cascading effects ccs2016

Cascading effects of critical transitions in social-ecological systems

Juan Carlos Rocha @juanrocha

The Anthropocene

Social challenge: Understand patterns of causes and consequences of regime shifts

How common they are? What are the main drivers? Where are they likely to occur? Who will be most affected? What can we do to avoid them? What possible interactions or cascading effects?

Science challenge: understand phenomena where experimentation is rarely an option, data availability is poor, and time for action a constraint

The Anthropocene

Can the occurrence of one regime shift in an area of the world increase or decrease the likelihood of other regime

shifts in apparently disconnected systems?

Can the study of regime shifts networks help us elucidate potential hypothesis for these tele-connections?

1. A comparative framework: the data 2. Forks: Global drivers & Impacts 3. Domino effects 4. Inconvenient feedbacks

Outline

@juanrocha

1. A comparative framework

1. A comparative frameworkBiggs, et al. 2015. bioRxiv:018473.

Regime Shifts DataBase

The shift substantially affect the set of ecosystem services provided by a social-ecological system

Established or proposed feedback mechanisms exist that maintain the different regimes.

The shift persists on time scale that impacts on people and society

@juanrocha

www.regimeshifts.org

http://www.regimeshifts.org

@juanrocha

Forks: when sharing a driver synchronize two regime shifts

Causal chains: the domino effect

Inconvenient feedbacks: when two shifts reinforce or dampen each other

RS1 RS2 RS3

D1

RS1 RS2D1 ...

RS1

RS2

D2D1

Arctic Icesheet collapse

Bivalves collapse

Coral bleaching

Coral transitions

Desertification

Encroachment

Eutrophication

Fisheries collapse

Floating plants

Foodwebs

Forest to cropland

Forest to savanna

Greenland icesheet collapse

Hypoxia

Kelp transitions

Monsoon

Peatlands

Soil salinization

Soil structure

Thermohaline

Tundra to forest

Arctic salt marsh

River channel change

2. Forks: Drivers, Risk & ResilienceRocha, et al. PLoS ONE 10:e0134639

Drivers Natural or human induced changes that have been identified as directly or indirectly producing a regime shift

Causal-loop diagrams is a technique to map out the

feedback structure of a system (Sterman 2000)

@juanrocha

Methods

•Bipartite network and one-mode projections: 25 Regime shifts + 57 Drivers

•104 random bipartite graphs

to explore significance of couplings: mean degree and co-occurrence statistics on one-mode projections.

•ERGM models using Jaccard similarity index on the RSDB as edge covariates & MDS

Regime shiftsDrivers

A 1 0 1 1 0 0 0 0 1 1 1 1 0 1 0 1

B 1 0 0 0 1 1 0 0 1 1 1 0 0 1 0 1

C

Regime Shift Database

Ecosystem services

Ecosystem processes

Ecosystem type

Impact on human well being

Land use

Spatial scale

Temporal scale

Reversibility

Evidence

...

@juanrocha

Agriculture

Aquaculture

Aquifers depletion

Climate change

Coastal erosion

Deforestation

Disease

Droughts

ENSO like events

Erosion

Estuarine fresh water inputEstuarine salinity

Fertilizers use

Fire frequency

Fishing

Floods

Flushing

Green house gases

Harvesting (animals)

Hunting

Ice melt water

Impoundments

Invasive species

Irrigation

Landscape fragmentation

Logging

Low tides

Nutrient inputs

Ocean acidification

Pollutants

Precipitation

Production intensification

Rainfall variability

Ranching (livestock)

