Ecosystems (2005) 8: 301–317
DOI: 10.1007/s10021-002-0270-x
Landscape-scale Variation in
Taxonomic Diversity in Four Groups
of Aquatic Organisms: The Influence
of Physical, Chemical, and Biological
Properties
Thomas R. Hrabik,* Ben K. Greenfield, David B. Lewis, Amina I. Pollard,
Karen A. Wilson, and Timothy K. Kratz
Center for Limnology, University of Wisconsin–Madison, 680 N. Park St., Madison, Wisconsin, 53706, USA
ABSTRACT
of organisms showed stronger and steeper species–
area relationships in low-conductivity lakes. Further, after variance owing to specific conductance
was removed, the presence of stream connections
was positively related to species richness for fish,
snails, and macrophytes as well as familial richness
in benthic invertebrates. Our results indicate that
lakes with relatively more groundwater input have
lower extinction rates for all four groups of taxa
and that lakes with stream inlets and outlets have
enhanced immigration rates for fish, snails, benthic
invertebrate families, and macrophytes. These
findings link processes of immigration and extinction of four groups of organisms of varying vagility
to landscape-level hydrologic characteristics related
to the glacial history of the region.
We evaluated several factors influencing the taxonomic richness of macrophytes, benthic invertebrates, snails, and fish in a series of northern
Wisconsin lakes. We chose the study lakes to
decouple the potential effects of ionic strength of
lake water and stream connection, two factors that
are usually highly correlated and therefore have
been confounded in previous studies. In addition,
our study lakes covered a wide range in a variety of
characteristics, including residential development,
abundance of exotic species, nutrient concentrations, predator abundance, and lake size. Species
richness within each of the four taxonomic groups
was significantly positively related to ionic strength
(as measured by specific conductance); we also
found secondary associations with other variables,
depending on the specific group of organisms. The
relationship between richness and lake area was
dependent on the specific conductance of the lake
and the vagility of the organisms; less vagile groups
Key words: landscape; groundwater; species
richness; aquatic diversity; vagility; northern
Wisconsin lakes.
INTRODUCTION
Defining the factors that influence the taxonomic
diversity of an area is a standing goal of ecology.
Island biogeography theory suggests that the
diversity of an insular environment, such as a lake,
island, or mountaintop, is determined by a balance
Received 9 December 2002; accepted 8 December 2003; published online
31 May 2005.
*Corresponding author; e-mail: [email protected]
Present address: T.R. Hrabik, Department of Biology, University of Minnesota, 211 Life Science Building, 10 University Drive, Duluth, Minnesota, 55812-2496, USA
301
302
T. R. Hrabik and others
of regional dispersal processes and local extinction
processes (MacArthur and Wilson 1967; Barbour
and Brown 1974; Magnuson and others 1998).
Determining how these two forces interact to
influence the diversity of particular assemblages
represents an important challenge to ecologists
(Ricklefs 1990; Shurin and others 2000).
Lake districts provide excellent laboratories for
determining the relative influence of regional dispersal versus local extinction as drivers of diversity.
Lakes are islands of water in a terrestrial sea
(Magnuson 1976), and they offer a diverse set of
habitats that are likely to differ in immigration and
extinction rates for particular groups of organisms.
Immigration rates are most dependent on the ease
with which organisms can move from lake to lake;
thus, the dispersal characteristics of the organisms,
the relative proximity and size of lakes, and the
presence/absence of stream connections between
lakes are likely to be important factors. Extinction
rates in lakes are likely influenced by lake-specific
characteristics, such as predator abundance, water
chemistry, and habitat availability, that determine
the relative suitability for particular organisms.
In glaciated areas, the value of a lake district as a
laboratory extends beyond examining how taxonomic diversity responds to dispersal versus
extinction processes. Lakes in glaciated regions vary
in both connectivity, or the potential for dispersal
through streams, and in local environment, and
both factors are influenced by the position of the
lake in the landscape. A lake’s location in the
landscape is not a mechanism immediately affecting species richness. Rather, lake location is a
master variable that governs other factors that are
more directly responsible for establishing the balance between immigration and extinction. Thus,
lake location may sit atop a hierarchy or causal
sequence that produces patterns in taxonomic
diversity. The arrangement of lakes in the landscape is a template that constrains or ‘‘guides’’ the
patterns that manifest in both the direct drivers of
taxonomic diversity as well as taxonomic diversity
itself.
In northern Wisconsin lakes, it is useful to define
lake landscape position as the location of a lake
relative to the regional hydrologic flow regime
(Kratz and others 1997; Riera and others 2000).
Lakes relatively high in the flow system receive
most of their water inputs from precipitation,
whereas lakes lower in the flow system receive a
substantial input of water from surface and
groundwater sources. A variety of limnological
characteristics are associated with a lake’s landscape position (Riera and others 2000). Lakes high
in the flow system are often small, have low ionic
strength, and lack stream connections to other
lakes. Conversely, lakes relatively low in the flow
system receive a greater proportion of their input
from ion-rich groundwater and are often larger,
have greater ion concentrations, and are interconnected by stream corridors (Kratz and others 1997;
Magnuson and Kratz 2000; Riera and others 2000).
In addition to limnological characteristics, the
diversity of predators (fish and crayfish), the magnitude of human activity, and the abundance of
macrophyte habitat all tend to increase from
highland to lowland lakes (Kratz and others 1997;
Reed-Anderson and others 2001; Lewis and Magnuson 2000).
Previous research has shown landscape-level
patterns in the taxonomic richness in lakes (Kratz
and others 1997; Lewis and Magnuson 2000; Riera
and others 2000). In particular, species richness of
fish and snails increases from highland to lowland
lakes. Although this pattern is well established, the
mechanisms underlying it remain unknown. Given
that lake landscape position largely dictates both
regional dispersal (stream corridors) and local
extinction (water chemistry, diversity of predators,
habitat availability), it is difficult to assess the relative influence of these two suites of factors on
species richness or diversity (Tonn and others 1990;
Magnuson and others 1998).
