Macrophyte functional groups elucidate the relative ...

Hydrobiologia https://doi.org/10.1007/s10750-018-3709-6

PRIMARY RESEARCH PAPER

Macrophyte functional groups elucidate the relative role of environmental and spatial factors on species richness and assemblage structure Claudio Rossano Trindade Trindade . Victor Lemes Landeiro . Fabiana Schneck

Received: 20 December 2017 / Revised: 6 July 2018 / Accepted: 7 July 2018 Ó Springer Nature Switzerland AG 2018

Abstract We evaluated the relative importance of environmental and spatial factors on species richness and assemblage structure of macrophytes in 29 coastal wetlands in southern Brazil. We used variation partitioning on total assemblage and three functional groups (emergent, floating, and submerged) and predicted that the relative importance of environment would be greater than that of space for all groups. Further, we predicted that both environment and space would show greater relative importance for floating and submerged than for emergent species, since the first ones depend more on local characteristics and on hydrocoric and zoocoric dispersal, while emergent species are less dependent on local characteristics and

disperse mostly by wind. Variation in species richness was partly explained only for floating macrophytes by the environmental fraction. Regarding assemblage structure, environmental variables were more important for floating species and spatial variables for submerged species than for emergent ones and total assemblage. Further, while floating species were structured only by local environmental variables, emergent species were influenced by climatic environmental variables. These results revealed different patterns among macrophyte functional groups in wetlands, highlighting the importance of accounting for ecological differences to further advance the understanding of the relative role of predictors to metacommunity structure.

Handling editor: Andre´ Padial

Keywords Metacommunity Spatial distribution pRDA Aquatic vegetation Functional groups

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10750-018-3709-6) contains supplementary material, which is available to authorized users. C. R. T. Trindade (&) F. Schneck Po´s-Graduaçaõ em Biologia de Ambientes Aqua´ticos Continentais, Instituto de Cieˆncias Biolo´gicas, Universidade Federal do Rio Grande – FURG, Rio Grande, RS 96203-900, Brazil e-mail: [email protected] V. L. Landeiro Po´s-Graduaçaõ em Ecologia e Conservaçaõ da Biodiversidade, Instituto de Biocieˆncias, Departamento de Botaˆnica e Ecologia, Universidade Federal de Mato Grosso – UFMT, Cuiaba´, MT 78060-900, Brazil

Introduction To further advance in the understanding of how metacommunities respond to environmental and spatial factors, studies started to evaluate metacommunities by separating them into functional groups (e.g., De Bie et al., 2012; Alahuhta & Heino, 2013; Padial et al., 2014; Go¨the et al., 2017; Heino et al., 2017). This rationale is based on the fact that species with different traits (e.g., size, dispersion mode and life strategies)

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may respond differently to environmental and spatial gradients (Alahuhta et al., 2013). For instance, aquatic small-bodied passive dispersers are more strongly affected by environmental variables, while largebodied active dispersers are more strongly affected by spatial variables (De Bie et al., 2012). In the freshwater realm, aquatic macrophytes are a diverse group of organisms that greatly influence the functioning of these systems through their role in biomass production (Zhou et al., 2017; Schneider et al., 2018), nutrient storage (Tang et al., 2017) and by promoting complexity and heterogeneity of habitats and thus influencing other communities (Thomaz & Cunha 2010; Carvalho et al., 2013; Rocha et al., 2017). Species richness and assemblage structure of this group are commonly determined by local environmental variables such as depth, pH, nutrients (nitrogen and phosphorus) and alkalinity (Capers et al., 2010; Alahuhta et al., 2016; Go¨the et al., 2017), and also by regional variables such as temperature and precipitation (Alahuhta et al., 2011; Grimaldo et al., 2016). Furthermore, aquatic macrophytes are a suitable group of organisms to study the relationship between species traits and metacommunity responses to environmental and spatial factors, since they present a wide array of strategies to live submersed, floating, or growing across the water surface in permanently or periodically inundated environments (Chambers et al., 2008). Floating and submerged macrophytes are generally more sensitive to water quality than emergent macrophytes (Akasaka et al., 2010; Go¨the et al., 2017; Tang et al., 2017) and their dispersal is highly dependent on water connectivity and aquatic birds (Soons et al., 2016). On the other hand, emergent macrophytes obtain nutrients from the sediment and atmospheric carbon dioxide, have great light availability and their dispersion is facilitated by wind (Soons, 2006). Indeed, recent studies showed that the relative importance of environmental and spatial variables in structuring macrophytes varies among functional groups (e.g., Capers et al., 2010; Alahuhta et al., 2016). For instance, Alahuhta et al. (2016) found that environmental variables were more important to explain variation in assemblage structure of submerged and floating species than of emergent species in lakes. Regardless of functional group identity, the effects of environmental and spatial constraints also depend on study region (e.g., Grimaldo et al., 2016) and spatial scale (e.g., Alahuhta & Heino, 2013). In the

