C -3
This matrix is commonly called the Q-matrix.
This is not a matrix of probabilities but a matrix of
rates, and the elements in a row sum to 0.
We know that not all changes occur at the same
rate and a variety of models have been proposed
that allow the specification of different rates.
The most general is one in which each different
substitution can occur at a different rate, which
depends upon the equilibrium frequency of that
nucleotide, symbolized as A for the equilibrium
frequency 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 Ti T
j A k C l G-j A-k C-l G
All other important models are special cases of
this general nonreversible model.
In Kimura’s two-parameter model, transitions
occur at one rate, , and transversions occur at a
different rate, .
-a-2b b a b
b -a-2b b a
Q = a b -a-2b b
b a b -a-2b
are 6 different rates. Time-reversible models
assume that the overall instantaneous rate of
change from base i to base j is the same as from
base 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 Tf T
c A e C f G-c A-e C-f G
reconstruct trees, one may either assign specific
values to those rates, or estimate the values from
These models implicitly assume that the rates are
the same at all sites.
It is also possible to include rate variation across
sites in the models.
Maximum likelihood (ML) tries to infer an
evolutionary tree by finding that tree that
maximizes the probability of observing the data.
For sequences, the data is the alignment of
nucleotides or amino acids.
We begin with an evolutionary model that gives the
instantaneous rates at which each of the 4 possible
nucleotides changes to each of the other 3 possible
nucleotides and a hypothetical tree of some topology
and with branches of some length.
There are three possible unrooted trees for 4 taxa,
one of which looks like the following for the site
If the model is time-reversible, we can root the
tree at any node. One possible rooted tree is:
but since there are four possibilities for X and
four for Y, there are 16 possible scenarios that
might lead to the previous tree, one of which is:
is the probability of observing an
A at the root (PA), which might be
1/4 or might be the overall frequency
of A, depending on the model, time and the
probability of each change along the branches
leading to the tips.
The probability of changing from an A at the root
to a G at the tip is calculated from the
instantaneous rate matrix in the chosen model and
the length of the branch from A to G and is PAG.
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 probabilities
of each of the scenarios must be determined to
obtain the probability of the tree as follows:
Ptree = Ptree1 + Ptree2 + . . . . + Ptree 16
This is the probability for that tree for observing the
data at one site, the site marked in red.
of the sites is the product of the probabilities for
each of the sites i from 1 to N as:
Ptree = Pi
most computers to handle, and because it is
computationally easier, the probability (or
likelihood) of a tree for each site i is usually
expressed as a log likelihood, lnLi, and the log
likelihood of the tree is the sum of the log
likelihoods for each of the sites as follows:
lnLtree = lnLi
the alignment under the chosen evolutionary model
given that particular tree with its branching order
and branch lengths.
ML programs seek the tree with the largest log
Bayesian inference is based on the notion of
posterior probabilities: probabilities that are
estimated, 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 coins
that are biased to turn up heads 80% of the time.
at random; then you are asked “What is the
probability 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 you
chose 10 times and then are asked the probability
that it is biased, you would revise your estimate
based on your model of the expected distribution of
outcomes from true and biased coins, and your
expectations of the initial proportion of true coins.
outcomes -- the posterior probability -- should
be a better estimate than the 0.1 probability
you estimated with no knowledge.
Suppose you observe the following results of your
coin tosses: HHTHHTTHHH.
We will use X to symbolize that result.
is true -- symbolized P[X|True] where | means
“given that” is:
P[X|True] = 0.510 = 9.76 X 10-4.
The probability of that result given a biased coin is:
P[X|Biased] = 0.87 X 0.23 = 1.6 X 10-3.
The posterior probability that the coin is biased is
given by the Bayes formula as:
(P[X|Biased] x P[Biased]) + (P[X|True] x P[True])
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] = 0.13 and your estimate of the
probability that this is a biased coin has increased
from 0.1 to 0.13 based on your observation of
in that the used postulates a model of evolution
and the program searches for the best trees that
are consistent with both the model and with the
data (the alignment).
