do spatial patterns of benthic diatom assemblages vary across regions and years?

Do spatial patterns of benthic diatom assemblages vary across regions and years?Author(s): Marius Bottin, Janne Soininen, Martial Ferrol, and Juliette Tison-RoseberySource: Freshwater Science, Vol. 33, No. 2 (June 2014), pp. 402-416Published by: The University of Chicago Press on behalf of Society for Freshwater ScienceStable URL: http://www.jstor.org/stable/10.1086/675726 .

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Do spatial patterns of benthic diatom assemblagesvary across regions and years?

Marius Bottin1,5, Janne Soininen2,6, Martial Ferrol3,7, and Juliette Tison-Rosebery4,8

1Irstea, UR REBX, 50 avenue de Verdun, 33612 Cestas cedex, France2Department of Geosciences and Geography, PO Box 64, University of Helsinki, Finland3Irstea, UR MALY, 3 bis quai Chauveau - CP 220, 69336 Lyon, France4Irstea, UR REBX, 50 avenue de Verdun, 33612 Cestas cedex, France

Abstract: Authors of several studies of the spatial distributions of microorganisms have shown strong geo-graphical patterns and stressed the importance of considering the spatial component explicitly when studyingassemblage–environment relationships. The processes underlying the patterns are still under debate because it isdifficult to separate the unique roles of dispersal limitation and mass effects from spatially structured variation inenvironment. We analyzed correlations between assemblage dissimilarity and geographical and environmentaldistances in a large French diatom database, subdivided into regions, years, and different water-quality levels,with multiple regression on distance matrices (MRM) and partial Mantel correlograms. Before we applied MRM,we identified the strongest environmental predictors with the BIO-ENV procedure, which selects the bestpredictors after testing correlations between distance matrices including every possible set of variables. Environ-mental control of assemblages was stronger than spatial factors in explaining assemblage patterns, but purelyspatial patterns also were significant at the national scale and within some regions. When we averaged environ-mental and biological data over 3 y, environmental variables accounted for more variability in assemblagestructure than relationships estimated with data from a single year. Assemblages in mountainous regions showedparticularly strong spatial patterns, perhaps because dispersal barriers hinder the exchange of colonists acrosssites. The strong spatial structure in the diatom data leads us to encourage researchers to divide ecoregions intosmaller areas, especially in mountainous landscapes, when studying assemblage–environment relationships. Wealso recommend the use of averaged biological and environmental data when developing biotypologies of bioticassemblages for environmental assessment and conservation.Key words: diatoms, rivers, spatial, barrier, dispersal, temporal

Biotic communities are assembled by the interplay of local(i.e., biotic interactions and environmental filtering) andregional (i.e., history, climate, and dispersal) forces. How-ever, macro- and microorganisms may differ in their re-sponses to such factors. The Baas–Becking hypothesispostulates that dispersal ability and local abundance ofmicroorganisms are so high that one can overlook the ef-fects of dispersal limitation and historical factors whenstudying their distributions (Finlay 2002, Fenchel and Fin-lay 2004). Hence, for example, diatom assemblages havemostly been studied through the prism of: “everything iseverywhere, but the environment selects”. From this pointof view, one might assume that diatom assemblage com-position is driven purely by environmental factors and thatthe noise observed in diatom–environment relationshipsis mostly a result of sampling variability of environmental

and biological measurements and unmeasured environ-mental variables.

Large diatom data sets have been used to construct re-lationships between species and environmental variables(such as ionic balance and nutrient concentrations; Pota-pova and Charles 2003), and the wide use of diatom as-semblages in bioassessment programs (Stoermer and Smol1999, Hill et al. 2000) might have strengthened the idea ofa direct relationship between assemblage composition andlocal environment. However, evidence has emerged thatprocesses other than environmental filtering act on dia-tom distributions. For example, biotic relationships (Burk-holder et al. 1990, McCormick 1996), historical events,such as long-term diversification, dispersal between thecontinents (Vyverman et al. 2007, Vanormelingen et al.2008), and dispersal limitation at a finer scale (Martiny

E-mail addresses: [email protected]; [email protected]; [email protected]; [email protected]

DOI: 10.1086/675726. Received 07 December 2012; Accepted 24 September 2013; Published online 20 February 2014.Freshwater Science. 2014. 33(2):402–416. © 2014 by The Society for Freshwater Science.



et al. 2006, Soininen 2007, Verleyen et al. 2009, Heino andSoininen 2010, Astorga et al. 2012) may account for someof the noise in species–environment relationships.

Authors of several studies on the relationships betweendiatom assemblages, environment, and space have shownstrong geographical patterns in diatom distribution (Pota-pova and Charles 2002), and have stressed the importanceof considering spatial structure explicitly (Tison et al.2005, Potapova and Charles 2007). The processes underly-ing the patterns are still being debated. However, it is diffi-cult to separate the unique roles of dispersal limitation(i.e., neutrality: variation of composition of assemblages iscaused by random dispersion of species) and mass-effect(i.e., metacommunity processes implying density-drivencolonization dynamics partly independent from environ-ment; Mouquet and Loreau 2003) from spatially struc-tured variations in environmental variables. Some impor-tant environmental factors also might act indirectly byplaying a role in the colonization–extinction dynamics. Forexample, the elevational gradient is correlated with manyimportant environmental variables (temperature, amountof nutrients, human population density, hydrology), butalso with dispersal processes associated with geographicalbarriers. Similarly, authors of several large-scale studieshave reported homogenization of communities by distur-bances (e.g., Tison et al. 2005, Donohue et al. 2009). How-ever, the relationships of these variables with spatial factorshave rarely been considered in microorganismal commu-nities.

