Ecography 33: 678687, 2010
doi: 10.1111/j.1600-0587.2009.05958.x
# 2010 The Author. Journal compilation # 2010 Ecography
Subject Editor: Helmut Hillebrand. Accepted 14 September 2009
Patterns of functional and taxonomic organization of stream fishes:
inferences based on a, b, and g diversities
Christopher L. Higgins
C. L. Higgins ([email protected]), Dept of Biological Sciences, Tarleton State Univ., Stephenville, TX 76402, USA.
The primary objective of this study was to determine whether total biodiversity (g) is partitioned into within-community
(a) and among-community (b) components differently for taxonomic and functional organization. I hypothesized that a
diversity will contribute more to the functional organization of g diversity and that b diversity will contribute more to the
taxonomic organization of g diversity. A secondary objective was to determine whether the relationship between
taxonomic and functional diversity is scale dependent. Species abundance data was obtained from fisheries surveys
conducted by the Texas Parks and Wildlife Dept that focused on least disturbed streams from 11 different ecoregions of
Texas, including 62 localities from 18 drainages. Functional and taxonomic organization of assemblages was quantified
with two different measures of biodiversity, including richness and the numbers equivalent of Shannon diversity. Scaledependent effects on these indices were assessed by multiplicatively partitioning g into a and b components. The
contribution of a and b components to g diversity differed between functional and taxonomic organization and among
different measures of biodiversity. Among-community components were more influential in structuring the taxonomic
organization of stream-fish assemblages, whereas within-community components were more important in structuring the
functional organization of assemblages. The relationship between taxonomic and functional diversity differed between a
and b components and between spatial scales. Indeed, ecological patterns not only change with spatial scale, but how they
change is dependent on which aspect of biodiversity is considered.
Ecologists increasingly recognize that local communities
(i.e. communities sampled at a small spatial extent) are
structured by a combination of historic, regional, and local
factors operating at different spatial scales (Ricklefs 1987,
Poff 1997, Whittaker et al. 2001, Stevens and Willig 2002,
Hoeinghaus et al. 2007). At larger spatial scales, historic
(e.g. speciation and dispersal) and regional (e.g. geology and
climate) factors determine the composition of available
species from which local communities are assembled. Local
factors, whether they are biotic interactions or abiotic
constraints, often dictate which of the species from the
regional pool will occupy a particular community and in
what abundance. Of course, regional factors can interact
with local factors altering the degree to which biotic or
abiotic determinants influence local community structure
and producing different patterns of biodiversity at different
spatial scales (Huston 1999). This understanding has led
ecologists to incorporate multiple spatial scales in their
assessment of patterns and processes associated with
biodiversity.
Patterns of biodiversity at larger spatial scales (g) are
a combination of within-community (a) and amongcommunity (b) components (Whittaker 1960, Lande
1996, Crist et al. 2003, Jost 2007). In certain systems,
patterns of g diversity are primarily a result of a diversity in
678
which case there is little turnover in composition or
abundance among communities (Gering and Crist 2002,
Cornell et al. 2007, Pegg and Taylor 2007). In other
systems, patterns of g diversity are primarily driven by b
diversity (Stendera and Johnson 2005, Eros 2007, Belmaker
et al. 2008), especially at larger spatial scales that incorporate more environmental heterogeneity (Freestone and
Inouye 2006). Of course, how individuals respond to
environmental differences among locations will influence
the degree to which a and b contribute to g diversity. In
this regard, different aspects of biodiversity (e.g. taxonomic
and functional) may respond differently to existing environmental variation, altering the degree to which we ascribe
local or regional determinants of biodiversity; however,
most studies that assess the hierarchical nature of biodiversity by partitioning g into a and b components only do
so from a taxonomic perspective, ignoring the functional
organization of biodiversity.
The functional organization of biodiversity is becoming
an increasingly important topic. First, functional organization characterizes different aspects of community structure
than taxonomic organization (Hoeinghaus et al. 2007,
Higgins and Strauss 2008). Second, functional characteristics allow comparisons to be made among broad geographic regions where communities comprise different taxa
(Simberloff and Dayan 1991). For example, Lamouroux
et al. (2002) observed intercontinental convergence in
functional traits related to hydraulics and geomorphology
despite phylogenetic and historical differences in fish
assemblages. Third, functional characteristics provide a
means of testing theoretical expectations of changes in
species traits along environmental gradients, such as those
generated from habitat templates (Southwood 1977), the
river continuum concept (Vannote et al. 1980), and
landscape filters (Poff 1997). For example, habitat templates predict changes in reproductive traits with habitat
stability, such as increasing parental care with decreased
variability (Townsend and Hildrew 1994). Of course, the
specific functional characteristics analyzed in a study will
depend on the hypotheses being tested.
