- Email: [email protected]
Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats, India
Journal Pre-proof Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats, India Shruti Vilas Paripatyadar, Sameer Mukund Padhye, Anand Dhananjay Padhye
PII:
S0075-9511(19)30084-2
DOI:
https://doi.org/10.1016/j.limno.2019.125730
Reference:
LIMNO 125730
To appear in:
Limnologica
Received Date:
29 April 2019
Revised Date:
3 November 2019
Accepted Date:
3 November 2019
Please cite this article as: Paripatyadar SV, Padhye SM, Padhye AD, Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats, India, Limnologica (2019), doi: https://doi.org/10.1016/j.limno.2019.125730
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats, India
Shruti Vilas Paripatyadara*, Sameer Mukund Padhyeb, Anand Dhananjay Padhyec
a Annasaheb Kulkarni Department of Biodiversity, MES’ Abasaheb Garware College, Karve Road,
ro of
Pune – 411 004. Maharashtra, INDIA. Email: [email protected] * Corresponding author
Nadu, INDIA. Email: [email protected]
-p
b Systematics, Ecology & Conservation Lab, Zoo Outreach Organization, Coimbatore 641035, Tamil
re
c Department of Zoology, MES’ Abasaheb Garware College, Karve Road, Pune – 411 004.
lP
Maharashtra, INDIA. Email: [email protected]
Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems
na
of the Western Ghats, India Abstract
ur
Partitioning beta diversity into its two components of spatial turnover and nestedness is a more robust method for checking spatial variability in biological communities than calculating the total beta
Jo
diversity alone. The relative contribution of spatial turnover and nestedness has been used to test the effects of climatic, environmental, spatial and temporal variables on community composition. In this study, we tested the effects of environmental factors and microhabitat features on total beta diversity and its spatial turnover and nestedness components using a comprehensive dataset of aquatic Heteroptera collected from four types of permanent freshwater habitats (i.e. streams, ponds, rock tanks and reservoirs) in the Western Ghats of India. We observed that communities in all four types of habitats were predominantly shaped by dissimilarity caused due to spatial turnover (>85%). Each type of habitat showed the presence of one or more species uniquely associated with it, which might 1
contribute to the turnover between communities. The abiotic environment (climatic factors, topological factors, soil characteristics and microhabitat features) as well as assemblage structure differed significantly between habitat types. Communities in each type of habitat were affected by different environmental factors, such as precipitation and temperature patterns for streams, altitude and rocky substrate for rock tanks, and soil characteristics and the presence of aquatic macrophytes for ponds and reservoirs. Assemblages observed in the four types of permanent habitats are thus compositionally distinct due to species replacements between local communities, which in turn are strongly influenced by environmental variables. Similar to previous studies, our results show that
ro of
spatial turnover largely measures the same phenomenon as total beta diversity on a regional scale.
Keywords: Active dispersers; permanent freshwater habitats; spatial turnover; abiotic environment;
-p
microhabitats.
re
1. Introduction
Beta diversity is an important measure of regional biodiversity and can be defined as the dissimilarity
lP
in community composition between a pair of sites (Gianuca et al., 2017). Beta diversity in turn depends on alpha diversity gradients and both of these components are a result of community assembly through local and regional filters. Understanding the beta diversity patterns is more helpful in
na
capturing the dynamic nature of biodiversity as compared to simple alpha diversity measures (Soininen et al., 2018). In the recent years, measuring beta diversity has gained popularity as a metric to infer the patterns shaping regional biodiversity, for testing ecological theories and for the practical
ur
management of regional diversity (see Baselga, 2010; Socolar et al., 2016; Soininen et al., 2018). Total beta diversity can be partitioned into its two additive components of ‘spatial turnover’ and
Jo
‘nestedness’, which signify the two antithetic processes of species replacement and species loss/gain (i.e. the differences in richness between samples) respectively (sensu Baselga, 2010). Spatial turnover implies simultaneous loss and gain of species between localities, reflecting species sorting due to environmental filtering, competition or dispersal abilities, while nestedness implies richness difference caused by the extent to which a species-poor site is a subset of a species-rich site, reflecting extinction-colonization dynamics (Legendre, 2014; Si et al., 2016; Soininen et al., 2018). In most studies on local to regional scale, spatial turnover has been shown to be the dominant component shaping beta diversity (see Soininen et al., 2018 and references therein), but as the spatial extent 2
increases to address biogeographical hypotheses, nestedness is also seen as a significant driver of variation between communities (Baselga, 2010). Baselga’s (2010) approach of partitioning total beta diversity was replicated by other studies to test the effects of climatic factors (Hortal et al., 2011; Dobrovolski et al., 2012; Tisseuil et al., 2012), environmental factors (Alahuhta et al., 2017), spatial factors (Viana et al., 2016; Hill et al., 2017) and temporal factors (Baeten et al., 2012; Angeler, 2013) on community composition. The contribution of spatial turnover and nestedness to overall beta diversity depends upon the dispersal abilities of taxa in freshwater ecosystems (Gianuca et al., 2017), with higher total beta diversity and its turnover component for actively dispersing taxa and lower overall beta diversity and
ro of
greater contribution of nestedness for passively dispersing taxa (see Tonkin et al., 2016; Hill et al., 2017). Species turnover also depends upon the type of habitat under study, as a function of environmental heterogeneity (see Qian and Ricklefs, 2012; Stegen et al., 2013; Maloufi et al., 2016). Moderately heterogeneous freshwater ecosystems such as lakes show lower overall beta diversity and turnover as compared to highly heterogenous ecosystems such as stream networks (Specziár et al.,
-p
2018).
In this study, we examined the effects of local as well as regional environmental factors on beta
re
diversity patterns in four types of tropical permanent freshwater ecosystems (i.e. streams, ponds, rock tanks and reservoirs) of the Western Ghats of India, which show remarkable heterogeneity in habitats,
lP
making them an excellent area to address ecological hypotheses. We chose permanent habitats because artificial (reservoirs) and natural permanent waterbodies are important habitats for a wide variety of aquatic organisms (Bronmark and Hansson, 1998; Jeffries, 2005; Oertli et al., 2005; De Roeck
na
et al., 2007; Apinda Legnouo et al., 2014) and have higher species richness as compared to temporary habitats (Nilsson and Svensson, 1995; Schneider and Frost, 1996; Fairchild et al., 2003). We chose aquatic Heteroptera (true bugs), which form one of major components of freshwater ecosystems, as
ur
our model system because their striking morphological adaptations to an aquatic mode of life make
Jo
them excellent models for ecological and biogeographical studies (Polhemus and Polhemus, 2008). Environmental factors do shape communities in heterogeneous landscapes and species track preferred environmental conditions (see Cottenie, 2005; Thornhill et al., 2017). This usually results in higher spatial turnover explaining the distinctness of local communities, as opposed to nestedness, which is prevalent in more homogeneous settings (Gianuca et al., 2017; Hill et al., 2017). We expected that the abiotic environment as well as the bug communities would differ significantly between each type of habitat under study (i.e. streams, ponds, rock tanks and reservoirs) and therefore, we hypothesized that the local communities in the four types of habitats were influenced mainly by the
3
environment and hence shaped much more by spatial turnover than by nestedness. In this study, we addressed the following questions: 1. To what extent does spatial turnover contribute to total beta diversity in each type of habitat and do the total beta diversity and its species replacement component increase from tanks and reservoirs through ponds to streams (due to differences in environmental heterogeneity)? 2. Which species or groups of species associate uniquely with each type of habitat and therefore contribute to turnover between communities? 3. Which environmental factors contribute to the differences between the four types of habitat and how do species interact with environmental conditions in each type of habitat? 2. Materials and Methods
ro of
2.1. Types of habitats: We selected four types of permanent or nearly permanent freshwater habitats (habitats in which water persisted for more than 10 months annually). These habitats were - a) streams, b) man-made tanks on hill forts (historical forts built on basaltic outcrops, with altitude more than 600 m ASL) constructed for water storage (hereafter referred to as fort tanks), c) ponds
-p
(waterbodies between 1m2 and 2ha in area, following Biggs et al., 2005) and d) reservoirs (man-made habitats bigger than those classified as ponds, with a retention wall and constructed for water storage); each with clearly definable microhabitats and abiotic as well as biotic environments. All the
re
waterbodies which were classified as ‘fort tanks’ could be categorised as ponds, but because of the uniqueness of these habitats (their situation on isolated rock outcrops), they were included in a
lP
separate category.
2.2. Field collections: Multiple samples of aquatic and semi-aquatic bugs were collected from 19
na
streams, rock tanks on 16 hill forts, 18 ponds and 16 reservoirs, using a D-net with a telescopic rod (GB nets, Cornwall, UK) between June 2013 to March 2016. One sample consisted of all bugs caught after 3 forward and 3 reverse sweeps, approximately covering an area of 2m2. The geographical
ur
coordinates, type of substrate (muddy/rocky) and the presence/absence of submerged aquatic vegetation were noted for each locality. In all, 250 samples from 69 different localities (3-4 samples
Jo
per locality) in and around the Western Ghats of Maharashtra were included in this study (see Figure 1; SF-Table 1). Adult specimens in the samples were identified to species level and species occurrences were used for further analysis. Relative abundances were not used because of the disparity in sampling effort due to variability in habitat size. 2.3 Data analysis: True species richness estimation was carried out for each type of habitat using nonparametric estimators - Chao2 and Jackknife2 along with Bootstrapping (Chao, 1987; Colwell and Coddington, 1994). Species occurrence data from all the localities were used (presence was coded ‘1’, while absence was coded ‘0’), considering each locality as a separate sample. One thousand 4
randomisations were carried out per sample, to obtain the standard deviations around the mean. The freeware EstimateS (Colwell, 2013) was used for this analysis (see SF-Table 4 for the results of this analysis). The package “betapart” (Baselga and Orme, 2012) in R was used to calculate and partition total beta diversity for each type of habitat to understand the relative contribution of species replacement or spatial turnover (βsim) and species loss or nestedness (βsne) to overall species turnover (βsor), as defined by Baselga (2010). Species occurrence data was used to calculate total species turnover and its two additive components. We used the method ‘beta.multi’ to calculate these coefficients, which takes into account all the given samples for producing the mean beta coefficient of pairwise beta
ro of
coefficients calculated for all possible sample combinations (Baselga and Orme, 2012). This procedure was repeated for 9,999 iterations. A Mantel test was performed for each type of habitat to check if there was a correlation between pairwise values of geographical distance between sites and spatial turnover (βsim) for all possible pairs (see SF-Table 5 for the results).
