Salmon-derived nutrients drive diatom beta-diversitypatterns

GUANGJIE CHEN*, EMILIE SAULNIER-TALBOT*, DANIEL T. SELBIE* , †, ERIKA BROWN*,DANIEL E. SCHINDLER‡, LYNDA BUNTING§, PETER R. LEAVITT§ , BRUCE P. FINNEY–

AND IRENE GREGORY-EAVES**Department of Biology, McGill University, Montreal, QC, Canada†Fisheries and Oceans Canada, Science Branch, Salmon and Freshwater Ecosystems, Cultus Lake Salmon Research Laboratory,Cultus Lake, BC, Canada‡School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, U.S.A.§Limnology Laboratory, Department of Biology, University of Regina, Regina, SK, Canada–Department of Biological Sciences, Idaho State University, Pocatello, ID, U.S.A.

SUMMARY

1. Pacific salmon are a textbook example of migratory animals that transfer nutrientsbetween ecosystems, but little is known about how salmon-derived nutrients (SDN) affectthe biodiversity of recipient freshwater ecosystems. We examined paleolimnologicalrecords from six Alaskan lakes to define how changes in SDN from sockeye salmon(Oncorhynchus nerka) influenced sedimentary diatom community structure and beta-diversity among lakes and through time.2. Using an isotopic mixing model, we showed that SDN loading could account for >80%of the lake total nitrogen budgets and strongly regulated diatom community composition.Spatial dissimilarity in diatom communities was positively related to differences in SDNamong lakes (r2 = 0.69, P < 0.01, n = 10). Likewise, temporal dissimilarity in diatomcommunities was positively related to differences in SDN in a sediment core withsubstantial variation in salmon spawner dynamics between 1700 and 1950 AD (r2 = 0.34,P < 0.01, n = 19). Finally, beta-diversity metrics quantifying temporal turnover within eachlake’s sediment record were also positively related to the variance in SDN loading amonglakes (r2 = 0.88, P < 0.05, n = 5). Mean SDN was only negatively correlated to temporaldiatom beta-diversity.3. Spatially subsidised systems often receive temporally variable resource inputs, and thus,it is not surprising that, unlike previous studies, we found that resource variability was thekey driver of community composition and beta-diversity. In habitats that receive stronglyfluctuating external nutrient loads, environment heterogeneity may overweigh stochasticcommunity processes. In addition, freshwater diatoms are characterised by great dispersalcapabilities and short life cycles and therefore may be a more sensitive indicator forevaluating the role of resource variability than previously used model organisms. Theseresults suggest that productivity–diversity relationship vary with the nature of nutrientloading and the life history of the community studied.4. Overall, our study highlights that the transport of nutrients by sockeye salmon acrossecosystem boundaries is a significant driver of algal community and biodiversity innursery lakes, mainly through changing the magnitude of nutrient variation. As such,freshwater species diversity in regions like the U.S. Pacific Northwest may become

Correspondence: Guangjie Chen, Department of Biology, McGill University, Montreal, Quebec H3A 1B1, Canada.

E-mail: guangjiechen@gmail.com

Freshwater Biology (2011) 56, 292–301 doi:10.1111/j.1365-2427.2010.02496.x

292 ! 2010 Blackwell Publishing Ltd

impoverished where there have been long-term declines in salmon populations anddecreases in nutrient variability among lakes.

Keywords: beta-diversity, diatoms, migratory animal, Pacific salmon, paleolimnology, salmon-derivednutrient (SDN)

Introduction

There is growing evidence that spatially separated

habitats can be linked through flows of organisms and

materials, which can have important impacts on

recipient food webs (Polis, Power & Huxel, 2004). In

particular, migratory animals, such as seabirds (Mi-

chelutti et al., 2009) and anadromous fish (Durbin,

Nixon & Oviatt, 1979; Naiman et al., 2002), can

transfer large amounts of nutrients across habitats

and ecosystems, affecting the productivity of recipient

ecosystems. For example, mass balance studies have

shown that nutrients derived from sockeye salmon

(Oncorhynchus nerka) carcasses can account for >50%

of the total nutrient flux to coastal nursery lakes (e.g.

Gresh, Lichatowich & Schoonmaker, 2000), and these

salmon-derived nutrients (SDN) can fertilise algal

production (Krokhin, 1975; Schindler et al., 2005).