Characterizing the temporal variation in the spatialstructure of assemblages also is important to understandthe role of deterministic (environmental) and stochastic(dispersal-related) processes on assemblages over time. De-pending on the importance of these different processes oncommunities, spatial structure may be detected even in arelatively small spatial extent, or environmental forcingand dispersal processes may shape the global distributionof diversity. Thus, examining how scale-dependent the spa-tial structure is among diatom assemblages might also shedlight on the processes underlying the spatial patterns.

Here, our aims were to: 1) examine the spatial structureof diatoms at the national scale in France and at finer, re-gional scales, 2) examine whether reference and impactedsites (i.e., more nutrient-rich sites) are spatially structuredto the same degree, and 3) examine the interannual varia-tion of the spatial patterns in the assemblages. We tested4 main hypotheses: 1) The influence of spatial factors onassemblages should decrease with decreasing spatial ex-tent (Soininen 2004, Verleyen et al. 2009), and assem-blages should be only weakly spatially structured within aregion, i.e., at the metacommunity scale (Donohue et al.2009, Soininen et al. 2011). We hypothesize that this trendoccurs because assemblages on larger scales are subject tostronger dispersal limitations and longer environmental

gradients (Soininen et al. 2011). 2) Human-impacted sitesshould have a weaker spatial structure and more homoge-nized assemblages across space because assemblages atthese sites comprise only the most pollution tolerant spe-cies throughout the study area (Soininen et al. 2004). Con-versely, at reference sites, assemblages include a larger suiteof species with associated larger variation in assemblages inspace. Moreover, at impacted sites, thicker biofilms shouldbe less affected than thin biofilms by finer-scale variationsin environment resulting in more homogeneous assem-blages (Burkholder et al. 1990, Passy 2007a). 3) Data ondiatom assemblages that are averaged over a longer timeshould show stronger relationships to the averaged physi-cochemical variables than to the temporal snapshots of theassemblages. We hypothesize that this pattern occurs be-cause averaging in time diminishes the effects of measure-ment errors and the potential influence of probabilisticevents. 4) Spatial patterns should be stronger in mountainassemblages than in lowland assemblages because dispersalis limited by topographic irregularities and because moun-tainous regions show strong environmental and topo-graphic variation at smaller scales.

To test these hypotheses, we analyzed a very largeFrench diatom data set. This large amount of data allowedus to address simultaneously the spatial patterns in theassemblages and potential underlying factors at multiplescales (i.e., across the whole study area and within separatesubsets of the data). We examined the spatial patterns atreference (near-pristine) and impacted sites separately. Wealso considered practical issues related to bioassessment,and we suggest methodological avenues to decrease thenoise in spatiotemporal diatom data and to separate ap-propriately regions in which assemblages are compared forbiomonitoring purposes.

METHODSData

We used a large diatom data set from France, collectedby the Water Agencies between 2007 and 2009. The data-base consisted of data collected from different networks allover France. Most of the data used in our study (>95%)originated from the “supervising and assessment network”(“Réseau de Contrôle et de Surveillance”). The aim of thisnetwork is to give a global view of water-quality patternsand trends at the national scale, in accordance to the Eu-ropean circular DCE 2006/16 (13 July 2006). Therefore,this network does not focus on particular conditions of thewater, such as local pollution or disturbances, but the datacovered both small headwater streams and large rivers.The database includes local physicochemical data from thesampling sites and diatom counts for georeferenced sites.The samples were collected from stones with a toothbrushand scalpel during the low-flow period to avoid seasonal

Volume 33 June 2014 | 403



variability in biotic assemblages and local physicochemicalvariables. All diatom samples were collected according toa standardized method (NFT 90-354; AFNOR 2007).

Before diatom compositional analysis, samples were di-gested in boiling H2O2 (30%) and HCl (35%) and mountedin a high-refractive-index medium (Naphrax®, refractive in-dex = 1.74; Northern Biological Supplies Ltd, Bolton, UK).Diatom species were identified at 1000× magnification(Leitz DMRB light microscope; Leica, Wetzlar, Germany)(400 valves per slide) with the aid mainly of keys publishedby Krammer and Lange-Bertalot (1986–1991) by examin-ing permanent slides of cleaned diatom frustules. As ex-pected for such a large data set, diatom species were identi-fied by different analysts. The number of valves identifiedranged from 386 to 603 (with 95% of the counts rangingfrom 396–419). To mitigate inconsistencies in taxonomicidentification across different analysts, we lumped togetherseveral subvarieties into their respective higher taxonomicunits (i.e., species or varieties). The resulting biological datamatrix, expressed in relative abundance of species, com-prised 1091 species in 2920 biological samples collectedfrom 1493 different sites. Diatom relative abundance datawere arcsin√(x)-transformed to reduce variations in abun-dance and to enhance the information brought by rare spe-cies.

