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Community Assembly: From
Small to Large Spatial Scales
Nicholas J. Gotelli
Department of Biology
University of Vermont
Burlington, VT 05405
Community Comparisons
Species
Bog
Forest
Amblyopone
pallipes
-
1
Dolichoderus
pustulatus
1
-
Lasius alienus
1
1
Lasius umbratus
-
4
Camponotus
herculaneus
-
3
82
-
Myrmica
detritinodis
-
1
Stenamma diecki
-
1
Aphaenogaster
rudis complex
-
1
Myrmica lobifrons
Bog sample grid
(5 m x 5 m)
Forest sample grid
(5 m x 5 m)
Community Comparisons
Species
Bog
Forest
Amblyopone
pallipes
-
1
Dolichoderus
pustulatus
1
-
Lasius alienus
1
1
Lasius umbratus
-
4
Camponotus
herculaneus
-
3
82
-
Myrmica
detritinodis
-
1
Stenamma diecki
-
1
Aphaenogaster
rudis complex
-
1
Myrmica lobifrons
1) Species Composition
Community Comparisons
Species
Bog
Forest
Amblyopone
pallipes
-
1
Dolichoderus
pustulatus
1
-
Lasius alienus
1
1
Lasius umbratus
-
4
Camponotus
herculaneus
-
3
82
-
Myrmica
detritinodis
-
1
Stenamma diecki
-
1
Aphaenogaster
rudis complex
-
1
Myrmica lobifrons
1) Species Composition
2) Relative Abundance
Community Comparisons
Species
Bog
Forest
Amblyopone
pallipes
-
1
Dolichoderus
pustulatus
1
-
Lasius alienus
1
1
Lasius umbratus
-
4
Camponotus
herculaneus
-
3
82
-
Myrmica
detritinodis
-
1
Stenamma diecki
-
1
Aphaenogaster
rudis complex
-
1
SPECIES RICHNESS
3
7
Myrmica lobifrons
1) Species Composition
2) Relative Abundance
3) Species Richness
Community Comparisons
Species
Bog
Forest
Amblyopone
pallipes
-
1
Dolichoderus
pustulatus
1
-
Lasius alienus
1
1
Lasius umbratus
-
4
Camponotus
herculaneus
-
3
82
-
Myrmica
detritinodis
-
1
Stenamma diecki
-
1
Aphaenogaster
rudis complex
-
1
SPECIES RICHNESS
3
7
Myrmica lobifrons
1) Species Composition
2) Relative Abundance
3) Species Richness
Determinants of Relative Abundance
• Invertebrate food web
associated with northern
pitcher plant Sarracenia
• Small scale
• Experimental
• CONCLUSION: Relative
abundance is best
explained by food web
models
Determinants of Species Richness
• Avifauna of South
America
• Large scale
• Correlative
• CONCLUSION: Species
richness patterns reflect
historical forces, not
contemporary climate
Food webs
• Diagrams of “who
eats whom”
• Alternative to
competitive-based
paradigm
Methods for Food Web Analysis
• Interaction
matrices
• Experimental
manipulations
New approach
• Manipulate entire communities in
ecological “press” experiment
• Compare relative abundances to
predictions of several biologically realistic
models
Carnivorous plants: well-known,
but poorly studied
Aaron M. Ellison
Harvard Forest
The Northern Pitcher Plant
Sarracenia purpurea
• Perennial plant of
low-N peatlands
• Lifespan 30-50 y
• Arthropod prey
capture in waterfilled pitchers
• Diverse inquiline
community in
pitchers
Sarraceniopus gibsoni
Wyeomyia smithii
The Inquilines
Blaesoxipha fletcheri
Habrotrocha rosa
Metriocnemus knabi
Food web structure
Moose Bog
Experimental Protocol
• 5 treatment manipulations applied
• 10 replicate plants per treatment
• Treatments applied to old, first, and second
leaves of each plant
• Treatments applied twice/week
• Inquilines censused once/week
• Treatments maintained 5/31/00 - 8/23/00
Habitat Volume Manipulations
C
Inquilines & liquid removed, censused,
returned (Control)
C-
Inquilines & liquid removed,censused.
Liquid replaced with equal volume of d H20
(Trophic Pruning)
A
Inquilines & liquid removed, censused,
returned, topped with d H20
(Habitat Expansion)
A-
Inquilines & liquid removed, censused.