In this study, we evaluated the relative influence
of dispersal versus extinction factors on taxonomic
richness and diversity across a spatially structured
landscape. Thus, we simultaneously determined
the most likely mechanism underlying the relationship between lake order and taxonomic diversity (sensu Kratz and others 1997; Lewis and
Magnuson 2000) and addressed the more general
‘‘dispersal versus extinction’’ debate (sensu Ricklefs
1990; Magnuson and others 1998; Shurin and
others 2000). In particular, we determined what
factors influence taxonomic richness and diversity
in fish, snails, macroinvertebrates, and macrophytes in a series of northern Wisconsin lakes. Our
goal was to investigate the mechanisms underlying
the established correlation between species richness and landscape position. Our hypothesis set
therefore derived from the factors correlated with
landscape position that are expected to influence
immigration and extinction rates. Thus, we expected that taxonomic richness and diversity
(evenness) in these lakes would be driven by (a)
dispersal opportunity; (b) limnological features,
such as ionic strength and transparency; (c) predator abundance; and (d) habitat diversity. Typically, the effects of these factors are difficult to
Landscape-scale Variation in Taxonomic Diversity
distinguish because they covary among a set of
lakes selected at random (Magnuson and Kratz
2000). By selecting study lakes along relatively
independent gradients, we were able to identify the
influence of different factors that are ordinarily
correlated in any random sample of lakes. Because
we examined lakes in a natural setting, however,
we could not control for each independent variable
of interest.
We developed a series of nonmutually exclusive
hypotheses based on each of these four factors:
Dispersal Opportunity
Hypothesis 1a. Lakes with stream connections
will have greater taxonomic richness and diversity
than isolated lakes because streams allow for higher
immigration rates.
Hypothesis 1b. Given similar limnological features, lakes with only outflowing streams will have
lower taxonomic richness and diversity than lakes
with inflowing streams.
Hypothesis 1c. Because vagile taxa can more
easily recolonize extinction conducive lakes, slopes
of the log-log species–area relationships will be
steeper for less vagile groups of organisms (for
example, snails and fish) than those that can fly or
have airborne propagule dispersal (for example,
invertebrates and macrophytes).
Limnological Features
Hypothesis 2a. Lakes with higher ionic strength
will have greater taxonomic richness and diversity
than more dilute lakes owing to lower extinction or
higher colonization rates in lakes with high ionic
strength water.
Hypothesis 2b. Lakes with greater nutrient
concentrations will have greater taxonomic richness and diversity than lakes with lower nutrient
concentrations.
Predator Abundance
Hypothesis 3a. Lakes with more intense predation will have lower taxonomic richness and
diversity.
Hypothesis 3b. Lakes with higher predator
abundance will have higher richness and diversity
resulting from keystone predator–prey responses.
Habitat Availability
Hypothesis 4a. Larger lakes, within the small
range of sizes that we sampled, will have greater
taxonomic richness and diversity than smaller lakes
because increased habitat complexity and higher
surface area for colonization of larger lakes results
in lower extinction rates.
303
Hypothesis 4b. Lakes with higher disturbance
resulting from lakeside cabin development, which
alters littoral zone habitat (Christensen and others
1996), will have lower taxonomic richness and
diversity because of reduced habitat availability.
Hypothesis 4c. Lakes with a greater abundance
of crayfish, which denude vegetated habitats, will
have lower diversity. (For snails and macrophytes,
this prediction also fits hypothesis 3 because crayfish feed on each.)
MATERIALS
AND
METHODS
The Region and Landscape
Characteristics
Our study lakes were located in the Bear River,
Manitowish River, and Tomahawk River catchments of the Northern Highland Lake District in
western Vilas and Oneida Counties, Wisconsin,
USA. This area contains over 700 lakes greater than
0.75 ha and has a maximum difference in elevation
among lakes of approximately 72 m (North Temperate Lakes Long-Term Ecological Research project
[NTL-LTER] database). Of these lakes, approximately 30% have stream outlets and or inlets (NTLLTER database). The Laurentian ice sheet retreated
from this area 10,000–13,000 years ago and left a
landscape characterized by many lakes embedded in
noncalcareous, sandy tills and outwash (Attig 1985).
Study Site Selection
We selected lakes with the primary objective of
contrasting the relative importance of hydrologic
isolation versus water chemistry in influencing taxonomic diversity. We used the presence or absence
of stream inlets or outlets as a measure of isolation
and specific conductance as an indicator of groundwater input and ionic strength. Both the presence of
stream connections and higher-conductivity water
are associated with lakes positioned low in the flow
system in northern Wisconsin lakes. To separate
these variables we specifically searched for and
sampled four groups of lakes: (a) low conductivity,
streams absent; (b) high conductivity, streams
present; (c) low conductivity, streams present; and
(d) high conductivity, streams absent. Lakes in categories (a) and (b) are common, but lakes in categories (c) and (d) are rare. We used a threshold of 50
lS to divide low- and high-conductivity lakes.
Summary statistics for a number of lake characteristics are given in Table 1. We did not sample lakes
with low pH (less than 6) to avoid confounding effects of species loss owing to this variable (see, for
example, Rahel and Magnuson 1983).
304
Table 1.
T. R. Hrabik and others
Range of Variables Used in the Multivariate Analyses of Aquatic Species Diversity
Water chemistry variables
Conductivity (lS/cm)
Total P (lg/L)
Total N (lg/L)
Chlorophyll-a (lg/L)
Water clarity
Secchi depth (m)
Color (absorbance at 440 nm)
Lake morphology
Surface area (ha)
Littoral area (ha)
Max. depth (m)
Lake connectivity
Lake order
Total no. of streams
Macrobiota
O. rusticus (#/trap)
Total crayfish (#/trap)
L. gibbosus (#/d)
Benthic omnivorous fish (#/d)
Predatory fish (#/d)
Human influence
Cabinsa
Minimum
Maximum
Mean
SD
Fish
Snails
Plants
Inverts
12
2.3
95.3
1.93
169
23.5
834.0
14.25
73.6
11.3
334.6
4.80
44.9
5.8
172.0
2.63
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
1.5
0.218
9.8
2.642
3.9
1.104
1.8
0.617
X
X
X
X
4.4
520.4
128.7
128.9
X
X
X
X
4.9
30.6
14.1
7.4
X
X
X
X
)3
0
4
4
0.62
1.2
1.84
1.1
X
X
X
X
X
X
X
X
0
0
0
0
0
56.8
56.8
212
1,311
248
7.2
8.8
27.1
397.9
62.2
13.9
13.8
50.5
377.9
52.9
X
X
X
X
X
0
0.73
0.15
0.20
X
X
X
X
X
X
a
Number of dwellings around the lake shore divided by the area of the lake
Fish Sampling
Fish sampling was conducted on 35 lakes during
the summer period at least 1 month after thermal
stratification in 1998, 1999, and 2000. This period
was selected in an effort to standardize the sampling effectiveness and catch rates for all fish species across lakes.