Physicochemical data consisted of average values ofeach variable for the period between 45 d before diatomsampling and 15 d after sampling (which corresponds to amean of 2.3 measurements for each value of the table). Weincluded 15 variables: water temperature, pH, altitude, dis-solved O2, suspended matter, biological O2 demand aver-aged over 5 d (BOD5), alkalinity, conductivity, NO3

−, NO2−,

NH4+, Kjeldahl N, orthophosphate, total P, and organic

C. When it significantly improved the normality of thevariables (suspended matter, conductivity, NO3

−, NO2−,

NH4+, Kjeldahl N, orthophosphate, total P, and organic C),

we log(x)-transformed physicochemical data. We scaledand centered (by subtracting the variable means and divid-ing each value by the standard deviation of the variable) allvariables to avoid overweighted variables in the dissimilar-ity calculations.

The largest annual data set included all sampling sitesand was collected in 2009. We refer to this data set as thecomplete 2009 data set. Data from the rest of the samplingyears included a subset of the sites sampled in 2009. Ourdata and analyses were separated by 3 categorical vari-ables: region, water-quality class, and year.

Variation among regions Our data included informationon different Hydro-Eco-Regions (HER), based on naturalcharacteristics of geology, relief, and climate (Wasson et al.2002). HERs have been described as a general geographicframework for all aquatic organisms, but HERs are not themost appropriate framework when considering diatoms.

Based on the relationships between assemblage composi-tion and the driving environmental gradients (mainly nat-ural alkalinity and altitude), Tison et al. (2005) defined 5main diatom biotypes across all the HERs. Based on thesebiotypes, we simplified the HER classification, resulting ina final set of 12 regions. Therefore, data from year 2009were divided into 12 regions for studying patterns withinregions (Fig. 1A, Table 1). In addition to geology, relief,and climate, we characterized the topographic variationof the regions by calculating their interquartile ranges inaltitude.

Reference vs impacted sites The influence of water qual-ity (mostly the effect of nutrient enrichment) on the spa-tial structure of the assemblages was assessed by separat-ing reference sites (near-pristine sites used in Europeanbioassessment programs) and impacted sites in the com-plete 2009 data set. European reference sites are selectedby a process comprising several steps (European Commis-sion 2003). Candidate reference conditions are checkedfor potentially harmful anthropogenic activities with geo-graphic screening of land cover. Then local water agenciesselect reference sites from these candidate river parts us-ing expert knowledge and quantitative criteria of ecologi-cal integrity (threshold values for chemical variables andbiological indices). To facilitate comparison, we randomlyselected the same number of reference and impacted sitesfrom each region to construct this data set (Fig. 1B).

Temporal variation A fraction of the sites was sampledrepeatedly over 3 consecutive years (2007, 2008, 2009)(Fig. 1C). We used these data to examine the influence oftemporal variability on diatom assemblage structure. Weused the same number of reference and impacted sites foreach region in these analyses to avoid a greater influenceof the more numerous low-water quality sites. Data fromyear 2009 were then compared to those of 2007 and 2008by creating 3 sets of data corresponding to 3 consecutiveyears including data from the same sites. We averagedvalues of diatom and physicochemical data over 3 sam-pling years to examine the effects of temporal variability.

Data analysisBIO-ENV procedure The BIO-ENV procedure (Clarke andAinsworth 1993) was first used to identify the subset ofenvironmental variables with the highest nonparamet-ric correlation with the assemblage dissimilarities. In thismethod, Spearman correlation indices are calculated be-tween assemblage dissimilarities (Bray–Curtis) and envi-ronmental distances (Euclidean) for all possible subsets ofthe variables. Including poor predictors in analyses in-creases noise in the relationships between environmentand assemblages, leading to poorer correlations than thoseobtained with important predictors only. This procedure

404 | Spatial patterns in diatom assemblages M. Bottin et al.



Figure 1. Maps of the sites sampled in France, depending of the 3 categorical variables : regions, quality and year. A.—Map of allsites sampled in 2009. Symbols correspond to the 12 regions identified as different data subsets. B.—Map of the reference andimpacted sites sampled in year 2009. C.—Map of the sites sampled consecutively in years 2007, 2008 and 2009.



Table

1.Mean(±

SD)values

forenvironm

entalcharacteristicsof

thedifferentregion

s

Region

Num

berof

sites

Altitud

e

pHAlkalinity

(mg/L)

Con

ductivity

(μs/cm

)TotalP

(mg/L)

NO

3−(m

g/L)

