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ORIGINAL
ARTICLE
Global variation in the beta diversity of
lake macrophytes is driven by
environmental heterogeneity rather
than latitude
Janne Alahuhta
1
* , Sarian Kosten
2
, Munemitsu Akasaka
3
,
Dominique Auderset
4
, Mattia M. Azzella
5
, Rossano Bolpagni
6
,
Claudia P. Bove
7
, Patricia A. Chambers
8
, Eglantine Chappuis
9
,
John Clayton
11
, Mary de Winston
11
, Frauke Ecke
12,13
, Esperanc
ßa Gacia
9
,
Gana Gecheva
14
, Patrick Grillas
15
, Jennifer Hauxwell
16
, Seppo Hellsten
17
,
Jan Hjort
1
, Mark V. Hoyer
18
, Christiane Ilg
10
, Agnieszka Kolada
19
,
Minna Kuoppala
17
, Torben Lauridsen
20
,En‒Hua Li
21
, Bal
azs A. Luk
acs
22
,
Marit Mjelde
23
, Alison Mikulyuk
16,24
, Roger P. Mormul
25
, Jun Nishihiro
26
,
Beat Oertli
10
, Laila Rhazi
27
, Mouhssine Rhazi
28
, Laura Sass
29
,
Christine Schranz
30
, Martin Søndergaard
20
, Takashi Yamanouchi
26
, Qing
Yu
31,32
, Haijun Wang
31
, Nigel Willby
33
, Xiao‒Ke Zhang
34
and Jani Heino
35
1
Geography Research Unit, University of
Oulu, PO Box 3000, FI‒90014 Oulu, Finland,
2
Department of Aquatic Ecology and
Environmental Biology, Institute for Water
and Wetland Research, Radboud University,
Heyendaalseweg 135, 6525AJ Nijmegen, The
Netherlands,
3
Institute of Agriculture, Tokyo
University of Agricultural and Technology, 3‒
5‒8 Saiwaicho, Fuchu, Tokyo 183‒8509, Japan,
4
Department F.-A. Forel for Environmental
and Aquatic Sciences, University of Geneva,
Bd Carl Vogt 66, CH 1205 Geneva,
Switzerland,
5
Department of Life and
Environmental Sciences, University of
Cagliari, Viale S. Ignazio da Laconi 11‒1113,
09123 Cagliari, Italy,
6
Department of
Chemistry, Life Sciences and Environmental
Sustainability, University of Parma, Parco
Area delle Scienze 11/A, 43124 Parma, Italy,
7
Departamento de Bot^
anica, Museu Nacional,
Universidade Federal do Rio de Janeiro,
Quinta da Boa Vista, 20940‒040 Rio de
Janeiro, RJ, Brazil,
8
Environment and
Climate Change Canada, 867 Lakeshore Rd,
Burlington, ON L7S 1A1, Canada,
9
Centre
d’Estudis Avanc
ßats de Blanes (CEAB),
Consejo Superior de Investigaciones Cient
ıficas
(CSIC), C/ Acc
es a la Cala St. Francesc 14,
17300 Blanes, Spain,
10
Hepia, University of
Applied Sciences and Arts Western Switzerland,
150 route de Presinge, CH‒1254 Jussy/Gen
eve,
Switzerland,
11
National Institute of Water and
Atmospheric Research Limited, PO Box 11115,
Hamilton, New Zealand,
12
Department of
Aquatic Sciences and Assessment, Swedish
ABSTRACT
Aim We studied global variation in beta diversity patterns of lake macrophytes
using regional data from across the world. Specifically, we examined (1) how
beta diversity of aquatic macrophytes is partitioned between species turnover
and nestedness within each study region, and (2) which environmental charac-
teristics structure variation in these beta diversity components.
Location Global.
Methods We used presence–absence data for aquatic macrophytes from 21 regions
distributed around the world. We calculated pairwise-site and multiple-site beta
diversity among lakes within each region using Sørensen dissimilarity index and par-
titioned it into turnover and nestedness coefficients. Beta regression was used to cor-
relate the diversity coefficients with regional environmental characteristics.
Results Aquatic macrophytes showed different levels of beta diversity within each
of the 21 study regions, with species turnover typically accounting for the majority
of beta diversity, especially in high-diversity regions. However, nestedness con-
tributed 30–50% of total variation in macrophyte beta diversity in low-diversity
regions. The most important environmental factor explaining the three beta diver-
sity coefficients (total, species turnover and nestedness) was elevation range, fol-
lowed by relative areal extent of freshwater, latitude and water alkalinity range.
Main conclusions Our findings show that global patterns in beta diversity of lake
macrophytes are caused by species turnover rather than by nestedness. These patterns
in beta diversity were driven by natural environmental heterogeneity, notably vari-
ability in elevation range (also related to temperature variation) among regions. In
addition, a greater range in alkalinity within a region, likely amplified by human
activities, was also correlated with increased macrophyte beta diversity. These findings
suggest that efforts to conserve aquatic macrophyte diversity should primarily focus
on regions with large numbers of lakes that exhibit broad environmental gradients.
