Quantifying cascading effects in trade networks

A case study on salmon



Juan C. Rocha

Telecoupling and teleconnections

“human actions in one place may create unintended consequences elsewhere”

  • By 2016 salmon trade accounted for 11.6B US$:
    • 93 countries
    • 406 bilateral relationships
  • In 2016 toxic algae bloom “red tide” killed 20% of Chile’s salmon:
    • 27M salmon = 70M pounds
    • >300 whales
    • 40k tons sardines
    • >800M dollars lost

Theory: what’s an attractor or manifold?

Sugihara et al. Detecting Causality in Complex Ecosystems. Science (2012)

Theory: Taken’s theorem

Sugihara et al. Detecting Causality in Complex Ecosystems. Science (2012)

Method: convergent cross-mapping

Sugihara et al. Detecting Causality in Complex Ecosystems. Science (2012)

Method: convergent cross-mapping

Sugihara et al. Detecting Causality in Complex Ecosystems. Science (2012)

Each link correspond to the relationship A -> B <- C, where the forecasting skill allow us to predict C dynamics given the time series of A with a \(\rho\) > 0.1 (t-test, p < 0.05)

Salmon fisheries around the world are tightly connected, the dynamics of a country production can easily influence fishing efforts in far away ecosystems [Teleconnections!]

Each link correspond to the relationship A -> B <- C, where the forecasting skill allow us to predict C dynamics given the time series of A with a \(\rho\) > correlation coefficient (t-test, p < 0.05)

Salmon fisheries around the world are tightly connected, the dynamics of a country production can easily influence fishing efforts in far away ecosystems [Teleconnections!]

41 out of 239 trading countries are causally related on the salmon trade network (~45%)

Prediction skill \(\rho\) is weakly correlated to network structure but not necessarily with trade volume

Lessons

Tack | Gracias

Questions?


email: juan.rocha@su.se
twitter: @juanrocha
slides: juanrocha.se/presentations/trade_networks
preprint: coming soon!


Stockholm Resilience Centre
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