Several types of gear were used to estimate fish
diversity in each lake; each gear was effective at
catching a different set of fishes. Vertical gill nets
were used to sample populations of pelagic fishes. A
spectrum of mesh sizes (19-, 32-, 51-, 64-, and 89mm stretch mesh) was used, with each mesh size
effectively catching a different size range of fish.
The nets were fished in the deepest basin of each
lake for one diel cycle; they were hung from foam
rollers and stretched from the surface to the bottom
of the lake. Fyke nets were used to sample fishes in
the shallow nearshore area of each lake. These nets
were constructed with 7-mm delta mesh, had a
mouth opening 0.75 m high · 1.25 m wide, with a
1 m · 5 m single lead, and were set so the lead net
ran perpendicular from shore with the mouth in
water approximately 1 m deep. We set three fyke
nets in each lake, one each in areas dominated by
muck, sand, and cobble, for one diel cycle. We set
three funnel-style minnow traps baited with 100 g
of beef liver alongside each fyke net. Night electrofishing was performed in the near shore area
between 0.3 and 2.0 m in depth. Two 30 min
transects were performed such that a variety of
substrate and habitat types were sampled. The
dipnets used to net fish had 4-mm delta mesh and
were capable of retaining small fishes (down to
around 25 mm). Our objective with electrofishing
was to cover a maximum number of habitat types
and to capture and identify as many species as
possible during each run while maintaining a
constant effort across lakes. Fish collected using all
gears were identified using the taxonomic key
found in Becker (1983). The suite of gears used and
effort was consistent across lakes. As such, the
number of each species caught was summed across
all gear in each lake to obtain a relative abundance
of fish of each species in each lake and an estimate
of species richness.
Snail Sampling
In August 1995–99, we surveyed 31 lakes once for
snails and several variables that potentially
influence snail distribution. Prior to sampling, lake
habitat (sand, cobble, woody debris, and
macrophytes) was determined by qualitatively
mapping the entire shoreline of a lake at approxi-
Landscape-scale Variation in Taxonomic Diversity
mately 30-m intervals or by consulting habitat
maps (Petrie and others 1993) that were confirmed
for accuracy. Sample sites were chosen randomly,
stratifying by habitat type. Snails were sampled
from the following four habitat types in each lake:
sand, cobble, woody debris, and macrophytes. The
first three categories were sampled at each of three
to five locations in each lake; the number of sites
was dependent on the identification of a new species at each additional site (also see Lewis and
Magnuson 2000; Lewis 2001). Sand and cobble
sites were usually free of structure such as coarse
woody debris and macrophytes. Preliminary sampling indicated that haphazardly located quadrats
resulted in a gross underestimation of species
richness. We sought to ensure that the search area
for these patchily distributed organisms was
appropriately scaled, depending on local snail
density. Therefore, we used search time as the
standard for effort, and each site was searched
using SCUBA gear over a depth range of 0.5–4 m
for 8 min. Timed search has been successfully applied to snail sampling in other physically complex
habitats (Tattersfield 1996; Lewis and Magnuson
2000; Lewis 2001). For the macrophyte habitat, we
sampled up to five sites in each lake, if available, by
sweeping a D-net through 1.5 m2 (bottom surface
area) of submerged macrophytes per site. Fewer
sites were sampled from lakes that had only one or
two habitat types per lake and in which only one
patch of a particular habitat could be found. Snails
were identified to species according to Burch
(1982), with supplemental information from
Clarke (1973).
Benthic Invertebrate Sampling
We used modified Hester-Dendy colonization substrates to sample benthic invertebrate communities
in 32 lakes from 1998 to 2000. Colonization substrates sample selectively (Turner and Trexler
1997) and thus provide a biased estimate of benthic
invertebrate assemblages. However, colonization
substrates allow standardized sampling effort in a
variety of habitat types within a lake, and they are
suitable for detecting differences among habitats
(Richards and others 1996). Two Hester-Dendy
samplers were set at a depth of 1 m on each of three
substrate types (cobble, sand, and silt) within each
lake for 4 weeks in July and August. Different
substrates were sampled to account for invertebrate
associations with specific substrate characteristics.
All benthic invertebrates were removed from the
colonization samplers, preserved in 70% ethanol,
identified, and enumerated. Aquatic insects were
305
identified to family; other invertebrates were classified to order (Thorp and Covich 1991).
Macrophyte Sampling
Macrophyte surveys were conducted in 29 lakes in
mid- to late July, near peak biomass for macrophytes, from 1998 to 1999. Macrophytes were
surveyed along a transect running perpendicular to
shore at two sampling sites per lake, one dominated
by sand and one dominated by muck substrate.
Transects were set to 100 m in length or until a
depth of 4 m was reached, whichever came first.
SCUBA divers counted species within a 0.25-m2
circular quadrat at 1-m intervals along each transect, noting depth range and distance along transect. If less than 10 quadrats fell between depth
ranges (0–1 m, 1-2 m, 2–3 m, 3–4 m), we sampled
additional quadrats an arm’s throw off the transect.
Percent cover for each plant species was determined for each quadrat. In 1998, cover was estimated on a 1–5 scale, where 1 was rare and 5 was
abundant. In 1998 and 2000, cover was estimated
as percent area covered (0%–100%). To join these
two data sets, we categorized the data into five
abundance categories, where 0%–20% equaled 1
on the 1–5 scale, 21%–40% equaled 2, and so on.
To calculate absolute abundance for each lake, we
totaled the abundance for all 10 replicates in the
lake and divided by the total number of quadrats
sampled (total absolute abundance). Uncommon or
unknown species were sampled for identification
and voucher specimens were identified using the
taxonomic keys in Gleason and Cronquist (1991).
Sampling of Lake Attributes
Study lakes were sampled between 1 June and 1
September for the following water chemistry
parameters: dissolved oxygen at depth (1-m intervals), temperature at depth (1-m intervals), vertically integrated chlorophyll, alkalinity, pH,
conductivity, total nitrogen, total phosphorus, color (absorbance at 440 nm), and secchi depth.
Samples were collected on each lake on a minimum of two sampling dates; most lakes were
sampled on three dates, with a minimum of 3
weeks between each sampling.
All water chemistry parameters except chlorophyll, temperature, dissolved oxygen, and secchi
depth were estimated at 1 m below the surface and
2 m above the bottom, with the bottom sample
collected only when the study lake was stratified.
Water from these two depths were collected using a
peristaltic pump and flexible tygon tubing. Color
(absorbance at 440 nm) and samples for total ion
306
T. R. Hrabik and others
concentration were filtered using 0.44-l Nuclepore
filters.
