# Presentation: 323049

## 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”

- “Kitchellization”

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

- Phytoplankton

- Zooplankton

- Forage Fish

- Large Fish

- Marine Mammals

- Sharks

**???**

## “Revenge??”

- “Revenge??”

- Phytoplankton

- Zooplankton

- Forage Fish

- Large Fish

- Marine Mammals

- Sharks

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

- 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

## Variation comes with history

- Variation comes with *history*

- This is maybe not so important in labs and lakes (“white” systems).
- But in large marine systems....

## History vs. the correlation method - equilib. Vs. time...

- TOP DOWN OR BOTTOM UP???

## How can variability vary?

- 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 Webs

**The 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 production*
*which 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...

## P/B vs. Trophic Level - ETP

## Insert bottom up variation

- Use “natural” signal (e.g. ENSO)

## Insert bottom up variation

- Use “natural” signal (e.g. ENSO)
- Vary amplitude, frequency, cadence

## Insert bottom up variation

- Use “natural” signal (e.g. ENSO)
- Vary amplitude, frequency, cadence

## Insert bottom up variation

- Use “natural” signal (e.g. ENSO)
- Vary amplitude, frequency, cadence

## 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

## Frequency and Variation

## Frequency and Variation

## Frequency II - CV (tuna)

## What about correlations and control??

## 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

- 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*

*P*

- B

- 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...) 

- 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...

- _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.**