-p
We calculated the Phi coefficient of association (De Cáceres and Legendre, 2009) to check if there were any taxa which associate uniquely with each type of habitat and therefore could be influencing the differences in beta diversity patterns. This analysis was done using the function ‘multipatt’ from
re
the package “indicspecies” (https://cran.r-project.org/web/packages/indicspecies/index.html) in R. We used the ‘r.g’ value for the ‘func’ argument of the ‘multipatt’ function in order to consider the
lP
unequal sample numbers. The function ‘multipatt’ determines the list of species which are associated with a particular group of sites (type of habitat in our case). A total of 5000 permutations were run for
na
obtaining the significance of association of each species with an alpha of p = 0.05. To understand the influence of large-scale abiotic variables in shaping the environment of different types of permanent habitats, data extracted from 40 different GIS layers were used for analysis. The
ur
GIS layers corresponding to soil characteristics, water storage in soil, topography and bioclimatic variables (temperature and precipitation) were downloaded from online sources (Worldclim – Global
Jo
Climate Data and ORNL DAAC) (see SF-Table 2 for details). These variables were chosen as they were seen to differ locally and might be important for defining the four types of habitats under study. The freeware DIVA GIS was used to extract the data from the GIS layers using the geographical coordinates of the localities, which were superimposed on the GIS layer to extract the data for each point locality. For streams, the data were extracted for the actual sampling locality and not for upstream points. Principal Components Analysis (PCA) was performed to extract the environmental variables which were significantly associated with the four types of habitats, from where the bugs were collected. Values of quantitative environmental variables were log transformed prior to the analysis, as they had 5
different units of measurement. Squared cosine values (which indicate the importance of a component for a given variable) were calculated for all GIS-based variables on all components, to determine which component had the largest squared cosine for each variable. The GIS-based variables, for which the largest squared cosine values were on one of the first three components, were used for further analysis. Co-linearity of the selected GIS-based variables was checked using Spearman correlation. Variables having coefficients of determination more than 0.75 were considered as colinear (see Kokociński et al., 2010 for a similar approach) and only the one which had the highest squared cosine value was used for Permutation based multivariate ANOVA (one-way PERMANOVA) and Canonical Correspondence Analysis (CCA).
ro of
Permutation based multivariate ANOVA (one-way PERMANOVA) was used to test whether there were significant differences between the four types of habitats under study. Sample-wise values of the selected non-colinear abiotic variables and species occurrence data were used to test for differences in abiotic environment (PERMANOVA using Gower index) and community composition (PERMANOVA
-p
using Jaccard index) respectively.
We used Canonical Correspondence Analysis (CCA) to understand the effects of different environmental variables on the distribution of aquatic bug species collected from the four habitat
re
types (as per ter Braak and Verdonschot, 1995). CCA was performed using species occurrence data from all samples along with the sample-wise values of the following quantitative and qualitative
lP
variables: rocky/muddy substrate, submerged aquatic vegetation, altitude, percentage of clay in soil, subsoil bulk density, mean diurnal range of temperature, isothermality, annual precipitation, precipitation of the warmest quarter and precipitation of the coldest quarter. Species which were
na
represented by a single sample were excluded from analysis to avoid their effect on the ordination. A permutation test was used (999 permutations) to obtain the significance of each axis for explaining the data. The first two ordination axes were plotted to visualise the scatter of the data and a scree
ur
plot was generated to understand the contribution of each canonical axis extracted by CCA. All these
Jo
analyses were carried out in the freeware PAST 3.13 (Hammer et al., 2001). 3. Results
Samples from the four types of habitats considered in this study yielded 48 species of aquatic bugs belonging to 30 genera and 14 families, representing all the three infraorders associated with water (i.e. Nepomorpha, Gerromorpha and Leptopodomorpha). Cumulative species richness was greater in streams and ponds than in fort tanks and reservoirs (see SF-Table 3).
6
Total beta diversity (βsor) was highest for streams (βsor = 0.929), followed by ponds, reservoirs and fort tanks (βsor = 0.915, 0.887 and 0.879 respectively). Overall, the communities in all four types of habitats were shaped predominantly by spatial turnover as opposed to nestedness. The contribution of spatial turnover and nestedness to total beta diversity in streams was 96.7% and 3.3% respectively (β sim = 0.899 and βsne = 0.030); for ponds, it was 91% and 9% respectively (βsim = 0.833 and βsne = 0.082); for reservoirs, it was 91.4% and 8.6% respectively (βsim = 0.810 and βsne = 0.076) and for fort tanks, it was 85.8% and 14.2% respectively (βsim = 0.754 and βsne = 0.125). The values of index of association (Phi coefficient) showed that the following species were uniquely associated with only one type of habitat: for streams - Ptilomera agriodes (Gerridae) and Rhagovelia
ro of
sumatraensis (Veliidae); for ponds - Anisops exiguus (Notonectidae), Neogerris parvulus (Gerridae) and Mesovelia vittigera (Mesoveliidae); for fort tanks - Ranatra elongata (Nepidae) and Enithares hungerfordi (Notonectidae) and for reservoirs - Aquarius adelaidis (Gerridae). Detailed results of this analysis are tabulated in Table 1.
-p
The first three principal components of the PCA had high eigenvalues (17, 6.6 and 3.6 respectively) and explained more than 66% of the total variation in the dataset (see SF-Figure 1). Principal components analysis showed that the habitats from where the aquatic bugs were collected were
re
characterised mainly by soil characteristics and climatic variables of temperature and precipitation. Out of the 40 GIS based variables chosen for this analysis (see SF-Table 2), 23 variables had the highest
lP
squared cosine values on the first three principal components and eight of them were found to be non-colinear.
na
PERMANOVA showed that there were significant differences between the four habitat types (i.e. streams, ponds, fort tanks and reservoirs) chosen for this study, both in terms of abiotic environment (F3,65 = 17.17, p = 0.0001) and community composition (F3,65 = 3.39, p = 0.001).
ur
For the dataset of species occurrence, the first two CCA axes had the eigenvalues 0.557 (p = 0.002) and 0.449 (p = 0.001) respectively (Figure 2). Considering the first two axes, variables - submerged
Jo
vegetation (r = 0.49), altitude (r = 0.58), clay percentage in soil (r = 0.69), subsoil bulk density (r = 0.71) and mean diurnal range of temperature (r = 0.69) were positively correlated with Axis 1, while isothermality (r = -0.75), annual precipitation (r = -0.37), precipitation of the warmest quarter (r = 0.35) and precipitation of the coldest quarter (r = -0.17) were negatively correlated with Axis 1. Variable rocky substrate (r = 0.50) was positively correlated with Axis 2 and variable muddy substrate (r = -0.51) was negatively correlated with Axis 2. The types of habitats studied here separated on the basis of substrate type, showing distinction between streams-fort tanks and ponds-reservoirs.
7
Considering the first two axes, the species commonly occurring in ponds and fort tanks were positively associated with submerged vegetation, altitude, clay percentage in soil, subsoil bulk density and mean diurnal range of temperature; while stream dwelling species were negatively associated with these variables, but positively associated with isothermality, annual precipitation, precipitation of the warmest quarter and precipitation of the coldest quarter. On the other hand, the species common to streams and fort tanks were positively associated with rocky substrate and the species preferring to inhabit ponds and reservoirs were positively associated with muddy substrate (Figure 2). 4. Discussion The values of total beta diversity and spatial turnover were quite high for our dataset of aquatic bugs,
ro of
a pattern seen in previous studies on active dispersers (see Tonkin et al., 2016; Hill et al., 2017). Aquatic bugs are active dispersers and can disperse locally in search of favourable conditions (Bilton et al., 2001). Their ability of active dispersal is also reflected in their beta diversity patterns. Podani and Schmera (2011) have emphasised the importance of community similarity in shaping communities
-p
along with beta diversity. But the high values of overall beta diversity (βsor) in our study indicated that the communities in all four types of permanent habitats under study are predominantly shaped by dissimilarity between local assemblages. The consistently higher contribution of spatial turnover to
re
overall beta diversity emphasises the compositional distinctness of local assemblages in the four types of habitats and community assembly through environmental filtering. The Phi index of association
lP
showed that some species or groups of species associate uniquely with only a single type of habitat and can be used as indicators of that habitat type, while some are indicative of a combination of two habitat types (see Table 1). The species or groups of species, which are uniquely associated with a
na
particular habitat type, might be responsible for the high spatial turnover between communities. They exhibit well defined niches, such as rheophilic adaptations, preference for larger habitats or association with aquatic macrophytes. Species which are associated with specific habitat features
ur
could be useful indicators (Carignan and Villard, 2002). Such indicators provide critical information about the structure, function and composition of a habitat and its community (Dale and Beyeler, 2001;
Jo
Lumbreras et al., 2016).
According to Carvalho et al. (2013), the spatial turnover component as calculated by Baselga’s (2010) method (i.e. βsim) does not truly represent dissimilarity caused by species replacement and hence partitioning using this method might not accurately represent the species replacement and species loss processes that structure communities. But, Soininen et al. (2018) have shown that the turnover and nestedness components respond differently to the predictor variables and therefore, partitioning
8
total beta diversity into its two components is a more robust measure of the spatial variability in biological communities, as opposed to the measurement of total beta diversity alone. Overall beta diversity and the contribution of its turnover component showed a positive association with environmental heterogeneity, as they were highest in streams, followed by ponds, which were seen to be hydrologically more dynamic habitats and with marked microhabitat variation, while they were lower in reservoirs and tanks, which were more stable, homogeneous habitats. Similar trends were reported for the beta diversity patterns of chironomid metacommunities in various types of freshwater ecosystems, where overall beta diversity and its replacement component increased from lakes, through wetlands to streams (Specziár et al., 2018). The near absence of nestedness in stream
ro of
communities hints at their being much more distinct than communities in lentic habitats. But we would like to adopt a conservative approach while discussing the results of species turnover analysis, as the high spatial turnover in all four types of habitats might partly result from incomplete sampling (see SF-Table 4 for the results of species richness estimation analyses). Nevertheless, species turnover patterns on a regional scale are important to devise efficient management plans that make most of
-p
the available resources (Socolar et al., 2016; Zbinden and Matthews, 2017).
