Influence of species invasion, seasonality, and connectivity on fish functional and taxonomic beta-diversity in a Neotropical floodplain

doi:10.21203/rs.3.rs-2596308/v1

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Influence of species invasion, seasonality, and connectivity on fish functional and taxonomic beta-diversity in a Neotropical floodplain

https://doi.org/10.21203/rs.3.rs-2596308/v1

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published 19 Jun, 2023

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Studies that combine functional and taxonomic beta-diversity are essential for explaining some ecological processes, including the process of species invasion. We evaluated whether environmental factors (such as lake connectivity, subsystem and hydrological period) and biological factors (occurrence and richness of non-native and native fish species) affect beta-diversity components (total, richness and replacement) of fish communities living in the Upper Paraná River floodplain in Brazil. For this, a distance-based redundancy analysis (dbRDA) was performed for both taxonomic (BDtax) and functional (BDfunc) approaches. In order to see which variables influence the local contribution to BDtax and BDfunc (LCBD), mixed effects regression models were fitted. Our results indicated a significant influence of environmental and biological factors on the taxonomic patterns of beta diversity and functional beta-diversity of fish between lakes. Environmental factors influenced the replacement component for both taxonomic and functional diversity, while biotic factors (occurrence and richness of non-native species) influenced the richness component. The richness of native and non-native species, the occurrence of non-native species and hidrological period showed significant influence on LCBD values. Although in our study the occurrence and richness of non-native species are related to beta-diversity, in the long term, these species can cause a decrease in functional and taxonomic beta-diversity, altering the ecological multifunctionality of the environment. Our study emphasizes that both changes in environmental factors and species diversity (such as the introduction of a non-native species) can impact the beta-diversity of Neotropical fish.

Invasion

alien species

beta-diversity partition

shallow lakes

community ecology

functional traits

Studying and analyzing how communities are distributed in space and time is the key to understanding processes underlying community structure (Gaston et al. 2000). Species richness has traditionally been used as a metric to represent community diversity, from which scale-dependent metric indices can be applied, such as alpha, beta and gamma diversity (Myers et al. 2000). Beta-diversity was initially conceptualized to quantify differences in species composition between two or more samples, either on a temporal or spatial scale (Whittaker 1960; Mori et al. 2018), focusing only on the taxonomic diversity of species. Nevertheless, since species vary in resource use, abundance, and distribution according to certain morphological, physiological and ecological characteristics, referred to as functional traits (Violle et al. 2007), studies that combine functional and taxonomic beta-diversity are essential for explaining some ecological processes (Zarabska-Bożejewicz and Kujawa 2018; Chao et al. 2019; Quirino et al. 2021).

The use of functional traits to determine beta-diversity allows the measurement of environmental impacts on biotic communities (Carvalho and Tejerina-Garro 2015; Lechêne et al. 2018). Decomposing taxonomic and functional beta-diversity into two components, “replacement” and “richness”, as proposed by Podani and Schmera (2011), has become quite common (e.g., Legendre 2014; Lansac-Tôha et al. 2019; López-Delgado et al. 2020). While the replacement component indicates the replacement of species or functional traits from one location to another, the richness component shows the gain or loss of species or traits (Podani and Schmera 2011; Carvalho and Tejerina-Garro 2015). Another aspect of beta-diversity to be explored is the local contribution to beta-diversity (LCBD), which aims to infer the degree of ecological uniqueness of each site (Legendre and De Cáceres 2013). A high LCBD value may indicate that the site has unique ecological conditions that lead to unique species or functional trait combinations (Landeiro et al. 2018; Quirino et al. 2021).

Anthropogenic interventions, such as agriculture, urbanization, fisheries and species introduction, can lead to significant changes in alpha- and beta-diversity (Socolar et al. 2016l et al. 2019). Increased human migration facilitated by technology over the last two centuries, as well as human population growth and urbanization, have become key factors in the transport and introduction of species (Levine et al. 2003; Simberloff 2009). Although difficult to measure immediately, the introduction of non-native species can have major impacts depending on the context of the environment, ranging from direct competition with native species to the extinction of the local biota (Simberloff 2009; Simberloff et al. 2013; Petsch et al. 2022a), potentially leading to global biotic homogenization (Olden and Rooney 2006; Vitule et al. 2012; Petsch et al. 2022a).

