predicting extinction
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Hookers Sea Lion (Phocarctos hookeri) Found only in NZ ... Contrast with western stock of Stellers Sea Lion. Predicting Extinction. 12. Our model ...

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predicting extinction

Predicting Extinction

The Hooker’s Sea Lion

Predicting Extinction

nature of extinction
Nature of extinction
  • The taxonomic group of interest has no members (in the wild or captivity?)
  • caused by an average negative rate of increase for a long period of time

Predicting Extinction

causes of extinction
Causes of extinction
  • Competition predation
  • Climate Change
  • Habitat loss
  • Exotic species introductions
  • Disease
  • Other catastrophic event
  • Exploitation

Predicting Extinction

modeling extinction
Modeling extinction
  • Random walk with negative or close to negative rates of increase

Predicting Extinction

key parameters
Key parameters
  • Average rate of increase
  • Process error
  • Starting population size
  • Pseudo-extinction threshold
  • Often ignored - red noise autocorrelation of process errors - show general model!

Predicting Extinction

what is missing
What is missing
  • Density dependence, especially decreasing rates of increase at very low densities
  • Catastrophic events

Predicting Extinction

hookers sea lion phocarctos hookeri
Hookers Sea Lion(Phocarctos hookeri)
  • Found only in NZ
  • Main breeding sites are in Auckland Islands
  • Historical range may have included main islands
  • depleted to near extinction in 18th and 19th centuries

Predicting Extinction

concerns
Concerns
  • Listed as vulnerable - then upgraded to threatened, based on the lack of breeding sites at places other than Auckland Islands
  • Population size estimated at 14,000 animals
  • about 80 per year killed as by-catch in squid fishery
  • NZ DOC wants to limit by-catch by closure of squid fishery

Predicting Extinction

goals
Goals
  • Allow population to increase so that colonization at a new site takes place
  • Best way to achieve this is by letting population reach 90% of K
  • Contrast with western stock of Stellers Sea Lion

Predicting Extinction

our model
Our model
  • Spatially explicit 8 populations
  • Dispersal between sites
  • Allowed for depensation
  • Allowed for catastrophic events
  • Used existing data in integrated Bayesian framework

Predicting Extinction

the problem
The problem
  • Estimate impacts of squid fishery by-catch on two major indicators
  • Probability of extinction
  • Probability of establishing new breeding colonies

Predicting Extinction

key components of approach
Key components of approach
  • Model to estimate parameters from available data
  • Forward projections to calculate impacts of by-catch and catastrophies
  • Literature review to determine intensity and probability of catastrophies
  • Literature review to determine what is known about population dynamics of otariids

Predicting Extinction

data available
Data available
  • Irregular pup counts at some of the locations

Predicting Extinction

key elements of model
Key elements of model
  • Age structured
  • 8 possible breeding sites
  • model dispersal between sites
  • allow for depensation
  • allow for catastrophic events

Predicting Extinction

why age structure
Why age structure?
  • The “important” parameter is rate of increase - a total numbers model would be appropriate
  • But -- the data are pup counts - keeping track of age structure lets us predict observed pups

Predicting Extinction

key parameters20
Key parameters
  • Pups per female
  • juvenile survival
  • adult survival
  • only one aggregate rate of increase is really estimable!

Predicting Extinction

density dependence
Density dependence
  • Wanted flexible model to allow for different shapes in production curve

Predicting Extinction

why spatial model
Why spatial model?
  • Additional breeding sites may make population less vulnerable to catastrophic events
  • Data come in different years from different sites, thus we can’t “pool” Auckland Islands data into one area

Predicting Extinction

alternative model of dispersal
Alternative model of dispersal
  • From Barb Taylor - build up at beaches until density is high - then large numbers move to new site - usually a few miles away
  • This could be modeled, but obviously would be unlikely to move animals outside the Auckland Islands
  • Might want to make the probability of dispersal a higher power of density