It differs somewhat from ML in that while ML
seeks the tree that maximizes the probability of
observing the data given that tree, Bayesian
analysis seeks the tree that maximizes the
probability of the tree given the data and the model
probabilities in that the sum of the probabilities
over all trees is 1.0 under the Bayesian approach,
which in turn permits using ordinary probability
theory to analyze the data.
Like Parsimony and ML, the Bayesian method is
character-based and is applied to each site along
NJ-Kimura 2 parameter
Management Units (MUs) and
in conservation biology, genetic surveys of
managed species are far from routine and there
is a perception that genetic analyses are of more
significance to long-term than short-term needs
and thus, are of lower priority than demographic
Why are the theory and practice so far apart?
of genetic analyses to practical issues in
wildlife management have not been adequately
explained and demonstrated.
mtDNA is a powerful tool in evolutionary biology
--rapid rate of base substitutions
--effectively haploid and maternal inheritance
reduces Ne and increases sensitivity to
--ease of isolation and manipulation.
practicle importance, but the conservation goals
must be clearly defined first and the analyses
designed to fit the goals.
It is important to distinguish between:
Gene Conservation -- the use of genetic
information to measure and manage genetic
diversity for its own sake.
complement to ecological studies of demography.
In many respects, molecular ecology is more
straight forward and is of more use to wildlife
managers faced with short-term management
With few noticeable exceptions, such as
translocations, managing genetic diversity in so far
as it relates to conserving evolutionary potential, is
more relevant to long-term planning and policy
than to short-term management of threatened
To measure genetic variation within populations,
especially ones thought to have declined
Identifying evolutionary divergent sets of
populations, including the resolution of
Evolutionary Significant Units.
to assess conservation value of populations or
areas from an evolutionary or phylogenetic
A common aim of quantifying mtDNA variation within
populations is to test for the loss of genomic
variability, perhaps as a consequence of reduction
in population size.
This will have conservation significance if the loss of
variation translates to reduced individual fitness.
lack of any theoretical or empirical evidence for a
strong correlation between mtDNA diversity and
diversity in the nuclear genome.
For example, low mtDNA diversity has been
reported in rapidly expanding species such as
northern elephant seals and parthenogenetic gekos
whereas moderate to high mtDNA diversity has
been observed in declining species subjected to
intense harvesting such as coconut crabs,
humpback whales or in species otherwise suggested
to be inbred.
nuclear gene diversity is some case but not others.
These observations indicate that putting
management priorities on the basis of within
population mtDNA diversity is inappropriate.
mtDNA and the identification of Evolutionary
A prerequisite for managing biodiversity is the
identification of populations with independent
subspecies, or evolutionary significant units (ESUs).
Following from the Rio Biodiversity Convention,
genetically divergent populations increasingly are
being recognized as appropriate units for
conservation, regardless of their taxonomic status.
mtDNA phylogenies can provide unique insights into
population history and can suggest hypotheses about
the boundaries of genetically divergent groups
(i.e., cryptic species).
nuclear markers to identify evolutionary distinct
populations for conservation because given the
lower effective number of genes or greater
dispersal by males than females, mtDNA can
diverge while nuclear genes do not.
This is exemplified by the ring species Ensatina
Allozyme Group A
Simplified mtDNA phylogeny from different
subspecies of the salamander ring species
E. schscholtzii overlain with major allozyme groups.
The concept of an evolutionary significant unit (ESU),
a set of populations with a distinct, long-term
evolutionary history, as a focus of conservation
effort fits well with the goal of recognizing and
However, the criteria for defining an ESU remains
to be established.
from any population that “contributes substantially
to the overall genetic diversity of the species and
is reproductively isolated” to “populations showing
phylogenetic distinctiveness of alleles across
The question that plagues the approach is “How
much difference is enough?”
for setting an amount of sequence divergence
beyond which a set of populations is recognized as
an ESU, although comparisons to divergences within
an among related species may provide an empirical
the geographic distribution of alleles in relationship
to their phylogeny, the rationale being that gene
flow must be restricted a long period
(2 - 4 Ne generations) to create phylogeographic
structuring of alleles.
This suggests a qualitative criterion -- ESUs should
show complete monophyly of mtDNA alleles --
thereby avoiding the quantitative question of
“How much is enough?”.
that well characterized species with paraphyletic
mtDNA lineages have been documented.