Temperature and oxygen profiles were performed using a YSI model 58 temperature/oxygen
meter or a YSI Sonde 600 · 1 field meter on each
limnological sampling date on each lake. Secchi
transparency was estimated using a black-andwhite disk that was 20 cm in diameter. Chlorophyll
samples were collected by pumping a measured
volume of water from the epilimnion, metalimnion, and hypolimnion through a glass fiber filter.
The location of the epi-, meta-, and hypolimnion
was determined from the temperature profile of
each lake on each sampling date, where strata
layers were identified based on temperature changes greater than 0.99C per meter. Chlorophyll
samples were collected by raising and lowering the
opening of the sampling tubing within each depth
strata until the filter clogged (that is, 15 psi on a
pressure gauge) or 3 L of water had been filtered.
The sample represented an integrated sample for
each layer of the water column.
Assumptions of Species Richness Data
Our objective in performing the biological surveys
was to apply standard sampling methodologies to
facilitate interlake comparisons, not to create
exhaustive species lists for each lake. We do not
suggest that our estimates of species richness for each
of the groups of aquatic organisms are actual counts
of all the species present in any given lake ecosystem.
However, we applied a similar amount of sampling
effort, using consistent methods on lakes sampled for
each group of organisms, and we assume that our
estimates of species richness are comparable across
each of the lakes sampled. Furthermore, our considerations of species diversity include the evenness
of the assemblage. We adopted this consideration
largely because an assemblage may be considered
more diverse if it contains many species at relatively
equal abundance when compared to an assemblage
with equal number of species that is dominated by
rare taxa. We computed two indices of diversity that
incorporate abundance using the richness and
abundance data for each group of organisms: the
Shannon-Weaver index and Hurlbert’s pie. The two
diversity indices were strongly correlated for each
group of organisms (R > 0.90 for all groups) so we
included analyses using only the Shannon-Weaver
index of diversity.
Statistical Analyses
We first compared species richness among the four
major categories of lakes (combinations of high or
low conductivity and streams present or absent).
We then compared the strength and slope of species–area curves for each group of organisms across
all lakes. Finally, we use classification and regression tree (tree) analyses to assess the relative
importance of each of 10 (nine for macrophytes)
independent variables in explaining the taxonomic
richness and diversity for each group of organisms.
For each taxonomic group, we used the split value
for the first variable selected by the tree model to
examine log-log species–area relationships for
subsets of lakes.
We chose tree models because they are nonparametric, valid for detecting nonlinear effects,
and enable analysis of both categorical and continuous variables (Clark and Pregibon 1992). Tree
analyses split the data set by recursive partitioning
into smaller and successively less variable portions
until the variability within each subset is less than a
user-defined level (Clark and Pregibon 1992). The
algorithm chooses the split, from all options of all
independent variables, that minimizes unexplained
variation in the dependent variable. Our criteria for
variable inclusion were that the proportional
reduction in error (PRE) (which is analogous to r2)
owing to any independent variable included in the
model was more than 5% of the total variation in
the data and that each new split used a minimum
of 10 data points. We used the tree model analysis
software offered by S-plus (v. 4.5) with leastsquares minimization to discriminate groupings of
the data based on the aforementioned levels of
variation reduction. This procedure enables the
model to initially fit a large tree that meets the
selection criteria. However, some of the ‘‘nodes’’ in
the tree may not contribute significantly to the
variation explained by the model. To eliminate
nodes and associated selected variables that provided no significant additional explanatory power
to the model, we used node deviance to facilitate
the identification of a model of the appropriate size
(Clark and Pregibon 1992). We also tested for
variable covariation by computing a correlation
matrix for all variables included in the analysis
(Table 2). If two variables that were significantly
correlated were included in the same tree model,
we excluded the variable that came in after the
initially selected variable.
The variable representing predator abundance
differed depending on the group of organisms being
analyzed. The abundance of pumpkinseed (where
abundance equals the number caught in a lake in all
sampling gears) (Lepomis gibbosus) was used as the
predator variable for snails (Lewis and Magnuson
2000). For the fishes, the predator variable was
Landscape-scale Variation in Taxonomic Diversity
Table 2.
307
Spearman-Rank Correlations for Independent Variables Included in Tree Model Analyses
Lake
Area
Lake area
Lake order
Maximum
depth
Phosphorus
Color
Chlorophyll
Specific
conductance
Cabin density
O. rusticus
abundance
Littoral area
Lake
Order
Maximum
Depth
P value Color
Specific
Cabin
O. rusticus
Chlorophyll-a Conductance Density Abundance
0.326
)0.002 )0.290
0.072
)0.063
0.135
0.254
0.401
0.287
0.499
0.690
)0.745
)0.506
)0.559
)0.134
0.680
0.538
0.286
0.474
)0.050 0.327
0.147
0.225
0.188
0.403
)0.178
)0.143
0.186
0.205
)0.135 0.044
0.156
0.050
0.301
0.231
0.071
0.941
0.387
0.033
)0.025
)0.083 0.130
0.241
0.142
represented in each lake by the abundance of fish
greater than 275 mm that were known to be
piscivorous. The predatory fish species included
walleye (Stizostedion vitreum), northern pike
(Esox lucius), muskellunge (Esox masquinongy),
smallmouth bass (Micropterus dolomeiu), and largemouth bass (Micropterus salmoides) (Becker 1983).
For benthic invertebrates, the predator variable included the abundance of fish greater than 70 mm
that were known to eat benthic invertebrates
(Becker 1983). Benthivorous fish included Iowa
darter (Etheostoma nigrum), johnny darter
(Etheostoma exile), logperch (Percina caproides),
troutperch (Percopsis omiscomycus), mottled sculpin
(Cottus bairdi), slimy sculpin (Cottus cognatus), bluegill (Lepomis macrochirus), black crappie (Pomoxis
nigromaculatum), pumpkinseed (Lepomis gibbosus),
rockbass (Ambloplites rupestris), yellow perch (Perca
flavescens), warmouth (Lepomis gulosus), and any
hybrids thereof. We used the abundance of the
exotic crayfish (Orconectes rustics) as the predator
variable for macrophytes. The abundance of this
organism was considered a disturbance variable for
other groups of organisms owing to its deleterious
effect on macrophytes (Olsen and others 1991).
RESULTS
Connectedness, Specific Conductance,
and Aquatic Diversity
There was a strong positive relationship between
lake conductivity and species richness (Figure 1).
Within isolated lakes, one way Kruskal-Wallis tests
indicated significantly higher richness in high
conductivity lakes for fish, snails, invertebrates,
and aquatic plants (P < 0.05). The same test showed
0.205
that, within the low-conductivity lake category,
lakes with streams had greater taxonomic richness
for fish (P < 0.01) and possibly for snails (P < 0.10).