Spatial turnover showed a significant, but weak positive correlation with geographical distance
re
between sites for the lentic habitats (ponds, fort tanks and reservoirs), while there was no correlation between spatial turnover and geographical distance between sites for streams (see SF-Table 5 for the
lP
results of the Mantel test). This might be because species adapted to lotic habitats are typically more strongly structured by environmental rather than spatial variables (Landeiro et al., 2012; Siqueira et al., 2012; Göthe et al., 2013), as compared to species inhabiting lentic habitats. But these trends
na
should be treated cautiously, as the Mantel test has its limitations when used for spatial analysis (see Legendre et al., 2015).
ur
Previously, significant differences have been reported for abiotic environment as well as assemblage structure of stream invertebrates among different ecoregions (Bini et al., 2014) and for environmental
Jo
characteristics and faunal communities of macroinvertebrates between urban and non-urban ponds (Hill et al., 2016). In our study, Principal Components Analysis (PCA) was used to single out the abiotic variables which define the four types of habitats. Based on the values of these selected abiotic variables, PERMANOVA showed that these four types of permanent habitats indeed have unique abiotic environments. It was also seen that the aquatic bug communities in each type of habitat differed significantly, reflecting the uniqueness of abiotic conditions in each type of habitat. High eigenvalues for the first two axes in Canonical Correspondence Analysis (CCA) indicate that there are clearly definable environmental gradients determining the distribution of species. Species adapted 9
to lotic conditions are positively associated with precipitation patterns and temperature variables such as isothermality, which are associated with the permanence of the habitat. However, for this study, we did not measure the variables at upstream points, which influence the values at downstream localities and might be important for explaining the patterns seen. Diversity in streams is positively correlated with hydrological conditions such as flow permanence and hence the hydroperiod, while negatively correlated with the duration and frequency of drying events (Schriever et al., 2015). Similarly, species inhabiting fort tanks were associated positively with altitude (as a proxy for their preferred climatic conditions). Positive association of some species with clay percentage in soil and subsoil bulk density might be due to their occurrence in habitats with clayey soil and a higher quantity of sediment. Previous studies have shown that factors such as depth and area of the waterbody,
ro of
canopy cover, percentage of open water, percentage of gravel, etc. also affect distribution patterns of aquatic insects (see Fairchild et al., 2003; Bloechl et al., 2010; Suskai et al., 2016).The results of the index of association analysis complement the distribution patterns seen in Canonical Correspondence Analysis (see Figure 2). Species which prefer rocky substrate are common to both streams and fort
-p
tanks. Species positively associated with submerged aquatic vegetation were common to fort tanks, ponds and reservoirs. Aquatic bugs are known to be associated with aquatic vegetation (Chen et al.,
re
2005) and their species richness is positively correlated with the presence of aquatic macrophytes (Karaouzas and Gritzalis, 2006; Sychra and Adámek, 2011).
lP
It is evident from this study that the aquatic bug communities in these tropical freshwater ecosystems are structured mainly due to a large amount of variation between local communities, which is caused by a strong influence of regional and local environmental variables as opposed to spatial factors. Some
na
species that are adapted to specific environmental conditions associate uniquely with each type of habitat and contribute to the overall species turnover.
ur
5. Conclusions
This study demonstrates that the individual communities in four kinds of habitats are shaped by
Jo
species replacements and the variation in species identities between local communities is mainly responsible for enhancing gamma diversity. As spatial turnover contributes much more to overall species turnover than nestedness, individual sites need to be preserved to conserve aquatic bug diversity across spatial scales. Also, each type of habitat is characterised by distinct environmental variables, which along with microhabitat features, determine the diversity and species distributions.
10
Funding: This work was partially supported by a grant from the Board of College and University Development, Savitribai Phule Pune University, India to ADP (Grant number: 14SCI000982).
Acknowledgments SVP acknowledges the Maharashtra State Biodiversity Board for the permit to collect aquatic bug specimens. SVP acknowledges the University Grants Commission, New Delhi, India, for the research fellowship. SVP thanks Mihir Kulkarni, Sayali Sheth, Shriraj Jakhalekar and Nikhil Modak for their help with fieldwork. SMP acknowledges Sanjay Molur, Zoo Outreach Organization for his support. The authors thank the authorities of Abasaheb Garware College, Pune, India for their help and support.
Jo
ur
na
lP
re
-p
ro of
The authors are grateful to the reviewers and the handling editor for their constructive comments.
11
References Alahuhta, J., Kosten, S., Akasaka, M., Auderset, D., Azzella, M.M., Bolpagni, R., ..., de Winton, M., 2017. Global variation in the beta diversity of lake macrophytes is driven by environmental heterogeneity rather than latitude. J. Biogeogr. 44(8), 1758-1769. https://doi.org/10.1111/jbi.12978 Angeler, D.G., 2013. Revealing a conservation challenge through partitioned long-term beta diversity: increasing turnover and decreasing nestedness of boreal lake metacommunities. Divers. Distrib. 19, 772–781. https://doi.org/10.1111/ddi.12029 Apinda Legnouo, E.A., Samways, M.J., Simaika, J.P., 2014. Value of artificial ponds for aquatic beetle
https://doi.org/10.1002/aqc.2413
ro of
and bug conservation in the cape floristic region biodiversity hotspot. Aquat. Conserv. 24, 522–535.
Baeten, L., Vangansbeke, P., Hermy, M., Peterken, G., Vanhuyse, K., Verheyen, K., 2012. Distinguishing between turnover and nestedness in the quantification of biotic homogenization. Biodivers. Conserv.
-p
21, 1399–1409. https://doi.org/10.1007/s10531-012-0251-0
Baselga, A., Orme, C.D.L., 2012. betapart: an R package for the study of beta diversity. Methods Ecol.
re
Evol. 3, 808–812. https://doi.org/10.1111/j.2041-210x.2012.00224.x
Baselga, A., 2010. Partitioning the turnover and nestedness components of beta diversity. Global Ecol.
lP
Biogeogr. 19, 134–143. https://doi.org/10.1111/j.1466-8238.2009.00490.x Biggs, J., Williams, P.J., Whitfield, M., Nicolet, P., Weatherby, A., 2005. 15 years of pond assessment in Britain: results and lessons learned from the work of Pond Conservation. Aquat. Conserv. 15, 693–
na
714. https://doi.org/10.1002/aqc.745
Bilton, D.T., Freeland, J.R., Okamura, B., 2001. Dispersal in freshwater invertebrates. Annu. Rev. Ecol.
ur
Syst. 32(1), 159–181. https://doi.org/10.1146/annurev.ecolsys.32.081501.114016 Bini, L.M., Landeiro, V.L., Padial, A.A., Siqueira, T., Heino, J., 2014. Nutrient enrichment is related to
Jo
two facets of beta diversity for stream invertebrates across the United States. Ecology, 95, 1569–1578. https://doi.org/10.1890/13-0656.1 Bloechl, A., Koenemann, S., Philippi, B., Melber, A., 2010. Abundance, diversity and succession of aquatic Coleoptera and Heteroptera in a cluster of artificial ponds in the North German Lowlands. Limnologica. 40(3), 215–225. https://doi.org/10.1016/j.limno.2009.08.001 Bronmark, C., Hansson, L., 1998. The Biology of Lakes and Ponds. Oxford University Press, Oxford.
12
Carignan, V., Villard M.-A., 2002. Selecting Indicator Species to Monitor Ecological Integrity: A Review. Environ. Monit. Assess. 78, 45–61. https://doi.org/10.1023/A:1016136723584 Carvalho, J.C., Cardoso, P., Borges, P.A.V., Schmera, D., Podani, J., 2013. Measuring fractions of beta diversity and their relationships to nestedness: a theoretical and empirical comparison of novel approaches. Oikos, 122(6), 825–834. https://doi.org/10.1111/j.1600-0706.2012.20980.x Chao, A., 1987. Estimating the population size for capture-recapture data with unequal catchability. Biometrics, 43(4), 783–791. https://doi.org/10.2307/2531532 Chen, P.P., Nieser, N., Zettel, H., 2005. Aquatic and semi-aquatic bugs (Heteroptera: Nepomorpha &
ro of
Gerromorpha) of Malesia. Brill, Leiden, Boston. Colwell, R.K., 2013. Estimate S: Statistical estimation of species richness and shared species from samples, ver. 9. Available at: http://purl.oclc.org/estimates
Colwell, R.K., Coddington, J.A., 1994. Estimating terrestrial biodiversity through extrapolation. Philos.
-p
T. R. Soc. Lon. B, 345(1311), 101–118. https://doi.org/10.1098/rstb.1994.0091
Cottenie, K., 2005. Integrating environmental and spatial processes in ecological community dynamics.
re
Ecol. Lett. 8, 1175–1182. https://doi.org/10.1111/j.1461-0248.2005.00820.x Dale, V.H., Beyeler, S.C., 2001. Challenges in the development and use of ecological indicators. Ecol.
lP
Indic. 1, 3–10. https://doi.org/10.1016/s1470-160x(01)00003-6
De Cáceres, M., Legendre, P., 2009. Associations between species and groups of sites: indices and
na
statistical inference. Ecology, 90, 3566–3574. https://doi.org/10.1890/08-1823.1 De Roeck, E.R., Vanschoenwinkel, B.J., Day, J.A., Xu, Y., Raitt, L., Brendonc, L., 2007. Conservation status of large branchiopods in the Western Cape, South Africa. Wetlands, 27, 162–173.
ur
https://doi.org/10.1672/0277-5212(2007)27[162:csolbi]2.0.co;2 Dobrovolski, R., Melo, A.S., Cassemiro, F.A.S., Diniz-Filho, J.A.F., 2012. Climatic history and dispersal
Jo
ability explain the relative importance of turnover and nestedness components of beta diversity. Global Ecol. Biogeogr. 21(2), 191–197. https://doi.org/10.1111/j.1466-8238.2011.00671.x Fairchild, G.W., Cruz, J., Faulds, A.M., Short, A.E.Z., Matta, J.F., 2003. Microhabitat and landscape influences on aquatic beetle assemblages in a cluster of temporary and permanent ponds. Freshw. Sci. 22(2), 224–240. https://doi.org/10.2307/1467994
13
Gianuca, A.T., Declerck, S.A.J., Lemmens, P., De Meester, L., 2017. Effects of dispersal and environmental heterogeneity on the replacement and nestedness components of β-diversity. Ecology. 98(2), 525–533. https://doi.org/10.1002/ecy.1666 Göthe, E., Angeler, D.G., Gottschalk, S., Löfgren, S., Sandin, L., 2013. The influence of environmental, biotic and spatial factors on diatom metacommunity structure in Swedish headwater streams. PLoS ONE, 8(8), e72237. https://doi.org/10.1371/journal.pone.0072237 Hammer, Ø., Harper, D.A.T., Ryan, P.D., 2001. PAST: paleontological statistics software package for education and data analysis. Palaeontologica Electronica, 4, 1–9. Hill, M.J., Biggs, J., Thornhill, I., Briers, R.A., Gledhill, D.G., White, J.C., Wood, P.J., Hassall, C., 2016.
ro of
Urban ponds as an aquatic biodiversity resource in modified landscapes. Global Change Biol. 23, 986– 999. https://doi.org/10.1111/gcb.13401
Hill, M.J., Heino, J., Thornhill, I., Ryves, D.B., Wood, P.J., 2017. Effects of dispersal mode on the
126(11), 1575–1585. https://doi.org/10.1111/oik.04266
-p
environmental and spatial correlates of nestedness and species turnover in pond communities. Oikos.
re
Hortal, J., Diniz-Filho, J.A.F., Bini, L.M., Rodríguez, M.Á., Baselga, A., Nogués-Bravo, D., Rangel, T.F., Hawkins, B.A., Lobo, J.M., 2011. Ice age climate, evolutionary constraints and diversity patterns of
lP
European dung beetles. Ecol. Lett. 14(8), 741–748. https://doi.org/10.1111/j.1461-0248.2011.01634.x Jeffries, M., 2005. Small ponds and big landscapes: the challenge of invertebrate spatial and temporal dynamics
for
European
conservation.