Keywords
alkalinity range, aquatic plants, elevation range, freshwater ecosystem,
hydrophytes, latitude, nestedness, spatial extent, species turnover
ª2017 John Wiley & Sons Ltd http://wileyonlinelibrary.com/journal/jbi 1
doi:10.1111/jbi.12978
Journal of Biogeography (J. Biogeogr.) (2017)
University of Agricultural Sciences (SLU), PO Box 7050, SE‒750 07 Uppsala, Sweden,
13
Department of Wildlife, Fish, and Environmental Studies,
Swedish University of Agricultural Sciences (SLU), SE‒901 83 Ume
a, Sweden,
14
Faculty of Biology, University of Plovdiv, Plovdiv 4000, Bulgaria,
15
Tour du Valat, Research Institute for the conservation of Mediterranean wetlands, Le Sambuc 13200 Arles, France,
16
Center for Limnology,
University of Wisconsin, 680 N Park St., Madison, WI 53704, USA,
17
Finnish Environment Institute, Freshwater Centre, PO Box 413, FI‒90014
Oulu, Finland,
18
Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, Institute of Food and Agricultural Services,
University of Florida, 7922 NW 71st Street, Gainesville, FL 32609, USA,
19
Department of Freshwater Assessment Methods and Monitoring,
Institute of Environmental Protection‒National Research Institute, Warsaw, Poland,
20
Department of Bioscience, Aarhus University, Vejsøvej 25,
8600 Silkeborg, Denmark,
21
Key Laboratory for Environment and Disaster Monitoring and Evaluation of Hubei Province, Institute of Geodesy
and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China,
22
Department of Tisza River Research, MTA Centre for Ecological
Research, Bem t
er 18/C, H‒4026 Debrecen, Hungary,
23
Norwegian Institute for Water Research (NIVA), Gaustadall
een 21, 0349 Oslo, Norway,
24
Wisconsin Department of Natural Resources, 2801 Progress Rd., Madison, WI 53716, USA,
25
Department of Biology, Research Group in
Limnology, Ichthyology and Aquaculture—Nupe
´lia, State University of Maringa
´, Av. Colombo 5790, Bloco H90, CEP–87020–900 Mringa
´,PR,
Brazil,
26
Faculty of Sciences, Toho University, 2–2–1 Miyama, Funabashi Chiba 274–8510, Japan,
27
Laboratory of Botany, Mycology and
Environment, Faculty of Sciences, Mohammed V University in Rabat, 4 avenue Ibn Battouta B.P. 1014 RP, Rabat, Morocco,
28
Department of
Biology, Faculty of Science and Technology, Moulay Ismail University, PB 509 Boutalamine Errachidia, Morocco,
29
Illinois Natural History
Survey, Prairie Research Institute, University of Illinois, 1816 South Oak Street, Champaign, IL 61820, USA,
30
Bavarian Environment Agency,
Demollstraße 31, 82407 Wielenbach, Germany,
31
State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology,
Chinese Academy of Sciences, Wuhan 430072, China,
32
University of Chinese Academy of Sciences, Beijing 100049, China,
33
Biological and
Environmental Science, University of Stirling, Stirling FK9 4LA, UK,
34
School of Life Sciences, Anqing Normal University, Anqing 246011, China,
35
Finnish Environment Institute, Natural Environment Centre, Biodiversity, PO Box 413, FI–90014 Oulu, Finland
*Correspondence: Janne Alahuhta, Geography Research Unit, University of Oulu, PO Box 3000, FI‒90014 Oulu, Finland.
E-mail: janne.alahuhta@oulu.fi
INTRODUCTION
Understanding broad-scale biodiversity patterns has become
a fundamental topic in biogeography and ecology. The
importance of explaining these patterns has increased in
recent years because they are intimately related to, for exam-
ple, ecosystem functioning (Symstad et al., 2003) and resili-
ence (Folke et al., 2004), biogeographical regionalization
(Divisek et al., 2016), niche conservatism (Alahuhta et al.,
2017), species conservation (Brooks et al., 2008) and ecosys-
tem services (Naidoo et al., 2008). Spatial variation in
broad-scale diversity patterns is typically driven by natural
history (e.g. past dispersal barriers and evolutionary
changes), interactions among species (e.g. competition, pre-
dation and mutualism) and biogeography (e.g. distribution
of climate zones, productivity and habitat heterogeneity)
(Willig et al., 2003; Qian & Ricklefs, 2007; Soininen et al.,
2007; Field et al., 2009; Baselga et al., 2012). Better knowl-
edge of patterns in biodiversity and their basis is also critical
for managing and adapting to invasive species, land use
changes, landscape and habitat degradation, and increasing
temperatures associated with global change (V€
or€
osmarty
et al., 2010, Vilmi et al., 2017). Therefore, studies focussing
on broad-scale diversity patterns may directly advance both
basic and applied research.
One intrinsic component of biodiversity is beta diversity
(i.e. among-site differences in species composition). In gen-
eral, beta diversity indicates the spatial variation of species
composition among communities across space (Anderson
et al., 2011), and is essentially related to two processes
(Baselga, 2010): species replacement (i.e. turnover, where
one species replaces another with no change in richness)
and nestedness (i.e. species richness differences due to spe-
cies gain or loss). Mechanisms responsible for species
replacement originate from environmental filtering, compe-
tition and historical events (Melo et al., 2009; Kraft et al.,
2011; Wen et al., 2016). Conversely, nestedness differences
stem from species thinning or from other ecological pro-
cesses (Baselga, 2010; Legendre, 2014), such as physical bar-
riers or human disturbance, that result in species-poor sites
being a subset of the richest site in the region. Independent
of the dissimilarity measure used to represent beta diversity,
it has been reported to decrease with latitude and increase
with elevation and area (Jones et al., 2003; Heegaard, 2004;
Qian & Ricklefs, 2007; Soininen et al., 2007; Kraft et al.,
2011). Explanations for these patterns in beta diversity stem
from effects of energy availability, water–energy dynamics,
climatic variability, habitat heterogeneity and human distur-
bance (Gaston, 2000; Willig et al., 2003; Socolar et al.,
2016). However, the majority of studies on beta diversity
have been conducted at small spatial extents or using coarse
resolution data across broad spatial scales (Kraft et al.,
2011; Dobrovolski et al., 2012), exposing the lack of beta
diversity studies using fine-resolution data at regional and
global scales.