River channelization

Roads and railways

Salt water intrusion

Sea level rise

Sea surface temperature

Sea water density

Sediments

Sewage

Soil moistureStorms

Temperature

Thermal anomalies in summer

Turbidity

Upwellings

Urban storm water runoff

Urbanization

Water depth

Water infrastructure

Water level fluctuation

Water stratification

Water vapor

Wetland Drainage

Wind stress

Arctic Sea Ice

Bivalves

Coral transitions

Drylands

Encroachment

Eutrophication

Fisheries

Floating plants

Forest to Savana

Greenland

Hypoxia

Kelps transitions

Mangroves

Marine Eutrhophication

Marine food webs

Moonson

Peatlands


Salt Marshes to tidal flats

Sea Grass

Soil salinization

Steppe to tundra

Thermohaline

Tundra to forest

WAIS

Agriculture

Aquaculture

Aquifers depletion

Climate change

Coastal erosion

Deforestation

Disease

Droughts

ENSO like events

Erosion


Fertilizers use

Fire frequency

Fishing

Floods

Flushing

Green house gases


Hunting

Ice melt water

Impoundments

Invasive species

Irrigation


Logging

Low tides

Nutrient inputs

Ocean acidification

Pollutants

Precipitation





Roads and railways


Sea level rise


Sea water density

Sediments

Sewage

Soil moistureStorms

Temperature


Turbidity

Upwellings


Urbanization

Water depth




Water vapor

Wetland Drainage

Wind stress

Arctic Sea Ice

Bivalves

Coral transitions

Drylands

Encroachment

Eutrophication

Fisheries

Floating plants

Forest to Savana

Greenland

Hypoxia

Kelps transitions

Mangroves


Marine food webs

Moonson

Peatlands



Sea Grass

Soil salinization

Steppe to tundra

Thermohaline

Tundra to forest

WAIS

AgricultureClimate change

DeforestationDisease

DroughtsErosion

Fertilizers use

FishingFloods

Green house gases

Landscape fragmentationNutrient inputs



SedimentsSewage

Temperature

Urbanization

> likelihood of drivers co-occurrence if drivers that can be managed at

local - regional scales and if they are indirect & generalist

Agriculture

Aquaculture

Aquifers depletion

Climate change

Coastal erosion

Deforestation

Disease

Droughts

ENSO like events

Erosion


Fertilizers use

Fire frequency

Fishing

Floods

Flushing

Green house gases


Hunting

Ice melt water

Impoundments

Invasive species

Irrigation


Logging

Low tides

Nutrient inputs

Ocean acidification

Pollutants

Precipitation





Roads and railways


Sea level rise


Sea water density

Sediments

Sewage

Soil moistureStorms

Temperature


Turbidity

Upwellings


Urbanization

Water depth




Water vapor

Wetland Drainage

Wind stress

Arctic Sea Ice

Bivalves

Coral transitions

Drylands

Encroachment

Eutrophication

Fisheries

Floating plants

Forest to Savana

Greenland

Hypoxia

Kelps transitions

Mangroves


Marine food webs

Moonson

Peatlands



Sea Grass

Soil salinization

Steppe to tundra

Thermohaline

Tundra to forest

WAIS

Agriculture

Aquaculture

Aquifers depletion

Climate change

Coastal erosion

Deforestation

Disease

Droughts

ENSO like events

Erosion


Fertilizers use

Fire frequency

Fishing

Floods

Flushing

Green house gases


Hunting

Ice melt water

Impoundments

Invasive species

Irrigation


Logging

Low tides

Nutrient inputs

Ocean acidification

Pollutants

Precipitation





Roads and railways


Sea level rise


Sea water density

Sediments

Sewage

Soil moistureStorms

Temperature


Turbidity

Upwellings


Urbanization

Water depth




Water vapor

Wetland Drainage

Wind stress

Arctic Sea Ice

Bivalves

Coral transitions

Drylands

Encroachment

Eutrophication

Fisheries

Floating plants

Forest to Savana

Greenland

Hypoxia

Kelps transitions

Mangroves


Marine food webs

Moonson

Peatlands



Sea Grass

Soil salinization

Steppe to tundra

Thermohaline

Tundra to forest

WAIS

Arctic Sea IceBivalves

Coral transitionsDrylands

Encroachment

Eutrophication

Fisheries

Floating plants

Forest to SavanaGreenland

HypoxiaKelps transitions

MangrovesMarine EutrhophicationMarine food webs

MoonsonPeatlands


Salt marshes to tidal flats

Sea Grass

Soil salinizationSteppe to tundra

ThermohalineTundra to forest

WAIS

Aquatic share more and more or less the same set of drives while terrestrial and sub-

