Conservation Biology Exam 2

Biodiversity and Variation (what types)

-Within Individuals(Genetic Level) (homologous chromosomes, genes and gene loci, alleles, homozygosity v. heterozygosity)

- Species Level

- Within and Among Populations

“Gene pools are becoming diminished and fragmented into gene
puddles”

Foose, 1983

Genetic drift

change in the frequencies of alleles in a population resulting from sampling error in drawing gametes from the gene pool to make zygotes and from chance variation in the survival and/or reproductive success of individuals; results in NONADAPTIVE EVOLUTION

ultimate origin of Vg (genetic diversity)

mutations

Polymorphism (P)
P = (np/k)

quantifies the fraction of gene loci in which alternative alleles of a gene occur
np: number of polymorphic loci
k: total number of loci being evaluated in the population
value ranges from 0-1
- 0 = no genetic diversity
- 1 = every gene evaluated has polymorphism

Fixation Index (Fst)

a general measure of heterozygosity in subpopulations

if Fst = 1, subpopulations are completely different genetically
if Fst = 0, subpopulations all have the same allele frequencies

Fst = (Ht - Hs) / Ht

example problem for Hs and Ht

Interpretation of Fst (guidelines from Wright)

<.05 gene flow is likely higher
.05-.15 gene flow moderate
>.15 gene flow likely lower

Additional considerations for evaluating Fst

• need to measure multiple genes - bigger sample size = more accuracy in measuring true genetic differentiation between subpopulations

• size of subpopulation (effective population size- how many individuals are contributing to the gene pool)

• sex ratios

• level of endangerment

• is isolation natural or induced by human activities

Mutation meltdown- sequence of events

• frequency of mating between close relative rises

• heterozygosity is reduced in offspring, reducing the ability of the population to respond to environmental change

• semi-lethal recessive alleles are expressed in a homozygous condition

• fecundity reduced

• mortality increases

• effective population size becomes even smaller, amplifying the whole process

Muller’s Rachet

a process in which, in the absence of recombination, results in an accumulation of irreversible deleterious mutations

management solutions for mutational meltdown

promote gene flow (corridors, translocations, ex-situ conservation)

exponential population growth key model

dN / dt = rN

dN/dt - he rate of change of the population size (N) with respect to time (t). It essentially tells you how much the population is growing at a given moment
r- per capita rate of increase

N- the population size at a given time

goals of conservation on a population level

a conservation biologist must know:

1. the number of individuals in the current population

2. demographic information regarding the population

3. the trend in population size

4. an estimate of the immediate risk of extinction (or of the population’s viability- PVA)

IUCN Categories based on species viability

• extinct (beyond reasonable doubt, the last individual has died)

• extinct in the wild (survive only in captivity and/or naturalized populations)

• critically endangered (considered to be facing an extremely high risk of extinction)

• endangered (considered to be facing a very high risk of extinction in the wild)

• vulnerable (considered to be facing a high risk of extinction in the wild)

• near threatened (do not currently qualify for a threatened category, but are close in the near future)

• least concern (do not qualify for a threatened category)

• data deficient

perturbations (disturbance) to populations

1. deterministic factors: those that affect the population in constant relation to the population size (density dependent control factors when populations are near or at K)

2. stochastic factors

   1. genetic: genetic variability, inbreeding, mutation meltdown

   2. environmental: natural catastrophes, unpredictable, density-dependent impacts on a population

   3. demographic: lag phases in population growth, changes in sex ratios, number of mature individuals, Allee effect and effective population size

The extinction vortex

• extrinsic factors (habitat destruction, pollution, overharvest, invasive species) cause population to shrink to low numbers

  • small, isolated population

    • inbreeding and drift reduce variability and individual fitness, population declines

      • demographic stochasticity, environmental stochasticity, allee effect further reduce population size

        • repeat downward spiral to extinction

population count

• quadrats, transects (botanical)

• capture-recapture methods

the lincoln index

N- number of individuals that live in the study site (population at time t)

n1- number of individuals captured during the first round of captures

n2- number of individuals captured during the second round of capture

m- number of marked individuals that were captured during the second round

Mark-recapture assumptions of the model

1. no effect of marking on the probability of recapture

2. no effect of marking on survival

3. mixing of marked and unmarked individuals

4. captured individuals are representative of the whole population

5. closed population model

6. marks are not lost

Sampling techniques: Simple Random Sampling (SRS)

strengths- best way to randomize sampling

weaknesses- assume homogenous distribution of population, some areas may be completely unsampled, sample size considerations

Sampling techniques: Stratified Random Sampling

best in a non-homogeonous environment

beta diversity level studies

utilize simple random sampling with equal quadrats in each type of area

Sampling techniques- Systematic Sampling

• random start, every x paces, every x tree, across a transect, etc

• measuring toward a system with constant rate of sampling, less random

• watch out for pseudoreplication ( don’t want to count the same individual more than once!!)

