ECOSYSTEM JELLO…

Kerim Aydin Bob Francis Pat Livingston

Ecosystem properties

• “Stability” Many Many terms– Resilience– Resistance

• Food web structure?– Diversity– High biomass or other adaptations in

susceptible groups• MEASURE VARIANCE

Need to understand control

• TOP DOWN?• BOTTOM UP?• MIDDLE OUT?

– Which (if any) is dominant in marine systems??

– Explanations may be easy to find and hard to confirm!

“Kitchellization”

• The process of losing beer money by making poor guesses about ecosystem responses

Phytoplankton

Zooplankton

Forage Fish

Large Fish

Marine Mammals Sharks

???

“Revenge??”

Phytoplankton

Zooplankton

Forage Fish

Large Fish

Marine Mammals Sharks

Top-down-bottom-up-ENSO-PDO-fishing?

Figure the Frequency!!!

Where can you go from a marine food web?

Food Webs Process models•Investigate specific environmental linkages•Address species concerns (endangered spp.)

Multi-species harvest models•Predict returns from harvest strategies

Biomass dynamics models•Measure broad ecosystem properties•Assess risks of regime shifts• Determine the need for mechanisms

Why use the most general models?Regimes Happen!

• There needs to be a way to abstract ecosystems, and look for risks.

• This is one example of using “whole ecosystem properties” to examine this risk.

Biomass dynamics models•Measure broad ecosystem properties•Assess risks of regime shifts• Determine the need for mechanisms

Food web forcing?

Phytoplankton

Zooplankton

Forage Fish

Large Fish

Marine Mammals Sharks

????

Correlation requires Variation

Phytoplankton

Zoop

lank

ton

PiscivoresPlan

ktiv

ores

Variation comes with history

• This is maybe not so important in labs and lakes (“white” systems).

• But in large marine systems….

0.50 50 100

Year

-EN

SO

History vs. the correlation method - equilib. Vs. time...• TOP DOWN OR BOTTOM UP???

Phytoplankton

Zoop

lank

ton

PiscivoresPlan

ktivor

es

00.20.40.60.8

11.21.41.61.8

1 140 279 418 557 696 835 974 1113

Phytoplankton

Large Flatfish

How can variability vary?

0.50 50 100

Year

-EN

SO

• Amplitude• Frequency• Cadence

What are the characteristics of a food web component?

What are the characteristics of a food web component?

• Biomass (“tons”)• Production (“tons/year”)• P/B (1/year)-

– inverse of replacement time– related to generation time

B

P/B

P

What are the characteristics of a food web component?

• Biomass (“tons”)• Production (“tons/year”)• P/B (1/year)• Trophic level

B

P/B

P

What are the characteristics of a food web component?

• Biomass (“tons”)• Production (“tons/year”)• P/B (1/year)• Trophic level

• Network characteristics• Dissipative characteristics

B

P/B

P

B vs. P/B

• R vs. K selected...

P P

P/B

B B

P/B

This experiment• Start with

two “Actual” Food WebsThe East. Bering Sea

Shelf system with pollock as the dominant fished species

The East. Tropical Pacific Tuna are dominant fished species

Both have shown interannual variation in primary productionwhich may be linked to climate signals.

Need rules for species interactions

• One possibility of many:– P/B then B of predator respond to increases of

prey biomass– “overly stable” - only looking for chances of

regimes – Ratio-dependent predator/prey model with

satiation (ECOSIM based)– Mimics surplus production (Pella-Tomlinson)

model when predator, prey fixed.– Next step is to add better age-structure

(single biggest weakness of the model).

????

Dynamics of overlap

Bi - Vij Vij

Bj

vijVij

vij (Bi-Vij)aijVijBj

dVij /dt = vij(Bi-Vij) - vijVij - aijVijBj

Assume fast equilibrium for Vij

V

B-V

“It’s cold down there!”

The appearance of Density Dependence

• dVij /dt = vij(Bi-Vij) - vijVij - aijVijBj = 0

• Vij = vijBi/(2* vij + aijBj)

• Cij (Bi,Bj) = aijvijBiBj

(2* vij + aijBj)Predator Biomass

Cij (or Minstant)

Prey biomass

Cij /Bj

P/B vs. Trophic Level - EBS

• (note missing microzooplankton)• B vs. Trophic Level? No firm relationship…

0

1

2

3

4

5

-6 -4 -2 0 2 4 6log [P/B]

Trop

hic

Leve

l

Phytoplankton

ZooplanktonForage fish

Large Flatfish

Pollock

Marine Mammals

P/B vs. Trophic Level - ETP

0123456

-5 0 5 10log [P/B]

Trop

hic

Leve

l

Phytoplankton

Zooplankton

Small Tuna

Large Tuna

Marine MammalsSharks

Insert bottom up variation

• Use “natural” signal (e.g. ENSO)