The primary objective of this study was to determine
whether total biodiversity (g) is partitioned into a and b
components differently for taxonomic and functional
organization. I hypothesized that a diversity would
contribute more to the functional organization of g
diversity and that b diversity would contribute more to
the taxonomic organization of g diversity. This hypothesis
stems from recent findings that functional groups divide
niche space more evenly than do species, especially at
larger spatial scales; this suggest that certain functional
groups may always be present in an assemblage, regardless
of geographic location of that assemblage, implying low
turnover (i.e. b diversity) in functional groups among
locations (Higgins and Strauss 2008). A subset of this
objective was to ascertain which environmental variables
help drive patterns of a and b diversity. A secondary
objective was to determine whether the relationship
between taxonomic and functional diversity is scale
dependent.
Methods
Data
I obtained data from fisheries surveys conducted by the
Texas Parks and Wildlife Dept (Linam et al. 2002). The
surveys focused on least disturbed streams of 11 different
ecoregions of Texas, including 62 localities from 18
drainages. Biologists from Texas Parks and Wildlife
sampled fish assemblages using seines (100% of sites),
backpack electrofishing (81% of sites), and boat electrofishing (8% of sites) with gear choice depending on
environmental characteristics such as flow regimes, water
chemistry, and habitat availability. The mean number of
seine (4.6 by 1.8 m, 4.8 mm mesh) hauls at sites where
electrofishing also occurred was 7.4 with a mean stream
reach of 61 m and the mean duration of shocking was 13
min. At sites where only seines were used to collect fishes,
the mean number of seine hauls was 8.0 with a mean stream
reach of 74 m. Although the desired number of seine hauls
was 6 and total shocking time was 15 min, they sampled
each locality until species accumulation peaked and sampled
all habitats within a locality in relative proportion to their
abundance. Although this sampling scheme may underestimate within-community (a) components and overestimate among-community (b) components of diversity,
it is the standard collection protocol described by the Texas
Natural Resource Conservation Commission (TNRCC
2007). They sampled streams during the summer months
(June through September) from 1988 through 1990.
Easily identified fish were enumerated and released
in the field, and the remaining fishes were preserved in
10% formalin and transported to the lab where positive
identification took place. Data also included a description of the sampling site, latitude and longitude, size
of drainage basin (km2), stream discharge (m3 s1), and
abundance of fish species at each site.
Formation of functional groups
I characterized the functional organization of stream-fish
assemblages with the same classification scheme used by
Hoeinghaus et al. (2007) in their assessment of local versus
regional determinants of taxonomic and functional organization of stream-fish assemblages. This scheme characterizes different aspects of assemblage structure than
taxonomic organization by distinguishing among groups
of fishes with similar ecological niches and population
dynamics (Hoeinghaus et al. 2007). In addition, lifehistory strategies are strongly associated with hydrodynamic forces (Blanck et al. 2007), which should vary
with geographic location. The specific functional groups
used in this study were piscivore-equilibrium (p-e),
piscivore-equilibrium/periodic (p-ep), piscivore-periodic
(p-p), piscivore-opportunistic/equilibrium (p-oe), omnivore-opportunistic (o-o), omnivore-opportunistic/periodic
(o-op), omnivore-periodic (o-p), omnivore-equilibrium/
periodic (o-ep), omnivore-equilibrium (o-e), invertivoreequilibrium (i-e), invertivore-opportunistic/equilibrium
(i-oe), invertivore-opportunistic (i-o), invertivore-opportunistic/periodic (i-op), invertivore-periodic (i-p), herbivoreperiodic (h-p). Following Hoeinghaus et al. (2007), all
individuals of each species were classified into only one
functional group. In addition, regional factors often promote
endemism, which is primarily a result of dispersal limitation,
speciation, and abiotic differences among locations. For the
remaining species, I used the trophic classification provided
in Linam and Kleinsasser (1998), and information provided
in Winemiller (1992) and Winemiller and Rose (1992) for
life-history strategies. One additional functional group
emerged that was not included in Hoeinghaus (2007). It
consisted of the fathead minnow Pimephales promelas, which
was classified as an omnivore with an opportunistic/periodic
life history.
Quantification of biodiversity
For each locality, I estimated two ecological indices of
assemblage structure, including richness (R) and Shannon
diversity (D) because they characterize different aspects of
community structure. Before estimating richness and
diversity, I converted each absolute abundance distribution
into a relative abundance distribution. I estimated richness
using the following equation: R axi1 p0i, where x is the
number of species within a site and p is the relative
abundance of that species. I estimated Shannon diversity
using the following equation: D exp [axi1 pi ln(pi )],
679
where x is the number of species within a site and p is the
relative abundance of that species. Both species richness and
species diversity are expressed as their own numbers
equivalent, which should estimate the ‘‘true’’ diversity of
an assemblage (Jost 2006). All calculations were conducted
using MATLAB v 6.0 software (Mathworks 1997). Because
multiple measures and different aspects of biodiversity were
incorporated into this study, I will use species richness (SR)
and species diversity (SD) as measures of taxonomic
organization of biodiversity. I will use functional richness
(FR) and functional diversity (FD) to characterize various
aspects of the functional organization of biodiversity.