Several human activities are responsible for the introduction of invasive fish species into freshwater environments, including aquaculture, sport fishing and dam construction (Langeani et al. 2007; Rahel 2007; Júlio Júnior et al. 2009). The problem is worse for freshwater environments because they are among the most susceptible to invasion (Cohen and Carlton 1998; Petsch 2016), experiencing greater impacts than invasions in terrestrial environments (Thomaz et al. 2015; Petsch 2016). These impacts occur because freshwater environments harbor the highest species richness per surface area on the planet (Dudgeon et al. 2006). As a result, species introduction has become a major cause of extinction in freshwater environments (Olden et al. 2010; Vitule et al. 2017), with direct effects on regional diversity.

River-floodplain systems occur in several freshwater environments and provide many ecosystem services for society (Petsch et al. 2022b). The hydrological regime is the main regulator of river-floodplain systems (Junk et al. 1989), being responsible for the seasonal changes in hydrometric level that produce oscillations in resource availability for fishes, as well as in biotic interactions and limnological conditions (Gomes et al. 2012; Quirino et al. 2017; Petsch et al. 2022b). However, most large rivers of the world are heavily regulated by dams that dampen seasonal pulses and affect the fish community (Winemiller et al. 2016). Consequently, while some lakes in floodplains remain connected to the main river, others become isolated (Lopes et al. 2022). This disconnection from the main channel reduces fish diversity in lakes, since predation and competition are intensified and food availability decreases (Junk et al. 1989; Liu and Wang 2010; Quirino et al. 2017).

In this study, we evaluated whether environmental factors and attributes of non-native species affect the components of beta-diversity of communities of fish living in littoral zones of lakes in a Neotropical floodplain. The Upper Paraná River floodplain in Brazil, is home to at least 66 non-native fish species (Ota et al. 2018), many of which are small-bodied. We used fish community to determine whether the occurrence and richness of non-native and native species, as well as environmental factors including lake connectivity, subsystem and hydrological period (seasonality), influence (i) the taxonomic and functional beta-diversity of fish; and (ii) the taxonomic and functional LCBD. Subsystem was considered as a factor because the studied floodplain possesses three subsystems with different limnological, geomorphological, and biological characteristics (see section 2.1 Study area).

2.1 Study area

The study was conducted in the Upper Paraná River floodplain, which is located in the upper part of the Environmental Protection Area of Islands and Floodplains of the Paraná River (22°45’S; 53°30’W - EPSG: 6933) and constitutes the last undammed stretch of the Paraná River in Brazilian territory, with a length of 230 km (Souza-Filho et al. 2004; Agostinho et al. 2007) (Fig. 1). This floodplain has three flood zones that form three subsystems from the main channels of the Paraná, Baía, and Ivinhema rivers, each of them holding several lakes (Souza-Filho et al. 2004). The habitat diversity of this floodplain includes the alluvial floodplain with numerous secondary channels, connected and isolated lakes, and the main channels of the Paraná, Baia, and Ivinhema rivers (Agostinho et al. 2007; Granzotti et al. 2018). Generally, the flood period occurs between November and March, while the dry period occurs between June and September (Agostinho et al. 2004). Although the lakes studied here belong to three different subsystems (Paraná River, Baía River, and Ivinhema River), strong flood pulses manage to homogenize the region completely, since all environments belong to the same hydrological regime (Thomaz et al. 2007).

2.2 Sampling

The fish studied here were sampled by the Long-Term Ecological Research Program of the Paraná River Floodplain (PELD-PIAP 441356/2020-6), funded by the National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq). Twenty-two lakes of different sizes were sampled, 11 connected to the main channel of the Paraná, Baía, and Ivinhema rivers and 11 isolated from them (Fig. 1; Table S1). Fish were sampled in the littoral areas of the 22 lakes using seine nets (10 m long, 0.5 cm mesh size) in March, June, September, and December 2010 and 2011. Only fish of small-bodied species and juveniles of large-bodied species associated with the littoral zones of the lakes were sampled. As some lakes were inaccessible for a few months, a total of 157 samples were taken. All sampled fish were anesthetized with benzocaine, euthanized, fixed in 10% formaldehyde and subsequently identified and classified as native and non-native, according to Ota et al. (2018). The entire process followed a protocol approved by the Ethics Committee on Animal Use of the University (CEUA / UEM nº 1420221018 - ID 001974). Species sampled as part of the PELD-PIAP were deposited in the Ichthyological Collection of the Limnology, Ichthyology and Aquaculture Research Center of the State University of Maringá (NUPÉLIA-UEM).