Aquat.
Conserv.
15,
541–547.
na
https://doi.org/10.1002/aqc.753
pond
Karaouzas, I., Gritzalis, K.C., 2006. Local and regional factors determining aquatic and semi-aquatic bug (Heteroptera) assemblages in rivers and streams of Greece. Hydrobiologia, 573, 199–212.
ur
https://doi.org/10.1007/s10750-006-0274-1
Jo
Kokociński, M., Stefaniak, K., Mankiewicz-Boczek, J., Izydorczyk, K., Soininen, J., 2010. The ecology of the invasive cyanobacterium Cylindrospermopsis raciborskii (Nostocales, Cyanophyta) in two hypereutrophic lakes dominated by Planktothrix agardhii (Oscillatoriales, Cyanophyta). Eur. J. Phycol. 45(4), 365–374, https://doi.org/10.1080/09670262.2010.492916 Landeiro, V.L., Bini, L.M., Melo, A.S., Pes, A.M.O., Magnusson, W.E., 2012. The roles of dispersal limitation and environmental conditions in controlling caddisfly (Trichoptera) assemblages. Freshwater Biol. 57(8), 1554–1564. https://doi.org/10.1111/j.1365-2427.2012.02816.x
14
Legendre, P., 2014. Interpreting the replacement and richness difference components of beta diversity. Global Ecol. Biogeogr. 23, 1324–1334. https://doi.org/10.1111/geb.12207 Legendre, P., Fortin, M.-J., Borcard, D., 2015. Should the Mantel test be used in spatial analysis? Methods Ecol. Evol. 6(11), 1239–1247. https://doi.org/10.1111/2041-210x.12425 Lumbreras, A., Marques, J.T., Belo, A.F., Cristo, M., Fernandes, M., Galioto, D., … Pinto-Cruz, C., 2016. Assessing the conservation status of Mediterranean temporary ponds using biodiversity: a new tool for practitioners. Hydrobiologia, 782, 187–199. https://doi.org/10.1007/s10750-016-2697-7 Maloufi S., Catherine A., Mouillot D., Louvard C., Couté A., Bernard C., Troussellier, M., 2016. Environmental heterogeneity among lakes promotes hyper b-diversity across phytoplankton
ro of
communities. Freshwater Biol. 61, 633–645. https://doi.org/10.1111/fwb.12731
Nilsson, A.N., Svensson, B.W., 1995. Assemblages of dytiscid predators and culicid prey in relation to environmental factors in natural and clear-cut boreal swamp forest pools. Hydrobiologia, 308, 183–
-p
196. https://doi.org/10.1007/bf00006870
Oertli, B., Biggs, J., Céréghino, R., Grillas, P., Joly, P., Lachavanne, J-B., 2005. Conservation and of
pond
biodiversity:
introduction.
Aquat.
Conserv.
15,
535–540.
re
monitoring
https://doi.org/10.1002/aqc.752
explaining
pattern
in
lP
Podani, J., Schmera, D., 2011. A new conceptual and methodological framework for exploring and presence
-
absence
data.
Oikos,
120(11),
1625–1638.
https://doi.org/10.1111/j.1600-0706.2011.19451.x
na
Polhemus, J.T., Polhemus, D.A., 2008. Global diversity of true bugs (Heteroptera; Insecta) in freshwater. Hydrobiologia, 595, 379–391. https://doi.org/10.1007/978-1-4020-8259-7_40
ur
Qian H., Ricklefs R.E., 2012. Disentangling the effects of geographic distance and environmental dissimilarity on global patterns of species turnover. Global Ecol. Biogeogr. 21, 341–351.
Jo
https://doi.org/10.1111/j.1466-8238.2011.00672.x Schneider, D.W., Frost, T.M., 1996. Habitat duration and community structure in temporary ponds. Freshw. Sci. 15, 64–86. https://doi.org/10.2307/1467433 Schriever, T.A., Bogan, M.T., Boersma, K.S., Cañedo-Argüelles, M., Jaeger, K.L., Olden, J.D., Lytle, D.A., 2015. Hydrology shapes taxonomic and functional structure of desert stream invertebrate communities. Freshw. Sci. 34, 399–409. https://doi.org/10.1086/680518
15
Si, X., Baselga, A., Leprieur, F., Song, X., Ding, P., 2016. Selective extinction drives taxonomic and functional alpha and beta diversities in island bird assemblages. J. Anim. Ecol. 85, 409–418. https://doi.org/10.1111/1365-2656.12478 Siqueira, T., Bini, L.M., Roque, F.O., Cottenie, K., 2012. A Metacommunity Framework for Enhancing the
Effectiveness
of
Biological
Monitoring
Strategies.
PLoS
ONE,
7(8),
e43626.
https://doi.org/10.3410/f.717988254.793472558 Socolar, J.B., Gilroy, J.J., Kunin, W.E., Edwards, D.P., 2016. How should beta-diversity inform biodiversity conservation? Trends Ecol. Evol. 31, 67–79. https://doi.org/10.1016/j.tree.2015.11.005 Soininen, J., Heino, J., Wang, J., 2018. A meta-analysis of nestedness and turnover components of beta across
organisms
and
ecosystems.
Global
Ecol.
Biogeogr.
27(1),
96–109.
ro of
diversity
https://doi.org/10.1111/geb.12660
Specziár, A., Árva, D., Tóth, M., Móra, A., Schmera, D., Várbíró, G., Erős, T., 2018. Environmental and
-p
spatial drivers of beta diversity components of chironomid metacommunities in contrasting freshwater systems. Hydrobiologia. 819(1), 123–143. https://doi.org/10.1007/s10750-018-3632-x
re
Stegen, J.C., Freestone, A.L., Crist, T.O., Anderson, M.J., Chase, J.M., Comita, L.S., … Vellend, M., 2013. Stochastic and deterministic drivers of spatial and temporal turnover in breeding bird communities.
lP
Global Ecol. Biogeogr. 22, 202–212. https://doi.org/10.1111/j.1466-8238.2012.00780.x Suksai, B., Sangpradub, N., Zettel, H., 2016. Assemblage of aquatic Heteroptera (Gerromorpha and Nepomorpha) in relation to microhabitats in the Phong River, Northeast Thailand. Entomol. Res. 46,
na
93–106. https://doi.org/10.1111/1748-5967.12152
Sychra, J., Adámek, Z., 2011. The impact of sediment removal on the aquatic macroinvertebrate assemblage
in
a
fishpond
littoral
zone.
J.
Limnol.
70,
129–138.
ur
https://doi.org/10.4081/jlimnol.2011.138
Jo
ter Braak, C.J.F., Verdonschot, P.F.M., 1995. Canonical correspondence analysis and related multivariate
methods
in
aquatic
ecology.
Aquat.
Sci.
57,
255–289.
https://doi.org/10.1007/bf00877430 Thornhill, I., Batty, L., Death, R.G., Friberg, N.R., Ledger, M.E., 2017. Local and landscape scale determinants of macroinvertebrate assemblages and their conservation value in ponds across and urban land-use gradient. Biodivers. Conserv. 26: 1065–1086. https://doi.org/10.1007/s10531-0161286-4
16
Tisseuil, C., Leprieur, F., Grenouillet, G., Vrac, M., Lek, S., 2012. Projected impacts of climate change on spatio-temporal patterns of freshwater fish beta diversity: a deconstructing approach. Global Ecol. Biogeogr. 21(12), 1213–1222. https://doi.org/10.1111/j.1466-8238.2012.00773.x Tonkin, J.D., Stoll, S., Jähnig, S.C., Haase, P., 2016. Contrasting metacommunity structure and beta diversity in an aquatic-floodplain system. Oikos. 125(5), 686–697. https://doi.org/10.1111/oik.02717 Viana, D.S., Figuerola, J., Schwenk, K., Manca, M., Hobæk, A., Mjelde, M., . . . Santamaría, L., 2016. Assembly mechanisms determining high species turnover in aquatic communities over regional and continental scales. Ecography. 39, 281–288. https://doi.org/10.1111/ecog.01231 Zbinden, Z.D., Matthews, W.J., 2017. Beta diversity of stream fish assemblages: partitioning variation spatial
and
environmental
factors.
Freshwater
Biol.
62,
1460–1471.
ro of
between
Jo
ur
na
lP
re
-p
https://doi.org/10.1111/fwb.12960
17
Figure Legends Figure 1: Map showing the study area and the collection localities. Different habitat types are depicted
re
-p
ro of
by different coloured symbols.
Figure 2: Ordination diagram for canonical correspondence analysis for all the bug species in the four
lP
permanent habitat types. The first two axes are shown here. The inset shows a scree plot, providing the proportional variation explained by each CCA axis. Species codes (numbers) are as given in SF-
Jo
ur
na
Table 3.