Increasing evidence indicates, however, that patterns in
beta diversity depend on the studied ecosystem, organisms
and geographical location (Soininen et al., 2007; Dobrovolski
et al., 2012; Viana et al., 2015; Wen et al., 2016). Many of
the reported patterns in beta diversity concern well-known,
and often charismatic, taxa of terrestrial ecosystems (Qian &
Ricklefs, 2007; Melo et al., 2009; Kraft et al., 2011; Wen
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
2
J. Alahuhta et al.
et al., 2016) but may be unrepresentative of patterns in beta
diversity for organisms in other ecosystems (Soininen et al.,
2007). Studies of beta diversity in freshwaters have often
proved to be incongruent with those of terrestrial assem-
blages (Heino, 2011; Hortal et al., 2015). A few studies have
suggested that ecological factors or data set properties associ-
ated with freshwater communities may override spatial pro-
cesses in determining beta diversity (Heino et al., 2015;
Viana et al., 2015). One possible explanation for these differ-
ences is that terrestrial ecosystems are more directly influ-
enced by climate, whereas water temperatures, which are
naturally more important to aquatic organisms, are more
stable. Moreover, the physiological constraints of access to
water and atmospheric gases are fundamentally different for
terrestrial and aquatic organisms. Consequently, there is a
need to study diversity patterns of freshwater assemblages at
regional and global scales to discover whether they follow the
general trends evident in terrestrial organisms.
Aquatic macrophytes are among the most under-repre-
sented groups in broad-scale studies of freshwater biodiver-
sity, yet they are an integral structural and functional
component of freshwater ecosystems (Chambers et al., 2008).
Few studies on macrophyte diversity have been conducted at
continental or global extents, and these have relied on data
scaled to coarse political or biogeographic regions (Chambers
et al., 2008; Chappuis et al., 2012), leading to potentially
spurious conclusions about species distributions at finer
scales (Hortal et al., 2015). Although aquatic macrophyte
diversity has been actively studied at local and regional
extents, these studies may suffer from ecosystem-specific
characteristics (i.e. varying environmental gradients lead spe-
cies to respond differently to abiotic factors among regions),
including variation in underlying environmental gradients
among regions (Heino et al., 2015; Viana et al., 2015). For
example, aquatic macrophyte diversity studied using similar
methods showed a clear decreasing latitudinal gradient in
one region, yet a reversed latitudinal gradient in another
(Alahuhta et al., 2013; Alahuhta, 2015). Thus, explaining and
testing hypotheses related to broad-scale patterns in diversity
is difficult with one or a few data sets, and a more general
overview demands comparative analysis of multiple data sets
(Crow, 1993; Kraft et al., 2011; Heino et al., 2015).
In this study, we examine pairwise- and multiple-site beta
diversity of aquatic macrophytes using data sets for 21
regions from around the world. Specifically, we consider two
questions: (1) How is beta diversity of aquatic macrophytes
partitioned between species turnover and nestedness across
study regions on a global scale? (2) Which environmental
factors explain variation in these beta diversity components
for aquatic macrophytes across study regions? Based on a
continental scale study (Viana et al., 2015), we expected that
spatial turnover accounts for most of the overall beta diver-
sity. We also assumed that latitude does not strongly struc-
ture macrophyte beta diversity (Crow, 1993; Chambers et al.,
2008). Instead, we hypothesized that macrophyte beta diver-
sity is mostly explained by variables reflecting variation in
local habitat conditions, thus indicating the effect of environ-
mental heterogeneity on beta diversity (Heegaard, 2004;
Viana et al., 2015).
MATERIALS AND METHODS
Macrophyte and explanatory variable data
We compiled lake macrophyte data for 21 regions with vari-
able sizes from around the world (Fig. 1). Although only one
or a few regions are included from some continents (e.g.
only Morocco from Africa), our data set covered all major
Brazil,
Paraná River
Brazil,
Amazon
Salga (Brazil, Uruguay
and Argentina)
US state of Florida
Canada
US state of Minnesota
US state of Wisconsin
Morocco
Spain
UK
Norway
Sweden
Finland
Denmark
Poland
New Zealand
Japan
China
Italy
Switzerland Hungary
0°
10°
20°
30°
40°
50°
60°
70°
80°
10°
20°
30°
40°
50°
60°
Figure 1 Study regions are represented in blue circles situated in the middle of convex hulls (n=21). Crosses in the right side panel
indicate which latitudinal bands are covered in our work.
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
3
Beta diversity of aquatic macrophytes
continents inhabitable by aquatic macrophytes (see Cham-
bers et al., 2008). The regions either closely but not entirely
followed a country’s political border (e.g. Finland and New
Zealand), or were delineated based on natural features (e.g.
the Paran
a River basin in Brazil and a small area in the
Nord-Trøndelag county of Norway). The lakes consisted
mostly of natural lentic water bodies (i.e. reservoirs were
excluded), but were influenced by anthropogenic pressures
to varying degrees (e.g. nutrient enrichment, introduced spe-
cies, water level fluctuation, isolation and fish farming). The
data consisted of presence–absence of vascular macrophyte
species that grow exclusively in freshwaters (i.e. hydro-
phytes). The species data were based on empirical or scien-
tific surveys which were performed all or in part by the
authors, with the exception of Canada, China and Japan
where data were compiled from existing literature (A list of
the data sources for two of these regions is found in
Appendix 1, Appendix S1 in Supporting Information).
Macrophytes were surveyed using broadly the same methods
within each region, enabling us to compare beta diversity
patterns across regions and to minimize the potential nega-
tive effects caused by contrasting regional survey methods.