Reference

Impacted

Interquartile

rang

eMean

Highlyacidicplains

Landes

011

1736.73±5.9

7.15

±0.16

7.03

±1.55

204.47

±29.09

0.06

±0.01

9.88

±2.46

Circumneutralm

id-altitud

e

Brittany

6140

5448.92±3.94

7.52

±0.03

13.84±0.36

331.11

±11.65

0.15

±0.01

20.63±1.17

Voseges

311

180.5

365.86

±29.54

7.38

±0.07

3.34

±0.44

117.46

±8.74

0.1±0.02

4.06

±0.42

Lim

ousinandAuvergn

e10

107

134

312.81

±14.11

7.56

±0.04

7.92

±0.45

202.75

±16.56

0.1±0.01

5.86

±0.46

Centre

118

2099.68±3.91

7.89

±0.15

11.57±1.06

247.21

±21.37

0.15

±0.03

8.1±0.94

ArdennesandFlanders

015

109

57.73±14.52

8.11

±0.05

25.62±1.52

763.56

±61.68

0.35

±0.09

17.46±2.07

Crystallin

ehigh

land

Pyrenees

228

352.75

576.73

±61.55

7.78

±0.05

8.63

±0.52

170.45

±17.46

0.04

±0

1.89

±0.32

MassifCentral

10101

479

529.25

±26.56

7.68

±0.05

7.76

±0.4

137.14

±10.24

0.05

±0.01

4.06

±0.31

Corsica

216

257

256.67

±81.24

7.83

±0.07

8.34

±0.88

194.89

±33.42

0.1±0

2.06

±0.06

Calcareou

salkalin

eplains

South

9209

122.25

129.84

±6.96

7.99

±0.02

15.86±0.39

469.62

±19.41

0.08

±0.01

10.16±0.76

North

19328

128

119.72

±4.21

7.96

±0.01

18.98±0.32

575.07

±15.49

0.12

±0.01

17.8±0.64

Calcareou

shigh

land

Alps

1271

479

541.93

±37.12

8.1±0.02

14.12±0.41

428.08

±15.56

0.05

±0.01

4.29

±0.65



indicates the strongest relationship possible between theassemblages and the environment, and thus allows thespatial control on the assemblages to be tested more effec-tively.

Dissimilarity matrices We constructed 3 dissimilarity(or distance) matrices for each set of data to describe bio-logical, environmental, spatial gradients, and their corre-lations. We used the Bray–Curtis index on transformeddiatom data between all site pairs to describe β-diversitygradients. We computed environmental gradients by cal-culating Euclidean distances on scaled, transformed, andBIO-ENV-selected environmental variables.WeusedEuclid-ean distance to calculate geographic distances. One couldargue that overland distances are not optimal to assessmovements of biota in river systems (Landeiro et al. 2011)because many diatom cells are more likely to disperse viawater than via wind (or animals; see Kristiansen 1996 fora more exhaustive list of dispersal processes), but Finnet al. (2006) showed that straight-line distance was aneffective surrogate for more complex connectivity mea-sures. Several previous investigators have used Euclideandistances for similar purposes (e.g., Heino and Soininen2010, Astorga et al. 2012). Euclidean distances representrealistic connectivity especially for small unicellular organ-isms that disperse across sites easily via the air. More-over, stream-network distances are not suitable for datain which sites span multiple drainage basins.

Multiple regressions on distance matrices We tested forcorrelations among biotic, environmental, and geographi-cal distance matrices with multiple regressions on distancematrices (MRM). MRM is an extension of partial Mantelanalyses (Lichstein 2007). MRM conducts multiple linearregression (although it can be applied with nonlinearmodels) between dissimilarity matrices. The regressioncoefficients are tested with an appropriate permutationprocedure (here 9999 permutations), which holds constantsome important properties of distance matrices. We useddissimilarities between biological assemblages as the re-sponse matrix in 3 different sets of predictors: 1) environ-mental distances in models called environmental models,2) geographical distances in models called spatial models,and 3) both geographical and environmental distances inmodels called spatial–environmental models. We used re-gression coefficients of the environmental and spatial dis-tance matrices to gauge the relative effect of environmentaland spatial factors on biotic assemblages (correspondingto pure environmental and pure spatial effects). To avoidbias in the comparison of coefficients, we scaled distancematrices before applying MRM.

Mantel correlograms We used partial Mantel correlo-grams to describe the extent to which spatial autocor-relation exists in the diatom data in different regions. Plot-

ting the partial Mantel statistics (correlation betweencompositional dissimilarity matrices and geographical dis-tances while controlling for environmental distances)against distance classes allowed us to show the geographi-cal distances at which assemblages were more (or less)similar than expected. The number of distance classes con-sidered was determined using Sturge’s rule (Legendre andLegendre 1998). We tested the Mantel statistics for eachdistance class with 1000 permutations and calculated thedegree of significance using Bonferroni correction formultiple tests. Positive values indicate that assemblageswithin the given distance class are more similar to oneanother than to assemblages outside that class. Negativevalues indicate that assemblages within the distance classare more dissimilar to one another than to assemblagesoutside that class. When significant, values indicate thatsimilarity among assemblages within the distance class isgreater (or lower) than expected by chance. We conductedpartial Mantel correlograms in R (version 2.12.1; R Projectfor Statistical Computing, Vienna, Austria) and with amodified version of the pmgram function in the ecodistpackage (Goslee and Urban 2007). We ran all data analy-ses under R with the vegan (Oksanen et al. 2011) and theecodist packages (Goslee and Urban 2007).

RESULTSBest environmental predictors (BIO-ENV procedure)

BIO-ENV analyses identified different sets of environ-mental variables depending on the region and samplingyear, resulting in selection of 1 to 9 variables (Table 2). Inthe complete 2009 data set (for the whole of France), thebest set of predictors included water pH, conductivity, or-ganic C, water temperature, and altitude. These variableswere also the 6 most frequent predictors over the differentdata sets.