Partitioning of biodiversity
Following Whittaker (1960) and Jost (2006, 2007),
I assessed scale-dependent effects of biodiversity by partitioning total biodiversity (g) into within (a) and among (b)
components using a multiplicative decomposition such that
g ab. I utilized three different spatial scales in this
study. The smallest spatial scale consisted of individual
assemblages at the locality or site level. The intermediate
spatial scale consisted of pooled assemblages within a river
basin. The largest spatial scale consisted of a single, pooled
assemblage across the entire sampling region. This design
allowed g diversity to be decomposed into within sites
(asites), among sites (bsites), within river basins (abasins),
and among river basins (bbasins) components. I estimated
SRa-sites by calculating the unweighted mean (i.e. each site
contributes equally to asites) number of species collected
1
across all 62 sites; SRa-sites /axi1 S, where x is the number
x
of sites and S is species richness for a given site.
I estimated SDa-sites by taking the exponent of the
unweighted mean species diversity across all 62 sites;
1
SDa-sites exp (axi1 H), where x is the number of sites
x
and H is the traditional estimate of Shannon diversity for a
given site. To estimate abasins, I first estimated the mean
relative abundance of each species across all sites within a
river basin to create a basin-wide relative abundance
distribution then calculated the unweighted average across
all 18 basins. I chose unweighted averages, as opposed to
weighted averages, because only then can beta diversity be
interpreted as a measure of inter-sample compositional
differentiation (Jost 2007). To estimate g, I estimated
the mean relative abundance distribution for the entire
sampling region then estimated each measure of biodiversity using standard procedures described above. I estimated
bsites by dividing abasins by asites and estimated bbasins by
dividing g by abasins. All of these calculations were
consistent with methodology provided in Jost (2006,
2007). All calculations were conducted using MATLAB
v 6.0 software (Mathworks 1997).
To determine whether observed patterns differed from
those resulting from random processes, I conducted three
different sets of simulations, one for each spatial scale
included in this study. The first set of simulations focused
on the site extent. For each of the 62 sites, I randomly
sampled individuals, with replacement, from the empirical
680
abundance distribution for that site, constraining the
number of individuals at each site to equal observed values.
This null model assumes individuals within a site are not
capable of colonizing other sites within the river basin (i.e.
local factors are primarily responsible for structuring local
communities). The second set of simulations focused on the
river basin extent. For each of the 62 sites, I randomly
sampled individuals, with replacement, from the pooled
abundance distribution that contained all individuals from
each site within a particular basin, constraining the number
of individuals at each site to equal observed values. This null
model assumes individuals within a river basin are capable
of colonizing any site within that basin, which combines
local and regional determinants of community structure.
The third set of simulations focused on the entire sampling
region. For each of the 62 localities, I randomly sampled
individuals, with replacement, from a pooled abundance
distribution that contained all individuals from each species
regardless of which river basin it was observed in. This null
model assumes individuals are capable of colonizing any site
within the entire sampling area regardless if they are
connected by waterways. This particular null model
corresponds to the first null hypothesis in Crist et al.
(2003). For each of these null models, I conducted 1000
iterations to generate 95% confidence intervals around the
individual point estimates, which were based on the
observed data matrix and did not vary among null models.
All simulations were conducted using MATLAB v 6.0
software (Mathworks 1997).
Relationship between taxonomic and functional
diversity
I conducted a series of linear regressions to assess the
relationship between taxonomic and functional organization of stream-fish assemblages at two different spatial
scales. The first series of analyses were conducted at the site
sampling extent and required regressing a and b components of functional organization, both richness and Shannon diversity, onto a and b components of taxonomic
organization. The second series of analyses were conducted
at the basin sampling extent. For each analysis, I obtained
95% confidence intervals around the point estimates for the
slope and y-intercept to determine whether the equations
for the best-fit lines differed between the two spatial scales
(i.e. scale dependent). All regression analysis were conducted using SPSS v16 software (SPSS 2007).
Results
General patterns of biodiversity
A total of 30 492 individuals representing 97 species were
collected across the entire sampling region. The three most
abundant species were western mosquitofish (Gambusia
affinis, 27.3% of all individuals), red shiner (Cyprinella
lutrensis, 20.0% of all individuals), and blacktail shiner
(Cyprinella venusta, 10.5% of all individuals). In addition to
being abundant, these species were some of the most widely
distributed as well. In fact, the only other species with as
broad a distribution were the longear sunfish Lepomis
megalotis, green sunfish Lepomis cyanellus, bluegill sunfish
Lepomis macrochirus, and largemouth bass Micropterus
salmoides. All of these species were native to Texas. In
fact, only five species were non-native based on Hubbs et al.