For high-conductivity lakes, those with streams
had significantly higher species richness of fish and
invertebrates (P < 0.05).
Species diversity was related to specific conductance within the study lakes. There was a positive
correlation between lake conductivity and species
diversity for fish, snails, and macrophytes (Figure 2). However, there was no relationship between lake-specific conductance and invertebrate
diversity among all lakes. Within isolated lakes,
one-way Kruskal-Wallis tests showed significantly
higher diversity in high-conductivity lakes for fish,
invertebrates, and aquatic plants (P < 0.05) but not
snails (P = 0.65). The same test showed that within
the low-conductivity lake category, lakes with
streams did not have significantly greater diversity
for fish, snails, invertebrates, or macrophytes (P >
0.10). Furthermore, high-conductivity lakes with
streams did not have significantly higher diversity
(for any group of organisms) than those without
stream connections (P > 0.14).
Direction of stream flow may influence the
immigration of new species into lakes. Furthermore, we could not eliminate all covariation between specific conductance and the extent of
connectedness to other lakes during the lake
selection process. To address the possibility that
lakes with outlets but no inlets have lower immigration rates than lakes with both inlets and outlets, we compared species richness and diversity
among lakes with similar specific conductance that
had either outlets alone or inlets as well as outlets.
We found no significant differences in either species richness or diversity when lakes of order zero
308
T. R. Hrabik and others
Figure 1. Species richness for four groups of organisms
segregated by levels of lake conductivity and the presence
or absence of stream connections to other lakes. Filled
boxes indicate values for low-conductivity lakes; open
boxes indicate values for high-conductivity lakes.
Figure 2. Species diversity for four groups of organisms
segregated by levels of lake conductivity and the presence
or absence of stream connections to other lakes. Filled
boxes indicate values for low-conductivity lakes; open
boxes indicate values for high-conductivity lakes.
(outlets alone) were compared to lakes of order 1–4
(inlets and outlets) (Kruskal-Wallis tests, P > 0.10)
(Figure 3).
The significance and strength of species–area
relationships varied among the four taxonomic
groups (Figure 4 and Table 3). For the fishes, species richness was significantly positively correlated
to the area of the lake, and area explained
approximately 46% of the variability in fish richness (Table 3). Snail richness was also positively
related to lake area; however, the trend was only
marginally significant (P = 0.09) (Table 3). Lake
area did not correlate significantly with species
richness of either benthic invertebrates or macrophytes (Table 3).
Output from tree-based models indicated a significant effect of specific conductance on species
richness and Shannon-Weaver diversity of fish,
snails, and macrophytes. Lake conductivity was the
most consistent initial variable selected by the tree
models among all independent variables considered
in the analysis of individual groups of organisms.
The presence of streams and predator abundance
also were each selected as the initial variable.
Variables included after the initially selected variable included the abundance of predatory species,
maximum depth, the density of cabins, phosphorus
concentration, and lake area. In the following
sections, we discuss the results of tree analyses in
more detail for each of the different taxa.
Fish Richness and Diversity
Tree model analysis indicated that specific conductance was the most significant source of variation in fish species richness within our study
lakes. Lakes with conductivity greater than 36 lS
had significantly more species than lakes with
lower conductivity (Figure 5 A). In the lakes with
conductance lower than 36 lS, piscivorous fish
abundance was positively correlated with fish
species richness. Lakes with specific conductance
higher than 93 lS contained significantly more
species than lakes with conductivity between 36
and 92 lS (Figure 5A). The total variation in fish
richness explained by conductivity and predator
abundance was approximately 77% and 5%,
respectively. The PRE by the tree model of fish
richness was 82.6% (Figure 5A). Variability in fish
diversity was related to lake conductivity and lake
maximum depth. Fish diversity was higher in
lakes with conductivity greater than 50 lS (Figure 5B). Of lakes with conductivity greater than
50 lS, those with maximum depth greater than 11
Landscape-scale Variation in Taxonomic Diversity
309
Figure 3. Species richness and
Shannon-Weaver diversity for aquatic
organisms in high specific conductance
lakes with outlets but no inlets and lakes
with both inlets and outlets.
Figure 4. Species–area relationships for four
groups of aquatic organisms found in northern
Wisconsin lakes.
m had higher fish diversity than shallower lakes
(Figure 5B). The PRE in the diversity of fishes
owing to conductivity and maximum depth was
20.7% and 14.7%, respectively. The total variation in fish diversity reduced by the tree model
was 35.4%.
310
T. R. Hrabik and others
Table 3. Results of a Linear Regression Analysis of Log-Species versus Log–Lake Areaa for four Groups of
Organisms
Group of Organisms
No. of Lakes
Parameter
Coefficient
SE
t
P
P (Total)
R2
36
Intercept (a)
Log–Lake Area (z)
0.35
0.33
0.149
0.065
2.37
4.98
< 0.024
< 0.000
< 0.00
0.44
31
Intercept (a)
Log–Lake Area (z)
0.594
0.226
0.324
0.130
1.834
1.739
0.078
0.094
0.094
0.10
29
Intercept (a)
Log–Lake Area (z)
0.985
0.048
0.124
0.053
7.933
0.900
0.000
0.376
0.376
0.03
28
Intercept (a)
Log–Lake Area (z)
1.213
)0.011
0.227
0.097
5.334
)0.108
0.000
0.915
0.915
0.00
Fish
Snails
Invertebrates
Macrophytes
a
Log-species = a+z Log-area.
Snail Richness and Diversity
Variation in snail species richness in the tree model
was associated with lake conductivity and the
abundance of L. gibbosus, a fish species identified as
a major predator of snails. In lakes with conductivity less than 36 lS, significantly fewer snail species were found. Among lakes with conductivity
greater than 36 lS, those with an L. gibbosus abundance greater than or equal to 10.5 fish caught per
day contained more snail species than lakes of that
conductance category with fewer L. gibbosus (Figure 5C). Variation in Shannon-Weaver diversity of
snails was explained by conductivity alone. In lakes
with conductivity less than 50 lS, there was a significantly less diverse snail community than in lakes
with conductivity higher than 50 lS (Figure 5C).
The total variation in snail richness reduced by the
tree model was approximately 79% (Figure 5C),
with conductivity and predator (L. gibbosus)
abundance representing 70.7% and 8.1%, of the
variance reduced, respectively (Figure 5C). Conductivity was the only significant variable in the
tree model for snail diversity, explaining 32.6% of
the total variation (Figure 5D).