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study of Grimaldo et al. (2016), macrophyte species richness and assemblage structure in temperate streams were structured by both environmental and spatial variables, while species richness in tropical streams was explained only by spatial variables that may be related to broad scale climatic variables. Finally, studies on these issues are focused on lakes and streams and little is known about the relative importance of environmental and spatial factors in structuring macrophyte assemblages in wetlands (sensu Mitsch & Gosselink, 2000; areas with saturated soils or with shallow water and vegetation adapted to wet conditions and flooding), which greatly differ from lakes and rivers in their hydrological dynamics and environmental conditions (Junk et al., 2014; Roznere & Titus, 2017), potentially influencing the responses of aquatic macrophytes to both environmental and spatial factors. Wetlands are critically endangered ecosystems which harbor a high biodiversity and play important ecological, economic, and social roles, making them priority areas for conservation (Maltchik et al., 2004; Junk et al., 2013). In Brazil, most of the wetlands are intermittent and characterized by large fluctuations in water level, related to flooding of rivers or excessive precipitation (Junk et al., 2014). In these ecosystems, the duration and frequency of the hydrological regime determines the local physical, chemical and biological conditions, influencing the diversity and structure of communities (Maltchik et al., 2007; Junk et al., 2014), including aquatic macrophytes (Rolon et al., 2008), which are strongly affected by the variation in precipitation related to the hydrological regime (Rolon et al., 2010). In the present study, despite not evaluating the temporal variation related to hydroperiod, we used variables related to spatial variation in precipitation and temperature and related to wetlands local drainage conditions (variation in altitude) as proxies for the importance of hydroperiod in structuring macrophyte assemblages. We evaluated the relative importance of environmental and spatial variables on species richness and assemblage structure of aquatic macrophytes in freshwater coastal wetlands along 640 km in southern Brazil. We predicted that variation in species richness and assemblage structure would be influenced mainly by environmental limnological and climatic variables, for both the total assemblage and for each of three functional groups (emergent, floating, and

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submerged). We also expected that functional groups would respond differently, such that both environmental and spatial predictors would be more important for floating and submerged species than for emergent species. That is, we expected that the floating and submerged groups would show higher explanation power of both predictors than the emergent group. This rationale is based on the fact that floating and submerged species depend more strongly on local limnological and hydrological characteristics of wetlands than emergent species, which would result in a larger environmental fraction for these two groups when compared to the emergent one. Further, floating and submerged species are mostly dependent on hydrocoric and zoocoric dispersal strategies, while emergent species present a greater capacity of dispersion facilitated by wind (Soons, 2006; Soons et al., 2016), resulting in a larger spatial fraction also for the floating and submerged groups in relation to the emergent group.

Materials and methods Study area The coastal plain of Rio Grande do Sul State is located between the coordinates (29°160 14.9600 S and 49°370 15.0800 W) and (33°370 25.1500 S and 0 00 53°13 46.56 W) in southern Brazil (Fig. 1). Originated in the Cenozoic, the coastline extends in the direction NE-SW, with approximately 640 km in length (Barboza et al., 2009; Vieira, 2012). The coastal plain developed by means of alluvial fans, and by the lateral addition of four depositional systems of the ‘‘barrier-lagoon’’ type, responsible for the formation of the large water bodies that characterize its landscape (Patos Lagoon, Lake Mirim and Lake Mangueira) (Barboza et al., 2009) and a great number of wetlands (Maltchik et al., 2003). The surface area of the coastal plain is of approximately 22,740 km2 of emerging lands and 14,260 km2 of water surface, totaling 37,000 km2 (Schwarzbold & Scha¨fer, 1984). The climate of the region is subtropical humid (Maluf, 2000). Annual precipitation varies between 1000 and 1500 mm and average annual temperature varies between 16 and 20°C, with mean temperatures between 22 and 26°C in the hottest months and between 10 and 15°C in

