Biogeclimatic Ecosystem Classificationin a changing world

Sybille Haeussler

UNBC

Smithers, BC

haeussl@unbc.ca

applying complex systems theory to ecosystem classification

A Brief History of BEC• Invented 1950-60s by Vladimir Krajina & students in

Botany Department at UBC

• Adopted by BC Government mid-1970s– Ecological Reserves program (Krajina, Foster, Pojar)

– BC Forest Service (Annas, Pojar, Klinka, Meidinger)

• Has served BC well– Golden Age of Silviculture - tree planting, site prep & rehab

(FRDA) – late 1970s to 1980s

– Old Growth Strategy (1992)

– Protected Area Strategy (1993) – gap analysis

– Forest Practices Code (1995) – natural disturbance units

– Land Use Plans (1991 - 2000) – LRMPs, SRMPs, LUPs

– Future Forests Strategy (eg, Kamloops TSA - 2008)

current 20852025 2055

Hamann & Wang 2006

Challenges of a Changing World1) Climate Envelope Predictions

2) Species will respond individualistically

3) Climate isn‟t the only thing that is changing

(disturbance regimes, habitat loss, invasive species)

"Application of the classification, in its current form, for identifying site quality and current climatic zonation should remain valid for several decades allowing time for a considered adjustment of the system."

BECweb climate change page

Challenges of a Changing Cohort

Vladimir J. Krajina

(1905-1993)Karel Klinka

ret. UBC ~2001

Jim Pojarleft MFR 2004

Allen Banner

ret.MFR 2010?

Del Meidinger

ret.MFR 2009

• BEC still taught to undergraduates – useful framework

• Not a serious topic for academic study (quaint?)

• What goes around, comes around ……

Paradigm Shifts

Lamarckian inheritance

acquired characteristics(Lysenko, Stalinist)

Soft inheritance

epigenetic (maternal) effectsgenes turned “on” or “off”

Mendelian inheritance

genetic selectionHard inheritance

encoded in DNA

The same applies to Ecosystems!

Individualistic “Gleasonian”

paradigm

Quantitative,Hard Science

Theory Building

Holistic “Clementsian”

paradigm

Descriptive,Soft ScienceClassification

Complex Systems Theory allows these two views to be reconciled

An Ecosystem as a Dissipative,Non-Equilibrium System

“an open, dynamical system operating far from thermodynamic equilibrium in an environment with which it exchanges energy and matter”

NASA Hurricane Dean

Complex Systems Science provides a framework for describing such systems mathematically and tools for modeling their dynamics

Keep this image of

shifting attractor in

your mind

AttractorTerm from Non-linear Dynamics (math & physics)

Definition:A set of states of a dynamic physical system toward which

the system tends to evolve, regardless of the starting

conditions of the system.

Different kinds of attractors:• Point attractor (eye of hurricane)

• Periodic attractor = limit cycle (system oscillates)

• Strange attractor = chaos (trajectory appears random)

Attractors in EcosystemsThe set of states toward which a dynamic ecosystem tends to

evolve, regardless of the starting conditions of the ecosystem.

Fast-changing Variables (100s of yrs):

Vegetation Succession Point attractor : climax ecosystem

Periodic attractor: shifting states

(eg Boreal mixedwood forest)

(eg North Coast bog – forest complexes)

Chaotic attractor: some tropical & temperate hardwood

systems

Slower-changing Variables (1000s of yrs):

Soil Profile Development Point Attractor: Podzol

Periodic attractor ? Luvisol

Succession and Soil Formation are Emergent

Properties of Terrestrial Ecosystems

Modeling Dynamical Systems:

• Dynamical systems are studied using differential equations that characterize the state of the system through time.

• The attractor represents the set of solutions for these

equations as time t ∞

• Ordinary differential equation (ODE) has one, independent variable: y = f(t).(Lotka-Volterra predator-prey model)

• Partial differential equation (PDE) has more than one variable: y = f(x, z, t)

• For complex, real-world systems, PDEs are/were either extremely difficult, or impossible to solve.

