Causes & Consequences of Regime Shifts: A Network Analysis of Global

Environmental Change

Juan-Carlos Rocha, Oonsie Biggs & Garry Peterson

The Anthropocene

The Anthropocene

The Anthropocene

Social challenge: Understand patters of causes and consequences of regime shifts !

How common they are? 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

Regime Shifts DataBase

Established or proposed feedback mechanisms exist that maintain the different regimes. !The shift substantially affect the set of ecosystem services provided by a social-ecological system

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

Methods

•Bipartite network and one-mode projections: 25 Regime shifts + 60 Drivers

•104 random bipartite graphs to explore significance of couplings: mean degree, co-occurrence & clustering coefficient statistics on one-mode projections.

•Multi-dimensional scaling

Regime shiftsDrivers

Regime Shift DatabaseA 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

Ecosystem services

Ecosystem processes

Ecosystem type

Impact on human well being

Land use

Spatial scale

Temporal scale

Reversibility

Evidence

...

Methods

•Bipartite network and one-mode projections: 25 Regime shifts + 60 Drivers

•104 random bipartite graphs to explore significance of couplings: mean degree, co-occurrence & clustering coefficient statistics on one-mode projections.

•Multi-dimensional scaling

Regime shiftsDrivers

Regime Shift DatabaseA 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

Ecosystem services

Ecosystem processes

Ecosystem type

Impact on human well being

Land use

Spatial scale

Temporal scale

Reversibility

Evidence

...

Agriculture

Atmospheric CO2

Deforestation

Demand

Droughts

Erosion Fishing

Floods

Global warming

Human population

Landscape fragmentation

Nutrients inputs Rainfall variability

Sea level rise

Sea surface temperature

Sediments

Sewage

Temperature

Upwellings

Urbanization

Arctic sea ice

Bivalves collapse

Coral transitions

Dry land degradation

Encroachment

Eutrophication

Fisheries collapse

Floating plants

Forest to savannas

Greenland

Hypoxia

Kelps transitions

Mangroves collapse

Marine Eutrophication

Marine foodwebs

Monsoon weakening

Peatlands

River channel change

Salt marshes

Sea grass

Soil salinization Soil structure

Thermohaline circulation

Tundra to Forest

Western Antarctic IceSheet Collapse

Simulation results for 25 Regime Shifts across the globe

1 3 5 7 9 11 14 17 21

Degree distribution

Degree

05

1015

2025

30

Clustering Coefficient

Clustering coefficient

Den

sity

0.25 0.30 0.35 0.40 0.45

010

2030

40

Drivers Network Co−occurrence Index

s−squared

Den

sity

3.0 3.2 3.4 3.6 3.8 4.0

01

23

4

Regime Shifts Network Co−occurrence Index

s−squared

Den

sity

16 17 18 19 20 21 22 23

0.0

0.2

0.4

0.6

Average Degree in simulated Drivers Networks

Mean Degree

Den

sity

27 28 29 30 31 32 33

0.0

0.2

0.4

0.6

Average Degree in simulated Regime Shifts Networks

Mean Degree

Den

sity

18 19 20 21 22 23 24

0.0

0.4

0.8

1.2

Global drivers of Regime Shifts

Agriculture

Climate change

Deforestation

Disease

Droughts

Erosion

Fertilizers use

Fishing

Floods

Green house gases

Landscape fragmentation

Nutrients inputs

Rainfall variability

Sea surface temperature

Sediments

Sewage

Temperature Turbidity

Urbanization

Few frequent drivers: Only 5 out of 60 drivers influence more than 1/2 of the regime shifts analyzed.

More shared drivers: 11 drivers interact with >50% of other drivers when causing regime shifts.

Food production & climate change drive the most frequent drivers of regime shifts

Global drivers of Regime ShiftsR

iver c

hann

el c

hang

eAr

ctic

Sea

Ice

Ther

moh

alin

eG

reen

land

WAI

SSt

eppe

to tu

ndra

Tund

ra to

fore

stC

oral

tran

sitio

nsM

angr

oves

Kelp

s tra

nsiti

ons

Fish

erie

sM

arin

e Eu

trhop

hica

tion

Eutro

phic

atio

nBi

valve

sSe

a G

rass

Floa

ting

plan

tsH

ypox

iaM

arin

e fo

od w

ebs

Peat

land

sSa

lt M

arsh

es to

tida

l fla

tsEn

croa

chm

ent

Soil

salin

izat

ion

Fore

st to

Sav

ana

Dry

land

sM

oons

on

Immigration and urbanization

Infrastructure development

Climate

Biogeochemical Cycle

Fishing and marine harvest

Food production

Resource exploitation

Ecological processes

Land Cover Change

Water

Nutrients and pollutants

Biophysical

Frecuency of disturbance

Biodiversity Loss

0 4 8Value

040

Color Keyand Histogram

Cou

nt Few frequent drivers: Only 5 out of 60 drivers influence more than 1/2 of the regime shifts analyzed.