(1991), which suggest patterns of taxonomic diversity were
largely driven by native species. The 97 species comprised
15 functional groups. The most abundant functional group
was the invertivores with the opportunistic/periodic lifehistory (43.2%), which consisted mostly of shiners (Cyprinella and Notropis). This functional group was followed by
invertivores with an opportunistic life history (32.5%),
which consisted entirely of western mosquitofish, and
invertivores with an equilibrium life history (6.8%), which
primarily consisted of bluegill and longear sunfish.
Partitioning of biodiversity
The average number of species collected within a site (asites)
was 13.66, regardless of geographic location; average species
diversity within a site was 4.55 (Table 1). Both of these
estimates were significantly higher than expected by chance
based on the site null model, but were significantly lower
than expected based on the basin and state null models.
Relative to g richness and g diversity, asites components
contributed to 14 and 22% of the overall taxonomic
organization, respectively. Species richness and species
diversity at the asites level were significantly correlated
with elevation and longitude but were not significantly
correlated with size of the drainage area, stream discharge,
or latitude (Table 2). In other words, taxonomic diversity
increased in eastern portions of the sampling region and at
lower elevations. Turnover in species richness among sites
(SRb-sites) was also correlated with elevation and longitude,
and turnover in species diversity among sites (SDb-sites) was
correlated with elevation, longitude, and stream discharge
(Table 3). The average number of functional groups
collected within a site (asites) was 7.73, regardless of
geographic location; average functional diversity within a
site was 3.10 (Table 1). Relative to g richness and g
diversity, asites components contributed to 52 and 57% of
the overall functional organization, respectively. Functional
richness at the asites level was significantly correlated
with elevation and longitude; functional diversity was
significantly correlated with drainage area and longitude
(Table 2). Turnover in functional richness among sites
(FSb-sites) was correlated with elevation and longitude,
whereas turnover in species diversity among sites (FHb-sites)
was correlated with elevation, longitude, and stream discharge (Table 3). Hence, taxonomic and functional organizations were similarly influenced by environmental factors.
The average number of species collected within a river
basin (abasins) was 23.00, which was significantly higher
than expected by chance based on the site null model and
significantly lower than expected based on the basin and
state null models. Average species diversity within a basin
was 6.07; this component did not differ from random
expectations based on the site null model, but was
significantly lower than expected based on the basin and
state null models. Relative to g richness and g diversity,
abasins components contributed to 24 and 30% of the
overall taxonomic organization, respectively. The number
of species collected within a river basin was positively
correlated with basin area (r 0.577, p0.012) but species
diversity was not (r 0.339, p0.168). The average
number of functional groups collected within a river basin
was 9.56, and the average functional diversity was 3.30.
Relative to g richness and g diversity, abasins components
contributed to 64 and 61% of the overall functional
organization, respectively. Functional richness and functional diversity within a river basin were significantly
correlated with basin area (r 0.816, p B0.001 and r
0.624, p 0.006, respectively). The effective number of
distinct communities within the entire sampling region
(bbasins) was 4.22 based on species richness and only 1.57
based on functional richness; a similar trend was observed
with species diversity (SDb-basins 3.37) and functional
diversity (FDb-basins 1.65), although the difference was
not as large. In other words, bbasins components contributed
to 76% (SR) and 70% (SD) of the taxonomic organization
at the state level, whereas bbasins components only
accounted for 36% (FR) and 39% (FD) of the functional
organization.
Table 1. Estimates of alpha, beta, and gamma diversities for the taxonomic and functional organization of stream-fish assemblages. Lower
and upper 95% confidence intervals are provided in parentheses.