the coldest months (Nimer, 1977). The number of frosts per year varies from one in the north to more than 15 in the south (Nimer, 1977). The vegetation belongs to the Pampean biogeographic province with predominance of subtropical grasslands. In addition to the predominant Pampa and Atlantic elements, the flora also has Andean, Austral-Antarctic and Holo-Artic elements. The vegetation in wetlands is physiognomically and floristically heterogeneous, changing according to drainage conditions or stages of succession (Cordazzo & Seeliger, 1995). Sampling of aquatic macrophytes For the selection of sampling sites, we used the definition of Mitsch & Gosselink (2000) which define wetlands as areas with saturated soils or with shallow water and vegetation adapted to wet conditions and flooding. We selected 29 freshwater wetlands along the 640 km of the coastal plain (Fig. 1), ranging in area from 0.19 to 14.12 ha. We visited the wetlands between January and February 2016, a period characterized by low precipitation. To register species richness and assemblage structure of macrophytes we performed an exhaustive visual survey by walking throughout each wetland and recording all the species we found. We stopped our search after 20 min without registering a new species. Since the survey period was characterized by low precipitation and the size of wetlands usually vary positively with hydroperiod (Babbitt, 2005), surveyed areas were usually smaller than the wetland total area. In this sense, we walked along the visually wet area of the wetlands and not along the entire dry area, so that the largest area covered was of approximately 5 ha. All species were classified in three functional groups (emergent, floating, and submerged) using Cordazzo & Seeliger (1995), Pott & Pott (2000), Pedralli & Teixeira (2003) and Amaral et al. (2008). Environmental and climatic variables We used a Horiba multi-parameter probe to obtain in situ the following limnological variables: dissolved oxygen (DO; mg l-1), pH, total dissolved solids (TDS; g l-1), and electrical conductivity (COND; lS cm-1). Water depth (cm) was obtained with a graduated ruler. In each wetland, we measured all the above variables

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Fig. 1 Study area in the coastal plain of Rio Grande do Sul State, southern Brazil. Location of the 29 coastal wetlands sampled (black circles). Areas from 1 to 9: south coast; areas from 10 to 22: middle coast; areas from 23 to 29: north coast

at five random points. We also collected three water samples for analyses of total nitrogen (TN; mg l-1; Allen et al., 1974) and total phosphorus (TP; mg l-1; Valderrama, 1981; Baumgarten & Rocha, 1996). In each wetland, at the central point, we recorded the coordinates using a GPS. Then, we used Google Earth Pro and Quantum Gis (QGis 2.14.1 Essen) to obtain wetland area based on the creation of polygons. We also obtained the following climatic data with 1 km2 resolution from WorldClim (Hijmans et al., 2005): annual mean temperature (AMT; mm), temperature seasonality (TS; standard deviation 9 100), maximum temperature of warmest month (MAXTW; °C), minimum temperature of coldest month (MINTC; °C), mean temperature of wettest quarter (MTWeQ; °C), mean temperature of driest quarter (MTDQ; °C), annual precipitation (AP; mm), precipitation seasonality (PS; coefficient of variation), precipitation of wettest quarter (PWeQ; mm), precipitation of driest quarter (PDQ; mm), precipitation of warmest quarter (PWQ; mm), precipitation of coldest quarter (PCQ; mm), annual evapotranspiration (EVAP; mm), and

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altitude (ALT; m.a.s.l.). The climatic variables selected for this study are important for structuring aquatic macrophyte communities, as evidenced by other authors (e.g., Grimaldo et al., 2016). According to Rojo et al. (2016), the effect of climate regime (alternating between wet and dry periods) on hydrology is a factor of great importance to be considered in analyses of aquatic metacommunities, so that we included in our analyses variables such as temperature and precipitation seasonality and annual evapotranspiration as proxies for hydroperiod and consequently for the variation of water level in wetlands. Data analysis We used a Principal Components Analysis (PCA; Legendre & Legendre, 1998) to summarize the variation in environmental (limnological and climatic) variables (all previously standardized through z-score) of wetlands along the coastal plain. Interpretation of results was based on the axes retained using the Broken-Stick criterion (Jackson, 1993). To summarize