18
Table 1: Phi coefficient of association (r) of species uniquely associated with each type of habitat with significance indicated by p-values. Group Streams
Species Ptilomera agriodes Rhagovelia sumatraensis Anisops exiguus Neogerris parvulus Mesovelia vittigera
r 0.552 0.351 0.420 0.350 0.339
p 0.001 *** 0.050 * 0.009 ** 0.025 * 0.042 *
Ranatra elongata 0.747 Enithares hungerfordi 0.310 Reservoirs Aquarius adelaidis 0.337 Fort tanks + streams Heleocoris elongatus 0.406 Metrocoris indicus 0.379 Fort tanks + Ponds Anisops barbatus 0.341 Anisops cavifrons 0.518 Anisops sardeus 0.416 Enithares ciliata 0.314 Fort tanks + Reservoirs Micronecta quadristrigata 0.404 Ponds + Reservoirs Ranatra filiformis 0.510 Laccotrephes griseus 0.338 Paraplea frontalis 0.338 Significance codes: *** - p = 0.001, ** - p = ≤ 0.01, * - p = ≤ 0.05
0.001 *** 0.032 * 0.037 * 0.007 ** 0.013 * 0.022 * 0.001 *** 0.005 ** 0.035 * 0.006 ** 0.001 *** 0.031 * 0.030 *
Ponds
Jo
ur
na
lP
re
-p
ro of
Fort tanks
19
PII:
S0075-9511(19)30084-2
DOI:
https://doi.org/10.1016/j.limno.2019.125730
Reference:
LIMNO 125730
To appear in:
Limnologica
Received Date:
29 April 2019
Revised Date:
3 November 2019
Accepted Date:
3 November 2019
Please cite this article as: Paripatyadar SV, Padhye SM, Padhye AD, Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats, India, Limnologica (2019), doi: https://doi.org/10.1016/j.limno.2019.125730
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems of the Western Ghats, India
Shruti Vilas Paripatyadara*, Sameer Mukund Padhyeb, Anand Dhananjay Padhyec
a Annasaheb Kulkarni Department of Biodiversity, MES’ Abasaheb Garware College, Karve Road,
ro of
Pune – 411 004. Maharashtra, INDIA. Email: [email protected] * Corresponding author
Nadu, INDIA. Email: [email protected]
-p
b Systematics, Ecology & Conservation Lab, Zoo Outreach Organization, Coimbatore 641035, Tamil
re
c Department of Zoology, MES’ Abasaheb Garware College, Karve Road, Pune – 411 004.
lP
Maharashtra, INDIA. Email: [email protected]
Environmental determinants of species turnover of aquatic Heteroptera in freshwater ecosystems
na
of the Western Ghats, India Abstract
ur
Partitioning beta diversity into its two components of spatial turnover and nestedness is a more robust method for checking spatial variability in biological communities than calculating the total beta
Jo
diversity alone. The relative contribution of spatial turnover and nestedness has been used to test the effects of climatic, environmental, spatial and temporal variables on community composition. In this study, we tested the effects of environmental factors and microhabitat features on total beta diversity and its spatial turnover and nestedness components using a comprehensive dataset of aquatic Heteroptera collected from four types of permanent freshwater habitats (i.e. streams, ponds, rock tanks and reservoirs) in the Western Ghats of India. We observed that communities in all four types of habitats were predominantly shaped by dissimilarity caused due to spatial turnover (>85%). Each type of habitat showed the presence of one or more species uniquely associated with it, which might 1
contribute to the turnover between communities. The abiotic environment (climatic factors, topological factors, soil characteristics and microhabitat features) as well as assemblage structure differed significantly between habitat types. Communities in each type of habitat were affected by different environmental factors, such as precipitation and temperature patterns for streams, altitude and rocky substrate for rock tanks, and soil characteristics and the presence of aquatic macrophytes for ponds and reservoirs. Assemblages observed in the four types of permanent habitats are thus compositionally distinct due to species replacements between local communities, which in turn are strongly influenced by environmental variables. Similar to previous studies, our results show that
ro of
spatial turnover largely measures the same phenomenon as total beta diversity on a regional scale.
Keywords: Active dispersers; permanent freshwater habitats; spatial turnover; abiotic environment;
-p
microhabitats.
re
1. Introduction
Beta diversity is an important measure of regional biodiversity and can be defined as the dissimilarity
lP
in community composition between a pair of sites (Gianuca et al., 2017). Beta diversity in turn depends on alpha diversity gradients and both of these components are a result of community assembly through local and regional filters. Understanding the beta diversity patterns is more helpful in
na
capturing the dynamic nature of biodiversity as compared to simple alpha diversity measures (Soininen et al., 2018). In the recent years, measuring beta diversity has gained popularity as a metric to infer the patterns shaping regional biodiversity, for testing ecological theories and for the practical
ur
management of regional diversity (see Baselga, 2010; Socolar et al., 2016; Soininen et al., 2018). Total beta diversity can be partitioned into its two additive components of ‘spatial turnover’ and
Jo
‘nestedness’, which signify the two antithetic processes of species replacement and species loss/gain (i.e. the differences in richness between samples) respectively (sensu Baselga, 2010). Spatial turnover implies simultaneous loss and gain of species between localities, reflecting species sorting due to environmental filtering, competition or dispersal abilities, while nestedness implies richness difference caused by the extent to which a species-poor site is a subset of a species-rich site, reflecting extinction-colonization dynamics (Legendre, 2014; Si et al., 2016; Soininen et al., 2018). In most studies on local to regional scale, spatial turnover has been shown to be the dominant component shaping beta diversity (see Soininen et al., 2018 and references therein), but as the spatial extent 2
increases to address biogeographical hypotheses, nestedness is also seen as a significant driver of variation between communities (Baselga, 2010). Baselga’s (2010) approach of partitioning total beta diversity was replicated by other studies to test the effects of climatic factors (Hortal et al., 2011; Dobrovolski et al., 2012; Tisseuil et al., 2012), environmental factors (Alahuhta et al., 2017), spatial factors (Viana et al., 2016; Hill et al., 2017) and temporal factors (Baeten et al., 2012; Angeler, 2013) on community composition. The contribution of spatial turnover and nestedness to overall beta diversity depends upon the dispersal abilities of taxa in freshwater ecosystems (Gianuca et al., 2017), with higher total beta diversity and its turnover component for actively dispersing taxa and lower overall beta diversity and
ro of
greater contribution of nestedness for passively dispersing taxa (see Tonkin et al., 2016; Hill et al., 2017). Species turnover also depends upon the type of habitat under study, as a function of environmental heterogeneity (see Qian and Ricklefs, 2012; Stegen et al., 2013; Maloufi et al., 2016). Moderately heterogeneous freshwater ecosystems such as lakes show lower overall beta diversity and turnover as compared to highly heterogenous ecosystems such as stream networks (Specziár et al.,
-p
2018).
In this study, we examined the effects of local as well as regional environmental factors on beta
re
diversity patterns in four types of tropical permanent freshwater ecosystems (i.e. streams, ponds, rock tanks and reservoirs) of the Western Ghats of India, which show remarkable heterogeneity in habitats,
lP
making them an excellent area to address ecological hypotheses. We chose permanent habitats because artificial (reservoirs) and natural permanent waterbodies are important habitats for a wide variety of aquatic organisms (Bronmark and Hansson, 1998; Jeffries, 2005; Oertli et al., 2005; De Roeck
na
et al., 2007; Apinda Legnouo et al., 2014) and have higher species richness as compared to temporary habitats (Nilsson and Svensson, 1995; Schneider and Frost, 1996; Fairchild et al., 2003). We chose aquatic Heteroptera (true bugs), which form one of major components of freshwater ecosystems, as
ur
our model system because their striking morphological adaptations to an aquatic mode of life make
Jo
them excellent models for ecological and biogeographical studies (Polhemus and Polhemus, 2008). Environmental factors do shape communities in heterogeneous landscapes and species track preferred environmental conditions (see Cottenie, 2005; Thornhill et al., 2017). This usually results in higher spatial turnover explaining the distinctness of local communities, as opposed to nestedness, which is prevalent in more homogeneous settings (Gianuca et al., 2017; Hill et al., 2017). We expected that the abiotic environment as well as the bug communities would differ significantly between each type of habitat under study (i.e. streams, ponds, rock tanks and reservoirs) and therefore, we hypothesized that the local communities in the four types of habitats were influenced mainly by the
3
environment and hence shaped much more by spatial turnover than by nestedness. In this study, we addressed the following questions: 1. To what extent does spatial turnover contribute to total beta diversity in each type of habitat and do the total beta diversity and its species replacement component increase from tanks and reservoirs through ponds to streams (due to differences in environmental heterogeneity)? 2. Which species or groups of species associate uniquely with each type of habitat and therefore contribute to turnover between communities? 3. Which environmental factors contribute to the differences between the four types of habitat and how do species interact with environmental conditions in each type of habitat? 2. Materials and Methods
ro of
2.1. Types of habitats: We selected four types of permanent or nearly permanent freshwater habitats (habitats in which water persisted for more than 10 months annually). These habitats were - a) streams, b) man-made tanks on hill forts (historical forts built on basaltic outcrops, with altitude more than 600 m ASL) constructed for water storage (hereafter referred to as fort tanks), c) ponds
-p
(waterbodies between 1m2 and 2ha in area, following Biggs et al., 2005) and d) reservoirs (man-made habitats bigger than those classified as ponds, with a retention wall and constructed for water storage); each with clearly definable microhabitats and abiotic as well as biotic environments. All the
re
waterbodies which were classified as ‘fort tanks’ could be categorised as ponds, but because of the uniqueness of these habitats (their situation on isolated rock outcrops), they were included in a
lP
separate category.
2.2. Field collections: Multiple samples of aquatic and semi-aquatic bugs were collected from 19
na
streams, rock tanks on 16 hill forts, 18 ponds and 16 reservoirs, using a D-net with a telescopic rod (GB nets, Cornwall, UK) between June 2013 to March 2016. One sample consisted of all bugs caught after 3 forward and 3 reverse sweeps, approximately covering an area of 2m2. The geographical
ur
coordinates, type of substrate (muddy/rocky) and the presence/absence of submerged aquatic vegetation were noted for each locality. In all, 250 samples from 69 different localities (3-4 samples
Jo
per locality) in and around the Western Ghats of Maharashtra were included in this study (see Figure 1; SF-Table 1). Adult specimens in the samples were identified to species level and species occurrences were used for further analysis. Relative abundances were not used because of the disparity in sampling effort due to variability in habitat size. 2.3 Data analysis: True species richness estimation was carried out for each type of habitat using nonparametric estimators - Chao2 and Jackknife2 along with Bootstrapping (Chao, 1987; Colwell and Coddington, 1994). Species occurrence data from all the localities were used (presence was coded ‘1’, while absence was coded ‘0’), considering each locality as a separate sample. One thousand 4
randomisations were carried out per sample, to obtain the standard deviations around the mean. The freeware EstimateS (Colwell, 2013) was used for this analysis (see SF-Table 4 for the results of this analysis). The package “betapart” (Baselga and Orme, 2012) in R was used to calculate and partition total beta diversity for each type of habitat to understand the relative contribution of species replacement or spatial turnover (βsim) and species loss or nestedness (βsne) to overall species turnover (βsor), as defined by Baselga (2010). Species occurrence data was used to calculate total species turnover and its two additive components. We used the method ‘beta.multi’ to calculate these coefficients, which takes into account all the given samples for producing the mean beta coefficient of pairwise beta
ro of
coefficients calculated for all possible sample combinations (Baselga and Orme, 2012). This procedure was repeated for 9,999 iterations. A Mantel test was performed for each type of habitat to check if there was a correlation between pairwise values of geographical distance between sites and spatial turnover (βsim) for all possible pairs (see SF-Table 5 for the results).