Null model
Diversity component
Taxonomic
R
Functional
D
R
D
Site level
asites
bsites
abasins
bbasins
g
13.66
1.68
23.00
4.22
97.00
(12.10, 12.58)
(1.69, 1.75)
(20.72, 21.72)
(4.30, 4.57)
(91.00, 97.00)
4.55
1.33
6.07
3.37
20.47
(4.36, 4.51)
(1.32, 1.37)
(5.81, 6.10)
(3.36, 3.50)
(20.00, 20.82)
7.73
1.24
9.56
1.57
15.00
(7.06, 7.37)
(1.23, 1.29)
(8.83, 9.33)
(1.61, 1.70)
(15.00, 15.00)
3.10
1.06
3.30
1.65
5.44
(3.01,
(1.05,
(3.20,
(1.63,
(5.34,
3.11)
1.09)
3.35)
1.69)
5.53)
Basin level
asites
bsites
abasins
bbasins
g
13.66
1.68
23.00
4.22
97.00
(27.66, 28.55)
(1.66, 1.72)
(46.89, 48.06)
(2.02, 2.07)
(97.00, 97.00)
4.55
1.33
6.07
3.37
20.47
(9.86, 10.19)
(1.63, 1.66)
(16.16, 16.82)
(1.54, 1.61)
(25.53, 26.50)
7.73
1.24
9.56
1.57
15.00
(10.05, 10.48)
(1.12, 1.16)
(11.50, 11.89)
(1.26, 1.30)
(15.00, 15.00)
3.10
1.06
3.30
1.65
5.44
(4.37,
(1.30,
(5.74,
(1.07,
(6.23,
4.50)
1.33)
5.91)
1.10)
6.43)
State level
asites
bsites
abasins
bbasins
g
13.66
1.68
23.00
4.22
97.00
(39.68, 41.24)
(1.29, 1.35)
(52.17, 54.94)
(1.69, 1.82)
(91.00, 97.00)
4.55
1.33
6.07
3.37
20.47
(12.87, 13.44)
(1.02, 1.06)
(13.32, 14.14)
(1.05, 1.09)
(14.41, 15.08)
7.73
1.24
9.56
1.57
15.00
(11.90, 12.40)
(1.05, 1.11)
(12.72, 13.50)
(1.11, 1.18)
(15.00, 15.00)
3.10
1.06
3.30
1.65
5.44
(4.56,
(1.00,
(4.57,
(1.00,
(4.67,
4.72)
1.03)
4.78)
1.03)
4.83)
681
Table 2. Correlation coefficients (r) and associated significance values (p) between pairwise combinations of environmental variables and
estimates of biodiversity at the asites level.
Environmental variable
Discharge
Drainage area
Elevation
Latitude
Longitude
Species richness
Species diversity
Functional richness
r
p
r
p
r
0.232
0.082
0.585
0.154
0.666
0.077
0.528
B0.001
0.231
B0.001
0.161
0.126
0.364
0.077
0.524
0.222
0.330
0.004
0.551
B0.001
0.017
0.036
0.537
0.219
0.479
Relationships between taxonomic and functional
diversity
Within-community (a) and among-community (b) components of functional organization were significantly
correlated with a components of taxonomic organization,
whether at the site or basin levels (Fig. 1); a components
of functional organization increased and b components
decreased as species richness and species diversity
increased. In fact, the same regression line could be used
to describe the relationships between FRa-sites and SRa-sites,
and FRa-basins and SRa-basins and between FDa-sites and
SDa-sites, and FDa-basins and SDa-basins (Table 4). This
suggests that the relationship between functional organization and taxonomic organization was not scale dependent,
at least for the within-community components. Amongcommunity components, however, were scale dependent
because the regression lines were not the same.
Within-community (a) components of functional
organization were significantly and negatively correlated
with b components of taxonomic organization at the basin
level, but not the site level (Fig. 2). Among-community
(b) components of functional organization were significantly and positively correlated with b components of
taxonomic organization at both the site and basin levels; the
same regression line could not be used for different measure
of biodiversity or at different spatial extents (Table 4).
Scale dependence was most definitely a factor in the
relationship between functional organization and turnover
in taxonomic organization.
Discussion
Partitioning of biodiversity
Taxonomic organization
Taxonomic organization at the largest spatial scale was
primarily driven by turnover among river basins. Several
factors potentially contributed to observed patterns, includ-
Functional diversity
p
0.897
0.779
B0.001
0.087
B0.001
r
p
0.094
0.278
0.215
0.078
0.292
0.480
0.029
0.093
0.545
0.021
ing the sampling scheme utilized by the Texas Parks and
Wildlife, the relative influence of local and regional factors
in structuring fish assemblages, differences in area among
the river basins, and the degree of endemic fishes within a
river basin.
The sampling scheme employed by the Texas Parks and
Wildlife likely underestimated within-community components of richness, especially at the site sample extent. For
example, Bonner et al. (2005) assess assemblage structure at
Independence Creek in Texas and collected 23 species
across seven different sites along the river. This estimate
differed dramatically from data reported by Texas Parks and
Wildlife, which estimated species richness to be 12 (based
on a single locality) in this river. If this discrepancy was
consistent across the entire sampling region, each estimate
of a diversity within a site would be equally affected. In this
case, estimates of biodiversity at the river basin extent and
state extent would also be underestimated. In fact, the total
number of species collected across the entire sampling
region was 97, which was much lower than the 161 species
included in the recent field guide on freshwater fishes of
Texas (Thomas et al. 2007). If you compare the relative
nature of these estimates, however, little differences emerge
(23/16114%, and 12/97 12%). Therefore, I doubt the
sampling scheme is responsible for observed differences in
spatial configuration between taxonomic and functional
organization.
