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Conservation at the Population Level

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Jennifer Dever

on 12 March 2015

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Transcript of Conservation at the Population Level

Conservation at the Population Level
Characteristics of a Population
Population Growth Factors
1) Density Dependent
Negative feedback
Competition, predation, herbivory, parasitism, disease

2) Density Independent
Disease, starvation

How bad is it?
Number of individuals in the current population
The trend in population size
An estimate of the immediate risk of extinction
When making conservation
decisions we need to know:
Population Density

Absolute Density - counting every individual in the population
Sampling: Capture-Recapture Method
major assumptions:
marking doesn't affect animals (behaviorally, physiologically, or ecologically)
marked animals are completely mixed in population
probability of capturing a marked animal is the same as capturing an unmarked animal (closed population)
marked animals don't lose their marks
marked mix naturally with unmarked
Quadrant techniques - useful sampling method for larger areas
Wildlife sightings -good for large animals living in open habitat
Indirect techniques - genetic markers; neutral theory applied
Demographics & Population Trends
Changes in population size reflect the relative rates of processes that add individuals to the population and eliminate individuals from it
- individuals added by birth
Fecundity - # of young or eggs produced per female per unit time
Effective Population Size
– birth, immigration, death, emigration - usually studied together because they are the means by which populations respond to short term changes in environment

the challenge of getting N right:
Requires multiple estimates of population sizes at different times
(how long???)
Sampling error
Process error = Random chance/environmental stochasticity
Population censuses for species of amphibians at Rainbow Bay, Savannah River Site, 1979–2004. For
R. catesbeiana
, the solid line represents the number of juvenile individuals trapped that year. For other species, plots show the number of breeding females (solid line) and juveniles (dashed line) caught in pitfall traps, and years with insufficient hydroperiod (squares) for successful development of larvae for that species (DASZAK et al., 2005)
more realistic than simple models
Demographic Matrix Models
useful starting point, but a simple model
unrealistic assumptions:
Density-independent population change
Deterministic population dynamics
Homogenous individuals

Exponential Growth Model
classification by:
life stages
Natural populations violate assumptions:
e.g. Arctic ground squirrels population impacted by availability, suitable burrowing habitat, and spacing behavior:
Experimental manipulation of food showed that squirrel populations in the boreal forest were limited by an interaction between food and predators (Karels & Boonstra, 2000)

Problems with Small Populations
Small populations are subject to rapid decline due to:
Loss of genetic variability and related problems
Demographic fluctuations due to random variations in birth & death rates
Environmental fluctuations due to variation in predation, competition, disease, natural catastrophes, etc.

4 Sources of “uncertainty” that can affect the size of a population
1. Genetic stochasticity
2. Environmental stochasticity
If rav > Ve then the expected persistence time of a population increases directly with increasing population size
If Ve > rav then the population reaches a situation where further increases in population size don’t significantly increase expected time of population persistence
3. Demographic stochasticity
4. Natural catastrophes

14 sq. km v. 10,000 sq km
Loss of Genetic Diversity
Genetic Drift:
Bottleneck: drastic reduction in population size
Founder effect: when a few individuals establish a new population that has less genetic variation than the larger original population
Inbreeding Depression

Outbreeding Depression

Heterosis -heterozygotes more fit; data suggests that heterozygotes tend to be more resistant to disease, grow faster, and survive longer
Outbreeding depression due to Introgression and hybridization:
When mating occurs between individuals that are too genetically dissimilar, Loss of fitness results – (outbreeding depression) adaptive genetic differences among populations are lost through interbreeding
“Swamping” of locally adapted genes – adaptive gene complexes in native populations are being displaced by the immigration of genes that are adapted to another environment .
Breakdown of biochemical or physiological compatibilities between genes in the different populations.

Benefits of PVA
Simulations of individual populations can be run using this random variation to determine the probability of population extinction within a certain period of time or the mean time to extinction.
Can determine which parameter or combination of parameters most influences extinction probabilities
Management regimes that affect population parameters can then be developed and analyzed
Can compare alternatives
Can evaluate the effectiveness of management efforts

How reliable is a PVA?
only as good as the data
sampling error and modeling assumptions dramatically affect predictions
PVA of howler monkey populations (Mandujano & Escobedo-Morales 2008)

Series of small, separate populations (in discrete habitat patches) united together (Levin 1970).
In this scenario, even if the individual populations go extinct, other populations survive and they supply dispersing individuals who recolonize “extinct” patches

Metapopulations are viewed as sets of populations persisting in a balance between local extinction and colonization. dp/dt = cp(1-p) - ep

Source populations = “good” quality habitats where reproduction exceeds mortality
Sink populations = habitat poorer in quality and mortality exceeds reproduction

Determining sources/sinks requires a great deal of information about the natural history of organisms

When a subpopulation is very small, local extinction can be prevented by occasional immigrants that arrive from neighboring patches
Major factor in maintaining small populations
Proportion of occupied habitat patches constant, even though populations in individual patches may go extinct
Extinction threshold – cp > ep
Remnant populations – long-lived species may persist because of life history traits rather than immigration; i.e. plant species that can survive a long drought

Rescue Effect
Significance of Metapopulation Theory:
Provides incentives for maintaining suitable habitat, even if fragmented, and even if “unoccupied”
Until recently, critical habitats were defined as the places where a species was most common
Fragmented group of population subunits could enhance population structure and persistence
Small habitats may have an important role in a populations overall persistence

from Gutjahr-Gobell et al., 2005
Conservation measures dealing with small populations:
Understanding Metapopulation dynamics
Determining the MVP & MDA
Population Viability Analysis

Minimum Viable Population
one that meets "the minimum conditions for the long-term persistence and adaptation of a species or population in a given place" (Soulé 1987).
N theoretically sufficiently large to protect against extinctions caused by harmful and unpredictable genetic, demographic or environmental factors over a given period of time (generally expressed in hundreds of years).
Population of sufficient size “to endure the calamities of various perturbations” more than just surviving under average conditions
Minimum Dynamic Area
area of suitable habitat necessary for maintaining the minimum viable population
Population Viability Analysis
PVA = quantitative assessment of the probability that a population will become extinct within some specified time frame
step 1) Construct a mathematical model using the following data:
Average mortality rates
Average recruitment rates
Current age distribution
Current population size
step 2) Add stochasticity to the model (Environmental, genetic, demographic stochasticity) – elements of variation, for a realistic approach; Allow model elements to vary at random between their observed range of annual values

Bengal Tiger, N <2,500
Logistic growth model
k - carrying capacity;
varies according to environment
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