-p
We calculated the Phi coefficient of association (De Cáceres and Legendre, 2009) to check if there were any taxa which associate uniquely with each type of habitat and therefore could be influencing the differences in beta diversity patterns. This analysis was done using the function ‘multipatt’ from
re
the package “indicspecies” (https://cran.r-project.org/web/packages/indicspecies/index.html) in R. We used the ‘r.g’ value for the ‘func’ argument of the ‘multipatt’ function in order to consider the
lP
unequal sample numbers. The function ‘multipatt’ determines the list of species which are associated with a particular group of sites (type of habitat in our case). A total of 5000 permutations were run for
na
obtaining the significance of association of each species with an alpha of p = 0.05. To understand the influence of large-scale abiotic variables in shaping the environment of different types of permanent habitats, data extracted from 40 different GIS layers were used for analysis. The
ur
GIS layers corresponding to soil characteristics, water storage in soil, topography and bioclimatic variables (temperature and precipitation) were downloaded from online sources (Worldclim – Global
Jo
Climate Data and ORNL DAAC) (see SF-Table 2 for details). These variables were chosen as they were seen to differ locally and might be important for defining the four types of habitats under study. The freeware DIVA GIS was used to extract the data from the GIS layers using the geographical coordinates of the localities, which were superimposed on the GIS layer to extract the data for each point locality. For streams, the data were extracted for the actual sampling locality and not for upstream points. Principal Components Analysis (PCA) was performed to extract the environmental variables which were significantly associated with the four types of habitats, from where the bugs were collected. Values of quantitative environmental variables were log transformed prior to the analysis, as they had 5
different units of measurement. Squared cosine values (which indicate the importance of a component for a given variable) were calculated for all GIS-based variables on all components, to determine which component had the largest squared cosine for each variable. The GIS-based variables, for which the largest squared cosine values were on one of the first three components, were used for further analysis. Co-linearity of the selected GIS-based variables was checked using Spearman correlation. Variables having coefficients of determination more than 0.75 were considered as colinear (see Kokociński et al., 2010 for a similar approach) and only the one which had the highest squared cosine value was used for Permutation based multivariate ANOVA (one-way PERMANOVA) and Canonical Correspondence Analysis (CCA).
ro of
Permutation based multivariate ANOVA (one-way PERMANOVA) was used to test whether there were significant differences between the four types of habitats under study. Sample-wise values of the selected non-colinear abiotic variables and species occurrence data were used to test for differences in abiotic environment (PERMANOVA using Gower index) and community composition (PERMANOVA
-p
using Jaccard index) respectively.
We used Canonical Correspondence Analysis (CCA) to understand the effects of different environmental variables on the distribution of aquatic bug species collected from the four habitat
re
types (as per ter Braak and Verdonschot, 1995). CCA was performed using species occurrence data from all samples along with the sample-wise values of the following quantitative and qualitative
lP
variables: rocky/muddy substrate, submerged aquatic vegetation, altitude, percentage of clay in soil, subsoil bulk density, mean diurnal range of temperature, isothermality, annual precipitation, precipitation of the warmest quarter and precipitation of the coldest quarter. Species which were
na
represented by a single sample were excluded from analysis to avoid their effect on the ordination. A permutation test was used (999 permutations) to obtain the significance of each axis for explaining the data. The first two ordination axes were plotted to visualise the scatter of the data and a scree
ur
plot was generated to understand the contribution of each canonical axis extracted by CCA. All these
Jo
analyses were carried out in the freeware PAST 3.13 (Hammer et al., 2001). 3. Results
Samples from the four types of habitats considered in this study yielded 48 species of aquatic bugs belonging to 30 genera and 14 families, representing all the three infraorders associated with water (i.e. Nepomorpha, Gerromorpha and Leptopodomorpha). Cumulative species richness was greater in streams and ponds than in fort tanks and reservoirs (see SF-Table 3).
6
Total beta diversity (βsor) was highest for streams (βsor = 0.929), followed by ponds, reservoirs and fort tanks (βsor = 0.915, 0.887 and 0.879 respectively). Overall, the communities in all four types of habitats were shaped predominantly by spatial turnover as opposed to nestedness. The contribution of spatial turnover and nestedness to total beta diversity in streams was 96.7% and 3.3% respectively (β sim = 0.899 and βsne = 0.030); for ponds, it was 91% and 9% respectively (βsim = 0.833 and βsne = 0.082); for reservoirs, it was 91.4% and 8.6% respectively (βsim = 0.810 and βsne = 0.076) and for fort tanks, it was 85.8% and 14.2% respectively (βsim = 0.754 and βsne = 0.125). The values of index of association (Phi coefficient) showed that the following species were uniquely associated with only one type of habitat: for streams - Ptilomera agriodes (Gerridae) and Rhagovelia
ro of
sumatraensis (Veliidae); for ponds - Anisops exiguus (Notonectidae), Neogerris parvulus (Gerridae) and Mesovelia vittigera (Mesoveliidae); for fort tanks - Ranatra elongata (Nepidae) and Enithares hungerfordi (Notonectidae) and for reservoirs - Aquarius adelaidis (Gerridae). Detailed results of this analysis are tabulated in Table 1.
-p
The first three principal components of the PCA had high eigenvalues (17, 6.6 and 3.6 respectively) and explained more than 66% of the total variation in the dataset (see SF-Figure 1). Principal components analysis showed that the habitats from where the aquatic bugs were collected were
re
characterised mainly by soil characteristics and climatic variables of temperature and precipitation. Out of the 40 GIS based variables chosen for this analysis (see SF-Table 2), 23 variables had the highest
lP
squared cosine values on the first three principal components and eight of them were found to be non-colinear.
na
PERMANOVA showed that there were significant differences between the four habitat types (i.e. streams, ponds, fort tanks and reservoirs) chosen for this study, both in terms of abiotic environment (F3,65 = 17.17, p = 0.0001) and community composition (F3,65 = 3.39, p = 0.001).
ur
For the dataset of species occurrence, the first two CCA axes had the eigenvalues 0.557 (p = 0.002) and 0.449 (p = 0.001) respectively (Figure 2). Considering the first two axes, variables - submerged
Jo
vegetation (r = 0.49), altitude (r = 0.58), clay percentage in soil (r = 0.69), subsoil bulk density (r = 0.71) and mean diurnal range of temperature (r = 0.69) were positively correlated with Axis 1, while isothermality (r = -0.75), annual precipitation (r = -0.37), precipitation of the warmest quarter (r = 0.35) and precipitation of the coldest quarter (r = -0.17) were negatively correlated with Axis 1. Variable rocky substrate (r = 0.50) was positively correlated with Axis 2 and variable muddy substrate (r = -0.51) was negatively correlated with Axis 2. The types of habitats studied here separated on the basis of substrate type, showing distinction between streams-fort tanks and ponds-reservoirs.
7
Considering the first two axes, the species commonly occurring in ponds and fort tanks were positively associated with submerged vegetation, altitude, clay percentage in soil, subsoil bulk density and mean diurnal range of temperature; while stream dwelling species were negatively associated with these variables, but positively associated with isothermality, annual precipitation, precipitation of the warmest quarter and precipitation of the coldest quarter. On the other hand, the species common to streams and fort tanks were positively associated with rocky substrate and the species preferring to inhabit ponds and reservoirs were positively associated with muddy substrate (Figure 2). 4. Discussion The values of total beta diversity and spatial turnover were quite high for our dataset of aquatic bugs,
ro of
a pattern seen in previous studies on active dispersers (see Tonkin et al., 2016; Hill et al., 2017). Aquatic bugs are active dispersers and can disperse locally in search of favourable conditions (Bilton et al., 2001). Their ability of active dispersal is also reflected in their beta diversity patterns. Podani and Schmera (2011) have emphasised the importance of community similarity in shaping communities
-p
along with beta diversity. But the high values of overall beta diversity (βsor) in our study indicated that the communities in all four types of permanent habitats under study are predominantly shaped by dissimilarity between local assemblages. The consistently higher contribution of spatial turnover to
re
overall beta diversity emphasises the compositional distinctness of local assemblages in the four types of habitats and community assembly through environmental filtering. The Phi index of association
lP
showed that some species or groups of species associate uniquely with only a single type of habitat and can be used as indicators of that habitat type, while some are indicative of a combination of two habitat types (see Table 1). The species or groups of species, which are uniquely associated with a
na
particular habitat type, might be responsible for the high spatial turnover between communities. They exhibit well defined niches, such as rheophilic adaptations, preference for larger habitats or association with aquatic macrophytes. Species which are associated with specific habitat features
ur
could be useful indicators (Carignan and Villard, 2002). Such indicators provide critical information about the structure, function and composition of a habitat and its community (Dale and Beyeler, 2001;
Jo
Lumbreras et al., 2016).
According to Carvalho et al. (2013), the spatial turnover component as calculated by Baselga’s (2010) method (i.e. βsim) does not truly represent dissimilarity caused by species replacement and hence partitioning using this method might not accurately represent the species replacement and species loss processes that structure communities. But, Soininen et al. (2018) have shown that the turnover and nestedness components respond differently to the predictor variables and therefore, partitioning
8
total beta diversity into its two components is a more robust measure of the spatial variability in biological communities, as opposed to the measurement of total beta diversity alone. Overall beta diversity and the contribution of its turnover component showed a positive association with environmental heterogeneity, as they were highest in streams, followed by ponds, which were seen to be hydrologically more dynamic habitats and with marked microhabitat variation, while they were lower in reservoirs and tanks, which were more stable, homogeneous habitats. Similar trends were reported for the beta diversity patterns of chironomid metacommunities in various types of freshwater ecosystems, where overall beta diversity and its replacement component increased from lakes, through wetlands to streams (Specziár et al., 2018). The near absence of nestedness in stream
ro of
communities hints at their being much more distinct than communities in lentic habitats. But we would like to adopt a conservative approach while discussing the results of species turnover analysis, as the high spatial turnover in all four types of habitats might partly result from incomplete sampling (see SF-Table 4 for the results of species richness estimation analyses). Nevertheless, species turnover patterns on a regional scale are important to devise efficient management plans that make most of
-p
the available resources (Socolar et al., 2016; Zbinden and Matthews, 2017).
