Compositional-turnover modelling: an applied perspective
Simon Ferrier
Tom Harwood, Karel Mokany, Kristen Williams, Andrew Hoskins, Renee Catullo
Glenn Manion (NSW OEH), Dan Rosauer (ANU)
CSIRO LAND & WATER FLAGSHIP
A long time ago ...
Ferrier (1984) The status of the Rufous Scrub-bird:
habitat, geographical variation & abundance.
PhD Thesis.
Ferrier et al (2002) Biodiversity & Conservation
Regional conservation planning in north-east NSW forests
throughout the 1990s
Modelled species
distributions &
vegetation
communities
Protection
targets
Conservation
prioritisation
(irreplaceability
analysis)
Timber resource
assessment
Ferrier, S et al (2000) Biological Conservation 93: 303-325
Negotiation
& selection of
new reserves
Innovations in SDM application during that period
- incorporating geographical space
Ferrier et al (2002)
Biodiversity &
Conservation
Innovations in SDM application during that period
- “stacked” predict-then-assemble approaches
Ferrier et al (2002) Biodiversity & Conservation
Late 1990s onwards: focus shifted to whole-landscape
decision-making processes
Ferrier, S & Wintle, B (2009) Quantitative approaches to spatial conservation prioritisation:
matching the solution to the need. In Spatial Conservation Prioritisation. OUP
Late 1990s onwards: focus shifted to whole-landscape
decision-making processes
Richmond-Tweed Catchments:
Key Habitats & Corridors Project
Hunter Central-Rivers CMA:
Planning for protection &
restoration of river / stream
biodiversity
Turak, Ferrier, et al (2011)
Freshwater Biology 56: 39-56
Whole-landscape modelling of biodiversity persistence as a
foundation for multiple forms of higher-level assessment
Ferrier, S & Drielsma, M (2012) Diversity & Distributions 16: 386-492
Resulted in further innovations in species modelling – e.g.
linking SDMs to dynamic metapopulation models
Drielsma, M & Ferrier, S (2009) Biological Conservation 142: 529-540
But what about “the other 99%” of compositional
diversity?
Moritz et al (2001)
Proc Ret
Soc
Moritz
al B
(2001)
Ferrier & Watson (1997)
www.environment.gov.au/archive/biodiversity/
publications/technical/surrogates/
Ferrier et al (2002)
Biodiversity & Conservation
Spectrum of distributional modelling strategies
Ferrier & Guisan (2006) Journal of Applied Ecology
• interested in individual species of particular concern
• reasonable number of records per species
Individual species distribution
(niche) modelling
“Predict first, assemble later”
techniques
Simultaneous multi-response
modelling of multiple species
“Assemble first, predict later”
techniques
Macroecological modelling of collective
biodiversity properties (richness,
compositional turnover etc)
• interested in biodiversity as a whole
• huge number of species, each with few (or no) records
Modelling of compositional turnover (dissimilarity)
across space
Location B
Compositional dissimilarity (beta diversity) between locations A and B =
f (differences in abiotic environment, biogeographic isolation, etc)
Location A
Local species richness (alpha diversity) at location A =
f (abiotic environment, biogeographic history, human disturbance, etc)
Statistical modelling of compositional turnover
e.g. generalised dissimilarity modelling (GDM)
Ferrier, S et al (2007) Diversity & Distributions
Biological
survey
data
Compositional
dissimilarity
dij = 1 − e −η
Ecological distance η = α +
Environmental
predictor A
Environmental
∑
Environmental
predictor C
f (predictor A)
p =1
predictor A
f (predictor B)
predictor B
n
predictor B
f p ( x pi ) − f p ( x pj )
Flexibility in types of response and predictor variables
Site i
Site j
Response:
Dissimilarity in species composition
or
Phylogenetic dissimilarity
or
Intraspecific genetic distance
or
Metagenomic distance
e.g.
C
d ij = 1 −
Predictors:
Multiple environmental gradients
and / or
Geographic separation
and / or
Biogeographic isolation
Ferrier, S et al (2007) Diversity & Distributions
A
B
e.g.