The set of best predictors varied strongly among re-gions. Altitude was a poor predictor for the 2 regions withthe widest altitudinal range: Alps and Massif Central. Onlydifferent fractions of N and P were included in the bestmodel in the Alps. Conductivity was the sole variablein the Massif Central subset. The best predictors variedslightly between reference and impacted sites. The smalldifferences included the fact that conductivity, total N, andorthophosphates were selected as best predictors in thereference data set, where they reflect differences in thenatural trophic states of the streams. All 6 variables se-lected for impacted sites also were selected for referencesites. The best predictors differed clearly between years,even though sampled sites were the same for all 3 consec-utive years.

Multiple regression on distance matricesComplete 2009 data set Spatial–environmental modelshad the highest R2 values, and spatial models had thelowest (Table 3). In spatial–environmental models, coef-




Table

2.Bestenvironm

entalpredictors

forassemblagecompo

sition

identified

byaBIO

-ENVprocedure.Selected

predictors

aremarkedby

acrossin

thedifferentdata

sets.

Alk

=alkalin

ity,Con

d=cond

uctivity,T

N=totalN,T

P=totalP,B

OD5=biological

O2demandaveraged

over

5d,

DO

=dissolvedO

2,S

Mat

=suspendedmatter,Tem

p=

temperature,A

lt=altitude.

Dataset

No.

sites

No.

variables

pHAlk

Con

dTN

NO

2NO

3NH

4PO

4TP

BOD5

Org

CDO

SMat

Tem

pAlt

2009

data

set

1129

5x

xx

xx

Regions

Highlyacidicplains

Landes

112

xx

Circumneutralm

id-altitud

e

Brittany

146

5x

xx

xx

Voseges

146

xx

xx

xx

Lim

ousinandAuvergn

e117

6x

xx

xx

x

Centre

192

xx

ArdennesandFlanders

152

xx

Crystallin

ehigh

land

Pyrenees

303

xx

x

MassifCentral

111

1x

Corsica

187

xx

xx

xx

x

Calcareou

salkalin

eplains

South

218

4x

xx

x

North

347

5x

xx

xx

Calcareou

shigh

land

Alps

832

xx

Reference

vsim

pacted

Reference

749

xx

xx

xx

xx

x

Impacted

746

xx

xx

xx

Tem

poralvariation

Averageddata

607

xx

xx

xx

x

2007

604

xx

xx

2008

609

xx

xx

xx

xx

x

2009

606

xx

xx

xx

408



Table

3.Results

ofthemultipleregression

son

distance

matrices(M

RM)in

thecomplete2009

data

setandin

thedifferentregion

s.Env

iron

mentcolumns

correspo

ndto

the

regression

coeffi

cients

ofenvironm

entaldistancesbetw

eensites,andcoordinate

columns

correspo

ndto

theregression

coeffi

cients

forspatialdistances.Average

distancesare

means

ofallpairwisedistancesbetw

eensitesin

theregion

s.In

mod

elequation

s,allintercepts

(not

show

n)werelower

than

10−15.W

henpartialregression

coeffi

cientare

sign

ificantly

differentfrom

0,asterisksshow

thelevelof

sign

ificanceforthepredictorconcerned.

ns=no

nsignificant,*

p<0.05,**p<0.01,***

p<0.001.

Dataset

Num

berof

sites

Maxim

umdistance

(km)

Average

distance

(km)

Env

iron

mental

mod

els

Spatialm

odels

Spatial–environm

entalm

odels

Env

iron

ment

R2

Coo

rdinates

R2

Env

iron

ment

Coo

rdinates

R2

Com

plete2009

1129

1316.74

399.63

0.42***

0.173

0.23***

0.053

0.39***

0.18***

0.205

Regions

Highlyacidicplains

Landes

1176.71

37.48

0.63***

0.4

-0.0051(ns)

2.60E-05

0.66***

0.13

(ns)

0.42

Circum-neutralmid-altitud

econtext

Brittany

146

374.80

141.61

0.46***

0.21

0.26***

0.068

0.41***

0.12***

0.22

Vosges

14154.28

66.35

0.48***

0.23

0.29*

0.085

0.43***

0.19

(ns)

0.26

Lim

ousinandAuvergn

e117

327.86

130.08

0.5***

0.25

0.2***

0.04

0.49***

0.18***

0.28

Centre

1985.94

36.73

0.58***

0.33

0.043(ns)

0.0018

0.58***

0.012(ns)

0.33

ArdennesandFlanders

15142.49

66.57

0.53**

0.29

0.017(ns)

0.00029

0.53**

0.007(ns)

0.29

Crystallin

eHighland

Pyrenees

30335.63

111.59

0.51***

0.26

0.18*

0.032

0.5***

0.13

(ns)

0.28

MassifCentral

111

314.22

123.04

0.38***

0.14

0.19***

0.036

0.36***

0.15***

0.16

Corsica

18142.94

52.23

0.3**

0.091

0.024(ns)

0.00057

0.3**

0.036(ns)

0.092

Calcareou

sAlcalineplains

South

218

718.95

226.01

0.3***

0.088

0.14***

0.02

0.28***

0.08**

0.094

North

347

767.66

285.04

0.37***

0.14

0.14***

0.019

0.36***

0.088***

0.14

Calcareou

sHighland

Alps

83307.45

116.65

0.2*

0.041

0.33***

0.11

0.19*

0.32***

0.14

409



ficients were consistently higher for environmental dis-tances than for spatial distances, showing a stronger linkbetween environment and assemblage compositions thanbetween space and assemblage compositions.