However, little is known of the effect of SDN on the

biodiversity of recipient freshwater ecosystems. At the

southern end of their range, many Pacific salmon

populations have been extirpated or are at risk of

extirpation (Gustafson et al., 2007). Thus, quantifying

how SDN loading affects the biodiversity of recipient

ecosystems is critical for understanding the conserva-

tion implications of salmon population declines.

Beta-diversity (variation in species composition

among sites or over time) is a key metric for

understanding the control of species composition in

biological communities and the functioning of eco-

systems (Magurran, 2004; Chalcraft et al., 2008). In

light of the large-scale environmental changes that are

currently challenging our planet, temporal analyses of

beta-diversity have been particularly insightful in

assessing community responses (Smol et al., 2005;

Hobbs et al., 2010; Korhonen, Soininen & Hillebrand,

2010; Soininen, 2010). Temporal beta-diversity has

been proposed as a more sensitive metric than

temporal changes in species richness because one

would expect that biological communities will expe-

rience changes in species dominance before any

constituent of the community is extirpated (Hille-

brand, Soininen & Snoeijs, 2010).

Observational and experimental studies have found

that beta-diversity is often related to mean ecosystem

productivity, but contrasting patterns have emerged

from different studies (Chase & Leibold, 2002; Chal-

craft et al., 2004, 2008; Donohue et al., 2009; Chase,

2010). For example, beta-diversity in aquatic commu-

nities has been shown to be both positively and

negatively related to mean ecosystem productivity

(Chase & Leibold, 2002; Donohue et al., 2009; Chase,

2010). Positive relationships between regional beta-

diversity and mean ecosystem productivity may be

because of an enhancement of stochastic community

processes (such as dispersal limitations, ecological

drift and differential extinction) in higher productivity

sites or intensified interaction between environmental

and stochastic community processes in productive

sites (Chase & Leibold, 2002; Chase, 2010). Con-

versely, a negative relationship between community

beta-diversity and mean ecosystem productivity has

been observed when anthropogenic nutrient loading

increased rapidly to high concentrations (Donohue

et al., 2009). These contrasting results, together with

an earlier study by Chalcraft et al. (2008), suggest that

the source, strength and dynamics of nutrient loading

are critical in predicting beta-diversity responses.

Nutrient loading in spatially subsidised ecosystems

such as sockeye salmon nursery lakes can be both

variable and non-directional over time, and thus, it

is not clear that these earlier studies allow us to

make predictions about community beta-diversity

responses. For example, the density of sockeye salmon

spawners can vary by orders of magnitude within a

few years (Burgner, 1991), greatly altering nutrient

concentrations in lakes (Gresh et al., 2000; Gregory-

Eaves et al., 2003). Therefore, to better isolate unique

effects of temporal and among-lake variation in nutri-

ent subsidies associated with salmon, we employed

centennial-scale lake sediment records to quantify

how the beta-diversity of diatom communities varied

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as a function of SDN. Recent developments in paleo-

limnology have shown that it is possible to track past

changes in SDN by conducting analyses of sedimen-

tary nitrogen isotope (d15N; Finney et al., 2000; Schin-dler et al., 2005). We conducted parallel sedimentary

diatom analyses to quantify diatom beta-diversity

patterns over three different scales: (i) across space, by

examining patterns among five lakes at a single point

in time (i.e. c. 1950 AD), (ii) through time (i.e.

c. 250 years), by analysing a sediment record that

captured large changes in SDN and (iii) across both

spatial and temporal gradients, by identifying the

pattern among five lakes over a c. 250-year period.

Methods

Study sites

Six lakes were selected along a large gradient of

modern sockeye salmon densities (c. 0–34.7 ·103 spawners per km2) from south-central and south-

western Alaska (Fig. 1 and Table 1). All lakes are deep

(mean depth = 29–49 m) and have a water residence

time of around 2–5 years, which are important for the

retention and attenuation of SDN loading (Holtham

et al., 2004). Frazer, Karluk and Red lakes are located in

the south-western corner of Kodiak Island (Fig. 1). We

used Frazer Lake as a reference system, as this lake had

no natural salmon population until 1951 when sockeye

were first introduced. Therefore, only the data from

the pre-salmon introduction period (i.e. prior to

c. 1950 AD) are used in this study. Among the five

salmon nursery lakes, Karluk and Red lakes have the

highest sockeye salmon escapement densities. Not

surprisingly, Karluk and Red lakes are currently more

nutrient-rich [mean summer total phosphorus (TP) is 8

and 34 lg L)1, respectively] than our other study

lakes. In contrast, Little Togiak and Nerka lakes have

lower TP concentrations and are located in the Bristol

Bay area of south-western Alaska (Fig. 1). Iliamna

Lake, the largest freshwater body in Alaska, has one of

the largest salmon runs in North America, but has only

an intermediate modern salmon density when these

data are scaled to its large surface area (2622 km2).

