Download Landscape genetics

Landscape genetics
Instructor: K. McGarigal
Assigned Reading: Manel et al (2003)
Objective: Provide an overview of the consequences of landscape pattern to the spatial genetic
structure of populations. Highlight the role of landscape structure in gene flow and approaches
for examining the relationship between spatial genetic structure and the structure of landscapes.
Topics covered:
1. What is landscape genetics?
2. What is gene flow?
3. Why is gene flow important?
4. How much gene flow is enough?
5. Gene flow in heterogeneous landscapes
6. Landscape genetics: statistical approaches and examples
Comments: Slides adapted from presentation by Dr. Michael Schwartz, USDA Forest Service
Rocky Mountain Research Station, Missoula, Montana.
15.1
1. What is Landscape Genetics
Landscape genetics is an approach for understanding of how geographical and environmental
features structure genetic variation at both the population and individual levels. Importantly,
landscape genetics:
a. does not require that discrete populations be identified in advance;
b. emphasizes the processes and patterns of gene flow and local adaptation; and
c. the analysis involves detection of genetic discontinuities and the correlation of these
discontinuities with landscape features.
Landscape genetics is an emerging discipline that combines the fields of population genetics and
landscape ecology.
15.2
2. What is Gene Flow?
Gene flow is the incorporation of genes into the gene pool of one population from other
populations. In this regard, it is important to distinguish between the processes of dispersal and
migration as defined from a genetics perspective.
•
•
Dispersal – the permanent movement away from the site where an organism was born (i.e.,
its natal site). Note dispersal refers to the movement of an individual away from its natal site;
it is not necessarily true that the individuals genes will be incorporated into the new
population, since this requires successful reproduction.
Migration – refers to the movement of genes from one population to another accomplished
by individuals that move and breed in a population other than their birth site. Migration, as
defined here, equals gene flow. Note, migration is defined somewhat differently by
biologists, where it generally refers to the periodic (typically seasonal) movement of
individuals between geographic locations.
15.3
3. Why is Gene Flow Important?
Gene flow is important for many reasons, including the following:
• Prevent inbreeding of populations – gene flow reduces the potential for inbreeding, the
reduction in fitness due to the random loss of heterozygosity (genetic diversity) associated
with small populations, by introducing new genes into a population..
• Prevent depression of population fitness – gene flow functions to increase the heterozygosity
of individuals and populations and thereby increase population fitness.
• Prevent the suite of demographic problems that arise as a consequence of inbreeding or alone
(e.g., allee) – gene flow helps to prevent inbreeding depression, as noted above, and can
reduce the potential for the Allee effect in small populations – the rapid loss of fitness (e.g.,
fecundity) in very small populations.
• Decrease extinction risk – by functioning to reduce inbreeding depression and the reduction
in fitness due to the loss of heterozygosity, gene flow can serve to decrease the risk of
population extinction.
15.4
Example: Greater prairie chicken (Westemeier et al. 1998)
In 1933, the greater prairie chicken in Illinois numbered 25,000; in 1962 it was down to 2,000;
and in 1993 it was down to just 50 individuals. Moreover, in 1960 there was a 90% hatching
success rate, but in 1990 that rate had fallen to 74%. The decrease in fecundity (i.e., fitness) was
hypothesized to be due to the loss of genetic diversity associated, which declined 30% between
1960-1990. To reverse the situation, managers introduced birds from Minnesota and Kansas to
infuse new genes into the population, and hatching rate success increased to 94%. The
population has since recovered from near extinction.
15.5
Example: Scandanavian adder (Maddsen et al. 2001)
An Scandanavian adder population crashed between 1983-1993. The population had very low
genetic diversity and a large number of stillborn offspring were observed, an indication of low
fitness due to inbreeding depression. To counter the population crash, managers introduced 20
males from a larger population for 3 years between 1996-1999. The immediate result was an
increase in male recruitment and a decrease in the number of stillborns. Note, the males were
returned to their native population after performing their duty.