A more likely explanation is that regional factors
contributed more to the taxonomic organization of
stream-fish assemblages than local determinants. Previous
studies examining the influence of local and regional factors
structuring taxonomic organization of stream-fish assemblages (Angermeier and Winston 1998, Hoeinghaus et al.
2007) also suggested regional factors were more important
than local determinants. Angermeier and Winston (1998)
examined local and regional influences on taxonomic
organization of stream-fish assemblages in Virginia, and
Hoeinghaus et al. (2007) examined them in Texas. Both
studies suggested, among other things, that regional factors
Table 3. Correlation coefficients (r) and associated significance values (p) between pairwise combinations of environmental variables and
estimates of biodiversity at the bsites level.
Environmental variable
Discharge
Drainage area
Elevation
Latitude
Longitude
682
Species richness
Species diversity
Functional richness
Functional diversity
r
p
r
p
r
p
r
p
0.035
0.196
0.370
0.008
0.431
0.788
0.126
0.003
0.948
B0.001
0.275
0.168
0.358
0.123
0.433
0.031
0.193
0.004
0.342
B0.001
0.206
0.083
0.526
0.043
0.590
0.109
0.519
B0.001
0.737
B0.001
0.489
0.248
0.307
0.107
0.375
B0.001
0.052
0.015
0.409
0.003
7
Functional diversity (site level)
Functional richness (site level)
12
10
8
6
4
2
0
5
4
3
2
1
0
5
10
15
20
Species richness (
25
0
2
4
6
8
10
Species diversity (
sites)
12
14
16
18
sites)
6
Functional diversity (basin level)
16
Functional richness (basin level)
6
14
12
10
8
6
4
2
5
4
3
2
1
0
0
10
20
30
Species richness (
0
40
5
10
Species diversity (
basins)
15
20
basins)
Figure 1. Relationships between within-community components of taxonomic organization and functional organization at two different
spatial scales for measures of richness and diversity. Closed circles and solid lines represent average within-community (a) components,
and open circles and dashed lines represent average among-community (b) components.
has been shown to affect the degree to which a and b affect
g diversities (Freestone and Inouye 2006). In addition,
regional factors often result in different river basins
containing endemic species not found in other geographic
areas, which is primarily a result of dispersal limitation,
speciation, and abiotic differences among locations. Many
of the rare species included in this study, which largely
consist of minnows and darters, are restricted in their
geographic distribution (Thomas et al. 2007). In any case,
(e.g. temperature extremes, stream order, and precipitation
gradients) were better predictors of local taxonomic
organization than were local factors. Of course, the
observed patterns could have resulted from differences in
the geographic area encompassed by the different river
basins, which in my study ranged from 977 to 49411 square
miles. Larger river basins typically contain more environmental heterogeneity than smaller river basins, which leads
to increased species richness; environmental heterogeneity
Table 4. Results from regression analysis for relationships between taxonomic and functional organization of stream-fish assemblages at
multiple spatial scales.
Variables
Regression results
Dependent
R2
F
p
SRa-sites
SRa-sites
SRa-basins
SRa-basins
FRa-sites
FRb-sites
FRa-basins
FRb-basins
0.431
0.322
0.715
0.681
45.38
28.55
40.12
34.17
B0.001
B0.001
B0.001
B0.001
5.258
2.103
5.951
2.230
(4.475,
(1.871,
(4.665,
(1.943,
6.042)
2.336)
7.298)
2.466)
0.181
0.042
0.157
0.025
(0.127, 0.234)
(0.058, 0.022)
(0.104, 0.209)
(0.045, 0.016)
SDa-sites
SDa-sites
SDa-basins
SDa-basins
FDa-sites
FDb-sites
FDa-basins
FDb-basins
0.523
0.128
0.842
0.735
65.91
8.84
85.45
44.41
B0.001
0.004
B0.001
B0.001
1.960
1.781
1.792
2.617
(1.588,
(1.512,
(1.306,
(2.291,
2.333)
2.051)
2.279)
2.943)
0.249
0.066
0.221
0.107
(0.187, 0.310)
(0.110, 0.022)
(0.170, 0.271)
(0.140, 0.073)
SRb-sites
SRb-sites
SRb-basins
SRb-basins
FRa-sites
FRb-sites
FRa-basins
FRb-basins
0.056
0.676
0.642
0.737
3.55
125.46
28.73
44.91
0.064
B0.001
B0.001
B0.001
8.431
0.855
12.888
1.051
(7.601, 9.262)
(0.723, 0.987)
(11.409, 14.307)
(0.840, 1.202)
0.281
0.266
0.604
0.108
(0.580, 0.017)
(0.219, 0.314)
(0.843, 0.365)
(0.074, 0.142)
SDb-sites
SDb-sites
SDb-basins
SDb-basins
FDa-sites
FDb-sites
FDa-basins
FDb-basins
0.059
0.706
0.747
0.866
3.75
144.42
47.34
103.51
0.057
B0.001
B0.001
B0.001
3.715
0.610
5.115
0.856
(3.168,
(0.452,
(4.518,
(0.672,
0.209
0.357
0.354
0.147
(0.424, 0.007)
(0.323, 0.452)
(0.463, 0.245)
(0.156, 0.238)
Independent
B0
B1
4.243)
0.768)
5.712)
1.121)
683
6
Functional diversity (site level)
Functional richness (site level)
12
10
8
6
4
2
0
4
3
2
1
0
0
2
4
Species richness (
6
0
2
sites)
4
6
Species diversity (
16
8
sites)
6
Functional diversity (basin level)
Functional richness (basin level)
5
14
12
10
8
6
4
2
0
5
4
3
2
1
0
0
2
4
6
8
Species richness (
10
12
14
basins)
0
2
4
6
8
Species diversity (
10
12
14
basins)
Figure 2. Relationships between among-community components of taxonomic organization and functional organization at two different
spatial scales for measures of richness and diversity. Closed circles and solid lines represent average within-community (a) components,
and open circles and dashed lines represent average among-community (b) components.