2.3 Functional characterization

Functional traits of the collected fish species were characterized according to Winemiller et al. (2015) as modified by Quirino et al. (2021) for small-bodied species from the same floodplain. The traits used were related to trophic niche (characterized by the trophic guild of the species), life history (evaluated by the presence or absence of parental care and type of spawning and fertilization) and ecomorphology [considering body shape, mouth position and maximum standard length (sl)]. All functional data were extracted from the literature (more details in Table S2).

2.4 Statistical Analysis

Taxonomic beta-diversity (hereafter referred to as BDtax) was calculated from a taxonomic dissimilarity matrix of pairs of sampling points by applying the Sørensen Index (Legendre 2014), suitable for presence/absence data, through the beta function (BAT package; Cardoso et al. 2015). A distance matrix was calculated using the functional traits of species and Gower distance through the “gowdis” function (FD package; Laliberté et al. 2014), which is suitable for mixed data (Gower 1966). A cluster analysis was performed with the distance matrix using the “hclust” function (STATS package; R Core Team 2020). The taxonomic dissimilarity matrix and results of the cluster analysis were used to calculate functional beta-diversity (hereafter referred to as BDfunc) using the “beta” function (BAT package; Cardoso et al. 2015). Following Podani and Schmera (2011), total beta-diversity (referred to as BDtotal) was partitioned into species richness difference (referred to as rich) and species replacement (referred to as repl), as described by Legendre (2014).

The percentage contribution of each component was obtained by SDR-simplex analysis. Podani et al. (2013) proposed a triangular graph representation of beta-diversity considering that rich and repl combine to equal BDtax, and similarity (S) is equal to 1-BD, such that S + Repl (R) + Rich (D) = 1. Each side of the triangle represents a component (S,D or R): richness difference on left side, with 0 at the top; replacement on right side, with 0 at the bottom; and similarity on the bottom side, with zero on the left). Points on the graph represent a comparison between two sampling points (see details in Podani et al., 2013). Thus, SDR-simplex plots were created using Sørensen dissimilarity coefficients for presence/absence data with the “ggtern” function (ggtern package; Hamilton and Ferry 2018).

To determine whether the occurrence and richness of non-native species, subsystem, seasonality (dry and rainy periods) and lake connectivity influence the BDtax and BDfunc of fish, distance-based redundancy analyses (dbRDA; Legendre and Anderson 1999) were performed using the “dbrda” function (vegan package; Oksanen et al. 2019). The real significance of the models, for both taxonomic and functional diversity, was determined by Permutation Multivariate Analysis of Variance (PERMANOVA; Anderson 2017). Graphical visualization of the two main axes of the dbRDA was accomplished using the ggplot2 package (Wickham 2016).

The local contribution to BDtax and BDfunc (LCBD; Legendre and De Cáceres 2013), as well as to their three components (total, repl and rich), was calculated using the “LCBD.comp” function (adespatial package; Dray et al. 2021). The relationships of taxonomic and functional LCBD, as well as each of their components, with environmental (connectivity, subsystem and seasonality) and biotic [non-native species occurrence (NNOccur) and richness (NNRich) and native species richness (NATRich)] factors were assessed by adjusted mixed effects regression models. Models with and without temporal random effects (month and year of sampling) were adjusted to determine the existence of possible temporal dependence between variables. The models were subsequently compared according to Akaike Information Criterion (AIC; Akaike 1974). As LCBD is an index with values from 0 to 1, a beta distribution was assumed for LCBD of BDtotal and BDrich, with a logit link in the “glmmTMB” function (glmmTMB package; Brooks et al. 2022). A normal distribution was assumed for LCBD of BDrepl, in the lme function (nlme package; Pinheiro et al. 2022), since these values met the assumption of normality and this model better fitted the data. Visual inspection noticed nonlinear relationships between NNRich and NATRich and some LCBD components, for which second and third-order polynomial models were adjusted for NNRich and NATRich. The best selected models had homogeneous residuals (visual inspection of residual graphs) and the lowest AIC values. All statistical analyses were performed using R software (R Core Team 2020).