Sediment core collection and analyses

Sediment cores, ranging between 16 and 47 cm in

length, were collected in a deepwater depositional

zone of each lake using gravity corers. These cores

were then sectioned at intervals between 0.17 and

0.50 cm using a vertical extruder. The age models

used in this study were based on the application of the

constant rate of supply model to the activity of bulk

sediment 210Pb. Analyses of 137Cs and known tephras

were used to independently validate these age mod-

els. We relied on previously published age models of

Karluk, Frazer, Little Togiak, Nerka and Red lakes

(Finney et al., 2000; Gregory-Eaves et al., 2003; Schin-dler et al., 2005; Brock et al., 2007). For Iliamna, the210Pb and 137Cs activity profiles that were the basis for

our age model are presented in Appendix S1.

Sedimentary d15N analysis followed standard meth-

ods as described in Finney et al. (2000). Briefly,

homogenised sediment subsamples were measured

using a Finnigan Delta Plus or Europa 20 ⁄20 contin-

uous flow isotope ratio mass spectrometer at Univer-

sity of Alaska Fairbanks, University of Washington

and University of Regina. All analyses have a

measurement precision of ±0.2&. The d15N values

are expressed in standard d notation relative to

standard nitrogen of atmospheric air. As the total

nutrient loadings in lakes are composed of both

salmon nutrient input and non-salmon sources (e.g.

the watershed load; Selbie et al., 2009), we calculated

the percentage of nitrogen (%N) derived from spawn-

ing salmon using a two-member mixing model

(Schindler et al., 2005). This mixing model requires

sedimentary d15N data from local non-salmon lakes as

an estimate for the background d15N. The referenceFig. 1 Location of six study lakes from Alaska included in thisstudy.

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lakes included in the mixing models showed little

variation in sedimentary d15N during the past

300 years. Specifically, the average value with one

standard deviation (SD) was 3.32 ± 0.16& for Frazer

(reference lake for Karluk and Red lakes), 2.97 ±

0.18& for Grant (reference lake for Nerka and Little

Togiak) and 3.02 ± 0.28& for Tazimina (reference lake

for Iliamna Lake; Finney et al., 2000; Brock et al., 2007).All of our lake records had low atomic C:N ratios with

limited ranges of variation (i.e. the average value with

1 SD varied between 7.4 ± 0.6 in Iliamna and

10.1 ± 0.5 in Nerka), suggesting very limited input

of catchment-derived organic matter into our study

lakes (Selbie et al., 2009). As was reported in Krummel

et al. (2009), we found that Red Lake, our most

productive lake with the fastest sedimentation rates,

displayed signs of incomplete diagenesis (e.g. ele-

vated N content and lower than expected d15N values

in the upper 8 cm), and therefore, we have excluded

the top 8 cm from our analyses.

Sediment subsamples were prepared for diatom

analysis following standard procedures (e.g. Gregory-

Eaves et al., 2003). Approximately, 0.01 g freeze-dried

and well-mixed sediments were first treated with 10%

HCl to bring carbonates into solution. The slurrieswere

then allowed to settle for 24 h, before the supernatant

was aspirated and the remaining slurry rinsed with

deionised water. This rinsing procedure was repeated

for 4–5 times. A solution of 30% hydrogen peroxide

(H2O2) was used to oxidise the sediment organic

matter. Once the reactionwas complete, slurry samples

were aspirated and rinsed as before. Diatom slides

were prepared by pipetting the well-mixed slurry on a

coverslip and then mounting the air-dried solution in

highly refractive medium Naphrax" (Northern Biolo-

gical Supply, Ipswich, UK) or Zrax (http://micrap.

selfip.com:81/micrapp/media.htm).