15.6
As this table illustrates (from Manel et al 2003), there are numerous empirical examples of
studies showing genetic rescue effects of induced gene flow to small populations.
15.7
4. How Much Gene Flow is Enough?
So, if gene flow is important, at the very least to reduce the potential for inbreeding depression in
small populations, how much is enough? While this is not an easy question to answer, the
general rule of thumb that has been put forth is that one migrant per generation is necessary to
prevent the adversities of inbreeding depression, while also allowing for divergence in allele
frequencies among subpopulations. Mills and Allendorf (1996) authors argue that one migrant
per generation is an appropriate minimum, but that the “rule” should be broadened to include a
maximum of 10 migrants per generation. Vucetich and Waite (2000) question whether that rule
of thumb is sufficient for fluctuation populations, and demonstrate that most populations
fluctuate enough to require >10 migrants per generation and many may even require >20 per
generation.
15.8
Example: Brassica campestris (Newman and Tallmon 2001)
In this inbreeding experiment involving the mustard Brassica campestris, five of six fitness
related components were negatively effected by inbreeding. The researchers experimentally
introduced migrant treatments of 0, 1, and 2.5. The results were dramatic. The 1 migrant
treatment and 2.5 migrant treatment produced higher fitness components than the 0 migrant
treatment, and there was no difference between 1 and 2.5 migrant treatments, suggesting that in
this case the 1 migrant per generation rule may be enough to prevent inbreeding depression.
15.9
Example: Peromyscus maniculatus (Schwartz and Mills 2005)
In this study involving Peromyscus maniculatus, the researchers compared survival rates in a
relatively isolated control population against two different treatments: a migrant treatment in
which individuals from another distant source population were introduced to the local
population, and an inbreeding treatment in which a population was experimentally inbreed. The
migrant treatment resulted in a dramatic increase in survival. Surprisingly, the inbreeding
treatment also resulted in an increase in survival, which the authors attribute to a number of
factors.
15.10
5. Gene Flow in Heterogeneous Landscapes
The question we are most interested in is whether landscape patterns, in particular those created
by human land uses, influence gene flow and to what extent. Consider the following series of
slides and try to answer the question, is gene flow occurring?
15.11
15.12
15.13
In order to answer the question, is gene flow occurring, we need to sidetrack and do a quick
genetics primer for those that either haven’t had genetics or had it too long ago.
Collection of genetic samples
The first thing we need to do is collect genetic samples. There are many ways to do this. Optimal
samples (i.e., containing the most DNA) include tissue and blood, but the collection of these
samples requires invasive sampling. Sub-optimal samples include hair, scat, urine, skins/museum
specimens, feathers and guano, and these can generally be obtained using non-invasive methods.
15.14
Types of DNA
1. Mitochondrial DNA (mtDNA) – is contained (obviously) in the mitochondria of cells and
there are thousands of copies per cell (at least 20 times more DNA than in cell nucleus).
Mitochondrial DNA is maternally inherited, so it is passed down directly from mother to
offspring, and is highly conserved; i.e., it is very stable and does not change much over
generations. Thus, the DNA in your mitochondria and very much the same as those that were
carried by your great, great, great, great, etc. grandmother.
2. Nuclear DNA – is contained (obviously) in the nucleus of cells and there are two copies per
cell. Nuclear DNA is inherited from both parents, one copy from each parent. There are highly
variable regions called microsatellites that are very useful for distinguishing individuals and for
differentiating very recent population divergences.
15.15
Species ID using mtDNA
mtDNA is especially useful for distinguishing among species, which is often the first thing that
must be done after collecting non-invasive samples in order to be sure of the species. There are
two common approaches for this purpose:
1. Restriction digests (RFLP) – in which known genes are extracted using restriction enzymes
and the size of the alleles are used to distinguish among species.
2. DNA sequence analysis – in which the exact nucleotide sequence for a section of DNA is
determined and differences in the sequence are used to distinguish among species.
15.16
Restriction Digests
Step 1. The first step is to separate the DNA from other cellular material. Note, at this stage all of
the DNA, both nuclear and mitochondrial, is mixed together.