3.1 Characterization of the fish community

Sample richness ranged from 1 to 24 species, while total richness for all 157 samplings was 74 species, of 28 families and seven orders (Table S2). Most fish were of small-bodied species (sl < 20cm), while 41.8% were juveniles of large-bodied species. There was a total of 33 non-native species (44.6%), with non-native sample richness ranging from 0 to 12 species and native sample richness ranging from 0 to 13 species. Of the 157 samplings, 130 (82.8%) had at least one non-native species.

3.2 Taxonomic and functional beta-diversity

Mean taxonomic BDtotal was high (0.83) compared to mean functional BDtotal (0.63). For taxonomic beta-diversity, there was a more outstanding contribution of BDrich (mean = 0.43) compared to BDrepl (mean = 0.39) while for functional beta-diversity there was a more outstanding contribution of BDrepl (mean = 0.36) compared to BDrich (mean = 0.27). The functional simplex diagram confirmed these results (Fig. 2), with points far away from the rich extreme (D) and near the similarity extreme (S).

3.3 Distance-based redundancy analysis

For the taxonomic approach, total dbRDA showed that BDtax is influenced mainly by NNRich, followed by NNOccur, connectivity, NATRich, subsystem (Paraná subsystem showed the opposite influence from that of the other two subsystems, as shown by the direction of the arrow in the graph, Fig. 3a), and seasonality (Fig. 3a; Table S3; PERMANOVA Pseudo-F_(7,149) = 6.40; p = 0.001). Repl is influenced by subsystem, connectivity and seasonality (Table S3; PERMANOVA Pseudo-F_(4,152) = 2.68; p = 0.001), while rich is influenced by NNRich, NNOccur and NATRich (Table S3; PERMANOVA Pseudo-F_(3,153) = 65,575; p = 0.001).

For the functional approach, total dbRDA showed that BDfunc is mainly influenced by NNRich, followed by NNOccur, connectivity, NATRich and subsystem (Fig. 3B; Table S3; PERMANOVA Pseudo-F_(6,150) = 10.07; p = 0.001). Repl is influenced by connectivity, subsystem and seasonality (Table S3; PERMANOVA Pseudo-F_(4,152) = 2.68; p = 0.001), while rich is influenced by NNRich, NNOccur and NATRich (Table S3; PERMANOVA Pseudo-F_(3,153) = 73.61; p = 0.001).

3.4 Taxonomic and Functional LCBD

The models without the temporal random parameter were the best, having lower AIC values. Therefore, it was assumed that the data do not have temporal dependence. The best model for each facet and their components, chosen according to lowest AIC, had some selected parameters in common (Table S4). NATRich, NNRich, and NNOccur were chosen in all models. Overall, LCBD and its components showed a similar pattern for taxonomic and functional approaches in response to NATRich and NNRich (Fig. 4, Table S4).

As for the influence of NATRich on the taxonomic approach, a significant quadratic relationship was verified for all LCBD components. For the LCBD of BDtotal and LCBD of BDrich this relationship presented a U-shape (Fig. 4, Table S4), indicating lower values of total and rich taxonomic LCBD for intermediate richness of native species. For the BDrepl LCBD, on the other hand, this relationship presented a hump-shape, indicating higher values for intermediate richness of native species (Fig. 4, Table S4).

For the functional approach, a negative linear relationship was observed between NATRich and LCBD of BDtotal (indicating a trend of decreasing total LCBD with increasing native species richness) (Fig. 4, Table S4). Besides that, a significant quadratic relationship between NATRich and BDrepl LCBD with a hump-shape was observed (indicating higher LCBD values for intermediate richness of native species) (Fig. 4, Table S4). Also observed was a third-order relationship NATRich between BDrich LCBD, since LCBD decays up to a certain native species richness (five species), from where it tends to increase slightly (Fig. 4, Table S4).

For the taxonomic approach, NNRich demonstrated a negative linear relationship with total LCBD (indicating a trend of decreasing total LCBD with increasing non-native species richness) (Fig. 4; Table S4). For BDrepl LCBD, NNRich demonstrated a significant quadratic relationship with a hump-shape, indicating higher repl LCBD for intermediate non-native species richness (Fig. 4; Table S4). For BDrich LCBD, NNRich had a significant U-shaped quadratic relationship, indicating lower rich LCBD values for intermediate richness of non-native species (Fig. 4; Table S4).

