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 l.jpg

Predicting Extinction

The Hooker’s Sea Lion

Predicting Extinction


Nature of extinction l.jpg
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


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Causes of extinction

  • Competition predation

  • Climate Change

  • Habitat loss

  • Exotic species introductions

  • Disease

  • Other catastrophic event

  • Exploitation

Predicting Extinction


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Modeling extinction

  • Random walk with negative or close to negative rates of increase

Predicting Extinction


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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


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What is missing

  • Density dependence, especially decreasing rates of increase at very low densities

  • Catastrophic events

Predicting Extinction


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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




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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


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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


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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


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The problem

  • Estimate impacts of squid fishery by-catch on two major indicators

  • Probability of extinction

  • Probability of establishing new breeding colonies

Predicting Extinction


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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


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Data available

  • Irregular pup counts at some of the locations

Predicting Extinction



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Key elements of model

  • Age structured

  • 8 possible breeding sites

  • model dispersal between sites

  • allow for depensation

  • allow for catastrophic events

Predicting Extinction


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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



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Key parameters

  • Pups per female

  • juvenile survival

  • adult survival

  • only one aggregate rate of increase is really estimable!

Predicting Extinction


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Density dependence

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

Predicting Extinction


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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



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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


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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


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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



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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


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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


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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


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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


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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


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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


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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


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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



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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


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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


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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






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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


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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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