Step 2. The next step is to add a primer pair (2 short pieces of DNA, approximately 20 base pairs
in length), which latch on to either side of an area of interest (typically a 100-800 bp length of
DNA).
Step 3. The next step is to make many copies of the genetic fragment using Polymerase chain
reaction (PCR).
Step 4. Finally, the last step is to separate the fragments by size using gel electrophoresus.
Essentially, this entails putting the genetic material on a gel and passing an electrical current
across it. The DNA fragments migrate across the gel according to their size; large fragments only
move a short distance, while small fragments move farther across the gel.
15.17
Depending on the gene analyzed, species or groups of related species (e.g., families) can easily
be distinguished by the size of the genetic fragment or allele (i.e., alternative form of a gene,
varying in length) present. For example, as shown the left, the members of the weasel family are
distinguished perfectly from the members of the felid family by this gene. As shown of the right,
other genes can be used to distinguish among species of the same family, such as shown here for
distinguishing among lynx, bobcat and mountain lion.
15.18
DNA Sequence Analysis
The alternative to restriction digests is complete DNA sequencing, in which the specific
nucleotide sequence for a particular segment of DNA is determined. As shown here using
sequence chromatographs, in which each base nucleotide (A, C, T, or G) is revealed by a unique
florescent signature, the Lynx and the Bobcat have a single nucleotide difference.
15.19
Now back to our original question: Is this river a barrier to gene flow? At this point, using
mtDNA analysis, let’s say that we have confirmed that four of the samples collected are from
lynx, and that one of the samples is on the opposite side of the river. Does this suggest gene flow
across the river?
Yes, IF the samples on opposite sides of the river are from related individuals. But how do we
determine if these samples are from the same individuals or from related individuals (as opposed
to the same species). This is where nuclear DNA comes in.
15.20
Individual ID using nuclear DNA
Nuclear DNA is especially useful for distinguishing among individuals or determining the
degree of relatedness among individuals. There are two common approaches for this purpose:
1. Microsatellites – highly variable regions of nuclear DNA that diverge rapidly because they are
not under selection. Microsatellites have been the mainstay of landscape genetics since
inception, but are currently being replaced by the more power analysis of SNPs (below).
2. Single nucleotide polymorphisms (SNPs) – DNA sequence variation in which a single
nucleotide (A, T, C or G) differs between individuals. Single nucleotides may be changed
(substitution), removed (deletion), or added (insertion) to a polynucleotide sequence within a
protein coding, non-coding region, or intergenic region between genes, and the magnitude of
differences in SNPs between two individuals can be used as a measure of relatedness.
15.21
Microsatellites
Here we will focus on microsatellites as they have been the mainstay for landscape genetics
work since its inception; only recently have SNPs offered an alternative and potentially more
powerful approach for quantifying the genetic differences between individuals.
A microsatellite is a highly variable region of nuclear DNA containing mono-, di-, tri- or tetranucleotide units repeated. Importantly, microsatellites are generally referred to as “junk” DNA
because these regions of the genome are presumably non-protein coding and/or not under natural
selection. As a result, these regions can change rapidly over time, allowing us to distinguish even
relatively recent genetic divergences.
The procedure for measuring microsatellites is essentially identical to that described earlier with
restriction digests. The DNA material is extracted, segmented into fragments (typically 50-300
bp in length) at established locations (restriction sites), multiplied using PCR, and measured
using gel electrophoresus.
15.22
The resulting gel depicts the allele (alternative form of a gene) size at a specific locus (location
of a gene on a chromosome). Diploid individuals containing two homologous chromosomes
contain two copies of each gene. If the two copies are the same, the individual is said to by
homozygous at this particular locus. If the two copies are different, the individual is said to by
heterozygous. In the samples shown here, note that some individuals are homozygous, while
others are heterozygous. In addition, note that there are only two different alleles present for this
particular gene. Based on the data shown, clearly, samples 4 and 6 cannot be from the same
individual, since one is homozygous while the other is heterozygous at this locus.