Predicting Extinction

key assumptions in dispersal model
Key assumptions in dispersal model
  • The proportion that disperse increases with density so that when density doubles the number dispersing goes up four times
  • Probability of dispersal from one area to another decreases with distance between sites

Predicting Extinction

why depensation
Why depensation?
  • We need to consider the possibility that rates of increase decline at low densities, this is a common hypothesis for causes of extinction
  • We used an exponential model but do not believe the particular shape is important
  • There is information about depensation from the data on New Zealand sea lions, and in the historical record

Predicting Extinction

model derivation
Model derivation
  • Assumes a random “mating” model, that the probability a female goes unmated is the probability of her not encountering a mate, and this encounter rate is random.

Predicting Extinction

our likelihood
Our likelihood
  • Chose normal likelihood with different s.d. for each population
  • s.d. was chosen based on a CV of 0.5 except for the three populations with 1 or 2 animals counted
  • For all except Sandy the empirical CV is about 0.5

Predicting Extinction

why catastrophic events
Why catastrophic events
  • Most of the concern about threat to NZ sea lions relates to the impact of catastrophic events
  • If we want to model extinction risk or changes in abundance we have to model catastrophic events

Predicting Extinction

our model31
Our model
  • The probability of a catastrophic event is the same in all years
  • All individuals of all ages are equally affected
  • Two choices - all areas affected equally, or Auckland Islands together, all others independent

Predicting Extinction

other models
Other models
  • The intensity or probability of a catastrophic event could be density dependent (disease and contact rates)
  • Only breeding (or non breeding) animals are affected

Predicting Extinction

why look at only big events
Why look at only “big” events
  • If we want to consider “small” events - i.e. pup die offs, 20% mortalities, then we would need to consider the possibility that these have occurred in the last 30-40 years, and therefore the observed rates of increase reflect “small” events
  • This is technically hard to do and should “automatically” be incorporated in observed rates of increase

Predicting Extinction

choices in the meta analysis of catastrophies
Choices in the meta analysis of catastrophies
  • Two types of major catastrophic events in otariids - the long slow declines of Western Steller’s and South American sea lions, and the El Nino type declines in the eastern Pacific.
  • We found 7 such events with 50% or greater mortality

Predicting Extinction

what denominator to use
What denominator to use
  • If we use only years where current scientific methods for pup counts were used, we obtain a denominator of 273 and a probability of 2.5%.
  • This obviously greatly overestimates the probability,

Predicting Extinction

how likely are we to have observed a massive mortality
How likely are we to have observed a massive mortality
  • Clearly none has happened for at least 30 years with NZ sea lion - yet we used only 2 years data for out 2.5% calculation
  • If we use the length of the historical record we obtain 0.28%
  • This is too low
  • We chose 1% effectively saying there is a 25% chance of having observed a massive mortality at any time in the historical record

Predicting Extinction

depensation
Depensation
  • Reviewed the entire published literature for all otariids (sea lions and fur seals)
  • found that numerous populations had been driven low enough to be thought extinct by exploitation
  • had all recovered from such low levels
  • other analysis in progress

Predicting Extinction

catastrophic events
Catastrophic events
  • Considered only events of 50% or greater mortality on reproductive individuals
  • Seven such events: what denominator to use
  • If we assume only when populations closely monitored we get 2% probability
  • Our best estimate is 1% probability

Predicting Extinction

general conclusions
General conclusions
  • Risk of extinction is quite low, IUCN criterion is 10% probability in 100 years, we are 1/20th of that
  • Highly unlikely that new breeding colonies will be formed in next 20 years
  • By-catch has very small impact on population, dynamics dominated by catastrophes

Predicting Extinction

model improvements
Model improvements
  • Add process error other than catastrophes
  • Likelihood for low counts
  • Accounting for small populations
  • Better quantification of priors -- especially for depensation and catastrophes

Predicting Extinction

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