The surveys were executed mostly between 1990 and 2012,
with the exception of Canada, China and Britain, where sur-
veys were done during 1970s and 1980s, between 1964 and
2014, and between 1980 and 1998, respectively.
We used convex hulls to delineate the minimal area con-
taining all survey locations within a region (see Appendix S2,
Heino et al., 2015). We then used the convex hulls to extract
environmental information for each region and calculated
mean and range values, depending on the variable in ques-
tion, for each of the 21 regions.
The explanatory variables calculated for each regional
convex hull included region spatial extent (km
2
), elevation
range (m, Hijmans et al., 2005), modelled alkalinity range
in lakes (mequiv. l
1
at 1/16 degrees resolution, Marc
e
et al., 2015), predicted range of soil organic carbon mass
fraction at depth of 1 m (1 km resolution, Hengl et al.,
2014), areal extent of freshwaters expressed as a proportion
of region spatial extent, herein referred to as proportion of
freshwater (%, 1 km resolution, Latham et al., 2014) and
latitude (i.e. coordinate Yoriginated from each region’s
centre point) (Table 1). In addition, we examined whether
areal extent of artificial surfaces (e.g. surfaces with houses,
roads or industrial sites, Latham et al., 2014) as a propor-
tion of region spatial extent (%), was correlated with the
beta diversity coefficients and other explanatory variables.
Regional spatial extent was a surrogate for sampling effort,
as it was strongly positively associated with both numbers
of lakes and number of species present within a region
(R
Spearman
≥0.64, P<0.001, see Appendix S3), but is also
an indicator of environmental heterogeneity (see also Gas-
ton, 2000). In addition, elevation range likely illustrates
variability in habitats suitable for different macrophytes
(Gaston, 2000; Melo et al., 2009), and it simultaneously
served as a proxy for variation in temperature (correlation
Table 1 Explanatory variables used in the study and the number of lakes and species within each region. Negative latitude (Y) values
were converted to positive in the analysis to strengthen the relationship between beta diversity coefficients and latitude. Extent: spatial
extent of a region, Organic C: soil organic carbon range, waters: areal extent of water within a region as proportion of total spatial
extent, Y: latitude.
Region
Number
of lakes
Number of
species
Alkalinity range
(mequiv. l
1
)
Elevation
range (m)
Extent
(km
2
)
Organic C
(mass fraction) Waters (%) Y
Brazil, Amazon 21 27 0.01 603 943 4 0.23 6.23
Brazil, Parana River 29 37 0.79 17 368 18 21.08 22.78
Canada 58 82 3.95 242 82,540 33 21.72 44.78
China 36 100 4.75 1374 151,400 20 13.36 30.78
Denmark 32 77 4.33 156 17,260 30 10.67 56.08
Finland 261 98 3.55 923 315,900 110 10.50 64.32
Hungary 50 39 0.59 375 25,740 12 1.56 47.28
Italy 22 60 4.04 3637 37,980 20 2.20 44.68
Japan 49 93 3.20 3683 216,600 28 1.40 38.24
Morocco 33 54 4.33 2322 36,520 7 0.51 34.18
New Zealand 205 88 4.58 2800 250,800 48 22.16 41.10
Norway 30 30 0.00 309 724 17 23.01 64.90
Poland 475 84 4.34 289 175,000 22 1.99 52.99
Salga project (Brazil, Uruguay
and Argentina)
67 28 3.63 2119 299,300 57 3.88 32.98
Spain 66 56 4.67 3129 34,480 19 2.98 42.04
Sweden 379 101 4.68 1853 403,600 68 10.99 62.24
Switzerland 92 60 3.18 3633 26,910 35 4.93 46.93
UK 1928 127 4.81 1219 174,000 81 2.28 54.24
US state of Florida 205 57 4.45 112 104,200 66 5.14 28.99
US state of Minnesota 441 65 4.31 477 152,700 58 7.09 46.26
US state of Wisconsin 409 102 3.93 397 141,900 22 5.62 44.72
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
4
J. Alahuhta et al.
with temperature range: R
S
=0.92, P<0.001). Elevation
range was also positively associated with mean altitude
(R
S
=0.73, P<0.001). Following Dormann et al. (2013),
multicollinearity was manifested at the level of R
S
|> 0.7|
and, in these cases, statistically less significant predictors of
beta diversity were excluded from final models (see
Appendix S2). Carbon compounds in water directly and
indirectly influence macrophytes (Alahuhta & Heino, 2013;
Kolada et al., 2014). We therefore used two different prox-
ies, water alkalinity and soil organic carbon, to represent
these local-scale components. Carbon dioxide and bicarbon-
ate concentration influence photosynthesis in aquatic
macrophytes, while organic carbon (i.e. carbon leached
from organic soils) absorbs light, a common constraint on
productivity (Madsen et al., 1996; Vestergaard & Sand-Jen-
sen, 2000). Water alkalinity is also affected by anthro-
pogenic land use (e.g. Vestergaard & Sand-Jensen, 2000;
Kolada et al., 2014), enabling us to infer the degree of
anthropogenic pressures on macrophyte beta diversity in
lakes located on homogenous geology but lacking lake-level
chemistry data. The relative areal extent of freshwaters
within a region was used to indicate availability of potential
habitat for macrophyte growth. Finally, changes in species
diversity with latitude are well known, with species diversity
often decreasing towards the Poles (Qian & Ricklefs, 2007).
Negative latitude values were converted to positive in our
analysis to compensate for limited data availability on
Southern Hemisphere regions, thereby strengthening the
relationship between macrophyte beta diversity and latitude.