So how many potential individuals to we have represented in this set of 10 samples based on this
one locus?
15.23
Samples 1, 8, and 9 are all homozygous containing two copies of allele 2, and thus could be from
the same individual.
Samples 2 and 6 are heterozygous containing a single copy each of allele 1 and 2, and thus could
be from the same individual.
Samples 3, 4, 7, and 10 are all homozygous containing two copies of allele 1, and thus could be
from the same individual.
Note, it is not possible to say with any confidence whether any of these samples are from the
same individual based on this single locus, but if we evaluate many loci our ability to
distinguishing among individuals increases dramatically.
15.24
Back to our question about whether the river is a barrier to gene flow. Let’s assume that our
analysis of microsatellites allowed us to determine that our 4 samples of lynx represent 3
different individuals, and that the same individual was detected on opposite sides of the river.
Given that individual #1 was seen on both sides of the river we now know that the river isn’t a
complete barrier to movement. Later we will quantify the extent to which rivers and other
landscape features act as resistance to movement of genes.
15.25
For gene flow to be occurring there must be reproduction, because that is the only way for genes
of one population to migrate to another. So, another question we might be interested in is
whether we have both males and females present in our samples.
Fortunately, there are specific genes that allow us to determine the sex of an individuals. There
are few different genes typically used for this purpose. For example, in the Zf and Amelogenin
genes, females are always homozygous and males are always heterozygous, and the SRY gene is
only present in males.
In our particular example, let’s say that we can confirm that we have both males and females
present.
15.26
Given that we have females present, the next question we might want to ask is whether there are
related individuals? If we know who the mamma and papa are, we can determine the potential
genotypes of their offspring. For example, if mamma is homozygous at a particular locus with
allele CA6 (6 di-nucleotide repeats) and papa is heterozygous at the same locus with alleles CA4
(4 di-nucleotide repeats) and CA5 (5 di-nucleotide repeats), then the offspring will all get one
allele of CA6 from their mamma and either CA4 or CA5 from their pappa. Thus, the offspring
have to either be CA6 - CA5 or CA6 - CA4.
In our particular example, let’s say that we can confirm that mamma is found on the opposite
side of the river from her son. Relatedness across a potential “barrier” does suggest gene flow is
occurring.
15.27
6. Landscape Genetics: Statistical Approaches and Examples
Manel et al. (2003) define the field of landscape genetics and provide a description of the basic
approach involving the following key features:
• The identification of spatial genetic patterns requires the collection of genetic data from
many individuals (or populations) whose exact geographical location is known
• Ideally, the individual is the operational unit of study. However, this can be extended to
populations (using allele frequencies) if enough populations can be sampled
• The advantages of using individuals as the operational unit are to avoid potential bias in
identifying populations in advance and to conduct studies at a finer scale
• After sampling, genetic and statistical tools are used to determine the spatial genetic pattern
and to correlate it with landscape or environmental features
15.28
The essence of the landscape genetics approach involves three major steps:
Step 1 involves identifying/quantifying the spatial genetic structure of the sample. Note, this has
been a principal interest of population geneticists for decades and thus is not unique to landscape
genetics. There are a variety of approaches for doing this depending, in part, on whether clearly
defined and discrete a priori populations exist. In this case, the genetic structure among the prespecified populations can be quantified using a variety of statistical measures, the most common
being Fst (see below). An alternative method involves the use of assignment tests (see below).
When a priori populations do not exist or there is no reason to expect disjunct population units to
be real (e.g., continuously distributed individuals), then there are a wide variety of other
statistical approaches for detecting and/or quantifying the spatial genetic structure of the sample.
We will discuss the use of Mantel tests below, but see Manel et al. (2003) for a description of the
other approaches.
15.29
Step 2 involves quantifying the spatial landscape structure, and this is the purview of landscape
ecology. There are a variety of approaches for doing this, but most involve incorporating the
notion of landscape resistance (i.e., the impediments to gene flow) caused by landscape features
(e.g., land cover, terrain).
