Population Viability Analysis

Population Viability Analysis

Critically Endangered ThreatenedEndangeredCriterion

Reduction in population size 10 yrs 3 generations

>80% >50% >30%

<100 km2 <5000 km2 <20,000 km2

<50 <250 <1000

>50% >20% >10%10 yrs 20 yrs 100 yrsOr 3 gen or 5 gen

Small range

Very Small population

Quantitative analysisProbability of extinction

IUCN RED LIST

Population Viability Analysis (L14 and L15)

What is it?How do the models workUsesIssuesAccuracyMore uses - case studies

Additional reading (FYI)Papers for next weekPrinciples of Conservation Biology pp 433-35Intro to Conservation Genetics Ch 20

Small population

Exotic species

Poaching

Pollution

Management options

Human impact

DiseaseLifehistory

Inbreeding

Environment

Fluctuating population size

Catastrophes

Habitat loss

PVA models examine the effects of different life histories, environmental and threat factors on the population size and extinction risk of populations

BASIC APPROACH

Collect population datasize and number populationsbirth and death rates habitat capacities (K)frequency and effect of threats

Put into PVA model package VORTEX, GAPPS, RAMAS, ALEX

Predict extinction probabilities

BASIC MODEL- count-based PVA

One Pop’n N Census

Breed Immigrate Supplement

Death Emigrate Harvest Truncate at K

Run for 50-100 Generations

BASIC MODEL - count based PVA

PVA models are NOT deterministic

Models need following dataVariance in fecundity Variance in survivalVariance in KForm of density dependenceFrequency/magnitude of catastrophesCovariance in demographic rates

Q. What does deterministic/variance/covariance mean?

Model

Initial N=375

Productivity and survival (26 yrs of data)

Catastrophes - cyclones in Jan/April

- chance of event 7.5%

- effect on mortality +22%

K = 450 (± 10%)

BASIC EXAMPLE

Brook and Kikkawa 1998

Silvereyes on Heron Island

Cyclone-Jan or April

One Pop’n N Census

Breed

Death Truncate at K

Run for 100 years

Set K

MODEL OUTPUT

N

Year

Chance of extinction in 100 yrs = 15%

ADDING COMPLEXITY

1. Individuals are different

Eg 1 Elephantsonly breed after 10+ yearsproductivity increases and then declineshave age specific mortality rates

Eg 2 Red cockaded woodpeckersbreed in groups may float prior to joining a group

one male breeds, others help

Q. How would you alter the PVA model?

MORE COMPLEX MODEL

N at eachAGE orSTAGE

CensusEachSt(age)

Breed Immigrate Supplement

Death Emigrate Harvest Truncate at K

Individuals age or change categories

Stage-structured PVAsAdditional Data Requirements

St(age)-specific productivity

St(age)-specific survival

Transition probabilities between stages

2. additional populations---> multi-site PVAs

Simple - presence/absence - use IFM to estimate extinction/

colonisation - model viability of metapopulation

Complex - spatially-explicit individual based models

- track individuals in complex landscape as they are born, move, die

- landscape can be “patches” or “real”

ADDING COMPLEXITY

Multi-site PVAsAdditional Data Requirements

IFM - ???

Spatially explicit individual based- ???

Data needed – check metapopulation and corridor lectures

WHAT ARE PVA modelsHOW DO THEY WORK

USES OF PVA1. Assessing extinction risk

ISSUES WITH PVAs

ACCURACY of PVAs

ALTERNATIVE USES

1. Assessing the extinction risk of population

Grizzlies in Yellowstone National Park Shaffer (1978,1983) and others since

Figure 55.11



Age-structured model (cubs, 1..4, 5…25)Sex (male, female)Data - 12 yrs Craighead, YNP Environmental stochasticity -->productivityDemographic stochasticity --->sex of cubsK= 230; M + F + cubs Craighead, YNP



Age-structured modelSexData - 12 yrs Environmental stochasticity

RESULTS Size Probability of persisting 100yrs10 020 0.3630 0.7440 0.9450 0.98

AREA REQUIRED

Update1975 - 136 bears in Yellowstone2004 - 580 and rising2005 - proposal to delist grizzlies from ESA2007- population 500+grizzlies delisted - no protection outside park

Issues with PVA models - 1

Population at risk are typically examined using density-dependent single species model.

Environmental factors are included by varying K

Interactions between populations and environment appear as reduction in realized rate of increase as population size approaches K


Data requirements are far greater than the amount of data available for most species

Rubbish in ----> rubbish out


PVAs typically do not incorporate all genetic effects on population viability

Q. What genetic processes could influence viability?

PVAs can incorporate inbreeding depressionBUT

how susceptible are populations?what fitness components are affected?how is inbreeding depression related to F?is purging likely?is inbreeding depression greater during catastrophes?etc etc

HOW ACCURATE ARE PVAs?

Brooks et al. 2000Used 21 long term (> 10 year) datsets

Used first half of data to set up modelsTested predictions from 5 PVA packages

= probability of population declinewith what actually happened afterwards

= did population decline


Brooks et al. 2000


Coulson et al. 2001 ResponseThe 21 studies selected were biased

the data available was of very high qualitymost species were not endangered

PVAs will frequently be unreliable because the data to estimate vital rates is limited and vital rates of endangered spp will change

Brooks et al. 2000“PVA is a valid and sufficiently

accurate tool for categorizing and managing endangered species”

What sort of PVA model is appropriate?

Puerto Rican parrotBlack footed ferret

DISCUSSWhat is gained from using PVAs?

Next MORE USES OF PVA


2. Comparing the relative risk in 2+ populations

3. Identify key life history stages to protect

4. Determining minimum reserve size5. Determining numbers to release6. Setting harvest guidelines7. Deciding how many (which)

populations are needed

Documents

Population Viability Analysis