continental are more drivers diverse. Higher driver co-occurrence if regime shifts

share: ecosystem type, ecosystem processes, impacts on ES and scales.

The governance & management challenge

• Managerial actions need to be coordinated across scales.

• 62% of drivers can be managed locally or regionally

• Addressing local & regional drivers can build resilience and delay the effect of global ones; but there is not blue print solutions.

@juanrocha

3. Cascading & domino effects [work in progress]

Causal-loop diagrams

Causal-loop diagrams is a technique to map out the

feedback structure of a system (Sterman 2000)

Agriculture

Coral abundance

CPUE

DeforestationDemand

Disease outbreak

ErosionFertilizers use

Fishing

Food supply

Global warming

Green house gases

Herbivores

Human population

Hurricanes

Logging

Low tides frequency

Macroalgae abundance

Nutrients input

Ocean acidification

Other competitorsPollutants

SedimentsSewage

Space

SST

Thermal annomalies

Top predators

Turbidity

Unpalatability

Urbanization

Zooxanthellae

A

B C

Agriculture

Fertilizers useDeforestation

Coral abundance

Zooxanthellae

SpaceDisease outbreak

CPUE Food supply

Erosion

DemandFishing

Logging

Herbivores

Sediments

Nutrients input

Top predators

Global warmingSST

Green house gasesOcean acidification

Macroalgae abundance

Human population

Hurricanes

Low tides frequencyUnpalatability

Turbidity

Other competitors

Pollutants

Sewage

Thermal annomaliesUrbanization

D

A worked example. A) shows a CLD for coral transitions as reported on RSDB. B) is a network representation of the same CLD where positive links are blue and negative red. C) identifies communities of drivers and processes based on a community detection algorithm. D) shows a network of 19 regime shifts CLD’s where drivers are identified in orange and other variables in yellow. The giant component of the network suggest a large potential pathways of connections between regime shifts drivers and processes, thus plausible cascading effects.

4. Inconvenient feedbacks [work in progress]

Desertification Soil Salinisation Desertification - Soil Salinisation

0

5

10

15

5 10 15 20Feedback length

Num

ber o

f fee

dbac

ks

NetworksRS1

RS2

RS.mix

Inc.Feed

Desertification - Soil Salinisation

Regime shifts are tightly connected both when sharing drivers and their underlying feedback dynamics. Great potential for cascading effects. Food production and climate change are the main causes of regime shifts globally. The management of immediate causes or well studied variables might not be enough to avoid such catastrophes. A graphical framework to explore potential regime shifts interconnections has been developed. An empirical frameworks to test the plausibility of such interconnections is still needed.

Conclusions

Questions?? e-mail: [email protected] twitter: @juanrocha

slides: http://criticaltransitions.wordpress.com/ | data: www.regimeshifts.rog

mailto:[email protected]?subject=

http://www.criticaltransitions.wordpress.com

http://www.regimeshifts.rog

Regime shifts are abrupt reorganisation of a system’s structure and function.

collapse

collapse

recovery

Prec

ipita

tion

Vegetation Prec

ipita

tion

Vegetation Prec

ipita

tion

Vegetation Prec

ipita

tion

Vegetation

Precipitation Precipitation Precipitation Precipitation

low high low high low high low high

Vegetation

low

high

Vegetation

low

high

Vegetation

low

high

Vegetation

low

high

StabilityLandscape

Equilibria

Regime shifts are abrupt reorganisation of a system’s structure and function.