The most common approach employed involves measuring the “cost” distance between samples
(populations or individuals) based on one or more alternative landscape resistance models. In
most cases, the landscape resistance models (and their parameterization) are hypothesize a priori,
but in a few rare, recent cases the resistance coefficients for the model have been estimated
empirically using maximum likelihood procedures to identify the resistant surface that best
explains the spatial genetic structure (from step 1). More on this approach below.
In a few cases, instead of measuring the “distance” between samples, the landscape context is
used to derive an index of isolation of each sample unit, and the isolation index is subsequently
compared to the spatial genetic structure in step 3. The difference between these approaches is
largely whether there is a single observation for each genetic sample (population or individual) the isolation index approach – or whether there is a distance matrix used to represent the
“distances” between all pairs of samples – the previous approach.
15.30
Step 3 involves correlating the spatial genetic patterns with the landscape structure and is the
cornerstone of landscape genetics – which is fundamentally about relating genetic structure to
landscape structure. Not surprisingly, there are a variety approaches for doing this.
The most common approach involves using Mantel tests and partial Mantel tests to compare a
genetic distance matrix (representing the genetic distance between all pairs of samples) to an
ecological distance matrix (represent the “cost” distance between all pairs of samples in a
resistant landscape). More on this approach below.
An alternative approach involves the use of constrained ordination techniques such as
redundancy analysis (RDA) and canonical correspondence analysis (CCA), in which the genetic
data is represented as a two-way matrix of samples by loci, and the constraints are landscape
variables measures for each sample location.
Lastly, in some cases, the approach is simply a visual inspection of the relationships using GIS
analysis.
15.31
Example: Population Fst (Spear et al. 2005)
When populations are clearly defined and the samples are collected from separate populations,
the most common landscape genetic approach involves using a statistic like Fst (there are many
variants) to measure the proportion of genetic variation due to among-population differences
relative to that due to within-population differences and then comparing the pairwise population
Fst values to some measure of ecological distance between populations – often based on the least
cost path distance between populations.
Fst is defined as the proportional reduction in heterozygosity due to population subdivision, and
it ranges from 0 to 1. Low levels of gene flow promote genetic divergence among populations
and drives Fst to 1. Specifically, Fst=1 happens when two populations share no alleles in
common, as shown in the top right-hand figure. Conversely, high levels of gene flow drives Fst
to 0. Specifically, Fst=0 happens when two populations have identical allele frequencies; i.e.,
they share all alleles in the same proportions, as shown in the bottom right-hand figure.
15.32
Spear et al. (2005) used this approach to measure the genetic “distance” between all pairwise
combinations of 10 populations of blotched tiger salamanders associated with discrete breeding
sites (seasonal ponds) in the northern Range of Yellowstone National Park. Specifically, they
computed the pairwise Fst values between each combination of ponds based on 8 polymorphic
microsatellite loci.
15.33
Next, they hypothesized several alternative models of ecological distance among ponds based on
prior information about the landscape factors likely to affect salamander movement among sites.
For example, the null model was simply the straight-line Euclidean distance between ponds (i.e.,
isolation by distance). They compared this model to several more complex models in which
distance was measured using topographic distance (i.e., isolation by topographic distance), in
which individuals dispersed via a pathway moving through proximate wetlands that have
historical records of salamander occurrence (i.e., stepping-stone route), or in which least cost
paths were based the likelihood of wetlands, slope, or a combination of the two (i.e, isolation by
landscape resistance).
Next, for each of the routes (excluding the null model), they estimated percent variation in
Fst explained by (i) mean wetland likelihood, (ii) percent of each cover type along the
route, (iii) elevation, and (iv) number of streams and rivers crossed by each route in addition to
topographical distance.
Finally, they used a series of Mantel and partial Mantel tests to evaluate the evidence in support
of each model based on AIC, and decomposed the variance explained by each model into the
landscape variables. Their results indicated that the straight-line route model containing
topographical distance, elevational difference, percent open shrub habitat, and number of river
and stream crossings explained the most variance and was the best model based on AIC.