Among-region variation In all 12 regions, assemblagedissimilarity was significantly related to environmental dis-tance, and in 8, assemblage dissimilarity was significantlyrelated to spatial distance (Table 3). In models with bothenvironmental and spatial distances, assemblage dissimi-larity was related to environmental distance in all regionsand to geographical distance in only 6 regions. The Alpswas the only region with a larger regression coefficient forspatial than for environmental distance. Smaller regions(regions in which average pairwise distance between siteswas <115 km) showed no effect of purely spatial factorsin spatial–environmental models, whereas purely spatialfactor were significant in larger regions. We checked forrelationships between regression coefficients for geograph-ical distances in spatial–environmental models and 2 re-gional variables: average distances within each region andaltitude interquartile range within a region. Relationshipsbetween average distance between sites within a region andthe same regression coefficient (Fig. 2A) were less clearthan relationships between altitude interquartile ranges ofregions and regression coefficients related to geographicdistances (Fig. 2B).

Reference vs impacted sites MRM results revealed dif-ferences in the relative importance of environmental andspatial factors in explaining assemblage dissimilarity atreference and impacted sites (Table 4). Environmentalmodels explained more of the assemblage dissimilarity atreference sites, whereas spatial models explained moreassemblage dissimilarity at impacted sites. The regressioncoefficient of the spatial–environmental models showedthe same pattern. Coefficients for environmental and geo-graphical distances were similar at impacted sites, whereasthe coefficient for geographical distances was much lessthan the coefficient for environmental distance and wasnot significant at reference sites.

Temporal variation The magnitudes of relationships be-tween assemblage dissimilarity and environmental dis-tance or between assemblage dissimilarity and geographicdistance were similar depending on sampling year (Ta-ble 5). The models described assemblage dissimilaritiesbetter when biological and environmental data were aver-aged over 3 y than for any 1 y.

Partial Mantel correlogramsIn the complete 2009 data set, Mantel r values were

significant for each distance class (Fig. 3). The partial cor-relogram showed decreasing Mantel r with increasing dis-

tance, i.e., assemblages became more different as spatialdistance increased. The first negative Mantel r was foundat ∼500 km. The correlograms also were often decreasing,except in the smallest regions, where Mantel r were rarelysignificant and without an evident pattern. The first signif-icant negative values of Mantel r (i.e., the smallest distanceclass in which assemblages were more different than ex-pected by chance when controlling for environment) werefound at different distance classes depending on region,e.g., at <200 km for mountainous regions, such as Li-mousin, Auvergne, Massif Central, and the Alps, but at>200 km in the Southern plains. In the Northern plains,the first negative distance class was found at >500 km.

DISCUSSIONEnvironmental and spatial control of assemblages

Our data showed notable environmental control of ben-thic diatom assemblages, both at the scale of the whole ofFrance and at the regional scale. The pattern was consis-tent in all regions, for varying water qualities, and throughthe study years. Our data also support the idea that diatomassemblages exhibit a strong spatial component at multiplespatial scales. Strong spatial patterns (even when consider-ing the environmental variables) in lotic diatom assem-blages also have been found by other researchers using alarge variety of methods such as Redundancy Analysis(Passy 2007b), Canonical Correspondence Analysis (Soi-ninen 2004, Smucker and Vis 2011), direct ordination withvariance partitioning (Potapova and Charles 2002, Soi-ninen 2004), analysis of spatial autocorrelation (Heino et al.2010), and Mantel tests (Astorga et al. 2012). Althoughthese studies had some limitations related to inference ofspatial processes (Gilbert and Bennett 2010, Smith andLundholm 2010, Tuomisto et al. 2012), conclusions drawnfrom many studies done with a variety of methods indicatethat lotic diatom assemblages are spatially structured overa wide range of scales. Moreover, diatom assemblages arecharacterized by high niche overlap, and few sites with nospecies in common exist. Therefore, issues related to thelength of compositional range raised by Tuomisto et al.(2012) do not concern most diatom assemblages. Two al-ternative hypotheses may explain these spatial patterns:1) diatom dispersal is influenced by mass-effect or neutral-ity or 2) assemblages are structured by some unmeasuredenvironmental variables or biotic processes (e.g., competi-tion, grazing, or light conditions).

Scale dependenceSpatial control of communities is scale-dependent, and

spatial structure is expected to be stronger at a large spa-tial scale than at a small scale because of stronger dispersallimitation and longer environmental gradients at largerscales (Green et al. 2004, Soininen et al. 2007, 2011, Ben-nett et al. 2010). In our data, the smallest regions showed




no significant effect of spatial distance on assemblage dis-similarities. The smallest regions in our data set often in-cluded <30 sites, so conclusions about the weaker spatialpatterns at smaller scales are somewhat speculative atpresent. However, an increase in the size of the regionswas not paralleled by an increase in the strength of thespatial patterns, as indicated by the magnitude of the re-gression coefficient of the geographical distances in thespatial–environmental model.

