ECEM 2014 – Ecological Modelling Beyond boundaries: next generation modelling

Marrakech, 27-30 October 2014

Modelling species niches and distributions: vision for the future

Antoine Guisan

Departement of Ecology and Evolution (DEE, FBM)

Institute of Earth Surface Dynamics (IDyST, FGSE)

OB

1. Setting the scene

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Growing interest in predicting species distributions

Côté & Reynolds (2002) Science

Species Distribution

Models (SDMs)

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Spectacular

increase of SDM

papers

Graphe tiré de Guisan et al. (2013) Ecology Letters

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G. E. Hutchinson (1903-1991)

Species only occur in suitable abiotic

conditions within their environmental

niche, and these can be discontinuous in

geographic space, not all accessible and

not all biotically suitable

Hutchinson (1957) Cold Spring Harbor Symposia on Quantitative Biology

Colwell & Rangel (2009) PNAS, Guisan et al. (2014) TREE

Temperature

Wa

ter fundamental

niche

N-dim

temperature

rain

fall

niche-

habitat

duality!

Species have niches: biological data

can hardly be spatially interpolated!

Potential

distribution of

the species

Field

observations

Environmental

maps

presence

absence

wa

ter

Temperature

Guisan & Zimmermann (2000) Ecological Modelling

Guisan & Thuiller (2005) Ecology Letters

Model of the observed

environmental niche …

… …

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Principle of species distribution models (SDMs)

Spatial

predictions

Data

collection

Statistical

modelling

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A major need for ecologically-meaningful

environmental maps

Radiations

Altitude

Climate: T, P, ..

Slope

Exposure

etc..

+ slope

+ solar traject.

+ meteo measures

e.g. www.unil.ch/rechalpvd

Soils

+ edaphic

measures

Guisan & Hofer (2003)

J. Biogeography

Frog

Virtually applicable to all organisms

Lütolf et al.

(2006) J. Appl. Ecol.

Pellet et al. (2004)

Conservation Biology

Jaberg & Guisan

(2001) J. Appl. Ecol.

Patthey et al. (In review)

J. Wildlife Management

Maggini et al. (2006)

J. Biogeography

Maggini et al.

(2002)

Biodiversity &

Conservation

Moretti et al. (2006)

J. Biogeography Petitpierre et al.

(2012) Science

Le Lay et al.

(2010) Ecogr.

SDMs

See Guisan & Thuiller (2005) Ecol. Lett., Elith & Leathwick (2009)

AREES, Franklin (2010) CUP book

Soil fungi?

Soil bacteria?

SDMs in fundamental sciences

Spatial

genetics

Community

assembly

Drivers of

biological

invasions

Species

distributions

Environmental

niche

Niche-habitat

duality

Dispersal/migrations

pathways

Biogeographic

patterns

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New potential distribution

in time or space

?

∆ Temperature

∆ Landuse

1A. modifying the

input climate maps

1B. providing input

maps for a distinct

study area

Deriving projections in space or time

Guisan & Zimmermann (2000) Ecological Modelling

Guisan & Thuiller (2005) Ecology Letters 9

2. reapplying

the model (i.e. the quantified niche)

Thuiller et al.(2005) GCB, Engler et al. (2011) GCB

Graphs reflect the rates of plant species extinctions

in mountain ranges per elevation belt

L. alpinus

Androsace

E. myosuroides

D. octopetala

niche assumed to be ‘projectable’ in

a new range or a new time period

Biological invasions (projecting in space)

climate change (projecting in time)

Addressing global change issues

10

present

2030

SDMs in Conservation Sciences

Discovering

populations

Habitat

restorations

Species

reintroduction

Anticipating

invasions

Climate

change

impacts

Reserve

selection Prioritization

Biodiversity

monitoring

2. Vision for my future research at UNIL (DEE and IDYST)

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Thibaud et al. (2014) Methods in Ecology & Evolution 13

GLM

2.1 Methodological challenges

- Novel developments needed

- E.g. working more with “Virtual

Ecologist” (VE) framework

- Improving tools (e.g. R libraries,

‘migclim’, ‘ecospat’)

Guisan et al. (20014) Trends in Ecology & Evolution 14

2.2 Theoretical challenges

Which niche are we modelling?

SDMs are

modeling the

realized

environmental

niche

not

colonized

available

climate excluded

by biotic

interactions

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Range/time 1 Range/time 2

change in niche limit

change in niche

centroid

reduced density of

occurrences (but

not empty)

Realized

niche in:

Effect of niche changes?

Broennimann et al. (2012) Global Ecol. & Biogeogr.

Guisan et al. (2014) Trends in Ecology & Evolution

Pearman et al. (2008) Ecol. Letters Petitpierre et al. (2012) Science

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Niche changes lower SDM performances

Juniperus

Corylus

Abies

Larix

Fagus

Carpinus

Picea

0.3

0.4

0.5

0.6

0.7

0.8

0 0.5 1.0 1.5 2.0 2.5 3.0

Intensity of niche change

Qu

alit

y o

f p

ast p

red

ictio

n

Dominant

Pioneer

regression line:

r-square = 0.81

P = 0.00345

In time (7 spp, Past -6K to Present)

In space (50 invasive plants EU – US)

Red: North-American spp invading Europe

Green: European spp invading North-Am.

niche overlap U

nfilli

ng

E

xp

an

sio

n

Maiorano et al. (2013) GEB, Modified from Nogues-Bravo (2009)

Partial

realized

niches

a1 a2 a3

Pooled

niche

(close to

fundamental?)

a1+ a2+a3

t1+ t2 + t3

Building the niche through time or space

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a = area

t = time

CF = compositional

factor (niche axis)

fundamental

niche

realized

niche

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Beech, Fagus sylvatica

K years BP

nic

he

ove

rla

p

(with c

urr

ent

nic

he)

Model

based on

pooled

niche

Model

based on

partial

niches

Maiorano et al. (2013) GEB,

2 Center for Macroecology, Evolution and Climate,

University of Copenhagen Denmark

2.3 From species to communities

SDMs

Stacked SDM

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2.4 SDMs to support decisions?

year

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without ‘decision’

with ‘decision’

Not so many used routinely!

Searching for

applications in

four fields of

conservation

Guisan et al. (2013) Ecology Letters

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Putting SDMs in a decision context

Identifying the problem

Defining the objectives

Defining possible actions

Consequences of actions

Trade-offs between costs and benefits of actions

SDMs e.g. which exotic

species may be

problematic?

SDMs e.g. reserve selection

options?

SDMs e.g. impacts of

translocation?

Un

certainty assessm

ent

SDMs e.g. mapping

confidence

limits for

predicted

distributions?

Decision

Guisan et al. (2013) Ecology Letters

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The ever insufficient science-policy dialog

Guisan et al. (2013) Ecology Letters

Science

• Scientists • Applied researchers • Modellers

Scientific

Knowledge and Tools

SDM

Development and Evaluation

Conservation

• Decision makers • Site managers • Practitioners

Conservation

Problems

Model-assisted

Decision-making

‘Translators’ • Individuals • Groups • Institutions

Uncovering requirements and defining needs for

synthesis

Synthe- sizing

Synthe- sizing

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2.5 Promoting multi-disciplinary research

Http://rechalpvd.unil.ch

Thanks for your attention

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