15.34
Example: Assignment test (Wang et al. 2009)
An alternative to the Fst approach when populations are clearly defined and the samples are
collected from separate populations is to use an assignment test to measure the level of gene flow
between populations.
Assignment tests use the genotype of individuals from several populations and determine from
which population each individual is most likely to have originated using an assignment index -the highest probability of an individual's genotype in any of the populations. The proportion of
individuals from population 1 that are assigned to population 2 can be interpreted as an estimate
of the gene flow from population 2 to 1. Note, in this fashion gene flow estimates need not be
reciprocal; i.e., gene flow from 2 to 1 can be different than from 1 to 2.
15.35
Wang et al (2009) used this approach to estimate gene flow among 16 populations of California
tiger salamanders in grassland vernal pool habitat at the Fort Ord Natural Reserve,
Monterey County, California. Specifically, they estimated asymmetrical gene flow rates between
each combination of ponds using assignment tests based on 13 polymorphic microsatellite loci.
15.36
Next, they computed the least-cost path distance between each combination of ponds based on
resistant surfaces derived from the cost of movement through different habitat types (grassland,
chaparral, oak woodland). To infer the appropriate costs for each habitat
type, they calculated least-cost distances over a range of values and compared them to those
predicted by the genetic analyses. There were essentially three steps to their analysis: (i) assign
hypothetical costs to each habitat type, (ii) calculate the least-cost distances between ponds using
those costs, and (iii) compare these least-cost distances to those predicted by gene flow
estimates.
By assigning a range of costs to each habitat type, they were able to construct thousands of cost
surfaces representing alternative cost structures. Because the assignment algorithm provides a
95% confidence interval in addition to the mean of gene flow between ponds, they were able to
establish a 95% confidence interval of relative rates predicted from the molecular data. For each
of the cost structures, they took the least-cost path distances and compared them to the distances
expected by the rates of gene flow resulting from the molecular analysis. If all of the least-cost
distances fell within their expected ranges, based on the 95% confidence interval, then they
accepted the habitat cost values used to generate those paths as reflecting biologically accurate
costs of dispersal. This resulted in a range of values for which the habitat costs matched
expectations.
15.37
They were only able to infer significant gene flow rates among four of the ponds; the remainder
were indistinguishable from “uninformative” data. The least-cost path analysis involving these
four ponds indicated that dispersal through chaparral is the least costly to A. californiense, and
that movement through grassland is approximately twice, and through oak woodland roughly
five times as costly as movement through chaparral. Specifically, a small range of costs values
(1.7–2.2 for grassland and 4.6–5.30 for oak woodland) produced cost distances that fell within
the 95% confidence interval inferred from the genetic data (Table 4).
The inferred dispersal corridors are plotted on the habitat map shown here as least-cost
paths.
15.38
Example: Causal modeling (Cushman et al. 2006)
Landscape genetics is perhaps best exemplified by situations in which discrete populations do
not exist. In the traditional population genetics approach, discrete populations are presumed to
exist, the genetic samples are collected from those discrete units, and gene frequencies are
compared among populations. The landscape genetics approach simply relates these genetic
differences to landscape structure.
In the hypothetical example shown here, the river and the mountain range are presumed to be
barriers to gene flow. Individual samples are presumed to be associated with one of the three
distinct populations. The typical analysis involves determining if the “populations” are
genetically distinct. This is the so-called “isolation by barrier” hypothesis.
15.39
But what if selective pressures exist on a simple geographic distance gradient, in which genetic
dissimilarities increase with increasing distance. This is the so-called “isolation by distance”
hypothesis, and it is commonly the “null” model against which more complex models are
compared. Note, the isolation by distance and the isolation by barrier models have been the
mainstay of spatial population genetics. Incorporating more complex treatments of heterogeneity
is the purview of landscape genetics.
15.40
Landscape genetics hypothesizes that selective pressures may exist on complex landscape
structure gradients, in which the landscape is perceived as complex resistant surface where the
resistance is determined by landscape features (e.g., land cover, terrain). This is the so-called
“isolation by landscape resistance” hypothesis, and it is the mainstay of landscape genetics.