For the functional approach, NNRich showed a significant U-shaped quadratic relationship with BDtotal LCBD and BDrepl LCBD, indicating lower values of total LCBD and replacement for intermediate richness of non-native species (Fig. 4; Table S4). For BDrich LCBD, NNRich demonstrated a negative linear relationship, indicating a decreasing trend of rich LCBD with increasing non-native species richness (Fig. 4, Table S4).

NNOccur did not show a significant relationship with taxonomic or functional total LCBD (Fig. 5), but partitioned LCBD had a significant relationship with it, both for taxonomic and functional diversity. In both approaches, BDrepl had a positive relationship with NNOccur, while BDrich had a negative relationship (Fig. 5; Table S4).

Seasonality was selected in five of the six best-fitting models, and it had a significant relationship with taxonomic LCBD of BDtotal and LCBD of BDrepl, and with functional LCBD of BDtotal (Fig. 6; Table S4). The rainy season had higher LCBD values than the dry season in all models. That is, the rainy season flood in this floodplain positively influences LCBD compared to the dry season. Connectivity was significant only for functional LCDB of BDrich, with isolated lakes having higher LCBD values than connected lakes (Fig. 6; Table S4). Despite having been selected in one of the models, subsystem did not show a significant relationship with BDrich LCBD (Table S4).

The results presented here indicate that connectivity, subsystem and seasonality, as well as non-native species occurrence (NNOccur), non-native species richness (NNRich), and native species richness (NATRich) have a significant influence on taxonomic and functional beta-diversity patterns of small-bodied fish between lakes. Environmental factors influenced the replacement component for both taxonomic and functional diversity, while biotic factors (NNOccur and NNRich) influenced the richness component of each. For sites that contributed the most to beta-diversity (LCBD), both native and non-native species richness were related to the three diversity components (total, richness and replacement BD) for taxonomic and functional approaches. In contrast, NNOccur was related to the LCBD of BDrepl and LCBD of BDrich of both approaches. Seasonality influenced both taxonomic and functional LCBD, while connectivity did not influence any component of LCBD.

Taxonomic and functional beta-diversity

Even though some species vary in resource use, abundance and distribution according to some functional traits (Violle et al. 2007), the present study found very similar patterns for the taxonomic and functional beta-diversity, being influenced by the same variables. This result is potentially worrying since any change in taxonomic diversity would likely have a direct impact on functional diversity. In general, taxonomic beta-diversity was greater than functional beta-diversity, which may indicate functional redundancy since changes in functional characteristics do not occur in proportion to changes in taxonomic diversity. In fact, tropical regions are known to have sizeable functional redundancy due to their extraordinary biodiversity (Toussaint et al. 2016), but this does not guarantee that a loss of taxonomic diversity would not impact the ecosystem services provided (Cadotte et al. 2011; Vitule et al. 2017).

High non-native species richness

Almost half of the fish species collected in the present study are not native to the region, and are present in most sampled lakes. At least 66 non-native fish species have been recorded for the Upper Paraná River floodplain (Ota et al. 2018), which may be potential competitors of native species for resources. These introductions are mainly associated with the construction of the Itaipu reservoir in 1982, which flooded the geographic barrier Salto de Sete Quedas that effectively separated two ichthyofaunistic regions, the Upper and lower Paraná River basins (Langeani et al. 2007; Júlio Júnior et al. 2009). The subsequent construction of a quasi-natural canal for fish transposition in 2002 became a big problem (Vitule et al. 2012). Introducing non-native species into a new environment can directly or indirectly change ecological multifunctionality (Constán-Nava et al. 2015; Moi et al. 2021; Petsch et al. 2022a). The large number of non-native species in the study area can become a critical problem since environments dominated by non-native species tend to be more susceptible to stochastic extinction of native species (Erős et al. 2020).

The influence of non-native species on beta-diversity

The present results show that both the richness of native species and the occurrence and richness of non-native species influenced beta-diversity similarly, mainly the rich component. A biotic acceptance mechanism, common in Neotropical environments, can explain this influence (Ortega et al. 2018; Muniz et al. 2021), since, in these environments, native and non-native fish species respond similarly to increases in available resources (Muniz et al. 2021). According to the biotic acceptance hypothesis, natural ecosystems favorable for the survival of native species, such as those with high resource availability, tend to accommodate the establishment and coexistence of non-native species (Stohlgren et al. 2006; Fridley et al. 2007; Santos et al. 2018).