More shared drivers: 11 drivers interact with >50% of other drivers when causing regime shifts.

Food production & climate change drive the most frequent drivers of regime shifts

How drivers tend to interact?

Arctic Sea Ice

Bivalves

Coral transitions Drylands

Encroachment

Eutrophication

Fisheries

Floating plants

Forest to SavanaGreenland

Hypoxia

Kelps transitions

Mangroves

Marine Eutrhophication

Marine food webs

Moonson

PeatlandsRiver channel change

Salt Marshes to tidal flats

Sea Grass

Soil salinization

Steppe to tundra

Thermohaline

Tundra to forest

Marine regime shifts share significantly more drivers suggesting high similarity on their feedback mechanisms.

Terrestrial regime shifts share fewer drivers. Higher diversity of drivers makes management more context dependent.

Multi-Dimensional Scaling

−0.4 −0.2 0.0 0.2 0.4

−0.4

−0.2

0.0

0.2

0.4

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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

River channel change

Salt Marshes to tidal flats

Sea Grass

Soil salinizationSteppe to tundra

Thermohaline

Tundra to forest

WAIS

Multi−Dimensional Scaling

Multi-Dimensional Scaling

−0.4 −0.2 0.0 0.2 0.4

−0.4

−0.2

0.0

0.2

0.4

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Ecosystem type

Marine and coastal

Freshwater lakes and rivers

Moist savannas and woodlandsDrylands and deserts

Grasslands

Tundra

Polar

0.8 0 0.8

0.8

00.

8

Multi-Dimensional Scaling

−0.4 −0.2 0.0 0.2 0.4

−0.4

−0.2

0.0

0.2

0.4

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Ecosystem services

FreshwaterLivestock

Fisheries

Climate regulation

Water purification

RecreationAesthetic values

0.8 0 0.8

0.8

00.

8

Multi-Dimensional Scaling

−0.4 −0.2 0.0 0.2 0.4

−0.4

−0.2

0.0

0.2

0.4

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Land Use

Small scale subsistence crop cultivation

Large scale commercial crop cultivation

Extensive livestock production

Fisheries2

Land use impact are primarily off site

0.8 0 0.8

0.8

00.

8

Multi-Dimensional Scaling

−0.4 −0.2 0.0 0.2 0.4

−0.4

−0.2

0.0

0.2

0.4

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Scale

Local Landscape

Sub continental Regional

Months

Years

Decades

0.6 0 0.6

0.6

00.

6

Managing regime shift drivers

Floating plantsBivalves collapseEutrophication

Fisheries collapseCoral transitions

HypoxiaEncroachment

Salt marshesSoil salinization

Soil structureForest to savannas

Dry land degradationKelps transitions

Monsoon weakeningPeatlands

Marine foodwebsGreenland

Thermohaline circulationRiver channel change

Tundra to ForestLocalNationalInternational

Drivers by Management Type

Proportion of RS Drivers

0.0 0.2 0.4 0.6 0.8 1.0

International cooperation to manage most drivers of 75% of regime shifts.

Regulating single drivers, such as Climate change, won’t prevent regime shifts.

Regulating local drivers can build resilience to global drivers

Avoiding regime shifts requires poly-centric institutions.

Regime shifts are tightly connected both when sharing drivers and their underlying feedback dynamics. The management of immediate causes or well studied variables might not be enough to avoid such catastrophes. Food production and climate change are the main causes of regime shifts globally. Marine regime shifts share more drivers, while terrestrial regime shifts are more context dependent. Management of regime shifts requires multi-level governance: coordinating efforts across multiple scales of action. Network analysis is an useful approach to study regime shifts couplings when knowledge about system dynamics or time series of key variables are limited.

Conclusions

Questions?? e-mail: juan.rocha@su.se twitter: @juanrocha

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

Questions?? e-mail: juan.rocha@su.se twitter: @juanrocha

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

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