15.41
Cushman et al (2006) used this landscape genetics approach and a method known as causal
modeling to examine the landscape factors affecting the spatial genetic structure of black bear
populations in the northern Idaho panhandle. The study design included an incomplete
systematic distribution of 266 sample plots in the Selkirk and Purcell Mountains spanning the
Kootenai River valley.
They collected genetic samples using non-invasive hair snares at each of the sample plots, which
resulted in samples from 146 individual bears.
15.42
They computed the pairwise genetic differences among bears based on 9 polymorphic
microsatellites. They computed an Fst of 0.02 for the differences between the Selkirk and Purcell
Mountains, indicating very low levels of genetic differentiation between these two putative
populations. However, assignment tests resulted in 74% of the Purcell animals being assigned to
the Purcell Mountains and 89% of the Selkirk animals being assigned to the Selkirk Mountains.
Thus, there appears to be some spatial genetic structure due to the these gross topographic
features. Lastly, the spatial autocorrelation in genetic similarity revealed significantly positive
autocorrelation up to a distance of approximately 10-14 kilometers, indicating an isolation by
distance pattern.
15.43
To examine the role of landscape heterogeneity in structuring the genetic dissimilarities among
bears, they computed the genetic distance between each pair of bears based on the percentage
dissimilarity in alleles across the 9 loci. This resulted in a genetic distance matrix for the 146
bears in which each row and column represents one of the individual bears and the elements are
the genetic dissimilarities. Note, this is a square symmetric matrix; i.e., the lower triangle is a
mirror image of the upper triangle.
15.44
Next, they built hypothetical landscape resistance surfaces by considering four factors: land
cover (3 levels), slope (3 levels), elevation (4 levels) and roads (3 levels). In a full factorial
design (3x3x4x3), this resulted in 108 hypothesized landscape resistance models. They
combined this with the two traditional models, isolation by distance and isolation by barrier (in
which the Kootenai River serve as the barrier between the Selkirk and Purcell Mountains), for a
total of 110 landscape models.
15.45
The slope factor included 3 levels and was modeled as a linear relationship between percent
slope and landscape resistance, in which the slope of the relationship was zero (null or no
resistance) or low or high.
The elevation factor included 4 levels and was modeled as an inverse Gaussian relationship
between elevation and landscape resistance, in which resistance was minimal a low,
intermediate, and high elevations, or in which there was no relationship with elevation (null).
The cover factor included 3 levels and was modeled as a categorical relationship between land
cover classes and landscape resistance, in which various non-forested cover classes were given
relatively low versus high resistance values, or in which all cover types were equally nonresistant (null).
The roads factor included 3 levels and was modeled as a categorical relationship between road
class (major roads versus others) and landscape resistance, in which major and minor roads were
given relatively low versus high resistance values, or in which there was no resistance to roads
(null).
15.46
Next, they created a resistant surface for each landscape model (i.e., factorial combination of
resistances associated with each of the four factors) and computed the pairwise least cost path
distances among all bear samples. This resulted in a least cost path distance matrix for the 146
bears in which each row and column represents one of the individual bears and the elements are
the ecological least cost path distances. Note, this is a square symmetric matrix of the same
dimensions as the genetic distance matrix.