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the variation in assemblage structure we used a Principal Coordinates Analysis (PCoA) with presence-absence data and the Sørensen dissimilarity index (Legendre & Legendre, 1998). We constructed maps for all significant environmental and climatic variables, significant PCNM values, species richness, and assemblage structure (using the scores of the PCoA first axis) along the 29 coastal wetlands. We used Jenks Natural Breaks optimization to determine the best arrangement of values into different classes (two or three classes depending on the dataset). To evaluate the relative importance of environmental and spatial variables on species richness and assemblage structure of aquatic macrophytes, we used multiple linear regression and Partial Redundancy Analysis (pRDA), respectively (Borcard et al., 1992; Legendre et al., 2005). The analyses were performed using all macrophytes and separately for each of the three functional groups. Before pRDA, we applied the Hellinger transformation to presence-absence data of each biotic matrix (Legendre & Gallagher, 2001; Peres-Neto et al., 2006; Legendre & De Caćeres, 2013). The environmental matrix used in multiple linear regression and pRDA was constructed after excluding redundant variables using the variance inflation factor (VIF) (see also Online Resource 1, Table S1, for Pearson correlations among all environmental variables). VIF is used as an indicator of multicollinearity between explanatory variables in multiple regressions, where VIF = 1 indicates that the predictor variable in question is not related to any other predictor variable of the model. On the other hand, VIF [ 10 suggests strong collinearity (Quinn & Keough, 2002), and those variables were excluded from analyses (Online Resource 1, Table S2). The final environmental matrix contained 13 variables: depth, dissolved oxygen, pH, total dissolved solids, total nitrogen, total phosphorus, wetland area, precipitation seasonality, precipitation of coldest quarter, mean temperature of wettest quarter, mean temperature of driest quarter, annual evapotranspiration, and altitude. We elaborated the spatial predictor matrix using Moran Eigenvector Mapping (MEM) through the central coordinates of wetlands using Principal Coordinate Analysis of Neighboring Matrices technique (PCNM; Borcard & Legendre, 2002). This function creates the PCNMs through a Principal Coordinate Analysis of a truncated distance matrix of spatial relationships between sampling locations.

We truncated our distance matrix using the minimum spanning tree criteria. The MEMs with higher eigenvalues represent large-scale variation, while those with small eigenvalues represent fine-scale variation. After we generated all matrices, we applied multiple regression analysis for species richness (all taxa and each functional group) and redundancy analysis for assemblage structure (all taxa and each functional group) to test the significance of each global model (i.e., a model using the complete set of spatial predictors and another model using the complete set of environmental predictors). We proceeded with the analysis only when the global model for a given set of predictors was significant (P \ 0.05). In this case, we proceeded with a forward selection of variables using two selection criteria: values of significance level of each explanatory variable (P \ 0.05) and the adjusted coefficient of multiple determination (adjusted R2) of the reduced model smaller than the adjusted R2 of the global model, as recommended by Blanchet et al. (2008). Then, multiple regression or pRDA was applied to partition richness or assemblage structure of macrophytes into four independent fractions: [E] pure environmental variables; [E–S] spatiallystructured environmental variables; [S] pure spatial variables and [RES] residual unexplained variation (Borcard et al., 1992). We tested the significance of the pure environmental and pure spatial fractions using 999 permutations and the explained variation was estimated using adjusted R2 (Peres-Neto et al., 2006). We performed all analyses in R environment (R Core Team, 2017) using function vif from car package (Fox & Weisberg, 2011), function forward.sel from package adespatial (Dray et al., 2016) to select environmental and spatial variables in species richness models, and the following functions from vegan package (Oksanen et al., 2017): pcnm to obtain the PCNM’s, rda and varpart to run the global and partial RDA models and ordiR2step to select environmental and spatial variables in assemblage structure models.

Results We observed a marked variation of limnological and climatic variables in wetlands along the coastal plain (Table 1 and Online Resource 2). The first two axes of the PCA were retained according the Broken-Stick criterion and explained 44.8% (Axis 1: 24.5%; Axis 2:

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Hydrobiologia Table 1 Environmental variables of the 29 coastal wetlands sampled in southern Brazil

Environmental variables

Min.

Max.