0.1

1

10

0 10 20 30 40 50 60 70 80 90Year

Prim

ary

Prod

uctio

n ~6 Year Period

Insert bottom up variation

• Use “natural” signal (e.g. ENSO)

• Vary amplitude, frequency, cadence0.1

1

10

0 10 20 30 40 50 60 70 80 90Year

Prim

ary

Prod

uctio

n ~12 Year Period

Insert bottom up variation

• Use “natural” signal (e.g. ENSO)

• Vary amplitude, frequency, cadence0.1

1

10

0 10 20 30 40 50 60 70 80 90Year

Prim

ary

Prod

uctio

n ~24 Year Period

Insert bottom up variation

• Use “natural” signal (e.g. ENSO)

• Vary amplitude, frequency, cadence0.1

1

10

0 10 20 30 40 50 60 70 80 90Year

Prim

ary

Prod

uctio

n ~48 Year Period

Results• Here’s a couple 100-year time

tracks...

• We care about:• Amount of variability transmitted (CV over

100 years)

Amplitude and cadence

– Increasing the amplitude of forcing increases the amplitude of response.

– Cadence is complex, and depends too heavily on (unknown) parameters.

Frequency and Variation

0 1 2 3 4 5

Trophic Level

CV Phytoplankton

Sm. Zooplankton

Pollock

Marine Mammals

Lg. Zooplankton

~6 year cycle

Frequency and Variation

-6 -4 -2 0 2 4 6

log [P/B]

CV Phytoplankton

Sm. Zooplankton

PollockMarine Mammals

Lg. Zooplankton~6 year cycle

Frequency and Variation

-5 0 5log [P/B]

CV

~6 year cycle

-5 0 5log [P/B]

CV

~24 year cycle

-5 0 5log [P/B]

CV

~12 year cycle

-5 0 5log [P/B]

CV~48 year cycle

Frequency II - CV (tuna)

-5 0 5 10log [P/B]

CV

~6 year cycle

-5 0 5 10log [P/B]

CV

~12 year cycle

-5 0 5 10log [P/B]

CV

~24 year cycle

-5 0 5 10log [P/B]

CV~48 year cycle

What about correlations and control?? 6-year period

-0.4-0.2

00.20.40.60.8

11.2

0.01 0.1 1 10 100

P/B (log scale)

Cor

rela

tion

with

Ph

yto.

bio

mas

s

12-year period

-0.4-0.2

00.20.40.60.8

11.2

0.01 0.1 1 10 100

P/B (log scale)

Cor

rela

tion

with

Ph

yto.

bio

mas

s

24-year period

-0.5

0

0.5

1

1.5

0.01 0.1 1 10 100

P/B (log scale)

Cor

rela

tion

with

Ph

yto.

bio

mas

s

One theoretical explanation...

• Simply hitting the resonant frequencies of each model component?– Useful for model (and real life)

analysis of important terms.– “Real life” P/B values may imply

natural resonant frequencies.

Small observations

• Missing seasonal/micronekton• Frequencies, P/B are the same unit (1/time)• Trophic Level less of a fit

Fish must follow history or be history

• If P/B of a species is in the range that it is “excited” by the balance of top-down/bottom up, does it need extra biomass to be stable (“avoid” regimes)?

• We don’t know the frequency of primary production variation in many systems.

• Need to look at more ecosystems

What responds to each frequency range?

• Forage fish, micronekton response peaks near ENSO-scale forcing.

– This doesn’t mean that they vary on an ENSO scale, but that they are most susceptible to crashes when the bottom-up forcing is at that scale.

What responds to each frequency range?

• Forage fish, micronekton response peaks near ENSO-scale forcing.

• Larger commercial fish response peaks at “regime” (10-50 year) forcing.

So what about fishing?

• General principle of surplus production As B goes down, P/B goes up, due to:

• more food per fish• smaller, faster growing fish

P PB B

P/B

P/B

Are our fish becoming anchovies?

• Beyond “multispecies MSY”, have we changed the natural time scale of animals (P/B, replacement, generation time) without changing the natural time scale (frequency) of input variation?

Shift happens (with a little help from…)

• Fishing may push a species in or out of the high CV range. – Will regime changes occur MORE or

LESS frequently with fishing?– It can occur in both directions: the fin

whale control?– What is the range of fishing change

compared to natural variability in P/B?

Preliminary until...

• More ecosystems and parameters• Devil in the details

• Model type, (L.V., ECOSIM, Spatial, parameters)

• Fishing changes (historical)• Still, true for “reasonable” forms

• Middle-out Forcing

CONCLUSIONS

• The frequency of primary production variation may be strongly connected to P/B.

• The frequency associated with regime shifts (10-50 year period) is the frequency at which most currently fished species show the strongest response.

• Fishing may push P/B into or out of the range of greatest variation, depending on the frequency of natural forcing in the ecosystem.