Assessment of habitat
specialization of Southeastern
trees using large-extent
co-occurrence data
David B. Vandermast, Jason D. Fridley,
Dane Kuppinger, and Robert K. Peet
University of North Carolina at Chapel Hill
How do we characterize habitat
generalists and specialists?
• In ecological literature species tendencies are
commonly characterized by range of habitat use,
particularly with respect to gradients
habitat
specialist
Environmental gradient
habitat
generalist
Habitat generalism vs. specialization
without reference to gradients?
• A species can be a generalist along one gradient
but specialist along another
• In theory, impossible or not feasible to measure
all relevant gradients
• Why not let patterns of species co-occurrence
reveal habitat generalists and specialists?
New approach: Use large-extent species
co-occurrence data as a “biological
assay” of habitat specialization
• Specialists occur in few habitats. Therefore:
– Compositional turnover within plots containing a
specialist should be low.
• Generalists occur in many habitats. Therefore:
– Compositional turnover within plots containing a
generalist should be high.
• How to quantify species-centered compositional
turnover?
How to calculate speciescentered turnover
• Whittaker’s additive partition of
diversity: β is species turnover among
plots
 = -()
where
 = the cumulative # species among plots,
and
() = mean plot species richness
What the Species β metric does
Take all plots of a focal species:
• Generalists should occur with more species over their
range.
– All else equal, generalists should have higher gamma and
beta values.
• BUT there are two ways this could happen NOT
associated with turnover among plots:
– 1. If a species occurs in a particularly species-rich habitat (a
high associated alpha diversity). Removed by partition.
– 2. If a species is well sampled relative to its overall
abundance in the region (a high plot frequency). Removed
by selecting a constant number of plots (50) for each
species, taking the mean of 1000 replicates.
Large extent co-occurrence data of
the Carolinas
• Carolina Vegetation Survey (CVS)
database of plots throughout North and
South Carolina, and Georgia
• Search limited to trees > 10 cm
• 2500 plots containing 112 tree species
• Use of large woody flora
– trees used as habitat indicators
– fewer species and co-occurrences and more
life history data than herbs
CVS Plot locations
Results were consistent with
predictions:
• Common wide-ranging understory
trees (American holly, ironwood)
have among the highest β values
• Species restricted to few habitat
types (longleaf pine, pond cypress)
have among the lowest β values
5 highest and 5 lowest β values

s.d.

# co-occ
# plots
Ilex opaca
108.42
5.32
14.55
162
368
Ulmus rubra
102.00
4.07
15.53
141
80
Prunus serotina
101.41
5.79
15.04
149
282
Morus rubra
100.46
3.51
15.55
135
96
Ostrya virginiana
99.51
5.00
15.96
132
117
Pinus palustris
38.39
4.54
4.74
78
528
Nyssa aquatica
34.47
1.64
8.63
44
57
Quercus laevis
34.26
3.23
5.67
55
183
Abies fraseri
30.89
0.45
8.92
39
51
Taxodium ascendens
25.41
3.48
3.24
39
136
Species
Meaning of species-centered β
Species
Ilex opaca

s.d.

# co-occ
# plots
108.42
5.32
14.55
162
368
In a random sample of 50 plots containing American
holly, it co-occurs with (on average) 108 species,
after subtracting for mean plot richness (14 species).
Meaning of species-centered β
Species
Ilex opaca

s.d.

# co-occ
# plots
108.42
5.32
14.55
162
368
In a random sample of 50 plots containing American
holly, it co-occurs with (on average) 108 species,
after subtracting for mean plot richness (14 species).
Compare specialist like pond cypress...
Species
Taxodium ascendens

25.41
s.d.

# co-occ
# plots
3.48
3.24
39
136
β is correlated with plot frequency
110
Species 
90
70
50
30
10
4
5
6
7
8 9
102
2
3
4
5
6
7
log10 (Total plot frequency)
8 9
103
2
β is not correlated with μ() after a
low threshold
110
Species 
90
70
50
30
10
0.0
2.5
5.0
7.5
10.0
12.5
Mean plot richness
15.0
17.5
Is Species β robust?
• If we resample using a smaller number of
plots, or a geographic subset of the data,
will species be similarly distributed along
the generalist-specialists gradient?
Random subsets of data generate
same relative β values
Species , 2480-plot data
110
90
70
50
30
10
20
40
60
80
Species , 1000-plot data
100
Species , full dataset (2480 plots)
Regional data subsets are similar but yield
interesting exceptions
100
Liquidambar styraciflua
Acer rubrum
Diospyros virginiana
Nyssa sylvatica
80
Pinus taeda
Magnolia virginiana
60
Quercus marilandica
Pinus serotina
Quercus margarettiae
40
Quercus incana
Pinus palustris
Quercus laevis
20
20
30
40
50
60
70
Species , Coastal Plain upland forests (477 plots)
Is β correlated with species life-history
traits and environmental ranges?
• Using primary sources:
– Climate change website (Iverson and Prasad)
– USDA PLANTS database
– Radford, Ahles, and Bell 1968
• We regressed species β against many
variables describing life history traits and
environmental tolerances
Habitat generalists were
strongly associated with:
• Life history traits:
–
–
–
–
• Environmental range:
Deciduousness***
Shade-tolerance***
Short lifespans*
Bird-dispersed seed**
*p<.05
**p<.01
***p<.001
– Annual temperature***
– Potential
evapotranspiration***
– Soil pH***
– % soil organic
matter***
Summary
• Use of additive partition of species richness
and co-occurrence data from a large-extent
database appears to be a robust method
for placing species along a continuum of
habitat generalism vs. specialization
• Our data indicate certain life history traits
and environmental ranges are strongly
correlated with species generalism
Acknowledgments
• 600+ CVS participants since 1988
• CVS supported by NSF
• UNC Plant Ecology Lab
• Peter White, UNC
• Tom Wentworth, NCSU