Depth (cm)

12

50

30 ± 10

Dissolved oxygen (mg l-1)

3.0

9.7

5.9 ± 1.4

pH

4.4

7.8

Electrical conductivity (lS cm-1)

25.0

587.8

Total dissolved solids (g l-1)

0.02

0.4

0.1 ± 0.1 3.6 ± 3.0

-1

Minimum, maximum, mean and standard deviation values

Total nitrogen (mg l )

0.6

12.3

Total phosphorus (mg l-1)

0.004

1.2

Area (ha) Annual precipitation (mm)

0.2 1172

14.1 1491

Precipitation seasonality (coefficient of variation)

7

18

6.1 ± 0.7 109.1 ± 105.7

0.2 ± 0.3 4.5 ± 4.0 1335.1 ± 111.4 12.3 ± 3.2

Precipitation of wettest quarter (mm)

312

404

372.2 ± 29.0

Precipitation of driest quarter (mm)

230

247

385.0 ± 35.6

Precipitation of warmest quarter (mm)

295

383

336.8 ± 30.0

Precipitation of coldest quarter (mm)

300

404

359.6 ± 31.1

Annual mean temperature (°C)

16.8

18.9

18.2 ± 0.7

Temperature seasonality (standard deviation 9 100)

279

383

339 ± 31 27.6 ± 0.7

Max temperature of warmest month (°C)

26.4

28.4

Min temperature of coldest month (°C)

8.1

11.4

9.6 ± 1.1

Mean temperature of wettest quarter (°C)

12.7

19.5

15.1 ± 1.8

Mean temperature of driest quarter (°C)

15.4

21.9

Annual evapotranspiration (mm)

1058.6

1192.7

Altitude (m)

2

22

20.3%) of the variation in environmental data (Fig. 2). Most environmental variables are spatially-structured, especially the climatic ones, characterizing three distinct regions on the coastal plain: north, middle, and south coast (Fig. 2). We recorded 114 species of aquatic macrophytes, varying between 10 and 35 species per wetland (Fig. 3). Of the total species recorded, 90 species were classified as emergent (79%), 14 species as floating (12%), and 10 species as submerged (9%) (Online Resource 3). The most frequent emergent species (occurring at 10–22 wetlands) were Alternanthera philoxeroides (Mart.) Griseb, Bacopa monnieri (L.) Pennell, Centella asiatica (L.) Urb., Cyperus esculentus L., Enydra anagallis Gardner, Hydrocotyle bonariensis Lam., Hydrocotyle ranunculoides L.f., Lilaeopsis sp., Ludwigia uruguayensis (Cambess.) H. Hara, Ludwigia peploides (Kunth) P. H. Raven, Luziola peruviana Juss. ex J. F. Gmel., Myriophyllum aquaticum (Vell.) Verdc., Polygonum ferrugineum Wedd., Polygonum punctatum Elliot, Pontederia cordata L., and Schoenoplectus californicus (C. A. Mey) Soja´k. Among the floating macrophytes,

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Mean ± SD

20.0 ± 2.3 1123.7 ± 39.2 10.1 ± 5.1

species that occurred between 11 and 20 wetlands were Azolla filiculoides Lam., Nymphoides indica (L.) Kuntze, Ricciocarpus natans (L.) Corda, Salvinia herzogii de la Sota, and Wolffiella oblonga (Phil.) Hegelm. Utricularia foliosa L. was the most frequent submerged macrophyte, occurring at 16 wetlands. Considering species richness, environmental and spatial global models were not significant to explain the variation of the entire assemblage and of the emergent and submerged functional groups (Table 2). Variation on species richness of the floating functional group was explained only by the environmental fraction (Table 2), in which total phosphorus and depth explained 40% of the variation (Table 2, Figs. 3 and 4). Depth positively affected floating macrophytes species richness, while total phosphorus showed a negative effect (Online Resource 4). Variation in total macrophyte assemblage structure (Fig. 3) was partially explained by pure environmental, pure spatial and spatially-structured environmental fractions, which accounted for similar variation (Table 3). Environmental variables that significantly explained variation in total assemblage structure were

Hydrobiologia Fig. 2 Principal Components Analysis (PCA) applied to the environmental variables from 29 coastal wetlands in southern Brazil. DO dissolved oxygen, TDS total dissolved solids, TN total nitrogen, TP total phosphorus, AREA wetland area, PS precipitation seasonality, PCQ precipitation of coldest quarter, MTWeQ mean temperature of wettest quarter, MTDQ mean temperature of driest quarter, EVAP annual evapotranspiration, ALT altitude. Numbers represent the wetlands according to Fig. 1

annual evapotranspiration, mean temperature of driest quarter, dissolved oxygen, total phosphorus, and precipitation of coldest quarter (Table 3, Fig. 4), while the selected spatial variables were PCNMs 1, 3, 4, and 7 (Fig. 5). Regarding the three functional groups, distinct patterns were observed. Pure environmental, pure spatial and spatially-structured environmental fractions explained together 15% of the variation in the emergent functional group (Table 3, Fig. 6). For this group, five climatic environmental variables were important (mean temperature of wettest quarter, precipitation seasonality, mean temperature of driest quarter, precipitation of coldest quarter and annual evapotranspiration) as well as broad scale spatial variables (PCNMs 1, 3, and 4). For the floating functional group only the pure environmental fraction was significant, with depth and altitude explaining 15% of the variation on assemblage structure (Table 3). On the other hand, only spatial variables were important for submerged macrophytes, with

PCNMs 10, 1, and 4 accounting for 19% of variation (Table 3, Figs. 5, 6).