At the national scale and within all regions except thesmallest ones, correlograms showed decreasing relation-ships of Mantel r with spatial distances. Such a patternshows that the degree of spatial structure in the commu-nities is somehow related to spatial extent. However, theshapes of the curves, and the distances where the firstsignificant negative Mantel r were found varied widely be-tween the regions studied. We speculate that these differ-ences are related to some key features of the regions, suchas connectivity between sites or topographic irregularities,

that can act as confounding factors when teasing apart thescale effect on spatial autocorrelation (see discussion onaltitude and topography below). Confounding factors alsomight explain the poor relationship between region sizeand the importance of spatial effects on communities.

Water qualityContrary to our hypothesis, spatial effects were stron-

ger at impacted than at reference sites. Homogenization ofassemblages by human impacts, such as eutrophication,has been documented in some studies (Rahel 2002, Don-ohue et al. 2009). Increased primary production subse-quent to high human input of nutrients is now consideredto be one of the most important stressors on river eco-systems (e.g., Mainstone and Parr 2002). However, in ourdata, spatial effects were more important and environmen-tal effects were less important at impacted sites. The rea-son for such an outcome may not be easily found, butsome investigators found similar patterns along water-

Figure 2. Relationship between partial regression coefficients of geographical distances in multiple regressions on distance matrices(MRM) and average geographic distances (mean of all the pairwise distances between sites) of the different regions (A) or altitudeinterquartile ranges of the different regions (B). See Fig. 1A for key to symbols (regions). Asterisks over symbols of regions describethe level of significance of the regression coefficients (* p < 0.05, ** p < 0.01, *** p < 0.001).

Table 4. Results of the multiple regression on distance matrices (MRM) for impacted and reference sites in year 2009. See Table 3 forexplanations of table structure and symbols.

TypeNumber of

sitesMaximum

distance (km)Average

distance (km)

Environmentalmodels Spatial models Spatial–environmental models

Environment R 2 Coordinates R 2 Environment Coordinates R 2

Referencesites

74 1231.46 390.68 0.5*** 0.25 0.21*** 0.044 0.48*** 0.084 (ns) 0.25

Impactedsites

74 1301.73 409.68 0.27*** 0.072 0.27*** 0.073 0.23*** 0.23*** 0.12




Table 5. Results of the multiple regression on distance matrices (MRM) for impacted and reference sites in years2007–2009. Numberof sites = 60, maximum distance between sites = 768.1 km, average distance between sites = 272.9 km.

Temporal variation

Environmental models Spatial models Spatial–environmental models

Environment R2 Coordinates R2 Environment Coordinates R 2

2007 0.53*** 0.29 0.3*** 0.088 0.49*** 0.15*** 0.3

2008 0.54*** 0.29 0.36*** 0.13 0.47*** 0.19*** 0.32

2009 0.57*** 0.32 0.32*** 0.11 0.52*** 0.14** 0.34

Averaged data 0.68*** 0.46 0.41*** 0.17 0.62*** 0.16*** 0.48

Figure 3. Partial Mantel correlograms in the complete 2009 data set and in the different regions. Black symbols indicate significant(p < 0.05) Mantel r after Bonferoni correction.



quality gradients (Pan et al. 2000, Chase and Leibold 2002).Chase (2007) showed that environmental harshness in-creased the importance of stochastic processes, and thus,strengthens the spatial patterns of data. In our data, im-pacted sites might represent harsh conditions if nutrientloads are very large or especially if associated with otherpollutants (such as toxic compounds). However, one alsocould argue that impacted sites are favored habitats formany species because communities may not face nutrientlimitation (Passy 2008). However, we did not explicitly ac-count for nutrient limitation, pollution, or eutrophicationin our analyses, so further conclusions would be specula-tive.

Moreover, the impacted sites studied covered a widerange of environmental conditions, and a wide range in se-verity of impacts might have had differing effects on as-semblages, resulting in unexpected patterns in our data.A better understanding of the effects of pollution wouldrequire a more precise distinction between water-qualitylevels, and a coupled analysis of the effects of nutrient en-richment and other types of pollution.

Temporal variation of spatial attributesThe relative importance of effects of spatial patterns

and environmental gradients on diatom assemblages wascomparable during the 3 consecutive years. However, asexpected, averaging environmental and assemblage datafrom these 3 y yielded an even better correlation betweenenvironment and assemblages without affecting the cor-relation between spatial factors and assemblages. Theseresults suggest that spatial patterns act as constant con-straints on assemblages. The contribution that spatial fac-tors have on noise in the environment–assemblage rela-tionships could be reduced by considering a longer periodof time, as suggested by Kelly et al. (2009). These con-siderations stem from the effect of dispersal-related fac-tors: the presence of some of the species might be spo-radic, and taking into account a longer period of timemight increase the probability of finding a species mostprobably present in a given environment. At the sametime, this particular pattern could result from the reduc-tion of measurement errors when we used averaged phys-icochemical and biological data, whereas spatial coor-dinates remain constant over time. Future investigatorsshould further examine spatiotemporal patterns of diatomassemblages, as suggested by Soininen (2010) and Passy(2007b), to increase the understanding of biogeographicalstructure of diatom diversity.

Altitude and topographyWe hypothesized that elevation gradients may shape β-

diversity patterns. As expected, we found that altitudi-nal gradients affected the patterns in diatom β diversity.