Benthic Invertebrate Familial Richness
and Diversity
Variability in the richness of benthic invertebrate
families was associated with the presence or absence of streams, specific conductance, and area.
The tree model of invertebrate families indicated
that familial richness was significantly higher in
lakes with stream connections (Figure 6A). Of
lakes that had stream connections to other lakes,
lakes with specific conductance higher than 76 lS,
contained more families of benthic invertebrates
than those with lower specific conductance (Figure 6A). In addition, in lakes within the highest
specific conductance category identified by the
model, lake area had a significant positive influence
on species richness. The PRE for the tree model of
benthic invertebrate familial richness was approximately 38%. Variability in the diversity of benthic
invertebrate families was associated with the
abundance of omnivorous fish (number of fish
caught per day) (Figure 6B). In each of two categories of lakes defined by the initial split in
omnivorous fish abundance, the number of benthic
invertebrate families was further associated with an
abundance of omnivorous fishes. Over all lakes
sampled for invertebrate familial diversity, lakes
with more than 221 omnivorous fish caught per
sample day had higher diversity (Figure 6B).
However, in lakes where the abundances of
omnivorous fish were less than 221 per sample day,
the correlation between fish and benthic invertebrate familial diversity was negative (Figure 6B).
This general pattern suggests a U-shaped pattern
such that benthic invertebrate familial diversity
was lowest at intermediate abundances of
benthivorous fishes. The total variation in diversity
of benthic invertebrate families reduced by the tree
model was approximately 31% (Figure 6B).
Plant Richness and Diversity
The tree-based model of plant species richness
identified lake conductivity as the most influential
variable, followed by density of cabins. Plant species richness was higher in lakes with conductivity
greater than 50 lS than in lakes with lower conductivity (Figure 6C). Among the lakes with
Landscape-scale Variation in Taxonomic Diversity
311
Figure 5. Dendograms of
classification and regression
trees derived from species
richness and ShannonWeaver diversity index
values found for fish and
snails and data on
independent variables
collected from each study
lake. Levels of each variable
where splits in the data
occurred, proportional
reduction in error reduced by
each variable, and total
proportional reduction in
error are shown within each
dendogram.
Figure 6. Dendograms of
classification and regression
trees fit to species richness
and Shannon-Weaver
diversity index values found
for benthic invertebrates and
macrophytes and data on
independent variables
collected from each study
lake. Levels of each variable
where splits in the data
occurred, proportional
reduction in error reduced by
each variable, and total
proportional reduction in
error are shown within each
individual dendogram.
conductance higher than 50 lS, lakes with conductivity higher than 117 lS had significantly more
plant species than lakes with lower conductivity. In
lakes with conductance between 50 and 117 lS,
lakes with a cabin density greater than 0.054 cabins/ha contained significantly fewer species than
lakes with a higher cabin density (Figure 6C). The
tree model of Shannon-Weaver index of plant
diversity initially selected lake conductivity, abundance of the exotic crayfish Orconectes rusticus, and
concentration of phosphorus. Plant diversity was
greater in lakes with a conductivity higher than 50
lS (Figure 6D). Of lakes with conductivity greater
than 50 lS, lakes with an abundance of O. rusticus
312
T. R. Hrabik and others
Figure 7. Species–area relationships for four
groups of aquatic organisms found in highconductivity and low-conductivity lakes
identified by the initial split in the tree models.
Filled circles indicate species richness values for
low-conductivity lakes; open circles indicate
species richness values for high-conductivity
lakes.
less than 5.0 (number per trap) had a greater
diversity of plants than lakes of that category with
higher crayfish abundance (Figure 6D). Furthermore, lakes with a crayfish abundance lower than
5.0 (number per trap) and a phosphorus concentration higher than 11 lg/L had greater plant
diversity.
Analyses of Subgroups of Data Defined by
Tree Models
Subsets of data delineated by tree models indicated
that the explanatory power of lake area was
dependent on the edaphic conditions within the
lakes. Categories of lakes defined by the initial
variable selected by the tree models showed different species–area relationships for each group of
organisms (Figure 7). The slope of the log-log
species–area relationship (z) was higher for the
low-conductivity lake category for all groups of
organisms, although there were significant differences in the slope for fish and snails only (Tables 4
and 5).
DISCUSSION
The Diversity of Lake Organisms within a
Landscape Context
The geomorphic and hydrologic template strongly
influences the characteristics of lakes and associated aquatic species diversity in northern Wiscon-
sin. In randomly selected lakes, lake position
relative to hydrology is correlated with lake size,
conductance, and connectedness to other lakes
(Riera and others 2000). Our aim in this study was
to decouple these correlated variables (particularly
conductance and connectedness to other lakes),
which have equal potential to influence lake biotic
communities. Our analyses indicate that lake conductivity is strongly linked to the richness and
diversity of aquatic organisms. Our results from
lakes along a broad gradient in specific conductance
indicate a significant positive relationship between
conductivity and species richness and diversity that
was likely the result of our study lakes being within
the low range of productivity as compared to the
range found in other geological provinces (Dodson
and others 2000; Mittlebach and others 2001). The
presence of stream connections, which represent
immigration and colonization routes, also positively influenced fish, snail, and benthic invertebrate richness, but did not significantly influence
the richness of macrophyte communities. These
results fit into the context of island biogeography
(MacArthur and Wilson 1967; MacArthur 1972)
and provide supporting evidence for three of our
hypotheses: (a) that specific conductance (a correlate of groundwater flow rates relative to direct
precipitation inputs) has a positive influence on
aquatic diversity in these systems that is likely
linked to extinction rates; (b) that stream connections are important for immigration routes, partic-
Landscape-scale Variation in Taxonomic Diversity
Table 4.
Models
313
Results of Homogeneity of Slope Analysis of Log-Richness in Groups of Lakes Identified by Tree
Group of Organisms
No. of Lakes
Parameter
Coefficient
SE
t
P
P (Total)
R2
36
Intercept (a)
Group
Log-area (z)
Log-area * Group
)0.170
1.283
0.516
)0.479
0.124
0.163
0.063
0.077
)1.37
7.86
8.16
)6.27
0.180
< 0.001
< 0.001
< 0.001
< 0.001
0.873
31
Intercept (a)
Group
Log-area (z)
Log-area * Group
)0.202
1.569
0.434
)0.490
0.374
0.544
0.148
0.218
)0.54
2.86
2.93
)2.23
0.593
<0.01
<0.01
0.035
< 0.01
0.418
29
Intercept (a)
Group
Log-area (z)
Log-area * Group
1.154
0.419
)0.064
)0.063
0.316
0.416
0.148
0.183
3.65
1.02
)0.43
)0.34
< 0.001
0.317
0.668
0.734
0.018
0.336
28
Intercept (a)
Group
Log-area (z)
Log-area * Group
0.964
0.704
0.038
)0.213
0.400
0.482
0.197
0.225
2.41
1.46
0.19
0.94
0.024
0.157
0.847
0.354
0.056
0.266
Fish
Snails
Invertebrates
Macrophytes
Groups of lakes were identified according to the level of conductivity for fish, snails, and macrophytes and the presence or absence of streams for invertebrates.