Discussion Our results showed that mainly large-scale patterns were evidenced when analyzing the total macrophyte assemblage, since most of the selected environmental variables seem to be spatially-structured. In this sense, climatic variables presented a great importance to explain variation on the assemblage structure of macrophytes, despite the heterogeneity of limnological characteristics among the wetlands. Such as in our study, climatic variables were also of primary importance in explaining river macrophyte structure (Grimaldo et al., 2016), indicating that climatic factors override local variables and are the main determinants of macrophyte assemblage structure at broad spatial scales. Specifically in wetlands, climatic variables

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Fig. 3 Species richness and assemblage structure of aquatic macrophytes at 29 coastal wetlands in southern Brazil. In richness maps, the size of the points indicates the variation on the number of species based on Natural Jenks breaks for two (submerged species) or three classes (total, emergent and

floating species). In the assemblage structure maps, the values indicate the scores of the first axis of a Principal Coordinates Analysis (PCoA). Points with the same size indicate assemblages with similar structure based on Natural Jenks breaks for three classes

Table 2 Results of multiple regression analysis of spatial and environmental models explaining macrophyte species richness variation for the whole macrophyte assemblage (all taxa) and

for each functional group in wetlands of the coastal plain of southern Brazil


pGlobal [E]

pGlobal [S]

Adj R2 [E]

p[E]

All taxa

–

0.087

0.518

–

–

Emergent

–

0.096

0.308

–

–

Floating

TP, DEPTH

0.037

0.348

0.399

0.0005

Submerged

–

0.090

0.208

–

–

Environmental variables selected in the final model. Values of significance (P \ 0.05) for the global environmental (pGlobal [E]) and spatial (pGlobal [S]) models. Adjusted R2 (Adj R2 [E]) and significance values (p[E]) for the pure environmental fraction TP total phosphorus

such as temperature, precipitation, and evapotranspiration may reflect the importance of hydroperiod, which plays a key role in the functioning of these ecosystems and greatly affects species richness and assemblage structure of macrophytes (Mitsch & Gosselink, 2000; Rolon et al., 2010). Changes in water balance may also influence wetlands depth,

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affecting species richness and assemblage structure (Roznere & Titus, 2017). Our prediction that variation in species richness and assemblage structure of the floating and submerged functional groups would be better explained than variation of emergent species was partly corroborated. Environmental variables explained a larger amount of

Hydrobiologia Fig. 4 Selected environmental variables at 29 coastal wetlands in southern Brazil. The size of the points indicates the variation of the values based on Natural Jenks breaks for three classes. MTWeQ mean temperature of wettest quarter, MTDQ mean temperature of driest quarter, PCQ precipitation of coldest quarter, PS precipitation seasonality, EVAP annual evapotranspiration

variance in structure for floating species and spatial variables for submerged species than for the emergent group and the total assemblage. Also, while floating species were structured only by local environmental variables, total assemblage and emergent species were influenced mostly by climatic environmental variables. However, the total explanatory power of predictors of variation in assemblage structure was not higher for the floating and submerged groups than for the emergent group, since approximately 15–18% of total variation was explained for each group. Contrary to our expectations, local environmental variables, as depth and nutrients, contributed only to explain variation in species richness and structure of

the floating functional group. Depth is known to have an important effect in the distribution of macrophytes (e.g., Alahuhta et al., 2016), especially in wetlands (Roznere & Titus, 2017). Considering the low depth of the wetlands in our study, deeper sites may represent greater habitat availability, allowing the coexistence of floating species with different depth requirements, such as free and rooted species. In addition to the importance of depth, we found that altitude was important to explain variation in assemblage structure of the floating group, despite the small variation in altitude on the studied coastal plain. This result may be related to geomorphological features of the coastal plain characterized by a barrier-lagoon system