However, altitude covaries with many other factors, suchas hydrology, light conditions, human population density,habitat diversity, and productivity, so disentangling its di-rect effect on biological communities is difficult. None-theless, altitude is a major factor when defining diatom-related ecoregions (i.e. regional separation allowing thedescription of the main natural gradients for assemblages;Potapova and Charles 2002, Tison et al. 2005). Altitudeper se is a poor predictor of assemblage variations in moun-tainous regions (Rimet et al. 2007, Jüttner et al. 2010). Inour data, altitude was not selected by the BIO-ENV proce-dure in the 2 regions with the highest altitudinal range.This unexpected result might have been because altitu-dinal gradients are related to geographical barriers, andspecies’ dispersal limitation thus causes unpredictable as-semblages (Hubbell 2001). Moreover, mountain environ-ments are highly heterogeneous. To analyze the effect ofaltitude in our data, we focused on 3 regions (Alps, MassifCentral, and Pyrenees) that can be considered as particu-larly mountainous, with an average altitude >500 m and aninterquartile range >350 m. Only the Pyrenees failed toshow significant spatial patterns in diatom assemblages.One possible explanation for this phenomenon is that allsamples from the Pyrenees region were collected on thenorthern slopes of the mountain because the southernslopes are Spanish. The Pyrenees are east–west oriented,so sites from the same side of the range are separated byweaker geographical barriers than sites from north–southmountainous massifs, such as the Alps or Massif Central.Moreover, altitude interquartile range, as a surrogate fortopographic irregularities, was significantly positively re-lated with the degree of spatial effect on assemblages.These results suggest that the spatial patterns are relatedto geographical barriers, i.e., dispersal-related factors.

Implications for bioassessmentFrom a more practical perspective, the spatial patterns

described in our study represent noise in the relationshipsbetween diatom assemblages and environmental variables.European bioassessment programs are based on methodsin which first steps consist of describing the natural vari-ability of assemblages to define proper reference sites forcomparison with impacted assemblages (European Com-mission 2000). Similar methods are used for diatoms andother organisms on other continents (Grenier et al. 2006,Stubauer et al. 2010, Molozzi et al. 2012, Lavoie et al.2013). The spatial structure of the assemblages might beproblematic during the initial implementation of thesemethods. Several authors have advised the separation ofdiatom data into regions to mitigate the spatial patterns inthe data (e.g., Potapova and Charles 2002, Soininen et al.2004, Soininen 2007). Our results tend to show that, de-pending on the characteristics of the regions (relief, topog-




raphy, and perhaps, region size), regional stratificationmight be insufficient and diatom assemblages within re-gions may show substantial spatial patterns. Such strongspatial structure might be especially evident in countriespresenting a broad range of ecoregional and topographicvariations, such as France.

Specific values for calculation of indices are often fittedbased on the national description of the species’ environ-mental preferences. The spatial patterns described in ourstudy raise the question of whether there are regional dif-ferences in environmental preferences. Until recently, in-cluding specific values fitted regionally in the calculationof indices was unrealistic because available data were notsufficient at regional scales, but large databases have beenrecently constructed. Future development of indices shouldassess whether such regional differentiations result in moreprecise quality evaluations.

PerspectivesFor reliable diatom-based bioindication, future investi-

gators and developers of bioassessment programs shouldassess the optimum size of regions that do not includestrong pure spatial patterns in the diatom data. The rela-tionship between topographic heterogeneity and the spa-tial control of assemblages is especially problematic be-cause mountainous regions may show stronger spatialstructures in diatom assemblages. In France and manyother countries, a large proportion of reference sites occurin mountainous regions because of high population densi-ties and often poor water quality at lower elevations. There-fore, the mountainous reference assemblages should becompared carefully with other assemblages. Refining eco-regional zonation in mountains might be useful for thistask, and partial Mantel correlograms with enough datacould be powerful tools for examining the importance ofspatial noise in these regions. Averaging the diatom dataover 3 y yielded strong relationships between environmen-tal variables and assemblages without adding space-relatednoise to the data. Such pretreatment of data could be use-ful in studies designed to describe general relationshipsbetween environment and assemblages or particular spe-cies.

Future investigators should attempt to assess whichdiatom species are the most dispersal limited, as initiatedby Heino and Soininen (2010). If the spatial patterns de-tected are related to dispersal processes, rare speciesshould be particularly spatially structured because of thelower local densities and lower probabilities that newsites will be occupied. Moreover, examination of whichbiological and ecological traits influence a species depen-dence on spatial effects might reveal whether species areespecially prone to dispersal limitation, or if spatial pat-terns could be explained by unmeasured environmental

factors. Such studies might also allow us to definewhether a particular assemblage is structured by spatialfactors, so that spatial noise in a data set could be re-duced. Species traits are highly important for communityecology given that environmental factors typically filterspecies traits, not necessarily species identity. Rebuildingcommunity ecology from traits, not species identity, mayshed more light on important processes shaping localcommunities.

ACKNOWLEDGEMENTSWe thank François Delmas and Michel Coste for their kind

help concerning the general discussion and taxonomic issues.We acknowledge all the regional agencies for the environment,Directions Régionales et Interdépartementales de l’Environne-ment et de l’Energie and Directions Régionales de l’Environne-ment, de l’Aménagement et du Logement, and water agenciesfor providing identification data. We are grateful to 2 anony-mous referees for their suggestions, which substantially im-proved the manuscript.

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do spatial patterns of benthic diatom assemblages vary across regions and years?

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