Beta diversity coefficients for different data sets
We determined beta diversity of aquatic macrophytes using
pairwise-site and multiple-site indices based on presence–ab-
sence species data within a region. In our study, the pair-
wise-site index indicated degree of absolute beta diversity
within each region, whereas the multiple-site index was used
to compare relative differences in beta diversity among
regions (Baselga, 2010). For both indices, the calculations
were based on the Sørensen dissimilarity, resulting in the fol-
lowing three dissimilarity coefficients: (1) Sørensen coeffi-
cient (i.e. a measure of overall beta diversity, b
sor/SOR
), (2)
Simpson coefficient (i.e. a measure of turnover immune to
nestedness resulting from species richness differences, b
sim/
SIM
), and (3) a coefficient measuring nestedness–resultant
beta diversity (b
sne/SNE
, Baselga, 2010; Legendre, 2014). The
Simpson coefficient defines species turnover without the
influence of richness gradients, whereas the nestedness-resul-
tant component of beta diversity is the direct difference
between b
sor/SOR
and b
sim/SIM
. For the pairwise-site index, we
averaged the pairwise dissimilarities between all lakes in a
region. Because the number of sites affects the multiple-site
index (Baselga, 2010), we resampled the 21 regional data sets
to standardize them to a common number of 21 lakes, the
minimum number of lakes found across the regional data
sets (in Brazil Amazon, Table 2), based on 1000 permuta-
tions in each region. Both beta diversity indices were
obtained using the R package ‘betapart’ (Baselga et al.,
2013). The three beta diversity coefficients were calculated
Table 2 Summary of best models explaining variation in aquatic macrophyte beta diversity for multiple-site and pairwise dissimilarities
within a region. Models were calculated for Sørensen dissimilarity (total beta diversity), Simpson dissimilarity (beta diversity due to
turnover) and nestedness dissimilarity (beta diversity due to nestedness-resultant richness differences). Best models with delta <2 are
presented, because these models are typically considered to have similar statistical support (Burnham & Anderson, 2002). Waters:
Proportion of water within a region, d.f.: degree of freedom, delta: AICc difference between model iand the model with the smallest
AICc, weight: Akaike weight, pseudo R
2
: maximum likelihood coefficients of determination were obtained through an iterative process.
AICc d.f. DAICc Weight Pseudo R
2
Multiple-site beta diversity
Sørensen
Elevation range 80.9 3 0 0.435 0.282
Elevation range +latitude 79.6 4 1.34 0.223 0.317
Elevation range +waters 79.1 4 1.74 0.182 0.326
Elevation range +alkalinity range 78.9 4 1.99 0.160 0.309
Species turnover
Elevation range 57.2 3 0 0.708 0.325
Elevation range +waters 55.4 4 1.77 0.292 0.366
Nestedness
Elevation range 83.9 3 0 1 0.280
Pairwise-site beta diversity
Sørensen
Elevation range 21.9 3 0 0.719 0.283
Elevation range +Latitude 20.0 4 1.88 0.281 0.301
Species turnover
Elevation range 14.7 3 0 1 0.326
Nestedness
Elevation range 62.8 3 0 1 0.269
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
5
Beta diversity of aquatic macrophytes
using the functions beta.pair and beta.sample for pairwise-
site and multiple-site indices, respectively.
Statistical analysis
We used beta regression to identify which predictor variables
explained beta diversity of aquatic macrophytes across the 21
regions. Beta regression, which is an extension of generalized
linear models (GLM), was developed for situations where the
dependent variable is measured continuously on a standard
unit interval between 0 and 1 (Cribari-Neto & Zeileis, 2010).
The models are based on beta distribution with parameteri-
zation using mean and precision parameters. Similar to
GLMs, the expected mean is linked to the responses through
a link function and a linear predictor. The purpose of the
link function is to stabilize the error variance and transform
the fitted values to the desired application range (Ferrari &
Cribari-Neto, 2004). Linear regression using a logit-trans-
formed response variable is still commonly employed to
analyse the type of response data considered in our work.
However, this is questionable, because it (1) may yield fitted
values for the variable of interest that exceed its theoretical
lower and upper bounds, (2) does not allow parameter inter-
pretation in terms of the response on the original scale, and
(3) measures proportions typically displaying asymmetry
and, hence, inference based on the normality assumption can
be misleading (Ferrari & Cribari-Neto, 2004). We therefore
used beta regression models with a logistic link function,
which is asymptotic in the range 0–1 (i.e. the predicted val-
ues are automatically in the desired application range).
The models with the most important explanatory variables
influencing the beta diversity coefficients were selected based
on the second order Akaike information criterion corrected
for small sample size (AICc) among all model combinations.
AICc takes into account sample size by increasing the relative
penalty for model complexity with small data sets, and its
use is recommended if, as in our case, the ratio between
sample size and model parameters is <40 (Burnham &
Anderson, 2002). We also examined the possibility of curvi-
linear relationships between beta diversity coefficients and
certain explanatory variables (i.e. region extent, organic car-
bon and latitude) by entering the quadratic terms of these
variables in our models, making the use of AICc even more
relevant. In addition, we calculated AIC differences, which
can be used to rank different models in order of importance
(AIC
i
–AICmin, with AIC
min
representing the best model with
respect to expected Kullback-Leibler information lost).
Akaike weights derived from AIC differences were estimated
for each model to extract additional information on model
ranking. We also present pseudo R
2
values, which are a
squared correlation of linear predictor and link-transformed
response and have the same scale as R
2
values (between 0
and 1) (Ferrari & Cribari-Neto, 2004). The relative impor-
tance of explanatory variables was evaluated by summing the
Akaike weights of the models in which a given variable
appears from the exhaustive list of models. A value of <2.0
was used as the threshold for deviation of AICc values
among candidate models (i.e. difference between model i
and the model with the smallest AICc, DAICc), because
models with AICc differing by <2.0 are typically considered
to have similar statistical support (Burnham & Anderson,
2002).