regional factors have been shown to be very important to
the overall structure of stream-fish assemblages, especially at
larger spatial scales.
Recently, Pegg and Taylor (2007) used additive partitioning to examine spatial configuration of the taxonomic
organization of stream-fish assemblages in the Missouri and
Illinois rivers of central United States. Unlike my study,
however, they concluded that a components contributed
more to g diversity than b components. The discrepancies
between the two studies were most likely an issue of spatial
scale, although differences between additive and multiplicative partitioning may also account for part of the
discrepancy because additive partitioning underestimates
the degree of community differentiation. Pegg and Taylor
(2007) considered the largest spatial scale to encompass a
single river basin; whereas I considered the largest spatial
scale to encompass multiple river basins. If I would have
considered a river basin to be the largest spatial scale, then
a components would have contributed more to g diversity
than b components. Together, both studies indicate that
average within-site components greatly contribute to the
patterns observed within a river basin, but my results extend
this pattern to include among-river basin components as
well. These differences emphasize the importance of
defining spatial extents of a study, especially when examining the hierarchical nature of patterns of biodiversity (Crist
et al. 2003, Cornell et al. 2007).
Functional organization
Functional organization at the largest spatial scale was
primarily a result of within-community components in
684
which there was virtually no turnover among sites within a
river basin and relatively little turnover among river basins.
The lack of among-site and among-basin contributions
to functional organization appears to be at odds with
ecological theory, which suggests that functional attributes
of species should change with differences in environmental
conditions that exists along gradients from headwaters to
downstream reaches (Vannote et al. 1980). However, a
close examination of the sampling scheme employed by the
Texas Parks and Wildlife Dept revealed that 14 of the 18
river basins were represented by four or fewer sampling
localities, which may not have adequately spanned an
ecologically meaningful environmental gradient. If these
data were removed and the analyses repeated, among-site
components (bsites) would have contributed to 34% of
overall functional richness (FRg) and 34% of functional
diversity (FDg). These proportions would be, in fact,
consistent with ecological theory. Despite the increased
contribution of bsites components after removing 36 of the
original 62 sites, the main conclusions of this study
remained unchanged; the largest component of taxonomic
organization resulted from differences among river basins
(SRb-basins 49% of SRg) or differences among sites within
a river basin (SDb-sites 45% of SDg), whereas the majority
of functional organization was contained within the average
site (FRa-sites 53% of FRg and FDa-sites 57% of FDg).
In fact, not only did this pattern hold when half the data
points were removed but it held for other classification
schemes as well, such as those that focused on ecosystem
function, or when ecoregions were considered rather than
river basins (Higgins unpubl.).
Despite the relatively small contribution of b components to g diversity, certain regional factors may help
structure the functional organization of stream-fish assemblages. For example, the average among-community (bsites)
contribution to g diversity might arise from in-stream
repetition of riffle, run, pool sequences or increases in
stream size along headwater to downstream gradients (Poff
and Allan 1995, Goldstein and Meador 2004). These
factors, especially in concert with flow regimes and energy
input, are known to produce a consistent pattern of
structure along a river (Vannote et al. 1980, Schlosser
1982, Matthews 1986, Pires et al. 1999, Hoeinghaus et al.
2003). Average among-basin (bbasins) contributions might
arise from abiotic gradients in temperature and precipitation (Hoeinghaus et al. 2007) or as my study indicated the
total area incorporated by the river basin. Of course, area
itself is probably not the structuring factor; rather, larger
areas encompass greater environmental heterogeneity,
which has been shown to shape scale-dependent patterns
of biodiversity (Freestone and Inouye 2006).