Definition of a Complex System 1. A system with many parts or components

2. The components must interact (non-independent, feedbacks)

3. Interaction among the parts causes the behaviour of the whole to be more than the sum of its parts.

The Many-Body Problem

Two-body system: Sun and Earth

Three-body system: Sun, Earth and Moon

Reductionist solution: 2 independent systems (ignore interactions)

Holistic solution: = many-body problem (Poincaré; Legrange – 2 volumes x 900 pages)

Imagine an ecosystem!

Modelling Ecosystems1. Holistic Approach:

Biogeocoenose/Organismal concept Sukachev, Clements, Krajina etc.

(mostly descriptive, static or equilibrium)

2. Reductionist Approach: State factor model

Jenny 1941, 1961; Major 1961

Ecosystem or soils = f (climate, parent material, topography, organisms, time)

Linear equation that disregards interactions:

E = a0 + a1c + a2p + a3g + a4o + a5t PEM!

Climate Envelope (Niche) Models

1985

2085

Flying BEC zones

Hamann & Wang 2006

1) Linear state factor model

assumptions

E = f (cl, pm, top, org, time)Vary climate while holding p, t, o constant.

No significant interactions with disturbance,

geology etc.

As climate gets warmer, zones move upslope &

north

No opportunity for Ecological Surprises

2) Assumes ecosystems at

equilibrium with climatereasonable for 1985, but not for 2085.

Excellent Null Model

But what is the alternative?

An Example of Linear Thinking

• As the climate warms, trees will move upward in elevation and the Alpine Tundra zone (sorry, BAF) will shrink

• Yes, over the long term, ….. but what about in our life time?

An Example of Non-Linear Change

Hudson Bay Mountain, Smithers Perkins Peak near Tatla Lake

Photos: Sierra Curtis-McLane, UBC

Jan 2009 inversion-induced dieback?

Google Earth

Much of the order/pattern we

see in the world comes, not

from top down control, but

from local-level (bottom-up)

interactions among system

components.

(self-organization)

Examples: „hearts & minds‟

ant colonies, global recession,

viral marketing, civil society

The MOST important

idea from Complex

Systems Science:

Three main factors controlling

variability among ecosystems:

1. Environmental variation

(temperature, moisture,

nutrients) Krajina/BEC

2. Disturbance dynamics since

Pickett & White (1985)

3. Self-organizing processes

largely ignored since

Clements … plant-soil

feedbacks

National Geographic, March 2004

Applying these ideas to BEC

Simplified Version of the State Factor Model

• E = f (cl, pm, topog, org, time) • E = f (b, r, t) b = biota = organisms (plant-soil functional groups) r = resources (temperature, moisture, nutrients) t = time (disturbance frequency, size, severity)

Because this is a Complex Systems Model, factor interactions are assumed

Plant-Soil-(Zootic) Feedbacks are the key to

understanding self-organization in terrestrial ecosystems

negative feedbacksdampening, stabilizing

positive feedbacksamplifying, destabilizing

positive followed by negative feedbackscreates sharp boundaries & patterns

Ehrenfeld et al. 2005

r a t e

o

f p

r o

c e

s s

Agent-Based Simulation Modeling

Adjust resource availability

Adjust disturbance-risk

Low Resources High

Adjust plant-soil feedback strength

DEMO of EBRT Model

Many Other Modeling Tools: Fitness Landscape Models – shifting attractors

widely used in genetics (Sewall Wright, Stuart Kauffman)

smooth landscape rugged landscape chaotic landscape(Great Plains) (BC pre-1970s) (BC 2000s?)

point attractor multiple attractors strange attractor

Highest complexity

tune parameters of model (climate, disturbance, feedbacks)

nutrientsm

ois

ture

dry

wet

poor rich

bog fen calcareous

. fen

lichens grassland

h e

a t

h

shru

bby t

hic

kets

lichen

woodlands

conifer

ericaceous

forest

mixedwood

forest

bog

forest

swamp

forest

riparian

forest

savanna

Generic

northern temperate

or

southern boreal

landscape

Shape of domains changes

with:

climate

nutrient deposition

disturbance regimes

species distributions

0

Dry

7

Wet

Grassland attractorChernozem-like soil

very active decomposition

Lots of animals

Aspen woodland attractorDark Gray Luvisol

nutrient-rich litter

Rapid decomposition

Hemlock – Moss AttractorPodzolic soil

dark & shady with organic matter

accumulation

Pine –Lichen attractorEluviated soil

Ground fires & nutrient loss

Mixed

Moist

Forest

Black spruce - SphagnumOrganic soil

paludificationICH zone

Complex landscape

http://panmental.de/ICSBtut/text66.html

Reimagining the Edatopic Grid as a

Dynamic Fitness Landscape

Animation by Michael Herdy, TU Berlin

Lichen woodland Grassland

Riparian forestBog forest

Mesic Mixedwood

forest

Haeussler et al. 2004; Hamilton & Haeussler 2008

De Groot et al. 2005; Haeussler 2007, 2008

Flooding / Sedimentation

No humus

Repeated input

Paludification

Peat

Positive feedback

(cf van Breeman 1995)

Podzolization

Mor humus

Positive feedback(cf Northup et al. 1995,97

Sedia & Ehrenfeld 2005)

Melanization

Mull humus

Positive feedback

Mor-Moder humus

Negative Feedback

(cf Bever et al. 2003)

Lichen woodland Grassland

Riparian forestBog forest

Mesic Mixedwood

forest

Haeussler et al. 2004; Hamilton & Haeussler 2008

De Groot et al. 2005; Haeussler 2007, 2008

Another example of linear thinking

“The relative relationship between sites at a local scale will remain stable into the future. E.g. drier, mesic, and wetter sites will retain their relative position and designation in the landscape.”

BECweb climate change page

What is the alternative?

The Wal-Mart effect(„mesophication‟ Nowacki & Abrams 2008)

nutrients

mo

istu

re

dry

wet

poor rich

lichen

woodlands

bog

forest

riparian

forest

mixed-conifer

forest

grassland

The Wal-Mart effect(„mesophication‟ Nowacki & Abrams 2008)

nutrients

mo

istu

redry

wet

poor rich

grasslandlichen

woodlands

bog

forest

riparian

forest

mixedwood

forest

Paludificationnutrients

mo

istu

redry

wet

poor rich

grasslandlichen

woodlands

riparian

forest

mixedwood

forest

Novel Ecosystems?nutrients

mo

istu

re

dry

wet

poor rich

lichen

woodlands

bog

forest

riparian

forest

mixed-conifer

forest

grassland

Degraded Scrub(too much

stress/disturbance for long-lived trees)

and finicky soil organisms

Summing Up Ecosystems respond both individualistically and

holistically to changes in resource availability, disturbance and species distributions

Complex systems models are needed to “predict” emergent behaviour resulting from interactions among ecological drivers.

Because BEC takes a holistic, top-down + bottom-up approach to ecosystems, we have a headstart in understanding and modeling ecological surprises.

However, BEC needs to change from a static climax-forest view of ecosystem dynamics to a non-equilibrium view based on shifting ecosystem attractors.

Suggestions for Change (Process)

BEC needs to Excite and Enlighten a New Generation

Incorporate dynamic, non-equilibrium concepts into BEC training materials & get it into classrooms

Harness skills of tech-savvy generation and use new media –REPLACE static 2-way grids with cool interactive graphics!

Entice field ecologists & geeks to work together (ecosystems are the ultimate mathematical challenge!)

• Example – Leo’s film (Wake up, Freak out, Then Get a Grip) http://wakeupfreakout.org/film/tipping.html

Suggestions for Change (Content) A shift from static Description to dynamic Prediction

Less emphasis on classifying & prescribing

More emphasis on understanding, interpretation, adaptation (& of course quantitative modeling )

Focus more attention on bottom-up processes

Site Level vs. Climate Level

Plant-soil interactions vs. Bioclimate envelopes

Site diagnosis rather than Mapping

Pay attention to slow-changing (soils, geology, nutrient cycling) as well as fast-changing (plants, pests, wildlife, humus) variables

Formally incorporate paleoecology & dendrochronology into BEC databases

Suggestions for Change

• Your ideas?

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•

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Thanks for listening

Contact Sybille at haeussl@unbc.ca