All statistical analyses were conducted in R 3.2.0 (R Core
Team 2015). Beta regression was performed using functions
in the R package ‘betareg’ (Cribari-Neto & Zeileis, 2010),
and candidate models were selected with the R package
‘MuMIn’ (Barton, 2014).
RESULTS
Beta diversity of aquatic macrophytes differed among the 21
study regions, a finding that was mostly attributable to spe-
cies turnover (Fig. 2), especially in high-beta diversity
regions, and applied to both pairwise and multiple-site
indices. Nestedness accounted only for a small fraction of
overall beta diversity (14% of pairwise-site dissimilarity on
average) and was most important (although still less than
species turnover) in regions with low overall pairwise-site
beta diversity. Macrophyte beta diversity patterns in the
majority of regions were thus explained by variation in
species composition among lakes, rather than differences in
species richness. Based on the pairwise-site index, the degree
of macrophyte beta diversity varied clearly among the 21
study regions. The greatest beta diversity was found in the
coastal South American lakes (Salga, 0.90) and Spain (0.92),
whereas values were lowest in both the Brazilian regions
(0.43–0.44) and China (0.43). The top models obtained
through beta regression explained similar amounts of varia-
tion and included the same important explanatory variables
(Table 2) for both pairwise-site and multiple-site beta diver-
sity indices. The best models accounted for 28–33% of varia-
tion in the Sørensen coefficient, 33–37% in the turnover
component and 27–28% in the nestedness component.
The most important explanatory variables for all the best
models across the two beta diversity indices and different
coefficients were elevation range (Fig. 3, see Appendix S4),
proportion of freshwater, latitude range (Fig. 3, see
Appendix S4) and alkalinity range, yet their relative impor-
tance varied somewhat. We found that overall beta diversity
(i.e. Sørensen coefficient) and species turnover increased with
increasing elevation range, latitude and alkalinity range, and
decreased with increasing proportion of freshwater. The neg-
ative relation between species turnover and proportion of
freshwater is probably due to connectivity, which typically
increases with proportion of freshwaters, resulting in
enhanced exchange of macrophyte species among lakes,
thereby lowering turnover. Nestedness was negatively related
to the first three variables but was positively associated with
proportion of freshwater. Although some explanatory vari-
ables (i.e. spatial extent, latitude and organic carbon range)
showed a curvilinear relationship with beta diversity coeffi-
cients in preliminary analyses, only the linear terms of these
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
6
J. Alahuhta et al.
variables were selected in the best models. Comparison
across all possible models showed that elevation range was
included in the majority of models, with proportion of fresh-
water, latitude and alkalinity range all being of secondary
importance (Table 3). By contrast, organic carbon and spa-
tial extent were weak predictors of beta diversity across the
coefficients.
In addition to relationships between beta diversity coeffi-
cients and environmental variability, certain environmental
variables were correlated with indicators of anthropogenic
pressures. Alkalinity range showed a positive relationship
with the relative areal extent of artificial surfaces as propor-
tion of region spatial extent (R
S
=0.46, P=0.04). Both alka-
linity range (R
S
=0.48, P=0.03) and temperature range
(R
S
=0.56, P=0.008) were associated with spatial extent,
such that the span in alkalinity and temperature was greater
in regions that covered a greater areal extent. These correla-
tions also impede the separation of possible independent
effects for these factors.
DISCUSSION
Aquatic macrophytes exhibited considerable regional varia-
tion in beta diversity, which was largely driven by species
turnover. Our results thus suggest that turnover in species
composition primarily accounts for macrophyte beta diver-
sity. Aquatic macrophytes have similarly shown high levels of
species turnover at a regional and continental extent (Hee-
gaard, 2004; Viana et al., 2015; Boschilia et al., 2016). How-
ever, our finding conflicts with previous global extent studies
on beta diversity in which nestedness contributed equally or
more than species turnover to total diversity of amphibians
(Baselga et al., 2012), fish (Leprieur et al., 2011), macroin-
vertebrates (Heino et al., 2015) and oribatid mites (Gergocs
& Hufnagel, 2015). In addition, nestedness has been found
to outweigh species turnover in areas affected by glaciations
until recent time (Baselga et al., 2012; Dobrovolski et al.,
2012). We found no sign of this, as nestedness was typically
lowest in regions that were wholly or partly ice covered
0
0.2
0.4
0.6
0.8
1
Brazil, Amazon
Brazil, Paraná River
Canada
China
Denmark
Finland
Hungary
Italy
Japan
Morocco
New Zealand
Norway
Poland
Salga project
Spain
Sweden
Switzerland
UK
US state of Florida
US state of Minnesota
US state of Wisconsin
Beta diversity
0
0.2
0.4
0.6
0.8
1
Beta diversity
Species turnover
Nestedness
(a)
(b)
Brazil, Amazon
Brazil, Paraná River
Canada
China
Denmark
Finla nd
Hungary
Italy
Japan
Morocco
New Z ealand
Norway
Poland
Salga pr oject
Spain
Sweden
Switzer land
UK
US state of Florida
US state of Minn esota
US state of Wisconsin
Species tu rnover
Nestedn ess
Figure 2 Simpson dissimilarity (beta
diversity due to species turnover) and
nestedness dissimilarity (beta diversity due
to nestedness-resultant richness differences)
that sum to Sørensen dissimilarity (i.e. total
beta diversity) based on multiple site (a)
and mean of pairwise (b) beta diversity
measures for each study region. Multiple-
site beta diversity was based on 21
randomly selected lakes for each region
(except for Brazil, Amazon which had a
total nof 21).