2A
2A + B + C
Modelling dissimilarity in species composition
77,000 records of 2,700 land-snail species
Generalised
dissimilarity
modelling (GDM)
4.00
3.50
Spatial pattern in
compositional turnover
f(rprecmean)
3.00
2.50
2.00
1.50
1.00
0.50
0.00
-0.002
-0.001
0
0.001
0.002
rprecmean
4.00
3.50
f(bio18)
3.00
2.50
2.00
1.50
1.00
0.50
Remotely derived environmental variables:
climate, terrain, soils, geographic isolation etc
0.00
500
1,000
1,500
2,00
bio18
4.00
3.50
f(megagi)
3.00
2.50
2.00
1.50
1.00
0.50
0.00
0
0.1
0.2
0.3
0.4
0.5
0.6
megagi
4.00
3.50
f(slope)
3.00
2.50
2.00
1.50
1.00
0.50
0.00
0
5
10
15
20
25
30
35
slope
etc ...
etc ...
Funded by Aust. Department of Environment
Modelling phylogenetic dissimilarity
Rosauer, D et al (2013) Ecography
Modelling intraspecific genetic variation
Modelling intraspecific genetic variation
Perspective on potential roles of GDM, circa 2007
Individual species
extrapolation
Constrained
environmental
classification
Biological
survey
data
Generalised
dissimilarity
modelling
Visualisation of spatial
pattern in community
composition
Conservation
assessment
Environmental
predictors
Climate-change
vulnerability assessment
Survey gap
analysis
Ferrier, S et al (2007) Diversity &
Distributions
Perspective on potential roles of GDM, circa 2007
Individual species
extrapolation
Constrained
environmental
classification
Biological
survey
data
Generalised
dissimilarity
modelling
Visualisation of spatial
pattern in community
composition
Conservation
assessment
Environmental
predictors
Climate-change
vulnerability assessment
Survey gap
analysis
Ferrier, S et al (2007) Diversity &
Distributions
Visualisation and classification of spatial pattern in
compositional turnover
Ferrier, S et al (2007)
Diversity & Distributions
Visualisation and classification of spatial pattern in
compositional turnover
A
Lowland, low gradient streams
B
C
Lowland hill-country streams
D
E
Lowland rivers & streams
- dry climates
F
G
H
I
Mid-elevation streams
- dry climates
Mid-elevation rivers & streams
- wet climates
J
K
L
M
Mid-elevation rivers & streams
- glacially influenced
Glacial rivers
N
O
P
High elevation streams
– non-glacial
Q
R
S
T
0.6
0.3
0.2
Environmental distance
Leathwick, JR et al (2011) Freshwater Biology 56: 21-38
0.1
0.0
High elevation streams
– glacial
Perspective on potential roles of GDM, circa 2007
Individual species
extrapolation
Constrained
environmental
classification
Biological
survey
data
Generalised
dissimilarity
modelling
Visualisation of spatial
pattern in community
composition
Conservation
assessment
Environmental
predictors
Climate-change
vulnerability assessment
Survey gap
analysis
Ferrier, S et al (2007) Diversity &
Distributions
Predictively mapping distributions of individual
species?
Ferrier, S et al (2007) Diversity &
Distributions
Elith, J et al (2006)
Ecography
Predictively mapping distributions of individual
species – potential to simulate unknown species?
