## Outline

1. What are regime shifts? and how do we compare them?
2. What are their main drivers?

3. How do we manage them in aquatic systems?

4. Can regime shifts be interconnected? – water as an important connector

5. Empirical evidence? – Work in progress

6. Bonus: how people behave when facing thresholds?

## Forest to savanna

Regime shifts are large, abrupt and persistence critical transitions in the function and structure of (eco)systems

## Coral transitions

Regime shifts are large, abrupt and persistence critical transitions in the function and structure of (eco)systems

## Fisheries collapse

Regime shifts are large, abrupt and persistence critical transitions in the function and structure of (eco)systems

## Human-centered operational definition:

Large, persistent (and usually abrupt) shifts in the set of ecosystem services produced by a SES” —Oonsie Biggs

Abruptness affects the capacity to adapt to changes

## Cascading regime shifts within and across scales

Juan C. Rocha, Garry Peterson, Örjan Bodin & Simon Levin

Science. 362, 1379–1383 (2018)

## How regime shifts will interact?

Whether the occurrence of one will increase the likelihood of another, or simply correlate at distant places

## How regime shifts will interact?

Whether the occurrence of one will increase the likelihood of another, or simply correlate at distant places

## Hypotheses

~45% of the regime shift couplings analyzed present structural dependencies in the form of one-way interactions for the domino effect or two-way interactions for hidden feedbacks

## Driver sharing

Aquatic regime shifts tend to have and share more drivers. The most co-occurring drivers are related to food production, climate change & urbanisation. 36% of pair-wise combinations are solely coupled by sharing drivers

## Domino effects

Evidence of cross-scale interactions for domino effects was only found in space but not in time. The maximum number of pathways found was 4, and the variables that produce most domino effects relate to climate, nutrients and water transport

## Hidden feedbacks

Most hidden feedbacks occur in terrestrial and earth systems. Key variables that belong to many of these hidden feedbacks are related to climate, fires, erosion, agriculture and urbanisation

## Conclusions

• How a regime shift somewhere in the world could affect the occurrence of another regime shift remains an open question and a key frontier of research.
• Developed network-based method that allow us to explore plausible cascading effects and distinguish potential correlations from true interdependencies.
• Regime shifts can be interconnected: they should not be study in isolation assuming they are independent systems. Methods and data collection that takes into account the possibility of cascading effects needs to be further developed.
• The frequency and diversity of regime shifts interconnections suggests that current approaches to environmental management and governance are substantially underestimating the likelihood of cascading effects.

## Criticism: plausible vs. probable

“…the use of qualitative information inevitably has limitations […] it can only provide a catalog of the possible”

A case study on salmon

## 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

## Method: convergent cross-mapping

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 > 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!]

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

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!]

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

## How do people behave when confronted with situations pervaded by thresholds?

• Regime shifts in marine environments
• Fisheries collapse
• Mangroves collapse
• Coral transitions
• Coastal eutrophication
• Hypoxia
• Potential impacts on society
• ~50M people depend on small-scale fisheries
• Mostly in developing countries

## History of regime shifts

• 256 fishers groups of 4 players
• Communication allowed
• Threshod: 100% probability of climate event
• Risk: 50% probability
• Uncertainty: 10-90% probability

## Treatment effects

• Individual extraction: $x_{i,t}$
• Proportion of extraction: $x_{i,t}/S_t$
• Cooperation: $C_{i,t} = \frac{x_{i,t}}{\frac{S_t - \theta}{N}}$

• Diff-in-diff regression: $\hat{Y_i} = \hat{\mu} + \hat{\gamma}G_i + \hat{\delta}T_i + \hat{\tau}G_iT_i$

It’s harder to coordinate under treatments, but agreements increase the probability to coordinate and react to lower stock sizes by reducing fishing preasure. Agreements also reduce the variance of extraction and the variance of cooperation. Changes in fishing effort depends on treatments while changes in cooperation depends on context.

## Lessons

• Fishermen facing thresholds fish less – they take care of the resources
• By reducing fishing effort or keeping close to the social optimal people do cooperate. However, cooperation by itself is not affected by our treatments, it seems to be driven more by personal and group dynamics.
• If the existence of threshold effects already triggers cooperative behavior in natural resource users, then communicating their potential effects on ecosystems and society is more important that quantifying the precise point at which ecosystems tip over. Specially because such thresholds are hard to observe, measure, and they change over time.

## What we learnt:

1. What are regime shifts? and how do we compare them?
2. What are their main drivers?

3. How do we manage them in aquatic systems?

4. Can regime shifts be interconnected? – water as an important connector

5. Empirical evidence? – Work in progress

6. How people behave when facing thresholds?

## Can we predict impacts of regime shifts with machine learning?

• Yes, at least for some of them
• Topic models
• Works on papers but still to be tested on the field

Unpublished: Rocha, J.C. & Wikström, R. Detecting Potential Impacts on Ecosystem Services Related to Ecological Regime Shifts – A Matter of Wording.

## Gracias | Tack

Questions?

email: juan.rocha@su.se