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
7
Beta diversity of aquatic macrophytes
during the last glaciation (e.g. Finland, Norway, Canada,
China, New Zealand, Switzerland, US state of Minnesota and
UK). Our study thus emphasizes that conclusions about glo-
bal patterns in beta diversity need verification across a
diverse range of organisms, instead of using only a few well-
studied terrestrial taxa, because variable patterns exist in nat-
ure and exceptions are as instructive as conformity.
Contrary to our a priori expectations based on trends
found in terrestrial taxa (Willig et al., 2003; Qian & Ricklefs,
2007; Soininen et al., 2007), beta diversity of aquatic
macrophytes increased (albeit weakly) towards the poles.
Based on Rapoport’s rule (Stevens, 1989), species ranges and
niche width should increase at higher latitudes, giving rise to
a decrease in beta diversity (Soininen et al., 2007). But, in
general, many aquatic assemblages do not exhibit the latitu-
dinal patterns observed for terrestrial taxa, such as mammals,
birds and vascular plants (Heino, 2011; Hortal et al., 2015).
Even regarding species richness, one of the most widely used
measures of diversity, aquatic macrophytes show differing
responses to latitude at continental and global scales
Mean elevation Elevation range Latitude
0 250 500 750 1000 0 1000 2000 3000 0 204060
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Beta diversity
Sørensen Species turnover Nestedness
Figure 3 Relationships between pairwise-site beta diversity dissimilarities (i.e. Sørensen, species turnover and nestedness) of freshwater
macrophytes and mean altitude, elevation range and latitude. Similar plot for multiple-site beta diversity coefficients can be found in
Appendix S4.
Table 3 Relative importance (I) of explanatory variables among all model compilations. 1.00 indicates that the particular variable is
selected in all models, whereas 0 represents that the variable is not selected in any of the models. ‘+’ indicates positive and ‘’ negative
relation between the beta diversity coefficient and that environmental variable. If a given variable was not included among the most
important beta diversity models (AICc <2.0), then the direction of influence was obtained from a full model including all the candidate
variables. I: importance, D: direction of influence, elevation: elevation range, alkalinity: alkalinity range, extent: spatial extent of a region,
organic C: soil organic carbon range, waters: areal extent of water within a region as proportion of total spatial extent.
Multiple-site beta diversity Pairwise-site beta diversity
Sørensen
Species
turnover Nestedness Sørensen
Species
turnover Nestedness
IDIDIDIDIDID
Elevation 0.80 +0.90 +0.85 0.82 +0.90 +0.89
Waters 0.33 0.30 0.23 +0.26 0.25 0.17 +
Latitude 0.32 +0.24 +0.18 0.26 +0.21 +0.18
Alkalinity 0.25 +0.22 +0.20 0.24 +0.22 +0.17
Organic C 0.16 0.19 0.20 0.16 0.16 +0.17
Extent 0.16 0.17 0.20 0.16 0.16 0.17 +
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
8
J. Alahuhta et al.
(Rørslett, 1991; Chambers et al., 2008; Chappuis et al.,
2012). In addition, contrasting latitudinal patterns in macro-
phyte beta diversity have been found within individual
regions (Heegaard, 2004; Viana et al., 2015), likely due to
different study scales and varying sampling techniques used.
Our study included only macrophyte data collected via con-
sistent methods (within each region) and showed that overall
beta diversity increases weakly from the equator towards the
poles. However, the relative importance of latitude in
explaining global macrophyte beta diversity was modest,
being selected only in two of eleven models. These two mod-
els concerned the overall (Sørensen) beta diversity. In con-
trast, species turnover and nestedness did not vary
consistently with latitudinal gradient. This is likely because
aquatic macrophytes are more responsive to local environ-
mental conditions than the broad-scale variation in climate
that underlies latitudinal gradients in the beta diversity of
other (terrestrial) organism groups. Aquatic environments
moderate extreme climatic conditions, leading to less varia-
tion in temperature in freshwater than terrestrial ecosystems,
and this may partly explain the conflict in latitudinal beta
diversity patterns between freshwater and terrestrial assem-
blages.
Although the relationship between latitude and macrophyte
beta diversity conflicted with that of many organisms, our
results support another reported beta diversity pattern. Habi-
tat heterogeneity has previously been shown to structure beta
diversity for terrestrial plants (Freestone & Inouye, 2006) and
butterflies at a regional extent (Andrew et al., 2012), birds
and mammals at a continental extent (Melo et al., 2009), and
oceanic bacteria (Zinger et al., 2011) and fish (Leprieur et al.,
2011) at a global extent. Variation in macrophyte beta diver-
sity in our study regions was predominantly determined by
environmental heterogeneity, primarily the degree of eleva-
tion variability (also correlated with temperature variability)
in a region. Thus, beta diversity of aquatic macrophytes (ex-
pressed as either multiple-site or pairwise-site diversity)
increased with variation in altitude. This positive association
between beta diversity and elevation range likely reflects the
greater variety of habitats or resources available with greater
variation in altitude. Wang et al. (2012) similarly found that
elevational beta diversity of aquatic micro- and macroorgan-
isms was primarily related to environmental heterogeneity at
a regional extent. Species distributions are typically con-
strained by harsh climatic conditions at high elevation (Gas-
ton, 2000), and various aspects of macrophyte physiology are
known to be temperature sensitive (Sculthorpe, 1967; Rooney
& Kalff, 2000). However, the buffering of temperature
extremes in aquatic environments allows for continued plant
growth over a wide elevation range. Greater variation in habi-
tats with increasing variation in elevation is also related to
geological and soil properties, as low-lying lakes will vary
more in water chemistry due to greater variation in soil and
geology, which in turn increase variation in water chemistry
(Wang et al., 2012), as well as from the added influence of
human activity. These factors magnify the elevation gradient
which enhances environmental heterogeneity and thus enables
the establishment of a greater variety of macrophyte species,
further increasing beta diversity within a region.