Spatial turnover showed a significant, but weak positive correlation with geographical distance
re
between sites for the lentic habitats (ponds, fort tanks and reservoirs), while there was no correlation between spatial turnover and geographical distance between sites for streams (see SF-Table 5 for the
lP
results of the Mantel test). This might be because species adapted to lotic habitats are typically more strongly structured by environmental rather than spatial variables (Landeiro et al., 2012; Siqueira et al., 2012; Göthe et al., 2013), as compared to species inhabiting lentic habitats. But these trends
na
should be treated cautiously, as the Mantel test has its limitations when used for spatial analysis (see Legendre et al., 2015).
ur
Previously, significant differences have been reported for abiotic environment as well as assemblage structure of stream invertebrates among different ecoregions (Bini et al., 2014) and for environmental
Jo
characteristics and faunal communities of macroinvertebrates between urban and non-urban ponds (Hill et al., 2016). In our study, Principal Components Analysis (PCA) was used to single out the abiotic variables which define the four types of habitats. Based on the values of these selected abiotic variables, PERMANOVA showed that these four types of permanent habitats indeed have unique abiotic environments. It was also seen that the aquatic bug communities in each type of habitat differed significantly, reflecting the uniqueness of abiotic conditions in each type of habitat. High eigenvalues for the first two axes in Canonical Correspondence Analysis (CCA) indicate that there are clearly definable environmental gradients determining the distribution of species. Species adapted 9
to lotic conditions are positively associated with precipitation patterns and temperature variables such as isothermality, which are associated with the permanence of the habitat. However, for this study, we did not measure the variables at upstream points, which influence the values at downstream localities and might be important for explaining the patterns seen. Diversity in streams is positively correlated with hydrological conditions such as flow permanence and hence the hydroperiod, while negatively correlated with the duration and frequency of drying events (Schriever et al., 2015). Similarly, species inhabiting fort tanks were associated positively with altitude (as a proxy for their preferred climatic conditions). Positive association of some species with clay percentage in soil and subsoil bulk density might be due to their occurrence in habitats with clayey soil and a higher quantity of sediment. Previous studies have shown that factors such as depth and area of the waterbody,
ro of
canopy cover, percentage of open water, percentage of gravel, etc. also affect distribution patterns of aquatic insects (see Fairchild et al., 2003; Bloechl et al., 2010; Suskai et al., 2016).The results of the index of association analysis complement the distribution patterns seen in Canonical Correspondence Analysis (see Figure 2). Species which prefer rocky substrate are common to both streams and fort
-p
tanks. Species positively associated with submerged aquatic vegetation were common to fort tanks, ponds and reservoirs. Aquatic bugs are known to be associated with aquatic vegetation (Chen et al.,
re
2005) and their species richness is positively correlated with the presence of aquatic macrophytes (Karaouzas and Gritzalis, 2006; Sychra and Adámek, 2011).
lP
It is evident from this study that the aquatic bug communities in these tropical freshwater ecosystems are structured mainly due to a large amount of variation between local communities, which is caused by a strong influence of regional and local environmental variables as opposed to spatial factors. Some
na
species that are adapted to specific environmental conditions associate uniquely with each type of habitat and contribute to the overall species turnover.
ur
5. Conclusions
This study demonstrates that the individual communities in four kinds of habitats are shaped by
Jo
species replacements and the variation in species identities between local communities is mainly responsible for enhancing gamma diversity. As spatial turnover contributes much more to overall species turnover than nestedness, individual sites need to be preserved to conserve aquatic bug diversity across spatial scales. Also, each type of habitat is characterised by distinct environmental variables, which along with microhabitat features, determine the diversity and species distributions.
10
Funding: This work was partially supported by a grant from the Board of College and University Development, Savitribai Phule Pune University, India to ADP (Grant number: 14SCI000982).
Acknowledgments SVP acknowledges the Maharashtra State Biodiversity Board for the permit to collect aquatic bug specimens. SVP acknowledges the University Grants Commission, New Delhi, India, for the research fellowship. SVP thanks Mihir Kulkarni, Sayali Sheth, Shriraj Jakhalekar and Nikhil Modak for their help with fieldwork. SMP acknowledges Sanjay Molur, Zoo Outreach Organization for his support. The authors thank the authorities of Abasaheb Garware College, Pune, India for their help and support.
Jo
ur
na
lP
re
-p
ro of
The authors are grateful to the reviewers and the handling editor for their constructive comments.
11
References Alahuhta, J., Kosten, S., Akasaka, M., Auderset, D., Azzella, M.M., Bolpagni, R., ..., de Winton, M., 2017. Global variation in the beta diversity of lake macrophytes is driven by environmental heterogeneity rather than latitude. J. Biogeogr. 44(8), 1758-1769. https://doi.org/10.1111/jbi.12978 Angeler, D.G., 2013. Revealing a conservation challenge through partitioned long-term beta diversity: increasing turnover and decreasing nestedness of boreal lake metacommunities. Divers. Distrib. 19, 772–781. https://doi.org/10.1111/ddi.12029 Apinda Legnouo, E.A., Samways, M.J., Simaika, J.P., 2014. Value of artificial ponds for aquatic beetle
https://doi.org/10.1002/aqc.2413
ro of
and bug conservation in the cape floristic region biodiversity hotspot. Aquat. Conserv. 24, 522–535.
Baeten, L., Vangansbeke, P., Hermy, M., Peterken, G., Vanhuyse, K., Verheyen, K., 2012. Distinguishing between turnover and nestedness in the quantification of biotic homogenization. Biodivers. Conserv.
-p
21, 1399–1409. https://doi.org/10.1007/s10531-012-0251-0
Baselga, A., Orme, C.D.L., 2012. betapart: an R package for the study of beta diversity. Methods Ecol.
re
Evol. 3, 808–812. https://doi.org/10.1111/j.2041-210x.2012.00224.x
Baselga, A., 2010. Partitioning the turnover and nestedness components of beta diversity. Global Ecol.
lP
Biogeogr. 19, 134–143. https://doi.org/10.1111/j.1466-8238.2009.00490.x Biggs, J., Williams, P.J., Whitfield, M., Nicolet, P., Weatherby, A., 2005. 15 years of pond assessment in Britain: results and lessons learned from the work of Pond Conservation. Aquat. Conserv. 15, 693–
na
714. https://doi.org/10.1002/aqc.745
Bilton, D.T., Freeland, J.R., Okamura, B., 2001. Dispersal in freshwater invertebrates. Annu. Rev. Ecol.
ur
Syst. 32(1), 159–181. https://doi.org/10.1146/annurev.ecolsys.32.081501.114016 Bini, L.M., Landeiro, V.L., Padial, A.A., Siqueira, T., Heino, J., 2014. Nutrient enrichment is related to
Jo
two facets of beta diversity for stream invertebrates across the United States. Ecology, 95, 1569–1578. https://doi.org/10.1890/13-0656.1 Bloechl, A., Koenemann, S., Philippi, B., Melber, A., 2010. Abundance, diversity and succession of aquatic Coleoptera and Heteroptera in a cluster of artificial ponds in the North German Lowlands. Limnologica. 40(3), 215–225. https://doi.org/10.1016/j.limno.2009.08.001 Bronmark, C., Hansson, L., 1998. The Biology of Lakes and Ponds. Oxford University Press, Oxford.
12
Carignan, V., Villard M.-A., 2002. Selecting Indicator Species to Monitor Ecological Integrity: A Review. Environ. Monit. Assess. 78, 45–61. https://doi.org/10.1023/A:1016136723584 Carvalho, J.C., Cardoso, P., Borges, P.A.V., Schmera, D., Podani, J., 2013. Measuring fractions of beta diversity and their relationships to nestedness: a theoretical and empirical comparison of novel approaches. Oikos, 122(6), 825–834. https://doi.org/10.1111/j.1600-0706.2012.20980.x Chao, A., 1987. Estimating the population size for capture-recapture data with unequal catchability. Biometrics, 43(4), 783–791. https://doi.org/10.2307/2531532 Chen, P.P., Nieser, N., Zettel, H., 2005. Aquatic and semi-aquatic bugs (Heteroptera: Nepomorpha &
ro of
Gerromorpha) of Malesia. Brill, Leiden, Boston. Colwell, R.K., 2013. Estimate S: Statistical estimation of species richness and shared species from samples, ver. 9. Available at: http://purl.oclc.org/estimates
Colwell, R.K., Coddington, J.A., 1994. Estimating terrestrial biodiversity through extrapolation. Philos.
-p
T. R. Soc. Lon. B, 345(1311), 101–118. https://doi.org/10.1098/rstb.1994.0091
Cottenie, K., 2005. Integrating environmental and spatial processes in ecological community dynamics.
re
Ecol. Lett. 8, 1175–1182. https://doi.org/10.1111/j.1461-0248.2005.00820.x Dale, V.H., Beyeler, S.C., 2001. Challenges in the development and use of ecological indicators. Ecol.
lP
Indic. 1, 3–10. https://doi.org/10.1016/s1470-160x(01)00003-6
De Cáceres, M., Legendre, P., 2009. Associations between species and groups of sites: indices and
na
statistical inference. Ecology, 90, 3566–3574. https://doi.org/10.1890/08-1823.1 De Roeck, E.R., Vanschoenwinkel, B.J., Day, J.A., Xu, Y., Raitt, L., Brendonc, L., 2007. Conservation status of large branchiopods in the Western Cape, South Africa. Wetlands, 27, 162–173.
ur
https://doi.org/10.1672/0277-5212(2007)27[162:csolbi]2.0.co;2 Dobrovolski, R., Melo, A.S., Cassemiro, F.A.S., Diniz-Filho, J.A.F., 2012. Climatic history and dispersal
Jo
ability explain the relative importance of turnover and nestedness components of beta diversity. Global Ecol. Biogeogr. 21(2), 191–197. https://doi.org/10.1111/j.1466-8238.2011.00671.x Fairchild, G.W., Cruz, J., Faulds, A.M., Short, A.E.Z., Matta, J.F., 2003. Microhabitat and landscape influences on aquatic beetle assemblages in a cluster of temporary and permanent ponds. Freshw. Sci. 22(2), 224–240. https://doi.org/10.2307/1467994
13
Gianuca, A.T., Declerck, S.A.J., Lemmens, P., De Meester, L., 2017. Effects of dispersal and environmental heterogeneity on the replacement and nestedness components of β-diversity. Ecology. 98(2), 525–533. https://doi.org/10.1002/ecy.1666 Göthe, E., Angeler, D.G., Gottschalk, S., Löfgren, S., Sandin, L., 2013. The influence of environmental, biotic and spatial factors on diatom metacommunity structure in Swedish headwater streams. PLoS ONE, 8(8), e72237. https://doi.org/10.1371/journal.pone.0072237 Hammer, Ø., Harper, D.A.T., Ryan, P.D., 2001. PAST: paleontological statistics software package for education and data analysis. Palaeontologica Electronica, 4, 1–9. Hill, M.J., Biggs, J., Thornhill, I., Briers, R.A., Gledhill, D.G., White, J.C., Wood, P.J., Hassall, C., 2016.
ro of
Urban ponds as an aquatic biodiversity resource in modified landscapes. Global Change Biol. 23, 986– 999. https://doi.org/10.1111/gcb.13401
Hill, M.J., Heino, J., Thornhill, I., Ryves, D.B., Wood, P.J., 2017. Effects of dispersal mode on the
126(11), 1575–1585. https://doi.org/10.1111/oik.04266
-p
environmental and spatial correlates of nestedness and species turnover in pond communities. Oikos.
re
Hortal, J., Diniz-Filho, J.A.F., Bini, L.M., Rodríguez, M.Á., Baselga, A., Nogués-Bravo, D., Rangel, T.F., Hawkins, B.A., Lobo, J.M., 2011. Ice age climate, evolutionary constraints and diversity patterns of
lP
European dung beetles. Ecol. Lett. 14(8), 741–748. https://doi.org/10.1111/j.1461-0248.2011.01634.x Jeffries, M., 2005. Small ponds and big landscapes: the challenge of invertebrate spatial and temporal dynamics
for
European
conservation.