15.47
Next, they tested the relationship between genetic distance and each of the ecological distance
models using Mantel and partial Mantel tests:
• Spatial Euclidean distance – in which the elements represent the Euclidean distance between
bears; this is the isolation by distance model
• Barrier distance – in which the elements equal 1 if the samples are on the same side of the
Kootenai River and 0 otherwise; this is the isolation by barrier model
• Least cost path distance – in which the elements represent least cost path distances between
bears based on one of the 108 landscape resistance models; this is the isolation by landscape
resistance model(s)
15.48
For this purpose, they used Causal Modeling to examine the support for 7 different
organizational models representing alternative hypotheses about the independent and joint
affects of Euclidean distance, the Kootenai River barrier and landscape resistance on genetic
structure. For each of these organizational models they listed several diagnostic results that
would be indicative of consistent support for that model. For example, the model highlighted in
red in the figure represents the isolation by landscape resistance model. The diagnostic results
include the following:
• L108G.B>0 – this indicates a significant partial Mantel test between one or more of the
landscape resistance models and genetic distance after accounting for (i.e, partial out) the
barrier effect
• L108G.D>0 – this indicates a significant partial Mantel test between one or more of the
landscape resistance models and genetic distance after accounting for (i.e, partial out) the
Euclidean distance effect
• BG.L108 ns – this indicates a non-significant (ns) partial Mantel test between the barrier
model and genetic distance after accounting for (i.e, partial out) each of the landscape
resistance models
• DG.L108 ns – this indicates a non-significant (ns) partial Mantel test between the Euclidean
distance model and genetic distance after accounting for (i.e, partial out) each of the
landscape resistance models
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The results of the Mantel tests for each of the 110 models were ranked in terms of strength of
support. The distance model was significant, as was the barrier model, as was all of the
landscape resistance models. In other words, there was significant support for all of the 110
alternative models. However, the barrier model was ranked 102 of 110. The Euclidean distance
model was ranked 35 of 110. Thus, there were 35 landscape resistance models with larger
Mantel r values than the distance model, suggesting that the landscape resistance model was
superior.
The causal modeling results based on the partial Mantel tests confirmed this relationship.
Specifically, the isolation by landscape resistance model was the only organizational model fully
supported by all of the diagnostic results. There were 12 landscape resistance models with
significant Mantel r statistics after partialling out the distance model. This is shown graphically
in the right-side figure, in which the shaded cubes represent the 12 significant partial models and
the blue to red gradient represents magnitude of significance.
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One of the products of this sort of analysis is a bear connectivity model that is empirically
derived to represent the landscape features that most affect gene flow in black bears – at least
among the landscape factors considered.
In this case, the best landscape mode is one in which there is a strong affinity for forest cover at
mid elevations on any slope and where roads have a neutral affect.
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One of the things they have done subsequently is to extrapolate this model to the entire northern
Rockies in order to examine potential corridors for connectivity between the greater Yellowstone
ecosystem and Canada. Indeed, by applying the landscape resistance model they derived and
simulating potential least cost movements between the two region they were able to identify a
few potential corridors of expected movement between the two regions. These corridors have
been compared to other proposed corridors, including those based entirely on subjective criteria
in addition to those empirically derived for other species using similar procedures.
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One of the implications of their work is to cast doubt over the traditional views of populations as
discrete entities, especially for species that are continuously distributed across their range (like
the black bear). In these situations, there may not be absolute boundaries to any “populations”.
Rather, “populations” may be better thought of as a moving local neighborhood in which the
spatial extent of the “population” varies with the spatial context of the landscape. The movie
shown here illustrates this concept.
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The take home messages from this section on landscape genetics include the following:
• Landscape genetics offers a cost-effective quantitative approach to empirically measure
relatively recent connectivity (i.e., gene flow). This is immensely important for two reasons.
First, measuring connectivity by direct observation of movement of individuals is extremely
challenging, costly and time-consuming. Second, direct movement studies are unable to
address movements integrated over many years to decades and centuries. Genes flow over
short and long periods of time, and landscape genetics allows us to assess this movement in a
way that direct movement studies can not.
• Landscape genetic tools can help us evaluate land use impacts (on gene flow) and identify
areas of important connectivity (e.g., corridors) for a focal species. This is immensely
important as well, as connectivity is the key to the conservation of ecological integrity and
there are few methods available that allow us to quantitatively examine connectivity.
• Synergy of landscape ecology & genetics is just being realized; recent explosion of analytical
methods. This is a very exciting time to be a landscape geneticist as the tools of the trade are
evolving rapidly and every day brings a new and exciting approach for examining the
relationship between spatial genetic structure and landscape structure.
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