Liquid replaced and topped with d H20
(Habitat Expansion & Trophic Pruning)
E
Inquilines & liquid removed, censused
(Habitat Contraction & Trophic Pruning)
Significant alterations in habitat
volume among treatments
Volume (cc)
40
Old Leaf
First Leaf
Second Leaf
30
20
10
0
C
C-
A
A-
Treatment
E
Mean responses per leaf
Treatment A1
Plant 5
Leaf A
Date
Species
5/31
6/5
6/7
6/12 6/20 6/26 7/4
7/10 7/17 7/24 7/31 8/7
8/14
8/23
Mean
Wyeomyia
0
0
0
0
11
12
5
4
4.000
Metriocnemus
1
4
0
1
0
10
2
3
2.625
Blaesoxipha
0
0
0
0
0
1
1
0
0.250
Sarracenioppus
0
1
0
0
0
0
0
0
0.125
Habrotrocha
1
0
0
0
7
21
15
3
5.875
10 300
0
0
0
200
0
20
66.250
3
0
1
3
8
22
4
5.250
4.0 8.2
0.0
7.0
6.7
6.0
9.6
15.6
7.138
Protozoa
Prey
Volume
1
Responses in abundance to both
leaf age and habitat manipulation
Wyeomyia Abundance
10
8
Old Leaf
First Leaf
Second Leaf
6
4
2
0
C
C-
A
A-
Treatment
E
Idiosyncratic responses of
individual taxa to manipulations
Habrotrocha Number
20
Old Leaf
First Leaf
Second Leaf
15
10
5
0
C
C-
A
A-
Treatment
E
Responses of prey abundance to
treatment and leaf age
16
Prey Number
14
Old Leaf
First Leaf
Second Leaf
12
Prey
10
8
6
4
2
0
C
C-
A
A-
Treatment
E
Summary of Effects of Treatment,
Age, and Prey Abundance
Wyeomyia
Metriocnemus
Treatment
Age
++
+
++
Habrotrocha
+++
++
+++
+
Sarraceniopus
Blaesoxipha
Volume
Prey
+++
Protozoa
Prey
TxA
++
++
++
++
Detecting Species Associations With
Multiple Regression Models
Dep
Var Pro
Independent Variable
Wye Met
Pro
Bla
+
+
Wye
Met
Sar
+
Hab
++ +++
—
+
Sar
Bla
Ha
++
Prey +++
Prey Vol
-
—
–
+
+++
+
+
+
+++
Standard Regression Methods
• Controls for covariation
• Assumes simple independent-dependent
data structure
• Does not allow for direct testing of different
hypotheses of community structure
P1
R
P2
P3
Path Analysis
• Controls for covariation
• Does not assume simple covariance
structure
• Allows for testing of different models of
community structure
V1
V4
V2
V3
Path models of community
structure
Habitat Volume, Prey Models
S3
S2
Habitat
Volume
S6
S4
S1
Keystone Species Models
S3
S2
Keystone
Species
(S1)
S6
S4
S5
Food Web Models
S1
S3
S2
S6
S4
S5
Top-Down Control
Food Web Models
S1
S3
S2
S6
S4
S5
Bottom-Up Control
Habitat Volume Model
Protozoa
Habrotrocha
(rotifer)
Blaesoxipha
(flesh fly)
0.099
-0.100
-0.004
0.135
Volume
Wyeomyia
(mosquito)
0.366
0.432
-0.001
Prey
Metriocnemus
(midge)
Sarracenioppus
(mite)
Blaesoxipha
(flesh fly)
0.258
-0.046
Wyeomyia
(mosquito)
Habrotrocha
(rotifer)
-0.264
-0.003
0.005
Protozoa
Metriocnemus
(midge)
0.176
-0.002
0.155
Prey
Sarracenioppus
(mite)
Blaesoxipha
(flesh fly)
0.258
-0.046
Wyeomyia
(mosquito)
Habrotrocha
(rotifer)
-0.271
-0.025
0.005
Protozoa
Metriocnemus
(midge)
0.034
-0.002
0.321
0.155
Prey
Sarracenioppus
(mite)
0.809
-0.350
Bacteria
Comparing Models
• Aikake’s Information Criterion Index (AIC)
• Balance between adding parameters and
reducing residual sum of squares
• Provides simple “badness of fit”
hypothesis test for path model
Cross-Validation Index
Sarraceniopus
Blaesoxipha
Protozoa
Metriocnemus
Habrotrocha
Wyeomyia
BOTTOM UP
BOTTOM UP (B)
TOP DOWN
TOP DOWN (B)
0.0
PREY
0.2
VOLUME
0.4
PREY + VOLUME
1.0
Random Colonization
Models
Keystone Species Models
Food Web Models
0.8
0.6
21
15