The influence of environmental factors on beta-diversity

Some environmental factors were found to be related to both taxonomic and functional beta-diversity, such as subsystem and lake connectivity. In contrast, seasonality was only related to the taxonomic beta-diversity. Environmental factors were found to be important mainly for the replacement component, showing that these factors are essential in exchanging species between environments. Indeed, environmental factors directly influence fish community diversity (e.g., Agostinho et al. 2004; Petsch 2016; López-Delgado et al. 2020; Lopes et al. 2022), sometimes even more than spatial factors (López-Delgado et al. 2020).

The hydrological regime is the main component of floodplain seasonality (Junk et al. 1989; Agostinho et al. 2004). The water flow of the Upper Paraná River floodplain is currently regulated by upstream dams, however, the hydrological regime is still the main force influencing the structure and functioning of communities (Agostinho et al. 2004). The alternation between dry and flood periods leads to both limnological and resource availability oscillations (Gomes et al. 2012; Quirino et al. 2017; Petsch et al. 2022b). While there is a greater homogenization of physical and biological characteristics in the flood period (Agostinho et al. 2004; Thomaz et al. 2007; Granzotti et al. 2018), the floodplain sites have less connection with the main channels in the dry period, becoming more susceptible to wind disturbances and tributary inputs, leading to greater dissimilarity (Agostinho et al. 2004; Thomaz et al. 2007; Granzotti et al. 2018). These limnological distinctions can act as environmental filters by selecting fish species that tolerate the conditions of each environment (Leibold et al. 2004; Fernandes et al. 2014), which may contribute to increasing beta-diversity.

Recent climate changes in the study area have been responsible for a decrease in rainfall through a decline in extreme rainfall events in the region (Zandonadi et al. 2016), making the seasons more similar. These changes can directly impact fish of the Upper Paraná River floodplain since the data presented here showed that seasonality influences their beta-diversity. Langer et al. (2018) found variation in annual rainfall to have a significant influence on fish beta-diversity and suggested that climate change and anthropogenic water level stabilization can impact this diversity. In addition, the insertion of dams upstream of the study site has dramatically modified variation in hydrometric level over the years, reducing the frequency and intensity of floods, which has directly influenced fish diversity (Gubiani et al. 2007; Alves et al. 2021).

For both taxonomic and functional beta-diversity, the Paraná subsystem showed an opposite influence on beta-diversity compared to the other two subsystems. This difference may be associated with two intrinsic environmental characteristics of this subsystem. The first is the presence of a cascade of reservoirs upstream of the Upper Paraná River floodplain, which directly influences the habitats of the Paraná subsystem, while having a substantially smaller influence for the Baía and Ivinhema subsystems (Agostinho et al. 2004; Roberto et al. 2009). The second concerns the fluctuation of the hydrometric level, which is independent in each of the three subsystems (Souza-Filho et al. 2004), leading to environmental changes and high fish diversity in these locations (Agostinho et al. 2004; Lopes et al. 2022).

Habitat connectivity was another factor explaining the beta-diversity of both facets. This was expected as the disconnection of lakes from a main channel generally reduces fish diversity (Liu and Wang 2010). Although these subsystems are connected, the presence of different types of habitats and tributaries within them increases environmental heterogeneity and, consequently, regional biodiversity (Agostinho et al. 2007), especially during dry periods when several environments are disconnected.

Local contribution to beta-diversity

In general, this study found that environmental and biological characteristics influence LCBD. High LCBD values are generally negatively related to species richness (Heino and Grönroos 2017; Landeiro et al. 2018). Here, both BDtotal LCBD and BDrich LCDB were, in general, negatively related to native and non-native species richness, at least to some extent. Sites with low species richness can contribute to beta-diversity as they harbor unique compositions (Legendre and De Cáceres 2013). Therefore, they may be ecologically unique and more susceptible to impacts. However, despite the negative response, after a certain number of species, the increase in species richness (native or non-native) generated an increase in LCBD values for taxonomic BDtotal and BDrich and functional BDtotal.