A
B
Number of species
0 - 50
50 - 100
100 - 150
150 - 200
200 - 220
Limited
survey data
Environmental
variables
Number of species
site
1
0
1
species
1
1
1
1
0
0
1
1
1
Statistical
models
0
0
1
0
0
1
0
0
1
0
0
0
0
1
1
0
1
1
1
0
1
1
0
1
0
0
0
0
1
1
1
0
1
0
0
0
0
0
0
1
0
0
0
1
1
1
1
0
0
1
0
1
0
1
1
0
0
1
1
0
1
0
0
0
0
1
0
0
0
1
0
1
0
0
1
1
0
0
1
0
0
1
1
0
0
1
0
1
0
0
0
0
1
0
0
0
1
1
1
1
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
1
1
1
1
0
0
0
1
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
1
1
0
1
0
0
0
1
0
0
1
0
0
1
0
0
1
0
0
0
0
1
1
0
1
1
1
0
1
1
0
1
0
0
0
0
1
1
1
0
1
0
0
0
0
0
0
1
0
0
0
1
1
1
1
0
0
1
0
1
0
1
1
0
0
1
1
0
1
0
0
0
0
1
0
0
0
1
0
1
0
0
1
1
0
0
1
0
0
1
1
0
0
1
0
1
0
0
0
0
1
0
0
0
1
1
1
1
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
1
1
1
1
0
0
0
1
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
1
0
1
0
0
0
0
0
site
site
site
DynamicFOAM
0 - 300
300 - 600
600 - 900
900 - 1200
1200 - 1350
0
1
1
0
1
0
0
0
1
0
0
1
0
0
1
0
0
1
0
0
0
0
1
1
0
1
1
1
0
1
1
0
1
0
0
0
0
1
1
1
0
1
0
0
0
0
0
0
1
0
0
0
1
1
1
1
0
0
1
0
1
0
1
1
0
0
1
1
0
1
0
0
0
0
1
0
0
0
1
0
1
0
0
1
1
0
0
1
0
0
1
1
0
0
1
0
1
0
0
0
0
1
0
0
0
1
1
1
1
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
1
1
1
1
0
0
0
1
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
1
0
1
0
0
0
0
0
400
Observed gamma-diversity
1
0
0
1
1
0
1
0
0
0
1
1:1
300
200
100
Predicted composition
site
species
1
1
1
0
1
1
1
1
1
0
0
1
0
0
0
1
1
1
1
0
1
0
1
1
1
1
0
0
1
0
0
1
1
0
1
0
1
1
1
0
0
1
1
1
0
0
0
0
1
1
1
0
1
1
1
0
0
1
1
0
1
0
0
0
1
0
0
1
0
0
1
0
0
1
0
0
0
0
1
1
0
1
1
1
0
1
1
0
1
0
0
0
0
1
1
1
0
1
0
0
0
0
0
0
1
0
0
0
1
1
1
1
0
0
1
0
1
0
1
1
0
0
1
1
0
1
0
0
0
0
1
0
0
0
1
0
1
0
0
1
1
0
0
1
0
0
1
1
0
0
1
0
1
0
0
0
0
1
0
0
0
1
1
1
1
0
0
0
0
0
1
0
1
1
1
0
1
0
0
0
1
1
1
1
0
0
0
1
0
1
0
0
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
0
0
0
1
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
1
0
0
0
0
0
1
0
0
0
1
0
0
0
0
1
0
1
0
1
0
0
0
0
0
0
0
100
200
300
Predicted gamma-diversity
400
Perspective on potential roles of GDM, circa 2007
Individual species
extrapolation
Constrained
environmental
classification
Biological
survey
data
Generalised
dissimilarity
modelling
Visualisation of spatial
pattern in community
composition
Conservation
assessment
Environmental
predictors
Climate-change
vulnerability assessment
Survey gap
analysis
Ferrier, S et al (2007) Diversity &
Distributions
Conservation assessment – national/continental scale
Conservation assessment – global scale
Ferrier et al (2004) BioScience
Perspective on potential roles of GDM, circa 2007
Individual species
extrapolation
Constrained
environmental
classification
Biological
survey
data
Generalised
dissimilarity
modelling
Visualisation of spatial
pattern in community
composition
Conservation
assessment
Environmental
predictors
Climate-change
vulnerability assessment
Survey gap
analysis
Ferrier, S et al (2007) Diversity &
Distributions
Projecting potential impacts of climate change –
correlative space-for-time substitution
Potential change in
plant community
composition
(2030 A1FI
scenario)
Projecting potential impacts of climate change –
correlative space-for-time substitution
Projecting potential impacts of climate change –
semi-mechanistic modelling
a
b
c
d
Using paleo-ecological data to test the predictive
performance of projections
A largely unforeseen use – testing fundamental
hypotheses around drivers of beta-diversity patterns