13
13
13
13
13
13
13
15
18
15
18
Cross-Validation Index
Sarraceniopus
Blaesoxipha
Protozoa
Metriocnemus
Habrotrocha
Wyeomyia
BOTTOM UP
BOTTOM UP (B)
TOP DOWN
TOP DOWN (B)
0.0
PREY
0.2
VOLUME
0.4
PREY + VOLUME
1.0
Random Colonization
Models
Keystone Species Models
Food Web Models
0.8
0.6
21
15
13
13
13
13
13
13
13
15
18
15
18
Wyeomyia Keystone Model
Protozoa
Habrotrocha
(rotifer)
Blaesoxipha
(flesh fly)
-0.061
-0.265
0.271
Wyeomyia
(mosquito)
0.014
0.228
-0.156
Prey
Metriocnemus
(midge)
Sarracenioppus
(mite)
Conclusions
• Invertebrates species of Sarracenia show
idiosyncratic responses to habitat volume,
leaf age
• Food web models provide a superior fit to
relative abundance data compared to
habitat volume models, keystone species
models
• Indirect evidence for strong bacterial links
Future Research
• Taxonomic resolution of protists, microbes
• Nutrient transfer and interactions between
plant and invertebrate food web
• Effects of food supplementation and
atmospheric inputs of nitrogen
Determinants of Species Richness
“There can be no question, I
think, that South America is
the most peculiar of all the
primary regions of the globe
as to its ornithology.”
P.L. Sclater (1858)
Gary Entsminger
Acquired Intelligence
Nicholas Gotelli,
University of Vermont
Gary Graves
Smithsonian
Rob Colwell
University of
Connecticut
Carsten Rahbek
University of Copenhagen
Thiago Rangel
Federal University of Goiás
Major Hypotheses
• Historical Factors
• Contemporary Climate
• Mid-domain Effect
Major Hypotheses
• Historical Factors
• Contemporary Climate
• Mid-domain Effect
“Current” Perspective:
Contemporary Climate Controls
Species Richness
“Climatic variables were the strongest predictors of
richness in 83 of 85 cases, providing strong support for
the hypothesis that climate in general has a major
influence on diversity gradients across large spatial
extents.”
Hawkins et al. (2004)
South American Avifauna
• 2891 breeding
species
• 2248 species
endemic to South
America and
associated landbridge islands
Minimum:
18 species
Maximum:
846 species
Minimum:
18 species
“Taiwan”
~ 4 grid cells
~ 471 bird species
Maximum:
846 species
Minimum:
18 species
Climate, Habitat Variables
Measured at Grid Cell Scale
800
600
600
Species Richness
Species Richness
800
400
200
200
0
0
0
2
4
6
8
10
Net Primary Productivity (tons / ha)
12
0
14
800
800
600
600
Species Richness
Species Richness
400
400
200
0
0
50
100
Precipitation (mm / yr)
150
200
100
150
200
Mean Annual Temperature (C) x 10
250
300
400
200
0
50
0
50
100
150
Climate PCA
200
250
Summary of
Simple Regression Statistics
PREDICTOR VARIABLE
R2
Topographic surface area (km²)
0.21
Net primary productivity (tons/yr)
0.67
Precipitation (mm/yr)
0.53
Temperature (mean annual, Cº)
0.48
Topographic relief (elevational range)
0.00
Ecosystem diversity (# ecosystem types)
0.07
All variables
0.79
However…
Conventional analyses mask
effects of species geographic
range!
Species vary tremendously in
geographic range size (= number of
grid cells occupied)
Myioborus cardonai
1 grid cell
Median range size
Anas puna
64 grid cells
Phalacrocorax brasilianus
1676 grid cells
1st
quartile
2nd
3rd
4th
quartile
Species Richness Gradients
Depend On Range Size Quartile!
