Structure of the presentation

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Biodiversity Conservation Laboratory, Department of Environmental Studies, University of the Aegean, 81100 Mitilene, Greece

Antonios, D. Mazaris and Yiannis, G. Matsinos

Investigating the effect of temporal variation upon reproductive performances of green

sea turtles (Chelonia mydas) by using an

individual based model.

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Structure of the presentation

•Sea turtles : critical features

•Purpose of the study

•Model description

•Results

•Concluding remarks

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Structure of the presentation

•Sea turtles : critical features

•Purpose of the study

•Model description

•Results

•Concluding remarks

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Sea turtle species

• long lived animals

• high fidelity to specific nesting areas

• only mature females come ashore for nesting

• great variability in reproductive performance

– Variable remigration interval (duration between two successive nesting seasons)

– Variable renesting interval (duration between two successive nesting attempts)

• great variation in reproductive output – Number of clutches laid

– Number of eggs per clutch

biological and behavioral characteristics :

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Sea turtle species

• Somatic growth rate is significantly reduced as animals get older (after maturation time)

• High reproductive value of each nesting individual

– During a nesting season an individual turtle may lay more than 600 eggs

• High mortality rates during the first years of their lives

– ‘from 1000 hatchlings entering the sea one of them will probably survive to adulthood’

some critical features:

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Sea turtle species

• Assessment of population trends is based on the number of nesting females

• Lacking information regarding:

– survival rates – life span

– age of maturation – re-nesting behaviour

– density dependence mechanisms – population structure

– population size

– age-specific distribution

problems arising when modeling sea turtles:

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Sea turtle species

• Re-nesting interval 1-6 yrs

• Predominantly herbivorous species Migratory routes

Somatic growth

Transition to next stages

• Life history can be easily divided into five stages

population dynamics of green turtles:

Are controlled by diet changes

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Sea turtle species

population dynamics of green turtles:

1st: eggs / hatchling 2nd: pelagic stage 3rd: benthic stage 4th: immature

5th: mature

1 2 3 4 5

Age (years)

~ 36~ 36

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Structure of the presentation

•Sea turtles : critical features

•Purpose of the study

•Model description

•Results

•Concluding remarks

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Purpose of the study

• How variability at the age of first reproduction influences population persistence?

• How re-nesting patterns linked with extinction

probabilities?

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Purpose of the study

• How variability at the age of first reproduction influences population persistence?

• How re-nesting patterns linked with extinction probabilities?

How individual variation is associated with population persistence?

Individual variation is reflecting different patterns of env. variability with temporal consideration

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Structure of the presentation

•Sea turtles : critical features

•Purpose of the study

•Model description

•Results

•Concluding remarks

(13)

Dynamic simulation model

• Underlying processes and environmental implications upon population dynamics are widely questioned

• Due to lacking information the development of theoretical model is strongly encouraged (i. e. extrapolation –

modification of critical demographic variables)

Why sea turtles?

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Dynamic simulation model

• Underlying processes and environmental implications upon population dynamics are widely questioned

• Due to lacking information the development of theoretical model is strongly encouraged (i. e. extrapolation –

modification of critical demographic variables)

Why sea turtles?

Why green turtles?

• In compare to other sea turtle species:

– biology and behaviour are well documented – existence of useful demographic information

• Density dependent growth has been documented

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Dynamic simulation model

• Modular stochastic IBM

objects:

Superinvidiviuals Individual animals Stage packs

Model structure:

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Dynamic simulation model

Primary units of the simulation:

Superinvidiviuals:

: The concept

The concept

• Newborns are modeled as an aggregation of animals sharing the same characteristics (age, growth, mortality rates etc)

• When individuals are born decisions about physiology, behaviour and development are made

Advantages Advantages

• Reduce computational burden

• Model abundant first age classes

Model structure:

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Dynamic simulation model

Primary units of the simulation:

Individual turtles:

The concept The concept

• Whether the age of S.I. exceeded the minimum maturation age (36 yrs) each animal tracked individually

• Each animal was individually subjected to all processes (growth, mortality, reproduction)

• Do not underestimate the reproductive value of breeding females

Model structure:

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Dynamic simulation model

Primary units of the simulation:

Superindividuals -Individual turtles:

Model structure:

1 2 3 4 5

Age (years)

~ 36~ 36

Age groups Individual turtles

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Dynamic simulation model

Secondary units of the simulation:

Stage packs:

The concept The concept

• Three stages (juveniles, subadults, adults) used for the description of stage- based packs

• Based on stage specific biomass, a density dependent effect on growth was modeled

• Based on stage specific biomass , a density dependent effect on reproductive performance was modeled

• Design and apply switching rules

Model structure:

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Dynamic simulation model Processes:

Start Initialize population

A g e

Individual density

Distinguish modular

units If age ≥36

S.I.

Individual else turtles Compute mortality

Include temporal variability Density dependence

Perfect cycles

Random sequence

Compute somatic growth

A g e

Size distribution

0 2 0 4 0 6 0 8 0 1 0 0

Examine somatic length If ≥Min. Maturation size

Not sexual mature

else Ready

to breed Examine energy budget If energy ≥min

energy required

else

Attempt to nest

Do not attempt to nest

Account for nesting success

Determine reproductive

output No. of clutches

Size of clutches

Compute egg / hatchling success

First year survivors compose a new S.I

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Dynamic simulation model Processes:

•Growth model

•Reproduction

•Temporal variation

•Density dependence

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2D Graph 3

Age (years)

0 10 20 30 40 50 60

Length (cm)

0 20 40 60 80 100

Mean annual growth rate

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Dynamic simulation model

• Exponential function

• Assumed a maximum life span of 60yrs

• Used a mean approximation of the maturation age

• Used size data of nesting females

• We assumed that mean and minimum (observed) lengths

correspond to maximum and mean lengths of first breeders

¾ Develop a size to age distribution

¾ Individual variation by sampling from a uniform distribution

Growth model

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Dynamic simulation model

• First breeding

• Re-nesting interval

• Reproductive output (number of clutches & size of clutches)

• Nesting success

Reproduction

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Dynamic simulation model

• Reproductive output (number of clutches & size of clutches)

• Nesting success

Reproduction

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Dynamic simulation model

The first breeding attempt was assumed to occur only if the individual had reached a threshold body condition

When entering the mature stage it was assumed that they have already started to accumulate energy to be devoted to their first breeding.

Dynamic energy budget of an individual was given by:

Thus, first breeding attempt is defined as:

Reproduction

t i incr n

t i incr i

initial t

i

E E E

E

_,

=

_,

+

_, _, ₋

+

_, _,

⎜ ⎜

⎜

⎝

⎛

≥

crit t

i

mature t

i

E E

and

L L

, ,

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Dynamic simulation model

An energy accumulation component was used to determine successive nesting attempts

It was assumed that the time between successive non - breeding periods has a cumulative effect upon the energetics.

Energy status, described by the function:

Therefore, completion of the energetic requirements for breeding occurred when individuals’ energy budgets exceed critical threshold level when.

o i mean

t i t

i t

i

B B B B

B

,

=

, env

+

, ₋_µ

+ +

,

Reproduction

crit t

i

B

_,

≥

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Dynamic simulation model

• Reproductive output

Number of clutches laid sampled within the range of observed values

Reproduction

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Dynamic simulation model

• Reproductive output

Number of clutches laid sampled within the range of observed values

Clutch size (no. of eggs / clutch) was determined as a power function of individuals length

Reproduction

)) ( (

, ,

i lth b t

n

i

a e

Egg = ⋅

^⋅

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Dynamic simulation model

Temporal Variability on growth and reproductive performances

Each simulation years was distinguished

between good and bad year

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Dynamic simulation model

Temporal Variability

Whether a good year occurred:

• The annual of energy increment would be sufficient to secure breeding every three successive years

• Maximum (age-specific) somatic growth

increment

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Dynamic simulation model

Whether a bad year occurred:

• The energy increment was equal to the value of energy needed for re-nesting after 6

successive years

• Minimum (age-specific) somatic growth

increment

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Dynamic simulation model

Whether a bad year occurred:

An autocorrelated type of disturbance was also assumed:

sampled growth rate -25% (After 1 yr)

sampled growth rate -50% (After 2 yrs)

sampled growth rate -75% (After 3 yrs)

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Dynamic simulation model

Succession of goodgood and badbad years:

1. Perfect cycle model 2. Random model

Perfect cycle model (a) 1 bad year

6 good years

Perfect cycle model (b) 1 bad year

10 good years

Random model

Good / bad years

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Dynamic simulation model

a) D.d effect on somatic growth is likely to occur whether pop. fluctuates near the 40% of its highest density (

Bjorndal et al., 2000)

b) The model was run - no temporal variation included

c) Abundance of the different stages (juveniles, immature, mature) was evaluated

d) Density (with respect to total population size of the different stages) was determined

e) We set a threshold by counting for the 40% of the maximum proportional contribution of the former stages to the total population size

Density dependence

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Dynamic simulation model

Density dependence

D.d. was modeled as an additional form of variability in environmental conditions rather than a distinctive

and independent process

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Dynamic simulation model

Juvenile – immature stages:

Reduction in growth rates (alike bad years)

Mature individuals:

Reduction in growth rates (alike bad years)

&

Delayed in re-nesting interval Density dependence

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Dynamic simulation model

• Population was initialised by using an exponential function with extreme values closely related to observed data (hatchings – nesting ind.)

• We modelled only female ind.

• Sex ratio 1:1

• Model was run in a 350 yr horizon

• Each simulation set was run 500 times.

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Results

probability of exctinction

scenarios

1 2 3 4 5 6 7 8

Probability of population exctinction

0.0 0.2 0.4 0.6 0.8 1.0

Prob. of exctinction within 250 years Prob. of exctinction within 350 years

1: no temporal variability

2: AC cycle 6:1, applied to the age of first breeding

3: AC cycle 10:1 applied to age of first breeding

4: random model of temporal variation applied to the age of first breeding

5: AC cycle 6:1, applied to both age of first breeding and re- nesting interval

6: AC cycle 10:1 as above 7: random model of temporal

variation as above

8: D.d buffer mechanism modeled to describe both age of first breeding and re-nesting interval.

Probability of population extinction

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Results

scenarios

1 2 3 4 5 6 7 8

0.0 0.2 0.4 0.6 0.8 1.0

Temporal variability as a buffer mechanism upon the age of first breeding has only a slight effect

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Results

scenarios

1 2 3 4 5 6 7 8

0.0 0.2 0.4 0.6 0.8 1.0

Variability in first breeding & re-nesting interval increased extinction probability

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Results

scenarios

1 2 3 4 5 6 7 8

0.0 0.2 0.4 0.6 0.8 1.0

Density dependent effects on growth and reproduction resulted to increased extinction probability

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Results

Increased variation during the first simulation years

Mean percentages of pop. get extinct thought time

percentage sof populaio get extinct

Trajectiories

0 50 100 150 200 250 300 350 400 450 500

Percentages of pop. get exctinct

0.0 0.2 0.4 0.6 0.8 1.0

no 6 ma 6ma ri 10 ma 10 ma ri ran ma ran ma ri density

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Results

Sharp population decline Increased egg production

Typical population trajectory

2D Graph 1

Simulation Time

0 50 100 150 200 250 300 350

Biomass index (no. of indv * length)

0 1e+6 2e+6 3e+6 4e+6 5e+6 6e+6

Eggs

0 1e+5 2e+5 3e+5 4e+5

Population size

0.0 5.0e+4 1.0e+5 1.5e+5 2.0e+5 2.5e+5 3.0e+5 3.5e+5

Biomass index Eggs Pop. size

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Results

Higher population declines are

subjected to reduction in the lower age classes

Population size and mean age distribution of the population.

Simulation time

50 100 150 200 250 300 350

Average population age

0 2 4 6 8 10 12 14 16 18 20

Population size

0 10000 20000 30000 40000 50000 60000 Mean age

Pop. size

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Concluding remarks

• Highlight the importance of breeding cycles on population persistence

• D.d. mechanisms have a significant effect on population persistence

The developed model stands apart from previous modelling

techniques applied to sea turtle populations

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