Based on the large contribution of a to the functional
organization of g diversity, I would argue the majority of
structuring factors operate at the local scale. These variables
might include conductivity, dissolved oxygen, or even
substrate availability (Hoeinghaus et al. 2007) or may relate
more generally to environmental stability, productivity, and
habitat diversity within a locality (Schlosser 1987). In any
case, the resulting organization of stream-fish diversity, at
least based on functional attributes, was relatively consistent
across locations. In terms of trophic ecology invertivores
were present at 100% of the sampling localities, piscivores
were present at 98%, omnivores were present at 90%, and
herbivores were present at 34%. This consistency suggests
trophic dynamics play a large role in structuring the
functional organization of stream-fish assemblages. Each
life-history strategy was found in at least 61% of all
sampling localities (opportunistic/equilibrium), with the
most widespread life-history strategies being equilibrium
(98% of all localities) and opportunistic/periodic (98% of
all localities). Hence, not only are trophic dynamics
important in structuring fish assemblages, but each assemblage contains fishes with various life-history strategies,
regardless of geographic location.
Relationship between taxonomic and functional diversity
The relationship between taxonomic and functional diversity was dependent on whether within-community (a) or
among-community (b) components were considered and
on the spatial extent used to sample the communities.
Within-community components of functional richness
increased as the number of species within an area increased,
but the rates of increase was less than one functional group
for every five species at both the site and basin extents.
In fact, the relationships between taxonomic richness and
functional richness and between taxonomic diversity and
functional diversity were identical at the site extent and the
basin extent, indicating that these relationships were not
scale dependent. Among-community components of functional organization, however, were scale dependent. The
rate or turnover among functional groups as a function of
increased taxonomic richness and diversity within a community were greater at the site extent than at the basin
extent. This suggest that more speciose sites, which typically
occur in downstream reaches with increased environmental
heterogeneity, contain most of the functional groups
available within that region within little turnover in
functional groups among river basins. This is in part
due to the fact that most species were functionally
redundant, or at least they shared similar ecological
niches and exhibited similar population dynamics. Functional redundancy is a characteristic of species within an
ecosystem where certain species contribute in equivalent
ways to an ecosystem function (functional effect traits)
or where certain traits of a species respond similarly
to environmental changes (functional response traits) such
that one species may substitute for another (Hooper et al.
2005).
Many studies use taxonomic diversity as a surrogate for
functional diversity. From a bottom-up approach, the
concepts of niche differentiation and limiting similarity
suggest that the functional traits of species must differ at
some point. This implies that functional richness should
increase as taxonomic richness increases, especially if
functional richness broadens the range of functional traits
present within an ecosystem (Schmid et al. 2002). Although
this relationship would result in a positive correlation
between functional and taxonomic diversity (Fig. 1, 2),
taxonomic diversity should not be an adequate surrogate for
functional diversity unless there is a linear increase in niche
space coverage with increased species richness (Diaz and
Cabido 2001). Diaz and Cabido (2001) discussed only two
possible ways in which this could happen: the first involving
the random occupation of niche space, and the second
requiring the niche space to be uniformly occupied. The
stream-fish assemblages used in this study are neither
randomly nor uniformly distributed in niche space (Higgins
and Strauss 2008). Consequently, taxonomic diversity
should not be used as a surrogate for functional diversity
of stream-fish assemblages, despite correlations between the
two aspects of biodiversity.
Conclusion
The results of this study are some of the first to document
differences in the hierarchical nature of patterns of
biodiversity based on taxonomic and functional organization. In my analysis of data from Texas stream-fish
assemblages, the degree to which a and b components
contributed to g diversity was dependent on which aspect
of biodiversity was considered. Among-community components were more influential in structuring the taxonomic organization of stream-fish assemblages, whereas
within-community components were more important in
structuring the functional organization of assemblages.
Although the two aspects of biodiversity are related, they
actually characterize different aspects of assemblage structure and provide complementary insight into the overall
understanding of biodiversity. Consequently, it is imperative that future studies addressing issues regarding biodiversity make explicit use of functional groups, or at least
685
incorporate the functional attributes of individuals. In
addition, future studies should continue to incorporate
multiple spatial scales to fully understand the origin and
maintenance of biodiversity in local communities.
Acknowledgements I thank C. P. Bloch, S. J. Presley, and M. R.
Willig for valuable insight into scale-dependent patterns of
biodiversity. I thank G. R. Wilde and R. E. Strauss for insight
into the functional organization of stream-fish assemblages.
Finally, I thank L. Jost and two anonymous reviewers for
comments on a previous version of this manuscript.
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