This positive relationship between LCBD and richness was also observed by Kong et al. (2017), who suggested that it reflects the introduction of novel species (e.g., migratory species) in communities. Thus, sites of both low and high-richness require special attention in terms of environmental protection (Legendre and De Cáceres 2013), as both may represent unique species and traits. On the other hand, the results of BDrepl LCBD showed a greater species substitution in an intermediate species richness, indicating that in a high richness there is only an increment of species, while in a low richness there is only the presence of common species. The results of the present study did not show a relationship between the occurrence of non-native species and total taxonomic and functional LCBD. Nonetheless, they did show a relationship between the LCBD of BDrepl and LCBD of BDrich, both functional and taxonomic with opposite trends. These results show the importance of decomposing beta-diversity into two components to fully understand the predominant processes occurring in a community (Legendre 2014). Thus, sites with the occurrence of non-native species proved important for the replacement of species. In contrast, places without non-native species are more important for the richness component. Since non-native species comprised about half of the species and were present in 76.5% of the sampled points studied, sampled points lacking them generally had lower species richness, leading to a peak of LCBD for the richness component.

The results presented here also showed an influence of environmental variables (seasonality and connectivity) on LCBD. There is less connectivity between environments and a decrease in food availability during the dry period, compared to the flood period (Junk et al. 1989; Quirino et al. 2017). In addition, there is an intensification of some biological interactions during the dry period, such as predation and competition for resources (Thomaz et al. 2008; Fernandes et al. 2014), making each environment more unique, a crucial factor for LCBD.

Although the present study found the occurrence and richness of non-native species to have a relationship with beta-diversity, these species can cause a decrease in functional and taxonomic beta-diversity in the long term (Angulo-Valencia et al. 2022). In fact, this invasion process changes the ecological multifunctionality of the environment (Constán-Nava et al. 2015; Moi et al. 2021; Petsch et al. 2022a). The results showed that the taxonomic and functional beta-diversity of small-bodied fish in lakes are influenced by environmental factors, such as connectivity, seasonality and subsystem, and by biotic factors, such as the occurrence and richness of non-native species. In addition, these parameters influenced the local contribution to beta-diversity. Therefore, this study emphasizes that changes in both environmental factors and species diversity (such as the introduction of a non-native species) can impact the beta-diversity of Neotropical fish.

Acknowledgments

The authors ~~also~~ thank the National Council for Scientific and Technological Development (CNPq), CAPES and Nupelia/UEM for financial support and infrastructure for developing this study. We also thank Danielle Katharine Petsch for helping us with some methodological points.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), an organ of the Brazilian Government for the training of human resources.

Competing Interests

The authors have no relevant interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Material preparation and analysis were performed by Matheus Henrique Ferreira Aleixo, Fernanda Moreira Florêncio and Fernando Miranda Lansac-Toha. The first draft of the manuscript was written by Matheus Henrique Ferreira Aleixo and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