Table 5.
Results of a Linear Regression Analysis of Log-Species versus Log-Lake Area in Lake Classes
Group of Organisms
No. of Lakes
Cond.
Log–Lake Area Coe. (z)
P
r2
11
23
Low
High
0.52
0.04
< 0.00
0.368
0.85
0.37
5a
22
Low
High
0.397
)0.06
0.050
0.681
0.05
0.01
15
14
Low
High
0.008
)0.07
0.890
0.513
0.00
0.04
9
19
Low
High
)0.064
)1.27
0.742
0.191
0.01
0.10
Fish
Snails
Invertebrates
Macrophytes
a
Lakes with zero species detected were omitted from the regression.
The coecient for log–lake area is equal to z. The first value in each column is the value for lakes classified in the lower level of the edaphic variable conductivity; the second value
is for the lakes classified at the higher level of the variable. Each conductivity class was identified by the tree models.
ularly in lakes with low groundwater input; and (c)
that predators may have a keystone effect on
benthic organisms within small lakes.
Specific Conductance as a Source of
Variability
Specific conductance, which is associated with
groundwater input, was the most consistent factor
associated with variability in aquatic richness and
diversity identified by our analyses. Our results
from analyses of broad categories of lakes defined
by specific conductance indicated a significant effect on richness for each group and for species
diversity in fish, snails, and macrophytes as well as
invertebrates found in lakes without streams. It is
unclear how specific conductance is mechanistically linked to increased taxonomic richness in the
314
T. R. Hrabik and others
lakes studied. However, specific conductance and
chlorophyll level are positively correlated (Table 2)
and lakes with higher specific conductance have
larger more structurally complex plant species (K.
Wilson unpublished). Increases in groundwater
flow in lake districts dominated by glacial till substrate enable increased productivity in the littoral
zone of lakes (Lodge and others 1989; Hagerthey
and Kerfoot 1998). The increased benthic primary
production and associated increases in littoral
habitat complexity in lakes with higher groundwater input likely result in the observed increases
in macrophyte richness and diversity. Increases in
littoral zone complexity linked to conductivity may
decrease extinction rates of other groups of organisms such as invertebrates, snails, and fish by providing refugia, thereby reducing the intensity of
biotic interactions. Furthermore, increases in
groundwater flow rates may lead to direct increases
in productivity throughout the lake by bringing in
trace elements such as phosphorus or others that
are required for algal production and increase total
primary productivity. Changes in primary productivity may influence the number of species, with
the extent and direction of the relationship varying
according to the level of productivity (Rosenzweig
and Abramski 1993; Pastor and others 1996; Weiher 1999; Mittelbach and others 2001). Where the
effect of productivity is positive, more productive
systems may have higher potential to support larger species pools (Lassen 1975; Rosenzweig 1995;
Dodson and others 2000). Therefore, groundwater
input rates appear to be an important determinant
of diversity in northern Wisconsin lakes not only
through straightforward increases in productivity,
but also by adding plant species that increase littoral zone habitat complexity.
The lack of a relationship between phosphorus,
the limiting nutrient in lakes (Schindler 1977), and
biotic richness may be related to high variability in
measurements. On the other hand, it may indicate
that phosphorus is only partially responsible for the
patterns observed. Variability in phosphorus is high
in studies where measurements are taken at a few
points throughout a season (as in this study) owing
to rapid uptake by phytoplankton and periphyton
(Gerloff and Skoog 1954; Klemer and Barko 1991).
Furthermore, phosphorus concentration is relatively low within the lakes studied here and is often
near the limit of detection, resulting in a low signalto-variance ratio. Thus, conductivity, which is
more consistent across time scales and more easily
measured, may offer a stronger, more consistent
indication of edaphic conditions that are linked to
variability in aquatic species richness and diversity.
Low groundwater flow rates in lakes may also
lead to stress for organisms that require a minimum
level of calcium for the construction of exoskeletons. Snails represent a taxonomic group experiencing this limitation in low-conductivity lakes
(Lodge and others 1987), and the increased diversity observed in high-conductivity lakes may be a
manifestation of a limitation in the lowest-conductivity lakes for this group of organisms.
Stream Connections and Immigration
Lakes with stream connections had higher species
richness of fish, snails, and invertebrates than lakes
with no inlets or outlets, indicating that immigration via streams is important for these groups of
organisms. However, the presence or absence of
streams did not appear to influence the evenness of
the assemblages. For fishes and snails, groups that
lack life history characteristics that allow effective
transport overland or through the air, our results
are consistent with the expectation that stream
corridors enhance immigration rates and lead to
higher richness, particularly in lakes with low
specific conductance. Many aquatic insects have
adult stages capable of flying, and we would expect
that stream corridors would be less important as
immigration corridors for these taxa. Nevertheless,
lakes connected to other lakes had higher taxonomic richness of invertebrates. We speculate that
streams represent sources for invertebrate species
that colonize lake ecosystems. Many types of benthic invertebrates thrive in stream environments,
and the observed increase in benthic invertebrate
richness might be a manifestation of additional
invertebrate taxa in streams adjoining lakes. However, we were unable to determine whether these
differences resulted from the additional organisms
in streams at the familial level.
Macrophyte species richness and diversity did not
increase significantly in response to the presence/
absence of stream connections. The most likely
mechanisms for the lack of an effect are related to
(a) decreases in water clarity associated with stream
connections, (b) high overland immigration rates
for this group that greatly exceed the immigration
via stream connections, or (c) the presence of seed
banks that greatly decrease extinction rates in this
group. We found a negative correlation between
water transparency and plant species richness in our
study lakes (plant richness versus secchi, Pearson
correlation, )0.331, P = 0.084); this suggests that as
transparency increased, plant richness decreased,
thus discounting the first possibility. It is more
plausible that airborne transport of seeds and
Landscape-scale Variation in Taxonomic Diversity
spores, via wind or waterfowl, provides an adequate
source of immigration to lakes and that the movement of plant fragments or reproductive propagules
via streams is less important for this group of
organisms. Also, long-term storage of seeds in lake
sediments likely decreases the rate of extinction for
this group of organisms relative to others considered
in this study. Thus, it is likely that the positive
influence of increased invasion rate and reduced
extinction rates resulting from life history traits may
mask the influence of streams on macrophyte
richness and diversity.
