EXAMPLE 2: Western Diamondback Rattlesnakes Lives at lower elevations in hot-dry desert and grassland areas across the US and northern Mexico No obvious drastic boundaries across its range that isolate populations… Just a huge range! High degree of morphological polymorphism across its range Lives at lower elevations in hot-dry desert and grassland areas across the US and northern Mexico No obvious drastic boundaries across its range that isolate populations… Just a huge range! High degree of morphological polymorphism across its range

South-Central Mexico (San Luis Potosi)

S-Eastern Arizona

Central Texas

East-Central Mexico (Veracruz)

Colorado Desert Sonoran Desert Chihuahuan Desert/ Trans Pecos Region Southern Plains/ Ozarks Coastal Plains PROPOSED BIOGEOGRAPHIC REGIONS

Crotalus tortugensis Crotalus ruber Crotalus atrox Is Crotalus atrox monophyletic...??

ML (and Bayesian) Phylogeny for all 50 C. atrox specimens plus outgoups mt ND4 gene (~850bp) ML (and Bayesian) Phylogeny for all 50 C. atrox specimens plus outgoups mt ND4 gene (~850bp)

GENE FLOW Between Desert Regions!!! C. tortugensis

Side Note: Gene VS. Species Trees The previous examples were based on one or two gene sequences The previous examples were based on one or two gene sequences We used these to infer the “species tree or phylogeny We used these to infer the “species tree or phylogeny We made the assumption that the Species Tree = Gene Tree We made the assumption that the Species Tree = Gene Tree This assumption is based on the assumption that the genes we examined evolved with the species over time This assumption is based on the assumption that the genes we examined evolved with the species over time The previous examples were based on one or two gene sequences The previous examples were based on one or two gene sequences We used these to infer the “species tree or phylogeny We used these to infer the “species tree or phylogeny We made the assumption that the Species Tree = Gene Tree We made the assumption that the Species Tree = Gene Tree This assumption is based on the assumption that the genes we examined evolved with the species over time This assumption is based on the assumption that the genes we examined evolved with the species over time

Taxonomic Practices Taxonomy Taxonomy – Naming and classification of organisms Binomial System of Nomenclature Binomial System of Nomenclature – Developed by Linnaeus – Either composed of Latin or Latinized (conjugation and gender specific endings from latin) Taxonomy Taxonomy – Naming and classification of organisms Binomial System of Nomenclature Binomial System of Nomenclature – Developed by Linnaeus – Either composed of Latin or Latinized (conjugation and gender specific endings from latin)

No two species can have the same Binomial name No two species can have the same Binomial name – Can have the same genus name for different organisms only for a single plant and single animal lineage Priority Priority – the valid name of a taxon is the oldest available name proposed – If it is found that what is considered 2 species actually is just one, the oldest name must be used, and the newer name is considered a junior synonym No two species can have the same Binomial name No two species can have the same Binomial name – Can have the same genus name for different organisms only for a single plant and single animal lineage Priority Priority – the valid name of a taxon is the oldest available name proposed – If it is found that what is considered 2 species actually is just one, the oldest name must be used, and the newer name is considered a junior synonym Taxonomic Practices

Species placed in different genera and are shown to be closely related may be shifted to the same genus, yet retain their species epithets Species placed in different genera and are shown to be closely related may be shifted to the same genus, yet retain their species epithets A species may be removed from a genus and placed into a different one if it is shown not to be closely related A species may be removed from a genus and placed into a different one if it is shown not to be closely related Forms originally described as different species may prove to be the same species and be synonymized Forms originally described as different species may prove to be the same species and be synonymized New species may be described New species may be described These general rules also apply to higher taxa above the level of genus (families, orders, etc.) These general rules also apply to higher taxa above the level of genus (families, orders, etc.) Species placed in different genera and are shown to be closely related may be shifted to the same genus, yet retain their species epithets Species placed in different genera and are shown to be closely related may be shifted to the same genus, yet retain their species epithets A species may be removed from a genus and placed into a different one if it is shown not to be closely related A species may be removed from a genus and placed into a different one if it is shown not to be closely related Forms originally described as different species may prove to be the same species and be synonymized Forms originally described as different species may prove to be the same species and be synonymized New species may be described New species may be described These general rules also apply to higher taxa above the level of genus (families, orders, etc.) These general rules also apply to higher taxa above the level of genus (families, orders, etc.) Taxonomic Revision

Phylogeny Hypothesis of evolutionary relationships Hypothesis of evolutionary relationships Phylogenetic tree = graphical summary of evolutionary history Phylogenetic tree = graphical summary of evolutionary history We have been using trees throughout the semester We have been using trees throughout the semester – Now we will examine how to construct them – Remember: Phylogenies, in almost all cases, are only estimates Hypothesis of evolutionary relationships Hypothesis of evolutionary relationships Phylogenetic tree = graphical summary of evolutionary history Phylogenetic tree = graphical summary of evolutionary history We have been using trees throughout the semester We have been using trees throughout the semester – Now we will examine how to construct them – Remember: Phylogenies, in almost all cases, are only estimates

Phylogenetics Under Darwin’s hypothesis of common descent  Species in the same genus stem from a recent ancestor Under Darwin’s hypothesis of common descent  Species in the same genus stem from a recent ancestor Hierarchical classification reflects not a mystical ordering of the universe, but rather a real historical process Hierarchical classification reflects not a mystical ordering of the universe, but rather a real historical process Under Darwin’s hypothesis of common descent  Species in the same genus stem from a recent ancestor Under Darwin’s hypothesis of common descent  Species in the same genus stem from a recent ancestor Hierarchical classification reflects not a mystical ordering of the universe, but rather a real historical process Hierarchical classification reflects not a mystical ordering of the universe, but rather a real historical process