Species Richness Correlates For
Range Quartiles
PREDICTOR VARIABLE
1st
quartile
2nd
quartile
3rd
quartile
4th
quartile
Topographic surface area (km²)
0.00
0.00
0.02
0.24
Net primary productivity (tons/yr)
0.00
0.00
0.05
0.82
Precipitation (mm/yr)
0.01
0.01
0.07
0.57
Temperature (mean annual, Cº)
0.00
0.00
0.00
0.69
Topographic relief (elevational
range)
0.31
0.39
0.16
0.14
Ecosystem diversity (# ecosystem
types)
0.21
0.23
0.19
0.00
All variables
0.48
0.58
0.47
0.85
Failure of Climate Variable to Predict Species Richness
of First Three Range Size Quartiles
PREDICTOR VARIABLE
1st
quartile
2nd
quartile
3rd
quartile
4th
quartile
Topographic surface area (km²)
0.00
0.00
0.02
0.24
Net primary productivity (tons/yr)
0.00
0.00
0.05
0.82
Precipitation (mm/yr)
0.01
0.01
0.07
0.57
Temperature (mean annual, Cº)
0.00
0.00
0.00
0.69
Topographic relief (elevational
range)
0.31
0.39
0.16
0.14
Ecosystem diversity (# ecosystem
types)
0.21
0.23
0.19
0.00
All variables
0.48
0.58
0.47
0.85
Contrasting Correlates for
Restricted vs. Widespread Species
PREDICTOR VARIABLE
1st -3rd
quartiles
4th
quartile
Total
Richness
Topographic surface area (km²)
0.00
0.24
0.21
Net primary productivity (tons/yr)
0.01
0.82
0.67
Precipitation (mm/yr)
0.04
0.57
0.53
Temperature (mean annual, Cº)
0.00
0.69
0.48
Topographic relief (elevational
range)
0.33
0.14
0.00
Ecosystem diversity (# ecosystem
types)
0.25
0.00
0.07
All variables
0.58
0.85
0.79
500
400
400
Species Richness (Quartiles 1-3)
Species Richness (Quartiles 1-3)
500
300
200
100
200
100
0
0
0
2
4
6
8
10
Net Primary Productivity (tons / ha)
12
0
14
500
500
400
400
Species Richness (Quartiles 1-3)
Species Richness (Quartiles 1-3)
300
300
200
100
0
50
100
150
200
Mean Annual Temperature (C) x 10
250
300
300
200
100
0
0
50
100
Precipitation (mm / yr)
150
200
50
300
550
800
1050
Climate PCA
1300
1550
1800
800
600
600
Species Richness
Species Richness
800
400
200
200
0
0
0
2
4
6
8
10
Net Primary Productivity (tons / ha)
12
0
14
800
800
600
600
Species Richness
Species Richness
400
400
200
0
0
50
100
Precipitation (mm / yr)
150
200
100
150
200
Mean Annual Temperature (C) x 10
250
300
400
200
0
50
0
50
100
150
Climate PCA
200
250
Correlates of Total Species
Richness Mirror Patterns for
Widespread Species (4th Quartile)
500
400
Species Richness (Quartile IV)
Species Richness (Quartile IV)
500
300
200
300
100
100
0
-100
0
2
4
6
8
10
Net Primary Productivity (tons / ha)
12
14
0
100
150
200
Mean Annual Temperature (C) x 10
250
300
500
500
400
Species Richness (Quartile IV)
Species Richness (Quartile IV)
50
300
200
300
100
100
0
-100
0
50
100
Precipitation (mm / yr)
150
200
0
50
100
150
Climate PCA
200
250
Topographic Relief
• Maximum
elevational range
within a grid cell
• Adjusted for
snowline at
latitude
• Correlated with
habitat diversity
• Barriers to
dispersal and
promoters of
speciation
Contrasting Correlates for
Restricted vs. Widespread Species
PREDICTOR VARIABLE
1st -3rd
quartiles
4th
quartile
Total
Richness
Topographic surface area (km²)
0.00
0.24
0.21
Net primary productivity (tons/yr)
0.01
0.82
0.67
Precipitation (mm/yr)
0.04
0.57
0.53
Temperature (mean annual, Cº)
0.00
0.69
0.48
Topographic relief (elevational
range)
0.33
0.14
0.00
Ecosystem diversity (# ecosystem
types)
0.25
0.00
0.07
All variables
0.58
0.85
0.79
250
200
80
Species Richness Quartile II)
Species Richness (Quartile I)
100
60
40
100
20
50
0
0
0
1000
2000
3000
Elevational Range (m)
4000
5000
6000
0
1000
2000
3000
Elevational Range (m)
4000
5000
6000
0
1000
2000
3000
Elevational Range (m)
4000
5000
6000
500
Species Richness (Quartile IV)
200
Species Richness (Quartile III)
150
150
100
50
400
300
200
100
0
0
0
1000
2000
3000
Elevational Range (m)
4000
5000
6000
Species Richness (Total)
800
600
400
200
0
0
1000
2000
3000
Elevational Range (m)
4000
5000
6000
Major Hypotheses
• Historical factors
• Contemporary Climate
• Mid-domain Effect
One-dimensional geographic domain
One-dimensional geographic domain
Species geographic ranges randomly
placed line segments within domain
One-dimensional geographic domain
Species geographic ranges randomly
placed line segments within domain
Peak of species richness in geographic
center of domain
Species
Number
One-dimensional geographic domain
Species geographic ranges randomly
placed line segments within domain
Peak of species richness in geographic
center of domain
domain
geographic range
domain
Pancakus spp.