Regional variation in water alkalinity, soil organic carbon
availability and spatial extent further indirectly would have
supported the habitat heterogeneity hypothesis in explaining
global patterns of macrophyte beta diversity. However, con-
trary to our expectations, these individual variables were not
important predictors of macrophyte beta diversity. Alkalinity
and soil organic carbon influence aquatic macrophytes
through their differing ability to use bicarbonate or carbon
dioxide as a source of carbon in photosynthesis (Madsen
et al., 1996), but also indirectly reflect human effects on
freshwaters. In-lake alkalinity often increases with eutrophi-
cation, while nutrient inputs from agriculture and human
effluents tend to be greatest in landscapes dominated by car-
bonate-rich minerals (Kolada et al., 2014; Alahuhta, 2015).
Similarly, regional spatial extent is often positively associated
with beta diversity, as in our work, because larger areas
incorporate higher levels of environmental heterogeneity
(Gaston, 2000; Anderson et al., 2011; Heino et al., 2015).
Moreover, spatial extent was also positively related to alkalin-
ity range and temperature range, both expressions of envi-
ronmental heterogeneity. These explanations suggest an
underlying effect of environmental heterogeneity on aquatic
macrophyte beta diversity that may also be affected by
human activities that impair water quality and physical char-
acteristics of near-shore habitats (Kosten et al., 2009;
V€
or€
osmarty et al., 2010; Alahuhta, 2015; Vilmi et al., 2017).
Besides discovering novel patterns in macrophyte beta
diversity, our main result has practical implications for envi-
ronmental management: the conservation of aquatic macro-
phyte assemblages that naturally exhibit high species
turnover will be most favoured by a regional approach, in
which multiple lakes that span a wide environmental gradi-
ent are protected within a region (Socolar et al., 2016). This
approach further underlines the need to maximize the total
area protected, independent of the geographical location.
Conversely, low-biodiversity regions characterized by high
nestedness require conservation actions that prioritize high-
diversity sites over those of lower diversity (Socolar et al.,
2016). In these low-biodiversity regions, the possible influ-
ence of land-based activities within a catchment should be
carefully evaluated and connectivity among high-diversity
habitats should be maintained.
ACKNOWLEDGEMENTS
We thank Andres Baselga for insightful comments on the
calculation of beta diversity. Comments from Christine Mey-
nard, Solana Boschilia, Chad Larsen and an anonymous
reviewer improved the manuscript considerably. We also
thank Lucinda B. Johnson and Sidinei M. Thomaz for pro-
viding Minnesota and part of the Brazilian data, respectively.
We appreciate assistance from Konsta Happonen in produc-
ing some of the figures. The gathering of the Finnish data
Journal of Biogeography
ª2017 John Wiley & Sons Ltd
9
Beta diversity of aquatic macrophytes
was partly supported by Biological Monitoring of Finnish
Freshwaters under diffuse loading -project (XPR3304)
financed by Ministry of Agriculture and Forestry and partly
by national surveillance monitoring programs of lakes. S.H.
and M.M. were supported by the EU-funded MARS-project
(7th EU Framework Programme, Contract No.: 603378).
SALGA-team, especially Gissell Lacerot, Nestor Mazzeo, Vera
Huszar, David da Motta Marques and Erik Jeppesen for
organizing and executing the SALGA field sampling cam-
paign and Bruno Irgang
†
and Eduardo Alonso Paz for help
with identification. Swedish macrophyte data were collected
within the Swedish Monitoring Program of macrophytes in
lakes funded by the Swedish Agency for Marine and Water
Management. S.K. was supported by NWO Veni grant
86312012. Macrophyte data from Brazilian Amazon were col-
lected within a limnological monitoring program funded by
Vale S.A. The vast majority of macrophyte data from Polish
lakes were collected within the State Environmental Monitor-
ing Programme and were provided by the Inspection for
Environmental Protection. Macrophyte data for British lakes
were collated by the Joint Nature Conservation Committee
from surveys resourced by the national conservation agen-
cies. Swiss macrophytes data were collected during a study
financially supported by the Swiss Federal Office for the
Environment. Wisconsin data collection was funded by the
Wisconsin Department of Natural Resources and supported
by the Wisconsin Cooperative Fishery Research Unit. The
Norwegian macrophyte data were collected within the Euro-
pean Union project ‘LAKES –Long distance dispersal of
Aquatic Key Species’, contract no. env4-ct-97-0585.
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the
online version of this article:
Appendix S1 Description of lakes and surveys.
Appendix S2 An example of convex hull.
Appendix S3 Correlation matrix among environmental
variables.
Appendix S4 Beta diversity and environmental determinants.
BIOSKETCH
Janne Alahuhta is a postdoctoral researcher in the Univer-
sity of Oulu. His research integrates biogeography, macroecol-
ogy, community ecology and conservation ecology to study
patterns and processes structuring aquatic plants at various
spatial scales. He is especially interested to understand how
global change affects aquatic macrophyte distributions across
temporal and spatial scales. The research group is devoted to
the study of aquatic plants and other freshwater assemblages
from different perspectives at various spatial scales.
Author contributions: J.A. and J.H. conceived the ideas; all
authors participated in the collection of the data; J.A. anal-
ysed the data; and J.A. led the writing to which other authors
contributed.
Editor: Christine Meynard
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