A less stringent criterion would be significant,
but not necessarily absolute, phylogenetic
separation of haplotypes between populations.
As already stressed, it is important to seek
corroborating evidence from nuclear loci and Avise
and Ball (1990) suggest that ESUs should exhibit
congruent phylogenetic structure with other genes.
take substantially longer to show phylogenetic
sorting between populations or species because of
the larger effective population size and slower
neutral mutation rate.
Defining evolutionary conservation value of
populations or areas:
An extension of the use of mtDNA variation to
recognize ESUs is to explicitly define conservation
value from an evolutionary perspective.
should be considered in prioritizing species for
management and this concept has been modified to
take account of evolutionary distance and is
particularly well suited to molecular data.
An exciting application of mtDNA phylogeography
is to define geographic regions within which
multiple species have genetically unique populations
or ESUs; moving from species to community
phylogeographic patterns among species to define
geographic regions within which a substantial
proportion of species have had evolutionary histories
separate from their respective conspecifics.
For example, analysis of mtDNA diversity in birds
and skinks endemic to the wet tropical rainforests
of north-eastern Australia have revealed a
geographically congruent genetic break on either
side of a dry corridor.
obvious -- regions with a high proportion of ESUs
should be accorded high conservation priority
even if they do not have an array of endemic
species as recognized by conventional methods.
This discussion on conservation “value” skirts
some 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?
value in terms of past history, the proportion of a
species total genetic diversity represented by a
particular set of populations.
We cannot, however, predict which, if any, of these
units will diversify to produce future biodiversity.
uncertainties, we must be very clear about the
nature of the advice we are providing when we
discuss conservation priorities from a molecular
This second general area of application uses
genetics as a tool for ecologists, in particular:
to define the appropriate geographic scale
for monitoring and managing.
2. to provide a means for identifying the origin
of individuals in migratory species.
to test for dramatic changes in population
size and connectedness.
simpler and much more relevant to short-term
management issues than are those related to gene
Defining Management Units:
A great deal of effort is spent on monitoring
populations as part of the species recovery
process. Yet, too often, little consideration is
given to the appropriate geographic scale for
monitoring or management.
been recognized that species typically consist of
multiple stocks that respond independently to
harvesting and management.
A simple but powerful and practical application of
genetics is to define such Management Units (MUs)
or stocks, the logic being that populations that
exchange so few migrants as to be genetically
distinct will also be demographically independent.
divergence in allele frequencies, regardless of the
phylogeny of the alleles because allele frequencies
will respond to population isolation more rapidly
than phylogenetic patterns.
mtDNA is especially useful for detecting boundaries
between MUs because it is usually more prone to
genetic drift than nuclear loci; meaning that a
greater proportion of the variation is distributed
populations will be more readily detected with
mtDNA than with nuclear genes, an important
consideration when sample sizes are limited as
is often the case with threatened species.
Identification & Use of Genetic Tags:
A practical and exciting use of genetics for short-
term management is to provide a source of
naturally occurring genetic tags, genetic variants
that individually or in combination diagnose
of a population at all ages, and can be used to
determine the source(s) of animals in harvest,
international commerce, or areas subjected to
impacts or management.
Genetic tags are particularly useful for migratory
species where impacts in one area (e.g., feeding
ground) can affect one or more distant MUs.
variation within areas is low relative to that
between areas, with the ideal situation being fixed
Where MUs are characterized by differences in
allele frequencies of shared alleles, maximum
likelihood methods can be used to estimate the
contribution of various MUs to a sample of
individuals taken from a particular feeding ground,
migratory route, or commercial harvest.
assessing the degree to which populations are
connected by migration and are changing in size.
Estimating these parameters via ecological studies
is an important but, very difficult and expensive
exercise, prompting a search for indirect methods
based on patterns of genetic variation.
At the same time, there has been rapid development
of methods for using information on allele
distributions and relationships to infer long-term
migration rates and trends in Ne.
insight into the long-term behavior of populations,
it is not clear that they can produce information
relevant to short-term management, especially
where populations are fluctuating in size and/or
connectedness as is often the case in