Phylogenetics Terms Monophyletic Group Monophyletic Group – All members are believed to stem from a single common ancestor, and the group includes this common ancestor Paraphyletic Group Paraphyletic Group – Group that is monophyletic except that some descendents of the common ancestor have been removed Polyphyletic Group Polyphyletic Group – consisting of unrelated lineages, each more closely related to other lineages not placed in the taxon Monophyletic Group Monophyletic Group – All members are believed to stem from a single common ancestor, and the group includes this common ancestor Paraphyletic Group Paraphyletic Group – Group that is monophyletic except that some descendents of the common ancestor have been removed Polyphyletic Group Polyphyletic Group – consisting of unrelated lineages, each more closely related to other lineages not placed in the taxon

Outbreeding Depression Describes the situation when crossing of populations results in reduced reproductive fitness Describes the situation when crossing of populations results in reduced reproductive fitness Evidence is rare Evidence is rare Is more frequent when populations have undergone significant adaptations to local conditions with limited dispersal Is more frequent when populations have undergone significant adaptations to local conditions with limited dispersal Describes the situation when crossing of populations results in reduced reproductive fitness Describes the situation when crossing of populations results in reduced reproductive fitness Evidence is rare Evidence is rare Is more frequent when populations have undergone significant adaptations to local conditions with limited dispersal Is more frequent when populations have undergone significant adaptations to local conditions with limited dispersal

Even if crosses of populations result in outbreeding depression, natural selection will usually lead to rapid recovery and often higher eventual fitness Even if crosses of populations result in outbreeding depression, natural selection will usually lead to rapid recovery and often higher eventual fitness

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

Although genetics has assumed an important role 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 analysis. Why are the theory and practice so far apart?

Moritz suggests that it is because the relevance of genetic analyses to practical issues in wildlife management have not been adequately explained and demonstrated. mtDNA is a powerful tool in evolutionary biology because: --rapid rate of base substitutions --effectively haploid and maternal inheritance reduces Ne and increases sensitivity to genetic drift. --ease of isolation and manipulation.

mtDNA can produce results of considerable 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: 1.Gene Conservation 1.Gene Conservation -- the use of genetic information to measure and manage genetic diversity for its own sake.

2.Molecular Ecology 2.Molecular Ecology -- genetic analyses as a 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 priorities.

Gene Conservation: Measuring & Managing Genetic Diversity 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 populations.

mtDNA has been used in 3 ways in this context mtDNA has been used in 3 ways in this context: 1.To measure genetic variation within populations, especially ones thought to have declined recently. 2.Identifying evolutionary divergent sets of populations, including the resolution of Evolutionary Significant Units Evolutionary Significant Units. 3.to assess conservation value of populations or areas from an evolutionary or phylogenetic perspective.

1.Genetic Variability within Populations: 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.

This is a weak application of mtDNA because of the 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.

Low mtDNA diversity is correlated with low 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. 2.mtDNA and the identification of Evolutionary distinct populations. A prerequisite for managing biodiversity is the identification 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 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).

However, mtDNA must be used in conjunction with 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 eschscholtzii.

xan esch oreg pic plat cro klau Allozyme Group A Allozyme Group B mtDNA = evolutinary entities Simplified mtDNA phylogeny from different subspecies of the salamander ring species E. schscholtzii overlain with major allozyme groups.

Recognition of ESUs: 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 maintaining biodiversity. However, the criteria for defining an ESU remains to be established.

It has been suggested that thresholds range 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 multiple loci.” The question that plagues the approach is “How much difference is enough?”

There is no theoretical or empirical justification 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 yardstick.

One approach to defining an ESU is to consider the geographic distribution of alleles in relationship to their phylogeny, the rationale being that gene flow must be restricted a long period (2 - 4 N e 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?”.

However, this criterion may be to stringent given 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.

However, alleles of nuclear genes are expected to take substantially longer to show phylogenetic sorting between populations or species because of the larger effective population size and slower neutral mutation rate. 3.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.

It has been proposed that phylogenetic uniqueness 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 genetics.

This involves testing for congruence of 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.

The significance of this for conservation is 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?

We can only measure evolutionary significance or 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.

Therefore, in the face of these inescapable uncertainties, we must be very clear about the nature of the advice we are providing when we discuss conservation priorities from a molecular evolutionary perspective.

Molecular Ecology: This second general area of application uses genetics 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 origin of individuals in migratory species. 3.to test for dramatic changes in population size and connectedness.

In general, these applications are conceptually simpler and much more relevant to short-term management issues than are those related to gene conservation. 1.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.

An exception is with fisheries, where it has long been recognized that species typically consist of multiple stocks that respond independently to harvesting and management. A simple but powerful and practical application of Management UnitsMUs genetics is to define such Management Units (MUs) stocks or stocks, the logic being that populations that exchange so few migrants as to be genetically distinct will also be demographically independent.

In contrast to ESUs, MUs are defined by significant 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 between populations.

So long as variation exists, differences between 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. 2.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 different MUs.

Genetic tags are indelible, present in all members 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.

Genetic tags are most effective where the variation within areas is low relative to that between areas, with the ideal situation being fixed genetic differences. 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.

Another problem faced by wildlife managers is 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 N e.

Although these new statistical tools can provide 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 conservation studies.