The Allee effect

• populations/species can get stuck in a lag phase

• fecundity drop off after large population because of carrying capacity

• analogy= empty club, no one stays

• ex: puffins

what factors and characteristics of a population contribute to its effective population (Ne)

1. absolute population size

2. uneven reproduction effort across individuals

3. sex ratio

4. population fluctuation over time

The effect on Ne of unequal sex ratios

Ne = (4NmNf) / (Nm + Nf)

The effect on Ne of population fluctuation over time

Ne = t / Σ (1/Nti)

t: number of generations (time steps)

Predicting how populations change through time

• what will they do in the future

• age-structure data = qualitative, typically human

• age/staged data = modules using linear algebra

• typically used for human populations, very difficult to determine with wildlife

age-stage based models of population growth

1. age-based

2. stage-based (egg, larvae, pupa, adult)

3. size based (small, medium, large)

Markov Chain

Age-based model of population growth

= 1: population will stay the same into the next time step

< 1: percentage decrease in the population (ex: .97= 3%)

age-based model assumptions

• population shrinks or grows at a constant rate (no population controls)

• deterministic population dynamics (no good years or bad years)

• homogenous individuals (all have the same reproductive success)

Leslie Matrix

*(how it fits together with the markov chain, must be able to plug into either), THEN plug in for lambda

Minimum viable population

• the population for any given species in any given habitat that is the smallest isolated population having 99% change of remaining extant for 1000 years despite foreseeable effects of demographic, environmental, and genetic stoachsticity

Population viability, essential pieces of info:

• the current N

• population trend “r” or lambda

• degree to with “r” or lambda fluctuates over t

primary objectives of population viability analysis

1. organize existing data about a population of conservation interest (IUCN)

2. estimate relative risk to the population based on different environmental, demographic, and genetic factors

3. adaptive management of populations by comparing model predictions to actual population behavior- ongoing experiments and monitoring

PVA is a model, quality can be effected by:

1. the quality of the model’s data and appropriateness of its structure to the populations studied

2. level of uncertainty associated with the model’s results

3. the quality of review the model was subject to prior to its final form and publication

Equilibrium theory of island biogeography (who_

• McArthur and Wilson 1967

• species equilibria for

  • small islands far from colonizing source

  • large island far from colonizing sourve

  • small island near colonizing source

  • large islands near colonizing source

immigration rate is determined by:

• distance of the island to the mainland

• number of species in the mainland pool that have not established themselves on the island (species richness matters)

• probability that a given species will disperse

extinction rate (on an island/patch) determined by:

• the area of the island

• the number of species present on the island (compression hypothesis)

• probability that a given species on an island will go extinct (generalist vs specialist species)

habitat loss

the complete elimination of habitat along with their biological communities and ecological function

isolation

potential barriers to gene flow

fragmentation

process by which larger, continuous habitats become subdivided into a greater number of small patches

edge habitats/ecotones

• mark the transition between two different habitats

• leads to edge effect dynamics (40% loss of habitat = 60% loss of interior habitat)

• consideration of natural edge habitats vs anthropogenic edge habitats

edge effects

• suite of physical and biological changes that tend to occur at habitat patch edges

• can potentially negatively impact diversity

• additional factors:

  • changes in water regimes (from moist to xeric soils)

  • changes in biotic interations

  • wind, drought, fire, light (could be positive), nitrogen/nutrients

Faunal Relaxation (stages and who’s theory)

Newmark = LESS EXTINCTIONS WITH MORE LAND

1. initial exclusion

2. extirpation due to lack of essential resources

3. perils of small Ne- genetic demographic, and stochastic problems

4. deleterious effects of isolation- rescue effect decreases (gene flow)

5. ecological imbalance (leads to continued decreases in “s”- species richness)

Andrewartha and Birch (1954) metapopulation

• a natural population occupying any considerable area will be made up of the number of local populations

Huffaker (1958)

mites and oranges experiment of habitat and non-habitat

Levins (1970)

• first definition of metapopulation (father of metapopulation theory)

• “any real population that is a population of local populations which are established by colonists, survive for awhile, send out migrants, and eventually disappear”

Levins basic population theory model

C- colonization

E- extinction rate

persistance at a patch requires that C > E

effects of colonization on extinction (do not need to know formulas, just how one affects the other

types of metapopulation

• classic metapopulation (Levins)

• mainland-island metapopulation (Boorman and Levitt)

• nonequilibrium metapopulation

• patch-population (panoptic)

• directed landscapes

Conservation Lessons from metapopulation dynamics

1. sites that are currently unoccupied may be essential for the long-term persistence of the metapopulation

2. reduced disperal success can cause a metapopulation to become extint

3. a metapopulation can become extinct long before all of the habitat patches are destroyed

4. the arrangement and connectivity of patches can be just as important as the absolute amount of habitat that remains

3 C’s of Managing Fragmentation and metapopulations

• connectedness (physical linage between habitats)

• connectivity (measures the processes by which subpopulations of organisms are interconnected into a functional demographic unit)

• corridor (linear landscape element that provides for movement between habitat patches (rosenberg et al. 1997)