For each sample, at least 400 diatom valves were

identified and counted along transects at a 1000·magnification with a Leica DM 2500 microscope

(Leica Mikrosysteme Vertrieb GmbH, Wetzlar, Ger-

many) with differential interference contrast optics.

We mainly adopted the diatom nomenclature and

taxonomy outlined in Krammer & Lange-Bertalot

(1986–1991). Similar to Gregory-Eaves et al. (2003),

we lumped Stephanodiscus minutulus and S. parvus in

our counts, as they were often indistinguishable

under the light microscope and have been reported

to have similar ecological preferences.

Numerical analyses

We used ordination and community dissimilarity

approaches to define changes in the composition of

diatom assemblages both among-lakes and within

each time series. Principle components analysis (PCA)

was used to define the main direction of variation in

the diatom assemblages across lakes (referred to

herein as the composite PCA) as they showed

relatively small turnover as shown by the axis-one

gradient length (i.e. <2.5 SD) in the detrended corre-

spondence analysis (Ter Braak & Smilauer, 2002).

A Jaccard’s dissimilarity distance metric (Magur-

ran, 2004) was applied to quantify the degree of

species turnover across spatial and temporal gradi-

ents, and then these values were correlated to our

metric of lake productivity and resource variability

(i.e. mean and absolute difference or variance in %N

derived from salmon, respectively). First, to quantify

beta-diversity across a spatial gradient, we conducted

pairwise sample analyses, comparing the c. 1950 AD

diatom assemblages for all possible lake combina-

tions. We selected this time interval because (i) it is

prior to the introduction of salmon into Frazer Lake

(and thus it can act as reference system) and (ii) the

dating of sediments at this time is robust (i.e. based on210Pb and 137Cs). Second, to quantify temporal beta-

diversity within a single lake record, we conducted

analyses of the Karluk Lake sediment record, which

had both strong variation in SDN (i.e. spanning a

gradient of 36–78% N derived from salmon) and a

high-resolution paleolimnological record over the past

c. 300 years with a relatively constant sedimentation

rate. Given that the strength of temporal species

turnover may vary depending on sampling duration

(Korhonen et al., 2010), we examined the relationship

between Jaccard’s dissimilarity and %N derived from

salmon with a time interval of approximately 10 years

(±1 year SD) between samples. We selected a decadal

time interval because previous analyses of salmon

time series data have shown significant variation at a

decadal scale (Mantua et al., 1997). We conducted

comparisons of adjacent samples in our temporal

analysis because species turnover through time is

unidirectional (Chalcraft et al., 2004). Third, to calcu-

late mean beta-diversity in each lake over the period

between 1700 and 1950 AD, we took the average of all

Jaccard’s dissimilarity distances between adjacent

samples in each time series.

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For all data analyses, we included taxa that had a

relative abundance of >1% in at least one sample

interval, which resulted in a common pool of 99 taxa.

This selection criterion was applied to stabilise the

variances. Square root transformation of species data

was also applied in all analyses. For all temporal

analyses, we examined trends during a common

temporal window across all lakes: the past c. 300 years

(c. 1700–2000 AD) for the PCA and then the past

c. 250 years (c. 1700–1950 AD) for beta-diversity anal-

yses, the latter of whichmatches the reference period of

Frazer Lake. Ordination and Jaccard’s dissimilarity

analyses were conducted in the R programme using

the Vegan package (R Development Core Team, 2008).

Results

Across all sites, diatom assemblages were composed

mainly of planktonic species (Fig. 2 and Appen-

dix S2). Small Cyclotella spp. were important compo-

nents of the assemblages in Frazer, Little Togiak,

Iliamna and Nerka lakes, whereas Stephanodiscus spp.

were abundant in Red and Karluk lakes. Application

of the broken stick model to our composite PCA

showed that the first three axes were statistically

significant (Jackson, 1993), and they explained 29.9,

16.4 and 13.1% of the total variance in the common

diatom assemblages across the six lakes, respectively.