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Hydrobiologia Table 3 Results of the partial redundancy analysis (pRDA) of spatial and environmental models explaining macrophyte assemblage structure variation for the whole macrophyte

assemblage (all taxa) and for each functional group in wetlands of the coastal plain of southern Brazil


Spatial variables (PCNM)

pGlobal [E]

pGlobal [S]

Adj R2 [E]

Adj R2 [E–S]

Adj R2 [S]

p[E]

p[S]

All taxa

EVAP, MTDQ, DO, TP, PCQ

1, 3, 4, 7

0.001

0.008

0.062

0.058

0.063

0.004

0.003

Emergent

MTWeQ, PS, MTDQ, PCQ, EVAP

1, 3, 4

0.001

0.007

0.045

0.073

0.036

0.019

0.026

Floating

DEPTH, ALT

–

0.033

0.378

0.151

–

–

0.002

–

Submerged

–

10, 1, 4

0.236

0.014

–

–

0.188

–

0.001

Environmental and spatial variables (PCNM) selected in the final model. Values of significance (P \ 0.05) for the global environmental (pGlobal [E]) and spatial (pGlobal [S]) models. Adjusted R2 values for pure environmental (Adj R2 [E]), spatiallystructured environmental (Adj R2 [E–S]) and pure spatial (Adj R2 [S]) fractions. Values of significance (P \ 0.05) for the environmental (p[E]) and spatial (p[S]) fractions EVAP annual evapotranspiration, MTDQ mean temperature of driest quarter, DO dissolved oxygen, TP total phosphorus, PCQ precipitation of coldest quarter, MTWeQ mean temperature of wettest quarter, PS precipitation seasonality, ALT altitude

Fig. 5 Spatial patterns of broad and intermediate scales plotted as eigenvector values in 29 coastal wetlands in southern Brazil. PCNMs 1, 3, 4, 7 and 10. The size of the points indicates the variation of the values based on Natural Jenks breaks for three classes

PCNM 1 -0.323 - -0.317 -0.317 - 0.048 0.048 - 0.177

PCNM 7 -0.741 - -0.741 -0.741 - 0.071 0.071 - 0.335

PCNM 3 -0.404 - -0.179 -0.179 - 0.125 0.125 - 0.294

PCNM 10 -0.401 - -0.202 -0.202 - 0.184 0.184 - 0.615

0

(Barboza et al., 2009), such that it is possible that wetlands at the higher relief sites are more susceptible to water loss and are more likely to dry during prolonged periods of drought, presenting intermittent characteristics. On the other hand, in wetlands located in depressions, the accumulation of water for a longer period would favor the occurrence of species more

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PCNM 4 -0.549 - -0.549 -0.549 - -0.276 -0.276 - 0.195

100 200 km

dependent on water, as is the case for floating species in comparison to emergent species. Further, the negative relationship between species richness of floating macrophytes and total phosphorus concentrations may be explained by the fact that it is common for many eutrophic environments to be dominated by one or a few floating species that form dense

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P r o p o r t io n o f v a r i a t i o n

1.00 0.90 RES

0.80 0.70

S

0.60

E-S

0.50

E

0.40 0.30 0.20 0.10 0.00 All taxa

Emergent

Floating

Submerged

Fig. 6 Partitioned variation of the assemblage structure of aquatic macrophytes, for all taxa and for the functional groups emergent, floating and submerged. [E] Pure environmental variables; [E–S] Spatially-structured environmental variables; [S] Pure spatial variables; [RES] Fraction not explained

macrophyte mats on the surface of aquatic environments (Albertoni et al., 2014). It could also be expected a similar negative effect for submerged species, especially considering that dense floating mats in high nutrient waters negatively affect gas exchange between the water column and the atmosphere and reduce light in the water column inhibiting submerged species (Bornette & Puijalon, 2011). However, despite we did not observe an effect of phosphorus in the submerged group, it should be highlighted that nutrient concentration is an indirect evidence of the effect of light attenuation on submerged macrophytes and that water transparency, not measured here, may play an important role in the distribution of this functional group (Santamarıá, 2002; Alahuhta et al., 2013). Finally, the absence of local environmental variables structuring emergent macrophytes may be related to the fact that these species are characterized by functional characteristics (Soons, 2006) and dispersal mechanisms (Santamarıá, 2002) which facilitate their colonization and establishment in wetlands with a wide range of environmental characteristics (e.g., Rolon et al., 2010). Although aquatic macrophytes are recognized as having efficient dispersal, being mostly structured by environmental variables (Capers et al., 2010), we found that broad and intermediate spatial scales explained between 3.6 and 19% of variation in assemblage structure. Since the selected PCNMs represent spatial variables in both broad and intermediate scales, it could be suggested that unmeasured spatially-structured environmental variables or a low dispersion capacity of the species play an important