die Pfankuchen
Guilde
Reduced species richness
at margins of the domain
Mid-domain
peak of species richness
in the center of the domain
© Matt Fitzpatrick, UT
2-dimensional MDE Model
• Random point of origination
within continent (speciation)
• Random spread of geographic
range into contiguous
unoccupied cells
• Spreading dye model
predicts peak richness in
center of continent (r2 = 0.17)
Realistic Hybrid “Range Cohesion
Model”
• Environmental variation is important (as in
CC models)
• Species geographic ranges are cohesive
(as in MDE models)
Conceptual Weakness of
Curve-Fitting Paradigm
Potential Predictor
Variables
(tonnes/ha, C°)
Predicted
Species Richness
(S / grid cell)
Observed
Species Richness
(S / grid cell)
Conceptual Weakness of
Curve-Fitting Paradigm
Potential Predictor
Variables
(tonnes/ha, C°)
minimize
residuals
Predicted
Species Richness
(S / grid cell)
Observed
Species Richness
(S / grid cell)
Conceptual Weakness of
Curve-Fitting Paradigm
Potential Predictor
Variables
(tonnes/ha, C°)
??
MECHANISM
??
minimize
residuals
Predicted
Species Richness
(S / grid cell)
Observed
Species Richness
(S / grid cell)
Alternative Strategy:
Mechanistic Simulation Models
Potential Predictor
Variables
(tonnes/ha, C°)
Predicted
Species Richness
(S / grid cell)
Explicit
Simulation
Model
Observed
Species Richness
(S / grid cell)
Range cohesion improves model fit
for all analyses
PREDICTOR VARIABLE
Environmental
Variable
Environmental
Variable +
Range
Cohesion
Topographic surface area (km²)
0.20
0.42
Net primary productivity (tons/yr)
0.79
0.83
Precipitation (mm/yr)
0.67
0.80
Temperature (mean annual, Cº)
0.67
0.74
Topographic relief (elevational range)
0.17
0.20
Ecosystem
types)
0.00
0.12
0.84
0.86
All variables
diversity
(#
ecosystem
Contemporary climate
+ range cohesion
Elevational range +
historical factors
Contemporary climate
+ range cohesion
Conclusions
• For most species of South American birds,
contemporary climate is uncorrelated with
species richness
• Elevational range and habitat diversity are
weakly correlated with species richness for all
groups
• Results implicate importance of historical
evolutionary forces in shaping species richness
• Hybrid models that include geographic range
cohesion improve fit
Future Research
• Phylogenetic constraints
• Mechanisms of speciation
• Analysis of isolated biomes
Community Comparisons
Species
Bog
Forest
Amblyopone
pallipes
-
1
Dolichoderus
pustulatus
1
-
Lasius alienus
1
1
Lasius umbratus
-
4
Camponotus
herculaneus
-
3
82
-
Myrmica
detritinodis
-
1
Stenamma diecki
-
1
Aphaenogaster
rudis complex
-
1
SPECIES RICHNESS
3
7
Myrmica lobifrons
1) Species Composition
2) Relative Abundance
3) Species Richness
• Invertebrate food web
associated with Sarrracenia
• Small scale
• Experimental
• Patterns of relative abundance
• CONCLUSION: Relative
abundance is best explained
by food web models
• Invertebrate food web
associated with Sarrracenia
• Small scale
• Experimental
• Patterns of relative abundance
• CONCLUSION: Relative
abundance is best explained
by food web models
•
•
•
•
Avifauna of South America
Large scale
Correlative
Patterns of species
richness
• CONCLUSION: Species
richness patterns reflect
historical forces, not
contemporary climate
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