Our composite PCA clearly identified that the key

environmental gradient across all samples was related

to SDN loading. Meso-eutrophic and eutrophic taxa

(e.g. S. minutulus ⁄parvus) were distinguished from

oligotrophic taxa such as Cyclotella spp. along PCA

axis 1 (Fig. 2a). Likewise, the PCA axis 1 separated

lakes based on their modern salmon densities. The

reference lake (Frazer) and unproductive lake (Little

Togiak) had negative PCA axis 1 loadings, whereas

the most productive systems generally had positive

loadings (Karluk and Red lakes). Consistent with

these qualitative observations, we found that the PCA

axis 1 sample scores were significantly correlated to

%N derived from salmon (r2 = 0.85, P < 0.001,

n = 127), suggesting that the PCA axis 1 sample

scores reflect changes in past SDN loading. The

second and third axes of the composite PCA were

not directly related to the %N derived from salmon

but reflect other dimensions of environmental change

(Fig. 2). For example, samples from the reference lake

(Frazer) and a low salmon density lake (Little Togiak)

showed more variation along PCA axes 2 and 3 than

the first axis. The PCA axis 3 shows a clear separation

between Iliamna Lake and other study lakes (Fig. 2b).

Aulacoseira distans, Cyclotella ocellata, Fragilaria nanana

and F. exigua either had relatively high abundances or

were only found in Iliamna Lake, reflecting its unique

lake typology (e.g. very large surface area and

volume, deep ephemeral thermocline, cold conditions;

(Burgner et al., 1969). Given the distinctness of both

the diatom community and the environmental condi-

tions of Iliamna Lake, we excluded this site from

further analyses.

Three lines of evidence demonstrate that both

among-lake and within-lake patterns of beta-diversity

exhibited significant and positive relationships with

the magnitude of change in %N derived from salmon

(Fig. 3a–c). First, the spatial beta-diversity among the

c. 1950 AD diatom communities was positively related

to differences in SDN (r2 = 0.69, P < 0.01, n = 10;

Fig. 3a). Second, a positive relationship was also

detected across temporal samples from Karluk Lake

(r2 = 0.34, P < 0.01, n = 19; Fig. 3b). Third, we detected

a significantly positive relationship in the spatio-

temporal analysis (r2 = 0.88, P = 0.018, n = 5; Fig. 3c).

In contrast, the relationships between beta-diversity

and mean %N derived from salmon were not consis-

tent (Fig. 3d–f). For example, we detected a negative

relationship between beta-diversity and mean SDN

across a temporal gradient (i.e. within the Karluk Lake

record; r2 = 0.26, P = 0.027, n = 10; Fig. 3e), but failed

to detect significant relationships both across lakes

around c. 1950 AD (P = 0.090; Fig. 3d) and in our

spatio-temporal analysis (P = 0.125; Fig. 3f).

Table 1 Selected limnological characteristics for the six studylakes. The mean epilimnetic total phosphorus (TP) is based onsummer measurements at each site

Lake

Surfacearea(km2)

Max depth(meandepth, m)

Spawnerdensity(1000 km)2)

Mean TP(lg L)1)

Frazer 16.6 59 (33) 0* 5.4†

Little Togiak 6 77 (30) 1.5 6.7‡

Iliamna 2622 301 (44) 2.2 –Nerka 201 164 (39) 2.4 6.5‡

Karluk 39.5 126 (49) 18.7 8.0†

Red 8.4 45 (29) 34.7 34.0†

*No sockeye salmon were present in Frazer Lake prior to1951 AD.†Schrof et al. (2000).‡Brock et al. (2006).

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Across each analysis, we detected different rela-

tionships between the mean SDN levels and the

metric of change in %N derived from salmon (i.e. the

% difference in SDN for the spatial and temporal

analyses, and variance in SDN in the spatio-temporal

analysis). The mean SDN levels and the difference in

SDN were negatively related through time (r2 = 0.27,

P = 0.024, n = 19; Fig. 3h). In contrast, we detected a

positive but non-significant or marginally significant

relationship between the mean %N derived from

salmon and the change in%N derived from salmon in

the spatial analysis (P = 0.155, n = 10; Fig. 3g) and in

our spatio-temporal analysis (P = 0.060, n = 5; Fig. 3i),

respectively.

Discussion

It is well established that SDN loading is an important

allochthonous nutrient subsidy to nursery lakes where

salmon densities are high (Koenings & Burkett, 1987;

Naiman et al., 2002) and can drive long-term nutrient

and algal dynamics (e.g. Gregory-Eaves et al., 2003). Inthis study, SDN loading contributed between 10 and

90% of the total nitrogen budget across salmon lakes

during the past 300 years (Fig. 3). Although we are

tracking changes in nitrogen (N) derived from salmon

in systems that are generally phosphorus (P) limited

(Goldman, 1960), we contend that this nutrient metric

is ecologically relevant because adult Pacific salmon

are rich in both P and N and have a relatively constant

N:P ratio. Furthermore, shifts in algal production (as

indicated by fossil pigments) are significantly related

to %N derived from salmon in most of our study

systems (Schindler et al., 2005; Brock et al., 2007; P.