role in the spatial organization of assemblages (PeresNeto & Legendre, 2010; Padial et al., 2014; Grimaldo et al., 2016), especially of submerged species. Similarly, Alahuhta et al. (2013) observed a substantial importance of space on the variation in species richness of macrophytes and suggested that this could be related to the lack of some spatially-structured environmental variables. In this sense, the relative importance of the spatial fraction may represent the increase in environmental heterogeneity adding new niches to different species and possibly increasing stochastic processes and/or effects of species dispersion limitation. Among the three functional groups, submerged macrophytes presented results that mostly deviated from our expectation, since none of the explanatory variables was important to explain variation in species richness and only spatial variables explained assemblage structure. Even though the selected spatial variables may represent unmeasured environmental variables that affect the occurrence of submerged species, such as water transparency (Alahuhta et al., 2013), this functional group is likely the most dispersal-limited among the three groups analyzed here. It could be suggested that the shallowness and intermittent nature of the studied wetlands in addition to the low degree of connectivity may limit these species to a stochastic occurrence during periods with sufficient water level depending on the seed banks stored in the sediment (Santamarıá, 2002) and priority effects. It is well known that seed dormancy is probably an important strategy for maintaining an assemblage after restoration of water volume in intermittent wetlands (Rolon & Maltchik, 2006). Further, some studies highlight the connectivity between habitats as a key factor structuring macrophyte assemblages (e.g., Padial et al., 2014; Schneider et al., 2018). For instance, Padial et al. (2014) emphasize the importance of direct hydrological connections for the dispersion of many organisms in floodplain river systems, especially fish and macrophytes. In this sense, future studies in wetlands should direct their efforts on the evaluation of water connectivity among habitats to further advance on the understanding of important aspects that affect the organization of macrophytes and especially the distribution of submerged species. Finally, the lack of environmental effect on the organization of submerged species was already observed by Alahuhta &

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Heino (2013), which concluded that this result could be related to the absence of nutrient analyses in their study. However, in our study, as mentioned above, total phosphorus and nitrogen were not important to explain variation in species richness and assemblage structure of this group, adding evidence on the importance of spatial constrains for submerged macrophytes. The large proportion of unexplained variation (* 80%) is commonly observed (e.g., Capers et al., 2010; Heino et al., 2010; De Bie et al., 2012; Rocha et al., 2017; Go¨the et al., 2017) and may be associated with events not measured, stochasticity of community organization or processes involving environmental variables that were not evaluated. For example, variables such as connectivity and land-use of the surrounding soil may be important factors for the organization of macrophyte assemblages (e.g., Rolon & Maltchik, 2006; Go¨the et al., 2017). In addition, the temporal scale could also reveal patterns in species richness and assemblage structure (Alahuhta et al., 2016) related to the colonization or development of species from seed banks, spores or vegetative propagules deposited in the sediment (Bonis et al., 1995; Albertoni et al., 2014). However, it is important to emphasize that the environmental variables used here were those commonly recognized as important for structuring macrophyte assemblages and that the studied wetlands varied widely in their environmental characteristics (see Table 1). We expected that the use of a deconstructive approach would increase the explanatory power of the models and refine the detection of significant environmental and spatial effects on species richness and assemblage structure. On one hand, there was no increase in the total explanatory power of environmental and spatial variables related to adaptative strategies and dispersal abilities of each group in relation to the explanatory power observed for the total assemblage. On the other hand, our results indicate that this deconstructive approach reveals different patterns among macrophyte functional groups in wetlands, especially regarding a clear differentiation between the importance of local and climatic environmental variables among the groups. Thus, we highlight the importance of taking into account ecological differences among functional groups to further advance on the understanding of the relative role of predictors to metacommunity structure.

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Acknowledgements Part of this study was funded by Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq; process 474279/2013-8). We thank Ana S. Rolon, Augusto Ferrari, Rafael A. Dias, Sonia Hefler and Alexandre M. Garcia for suggestions in previous versions of the manuscript. We also thank Sonia Hefler for helping in the identification of botanic material.

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