Aquat.
Conserv.
15,
541–547.
na
https://doi.org/10.1002/aqc.753
pond
Karaouzas, I., Gritzalis, K.C., 2006. Local and regional factors determining aquatic and semi-aquatic bug (Heteroptera) assemblages in rivers and streams of Greece. Hydrobiologia, 573, 199–212.
ur
https://doi.org/10.1007/s10750-006-0274-1
Jo
Kokociński, M., Stefaniak, K., Mankiewicz-Boczek, J., Izydorczyk, K., Soininen, J., 2010. The ecology of the invasive cyanobacterium Cylindrospermopsis raciborskii (Nostocales, Cyanophyta) in two hypereutrophic lakes dominated by Planktothrix agardhii (Oscillatoriales, Cyanophyta). Eur. J. Phycol. 45(4), 365–374, https://doi.org/10.1080/09670262.2010.492916 Landeiro, V.L., Bini, L.M., Melo, A.S., Pes, A.M.O., Magnusson, W.E., 2012. The roles of dispersal limitation and environmental conditions in controlling caddisfly (Trichoptera) assemblages. Freshwater Biol. 57(8), 1554–1564. https://doi.org/10.1111/j.1365-2427.2012.02816.x
14
Legendre, P., 2014. Interpreting the replacement and richness difference components of beta diversity. Global Ecol. Biogeogr. 23, 1324–1334. https://doi.org/10.1111/geb.12207 Legendre, P., Fortin, M.-J., Borcard, D., 2015. Should the Mantel test be used in spatial analysis? Methods Ecol. Evol. 6(11), 1239–1247. https://doi.org/10.1111/2041-210x.12425 Lumbreras, A., Marques, J.T., Belo, A.F., Cristo, M., Fernandes, M., Galioto, D., … Pinto-Cruz, C., 2016. Assessing the conservation status of Mediterranean temporary ponds using biodiversity: a new tool for practitioners. Hydrobiologia, 782, 187–199. https://doi.org/10.1007/s10750-016-2697-7 Maloufi S., Catherine A., Mouillot D., Louvard C., Couté A., Bernard C., Troussellier, M., 2016. Environmental heterogeneity among lakes promotes hyper b-diversity across phytoplankton
ro of
communities. Freshwater Biol. 61, 633–645. https://doi.org/10.1111/fwb.12731
Nilsson, A.N., Svensson, B.W., 1995. Assemblages of dytiscid predators and culicid prey in relation to environmental factors in natural and clear-cut boreal swamp forest pools. Hydrobiologia, 308, 183–
-p
196. https://doi.org/10.1007/bf00006870
Oertli, B., Biggs, J., Céréghino, R., Grillas, P., Joly, P., Lachavanne, J-B., 2005. Conservation and of
pond
biodiversity:
introduction.
Aquat.
Conserv.
15,
535–540.
re
monitoring
https://doi.org/10.1002/aqc.752
explaining
pattern
in
lP
Podani, J., Schmera, D., 2011. A new conceptual and methodological framework for exploring and presence
-
absence
data.
Oikos,
120(11),
1625–1638.
https://doi.org/10.1111/j.1600-0706.2011.19451.x
na
Polhemus, J.T., Polhemus, D.A., 2008. Global diversity of true bugs (Heteroptera; Insecta) in freshwater. Hydrobiologia, 595, 379–391. https://doi.org/10.1007/978-1-4020-8259-7_40
ur
Qian H., Ricklefs R.E., 2012. Disentangling the effects of geographic distance and environmental dissimilarity on global patterns of species turnover. Global Ecol. Biogeogr. 21, 341–351.
Jo
https://doi.org/10.1111/j.1466-8238.2011.00672.x Schneider, D.W., Frost, T.M., 1996. Habitat duration and community structure in temporary ponds. Freshw. Sci. 15, 64–86. https://doi.org/10.2307/1467433 Schriever, T.A., Bogan, M.T., Boersma, K.S., Cañedo-Argüelles, M., Jaeger, K.L., Olden, J.D., Lytle, D.A., 2015. Hydrology shapes taxonomic and functional structure of desert stream invertebrate communities. Freshw. Sci. 34, 399–409. https://doi.org/10.1086/680518
15
Si, X., Baselga, A., Leprieur, F., Song, X., Ding, P., 2016. Selective extinction drives taxonomic and functional alpha and beta diversities in island bird assemblages. J. Anim. Ecol. 85, 409–418. https://doi.org/10.1111/1365-2656.12478 Siqueira, T., Bini, L.M., Roque, F.O., Cottenie, K., 2012. A Metacommunity Framework for Enhancing the
Effectiveness
of
Biological
Monitoring
Strategies.
PLoS
ONE,
7(8),
e43626.
https://doi.org/10.3410/f.717988254.793472558 Socolar, J.B., Gilroy, J.J., Kunin, W.E., Edwards, D.P., 2016. How should beta-diversity inform biodiversity conservation? Trends Ecol. Evol. 31, 67–79. https://doi.org/10.1016/j.tree.2015.11.005 Soininen, J., Heino, J., Wang, J., 2018. A meta-analysis of nestedness and turnover components of beta across
organisms
and
ecosystems.
Global
Ecol.
Biogeogr.
27(1),
96–109.
ro of
diversity
https://doi.org/10.1111/geb.12660
Specziár, A., Árva, D., Tóth, M., Móra, A., Schmera, D., Várbíró, G., Erős, T., 2018. Environmental and
-p
spatial drivers of beta diversity components of chironomid metacommunities in contrasting freshwater systems. Hydrobiologia. 819(1), 123–143. https://doi.org/10.1007/s10750-018-3632-x
re
Stegen, J.C., Freestone, A.L., Crist, T.O., Anderson, M.J., Chase, J.M., Comita, L.S., … Vellend, M., 2013. Stochastic and deterministic drivers of spatial and temporal turnover in breeding bird communities.
lP
Global Ecol. Biogeogr. 22, 202–212. https://doi.org/10.1111/j.1466-8238.2012.00780.x Suksai, B., Sangpradub, N., Zettel, H., 2016. Assemblage of aquatic Heteroptera (Gerromorpha and Nepomorpha) in relation to microhabitats in the Phong River, Northeast Thailand. Entomol. Res. 46,
na
93–106. https://doi.org/10.1111/1748-5967.12152
Sychra, J., Adámek, Z., 2011. The impact of sediment removal on the aquatic macroinvertebrate assemblage
in
a
fishpond
littoral
zone.
J.
Limnol.
70,
129–138.
ur
https://doi.org/10.4081/jlimnol.2011.138
Jo
ter Braak, C.J.F., Verdonschot, P.F.M., 1995. Canonical correspondence analysis and related multivariate
methods
in
aquatic
ecology.
Aquat.
Sci.
57,
255–289.
https://doi.org/10.1007/bf00877430 Thornhill, I., Batty, L., Death, R.G., Friberg, N.R., Ledger, M.E., 2017. Local and landscape scale determinants of macroinvertebrate assemblages and their conservation value in ponds across and urban land-use gradient. Biodivers. Conserv. 26: 1065–1086. https://doi.org/10.1007/s10531-0161286-4
16
Tisseuil, C., Leprieur, F., Grenouillet, G., Vrac, M., Lek, S., 2012. Projected impacts of climate change on spatio-temporal patterns of freshwater fish beta diversity: a deconstructing approach. Global Ecol. Biogeogr. 21(12), 1213–1222. https://doi.org/10.1111/j.1466-8238.2012.00773.x Tonkin, J.D., Stoll, S., Jähnig, S.C., Haase, P., 2016. Contrasting metacommunity structure and beta diversity in an aquatic-floodplain system. Oikos. 125(5), 686–697. https://doi.org/10.1111/oik.02717 Viana, D.S., Figuerola, J., Schwenk, K., Manca, M., Hobæk, A., Mjelde, M., . . . Santamaría, L., 2016. Assembly mechanisms determining high species turnover in aquatic communities over regional and continental scales. Ecography. 39, 281–288. https://doi.org/10.1111/ecog.01231 Zbinden, Z.D., Matthews, W.J., 2017. Beta diversity of stream fish assemblages: partitioning variation spatial
and
environmental
factors.
Freshwater
Biol.
62,
1460–1471.
ro of
between
Jo
ur
na
lP
re
-p
https://doi.org/10.1111/fwb.12960
17
Figure Legends Figure 1: Map showing the study area and the collection localities. Different habitat types are depicted
re
-p
ro of
by different coloured symbols.
Figure 2: Ordination diagram for canonical correspondence analysis for all the bug species in the four
lP
permanent habitat types. The first two axes are shown here. The inset shows a scree plot, providing the proportional variation explained by each CCA axis. Species codes (numbers) are as given in SF-
Jo
ur
na
Table 3.
18
Table 1: Phi coefficient of association (r) of species uniquely associated with each type of habitat with significance indicated by p-values. Group Streams
Species Ptilomera agriodes Rhagovelia sumatraensis Anisops exiguus Neogerris parvulus Mesovelia vittigera
r 0.552 0.351 0.420 0.350 0.339
p 0.001 *** 0.050 * 0.009 ** 0.025 * 0.042 *
Ranatra elongata 0.747 Enithares hungerfordi 0.310 Reservoirs Aquarius adelaidis 0.337 Fort tanks + streams Heleocoris elongatus 0.406 Metrocoris indicus 0.379 Fort tanks + Ponds Anisops barbatus 0.341 Anisops cavifrons 0.518 Anisops sardeus 0.416 Enithares ciliata 0.314 Fort tanks + Reservoirs Micronecta quadristrigata 0.404 Ponds + Reservoirs Ranatra filiformis 0.510 Laccotrephes griseus 0.338 Paraplea frontalis 0.338 Significance codes: *** - p = 0.001, ** - p = ≤ 0.01, * - p = ≤ 0.05
0.001 *** 0.032 * 0.037 * 0.007 ** 0.013 * 0.022 * 0.001 *** 0.005 ** 0.035 * 0.006 ** 0.001 *** 0.031 * 0.030 *
Ponds
Jo
ur
na
lP
re
-p
ro of
Fort tanks
19