Leavitt, unpubl. data). As such, our results build on a

growing body of literature that shows that salmon

nutrient flux is a main driver of algal composition and

production in Alaskan lakes with sizable salmon

nutrient loadings (Gregory-Eaves et al., 2003; Schindleret al., 2005; Brock et al., 2007). However, our analysis is

(a) (b)

Fig. 2 Principle components analysis (PCA) of the diatom assemblages during the past c. 300 years across six lakes showing thefirst two axes (a) and the first and third axes (b). Different symbols and colours represent sedimentary samples from each of the sixlakes. A line connects all samples from each lake with closed red and black colours indicating the surface and bottom samples,respectively. The arrows indicate the dominant diatom taxa with absolute eigenvalues above 0.5 along each PCA axis, withthe vector of the taxon STEMIP truncated because of its long species score of >3. The species codes in bold font representplanktonic taxa. The full names for diatom taxa shown are: ACHMIN Achnanthes minutissima, AULDIS Aulacoseira distans, AULISLA. islandica, AULSUB A. subarctica, CYCCOM Cyclotella comensis, CYCOCE C. ocellata, CYCPSE C. pseudostelligera, CYCTRI C.tripartita, FRABIN Fragilaria brevistriata var inflata, FRABRE F. brevistriata, FRACON F. construens, FRACPS F. construens/pseudo-construens, FRACRO F. crotonensis, FRAEXI F. exigua, STEHAN Stephanodiscus hantzschii, STEMED S. medius, STEMIP S. minutulus/parvus group, STENIA S. niagare. Note: using lake-specific PCAs, we found the %N derived salmon was strongly correlated to PCAaxis 1 scores for each of the following salmon nursery lakes: Little Togiak, Nerka, Karluk and Red lakes (r2 = 0.26–0.88, P < 0.05).

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the first to demonstrate that SDN input (measured as

%N derived from salmon) significantly affects diatom

beta-diversity, mainly through variation in salmon

nutrient input (i.e. environmental heterogeneity).

Over both time and space, we found that nutrient

variability, rather than mean ecosystem productivity,

showed a consistent relationship with diatom beta-

diversity in our salmon systems (Fig. 3). These results

agree with recent research that recognised the impor-

tance of the magnitude of environment variability in

shaping community patterns relative to long-term

mean conditions (Knapp et al., 2002; Shurin et al.,2010). Over time, environmental variation can provide

more temporal niche opportunities, which in turn can

allow different species to coexist (Chesson & Huntly,

1997; Anderies & Beisner, 2000) and can facilitate

temporal turnover. Nonetheless, several studies have

found that mean productivity was a significant pre-

dictor of community beta-diversity over space (Chase

& Leibold, 2002; Harrison et al., 2006; Donohue et al.,2009; Chase, 2010) and through time (Chalcraft et al.,

2004) and failed to detect the role of environmental

variability in driving beta-diversity. However, in

many of these cases, study sites with the same

average productivity were selected based on their

environmental similarity, and therefore, environmen-

tal variation was not allowed to play a role or did not

have a significant effect over the relatively short time

period of observation. In addition, our use of micro-

scopic algae reflects an important distinction from

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

Fig. 3 Scatter plots showing the correlations between the Jaccard’s dissimilarity, variation in %N derived from salmon and relatedmean %N among lakes (i.e. between pairwise samples from five study lakes at c. 1950 AD; plots a, d and g), through time (i.e.between adjacent samples of Karluk Lake at an approximately 10-year time interval during 1700–1950 AD; plots b, e and h), andacross both space and time (i.e. across five study lakes during 1700–1950 AD; plots c, f and i). Least squares fitting is added in theplot with a solid line representing a significant relationship (P < 0.05). Absolute difference in %N derived from between samples isused to indicate nutrient variability among lakes and through time, respectively. In the spatial-temporal analysis, averaged Jaccard’sdissimilarity distance and the variance in %N derived from salmon among samples are used to represent mean species turnoverand nutrient variability in each lake, respectively. Data with square root transformation are indicated with SQRT.