Predation Intensity and Species Richness
and Diversity
The abundance of predators, although of secondary
importance to lake characteristics, was associated
with variability in species richness and diversity.
Species richness in snails was significantly higher in
lakes with a greater abundance of L. gibbosus, a fish
species that is known to feed heavily on snails.
Also, invertebrate evenness appeared to be higher
in the lakes with the highest abundance of
omnivorous fishes. In each case, the relationship
was positive, suggesting that predators may operate
as keystone species for benthic organisms in lake
islands.
Species–Area, Vagility, and Hydrologic
Connections
The species–area relationships found in our study
suggest differential responses among taxa and
groups of lakes that may be dependent on habitat
availability, the vagility of the aquatic organisms,
and the relative groundwater flow rates. Species
richness in fish and snails was correlated to lake
area, particularly when segregated into classes of
lakes with high and low conductivity. Lakes with
low specific conductance had steeper slopes of
species–area relationships than lakes with high
specific conductance for each group of organisms.
However, familial richness of benthic invertebrates
and species richness of aquatic plants were not related to lake area in either class of lakes. The lack of
a species–area relationship for macrophytes and
benthic invertebrates—groups that are known to
be highly associated with the littoral zone—may
indicate that littoral area does not increase proportionately with lake area. Within our study lakes,
however, littoral area is strongly correlated to lake
area (Table 2), suggesting that other variables were
associated with the observed variability in the
number of species in each group.
315
Ecosystems with a greater proportionate increase
in habitat with area may have a higher slope (z) of
the log-log species–area relationship (Rosenzweig
1995). Accordingly, the z term for the low-conductivity subgroup of the lakes was significant (P <
0.05) for fish and snails. This general pattern in z for
fish and snails suggests that the influence of area
for fish and snails is stronger for lakes with low
specific conductance and is consistent with the
hypothesis that habitat complexity and extinction
rates are lower in larger lake ecosystems, given
similar chemical conditions. Although we did not
measure habitat diversity or heterogeneity, it is
possible that increases in lake area provide greater
habitat heterogeneity or diversity in low-conductivity lakes. We observed higher species richness
and diversity for organisms in lakes with higher
specific conductance with or without stream connections, suggesting that habitat heterogeneity
may be enhanced in these lakes as well. The effect
of increasing lake area was much stronger for fish
and snails than for benthic invertebrates and
macrophytes in lakes with low specific conductance, suggesting that the vagility of the organisms
plays a role in determining species richness. The
latter two groups have life stages that can exist for
extended periods of time out of water or have life
stages capable of extended overland travel. Higher
immigration rates for these vagile groups may reduce the importance of lake size on species richness.
Disturbance variables, including exotic crayfish
abundance and cabin density, had secondary effects
on species richness and diversity relative to other
variables measured in this study. The abundance of
exotic crayfish strongly influences the abundance
and diversity of aquatic plants in many ecosystems
(Lorman 1980; Lodge and Lorman 1987) and had a
negative effect on plant diversity in this study.
Furthermore, the removal of macrophytes by cabin
owners can have a negative effect on macrophyte
abundance and diversity (Radomski and Gorman
2001), and cabin density was negatively associated
with plant species richness in a subset of our study
lakes. However, in this analysis, the effect of disturbance variables appeared to be of secondary
importance to lake characteristics (specific conductance and the presence of stream connections).
Possible explanations for this pattern include: (a)
that species richness and diversity are not good
indicators of disturbance, (b) none of our study
systems were heavily impacted by development,
and (c) that lake characteristics mediate the signal of
disturbance. Disturbance influences species richness in many taxa (Rosenzwieg 1995), and the level
316
T. R. Hrabik and others
of lakeside development on several of the lakes in
our data set approaches those observed in urban
areas. Thus, it is more likely that lake characteristics, such as specific conductance, mediate the
influence of rusty crayfish or littoral zone manipulation by lakeside cabin owners. However, the
negative effects of lakeside development may be
operating presently and may be counteracted by the
resilience of these communities to disturbance that
dampen losses in diversity over short time scales.
In summary, a lake’s relative position in the
hydrologic flow system is an important determinant of taxonomic richness of fishes, snails, invertebrates, and macrophytes in northern Wisconsin
lakes. Local lake characteristics appear to be more
important than isolation variables. Our findings are
consistent with those identified by Magnuson and
others (1998), who found strong linkages between
lake characteristics and fish assemblages. The
influence of isolation and extinction on species
richness, within the context of island biogeography, appears to be tempered by life history characteristics; organisms with high vagility may have
immigration rates that greatly exceed extinction
processes in lakes with a higher association with
groundwater. Disturbance variables such as cabin
density and the abundance of exotic crayfish had a
secondary, negative effect on the diversity and
richness of macrophytes. Taken together, our results show that the geologic and hydrologic characteristics of the landscape are directly associated
with the processes shaping aquatic biodiversity on
local and landscape scales. The overall characteristics of biotic communities appear to be set initially
by the hydrologic template; this biotic assemblage
may then be acted on by anthropogenic sources of
variability, such as cabin development and the
addition of exotics. Lakeside development within
the study region is relatively recent, and biotic
communities may be responding to these influences presently. As such, we think that there may
be a lag between development and its cumulative
effects on species diversity in aquatic systems. Cabin development on lakeshores and the continued
spread of exotic crayfish in north temperate regions
may have negative effects on aquatic biodiversity
over longer time scales because these factors reduce
species richness and diversity in macrophyte communities that likely provide habitat for other
groups of aquatic organisms.
(NSF) Graduate Research Traineeship on the integration of lake and stream ecology, and the LongTerm Ecological Research Program (NSF). We thank
John Magnuson, Thomas Frost, Katherine Webster,
and Joan Riera for critical discussion of ideas,
writing, and analyses. We also thank Tim Mienke,
Pam Montz, Carl Bowser, and Jim Thoyre for providing equipment and expertise during limnological
sampling and data analysis. We are grateful to Troy
Jaecks, Kevin Kapuscinski, Carrie Byron, and the
many others who labored during extensive field
sampling efforts. Janet Blair provided the organization of equipment and schedules.
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We acknowledge the support of the Anna Grant
Birge Fellowship, the National Science Foundation
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