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previous analyses, which have been based primarily

on vascular plants (Chase & Leibold, 2002; Chalcraft

et al., 2004; Harrison et al., 2006; Chase, 2010), becauseboth of these kinds of primary producers differ

strongly in dispersal rates and in their generation

times (Korhonen et al., 2010). At a regional scale, the

dispersal capacity of diatoms can be great (Keatley,

Douglas & Smol, 2008; Soininen & Weckstrom, 2009;

Verleyen et al., 2009; Bennett et al., 2010), and thus,

diatom communities have the potential to react much

more quickly to environmental heterogeneity. Fur-

thermore, vascular plants generally show lagged

responses to annual variation in resource availability,

owing in part to their relatively long life cycles (Adler

& Levine, 2007) compared to diatoms (Reynolds,

2006). Finally, although a larger number of lakes may

have increased our ability to detect additional rela-

tionships between mean productivity (as %N derived

from salmon) and beta-diversity, the present study

had adequate power to demonstrate that variation in

SDN was a stronger driver of diatom beta-diversity.

Through careful site selection, we were able to

largely control for variables that could otherwise

confound or override the salmon nutrient fertilising

effect. For example, N mass budgets revealed that

nutrient loss because of smolt emigration was only a

small (i.e. <10%) component of total N flux in several

of our study sites (Moore & Schindler, 2004). Similarly,

although regional climatic change can structure dia-

tom beta-diversity patterns across broad latitudinal

gradients (Smol et al., 2005), we restricted our study

sites to narrow latitudinal and altitudinal gradients to

minimise climatic influence. Furthermore, analysis of

our reference systems confirms that direct effects of

climate have been minimal during the last 300 years,

with little change in nutrient biogeochemistry (i.e.

d15N, %N and C:N) or diatom community composi-

tion (Gregory-Eaves et al., 2003). Finally, although it is

possible that some of the among-lake differences in

beta-diversity (Fig. 3) might be a result of differences

in climate between the two regions where our sites are

located (i.e. Bristol Bay and Kodiak Island; Fig. 1), we

think that this is not of singular importance as the

greatest difference in beta-diversity among sites

occurred between sites in close proximity (i.e. Red

and Karluk lakes on Kodiak Island).

In conclusion, our paleoecological analysis showed

that salmon nutrient subsidieswere a significant driver

of diatom community composition and beta-diversity

in Alaskan lakes during the past several centuries. In

contrast to previous studies, we found strong support

for the hypothesis that beta-diversity is regulated by

variation in nutrient flux, which suggests that the

dynamic nature of resource subsidies derived from

migrating animals like Pacific salmon is a key driver of

community dynamics. In the context of ecosystem

management of salmon nursery lakes and their water-

sheds, our results strongly suggest that declines in

salmon populations and the formation of nutrient

deficits, as has happened in southern end of the Pacific

salmon range (Gresh et al., 2000), will eventually result

in a decrease in species turnover in freshwater ecosys-

tems. Thus, eliminating the spatial subsidy of nutrients

derived from salmon can ultimately erode the biodi-

versity of ecosystems that receive these nutrients.

Acknowledgments

This study was supported by the Gordon and Betty

Moore Foundation, Canadian Foundation for Innova-

tion, Natural Sciences and Engineering Research

Council and National Science Foundation. We thank

Lauren McGruthers and Bjoern Wissel for conducting

part of the diatom and stable isotope analyses,

respectively. We are grateful to Kathleen Ruhland

for statistical advice. We also thank Beatrix E. Beisner

and two anonymous reviewers for constructive com-

ments on earlier drafts of the manuscript.

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Supporting Information

Additional Supporting Information may be found in

the online version of this article:

Appendix S1. Total unsupported 210Pb and 137Cs

radioisotope activities for the sedimental profile of

Iliamna Lake.

Appendix S2. Diatom profiles of Little Togiak (a),

Nerka (b), Iliamna (c) and Red (d).

As a service to our authors and readers, this journal

provides supporting information supplied by authors.

Such materials are peer-reviewed and may be reor-

ganised for online delivery, but are not copy-edited or

typeset. Technical support issues arising from sup-

porting information (other than missing files) should

be addressed to the authors.

(Manuscript accepted 21 August 2010)

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