Agostinho AA, Pelicice FM, Petry AC et al (2007) Fish diversity in the upper Paraná River basin: Habitats, fisheries, management and conservation. Aquat Ecosyst Heal Manag 10:174–186. https://doi.org/10.1080/14634980701341719
Agostinho AA, Thomaz SM, Gomes LC (2004) Threats for biodiversity in the floodplain of the Upper Paraná River: Effects of hydrological regulation by dams. Ecohydrol Hydrobiol 4:267–280
Akaike H (1974) A New Look at the Statistical Model Identification. IEEE Trans Automat Contr 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
Alves JC, Andreotti GF, Agostinho AA et al (2021) Effects of the El Niño Southern Oscillation (ENSO) on fish assemblages in a Neotropical floodplain. Hydrobiologia 848:1811–1823. https://doi.org/10.1007/s10750-021-04555-4
Anderson MJ (2017) Permutational Multivariate Analysis of Variance (PERMANOVA). Wiley StatsRef Stat Ref Online 1–15. https://doi.org/10.1002/9781118445112.stat07841
Angulo-Valencia MA, Dias RM, Alves DC et al (2022) Patterns of functional diversity of native and non-native fish species in a neotropical floodplain. Freshw Biol 67:1301–1315. https://doi.org/10.1111/fwb.13918
Brooks M, Bolker B, Kristensen K, Maechler M (2022)Generalized Linear Mixed Models using Template Model Builder
Cadotte MW, Carscadden K, Mirotchnick N (2011) Beyond species: Functional diversity and the maintenance of ecological processes and services. J Appl Ecol 48:1079–1087. https://doi.org/10.1111/j.1365-2664.2011.02048.x
Cardoso P, Rigal F, Carvalho JC (2015) BAT – Biodiversity Assessment Tools, an R package for the measurement and estimation of alpha and beta taxon, phylogenetic and functional diversity. Methods Ecol Evol 6:232–236. https://doi.org/10.1111/2041-210X.12310
de Carvalho RA, Tejerina-Garro FL (2015) Relationships between taxonomic and functional components of diversity: implications for conservation of tropical freshwater fishes. Freshw Biol 60:1854–1862. https://doi.org/10.1111/fwb.12616
Chao A, Chiu C-H, Villéger S et al (2019) An attribute-diversity approach to functional diversity, functional beta diversity, and related (dis)similarity measures. Ecol Monogr 89:1–29. https://doi.org/10.1002/ecm.1343
Cohen AN, Carlton JT (1998) Accelerating invasion rate in a highly invaded estuary. Science 279:555–558. https://doi.org/0.1126/science.279.5350.555
Constán-Nava S, Soliveres S, Torices R et al (2015) Direct and indirect effects of invasion by the alien tree Ailanthus altissima on riparian plant communities and ecosystem multifunctionality. Biol Invasions 17:1095–1108. https://doi.org/10.1007/s10530-014-0780-4
Dray S, Bauman D, Blanchet G et al (2021) adespatial:Multivariate Multiscale Spatial Analysis
Dudgeon D, Arthington AH, Gessner MO et al (2006) Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol Rev Camb Philos Soc 81:163–182. https://doi.org/10.1017/S1464793105006950
Erős T, Comte L, Filipe AF et al (2020) Effects of nonnative species on the stability of riverine fish communities. Ecography 43:1156–1166. https://doi.org/10.1111/ecog.04985
Fernandes IM, Henriques-Silva R, Penha J et al (2014) Spatiotemporal dynamics in a seasonal metacommunity structure is predictable: The case of floodplain-fish communities. Ecography 37:464–475. https://doi.org/10.1111/j.1600-0587.2013.00527.x
Fridley JD, Stachowicz JJ, Naeem S et al (2007) The invasion paradox: reconciling pattern and process in species invasion. Ecology 88:3–17
Gál B, Szivák I, Heino J, Schmera D (2019) The effect of urbanization on freshwater macroinvertebrates – Knowledge gaps and future research directions. Ecol Indic 104:357–364. https://doi.org/10.1016/j.ecolind.2019.05.012
Gaston KJ, Blackburn TM, Greenwood JJD et al (2000) Abundance-occupancy relationships. J Appl Ecol 37:39–59. https://doi.org/10.1046/j.1365-2664.2000.00485.x
Gomes LC, Bulla CK, Agostinho AA et al (2012) Fish assemblage dynamics in a Neotropical floodplain relative to aquatic macrophytes and the homogenizing effect of a flood pulse. Hydrobiologia 685:97–107. https://doi.org/10.1007/s10750-011-0870-6
Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338. https://doi.org/10.2307/2333639
Granzotti RV, Miranda LE, Agostinho AA, Gomes LC (2018) Downstream impacts of dams: shifts in benthic invertivorous fish assemblages. Aquat Sci 80:1–14. https://doi.org/10.1007/s00027-018-0579-y
Gubiani ÉA, Gomes LC, Agostinho AA, Okada EK (2007) Persistence of fish populations in the upper Paraná River: effects of water regulation by dams. Ecol Freshw Fish 16:191–197. https://doi.org/10.1111/j.1600-0633.2006.00211.x
Hamilton NE, Ferry M (2018) Ggtern: Ternary diagrams using ggplot2. J Stat Softw 87:1–17. https://doi.org/10.18637/jss.v087.c03
Heino J, Grönroos M (2017) Exploring species and site contributions to beta diversity in stream insect assemblages. Oecologia 183:151–160. https://doi.org/10.1007/s00442-016-3754-7
Júlio Júnior HF, Tós CD, Agostinho AA, Pavanelli CS (2009) A massive invasion of fish species after eliminating a natural barrier in the upper rio Paraná basin. Neotrop Ichthyol 7:709–718. https://doi.org/10.1590/S1679-62252009000400021
Junk WJ, Bayley PB, Sparks RE (1989) The flood pulse concept in river-floodplain systems. Can J Fish Aquat Sci 106:110–127. https://doi.org/10.1007/s10750-021-04555-4
Kong H, Chevalier M, Laffaille P, Lek S (2017) Spatio-temporal variation of fish taxonomic composition in a South-East Asian flood-pulse system. PLoS ONE 12:1–16. https://doi.org/10.1371/journal.pone.0174582
Laliberté E, Legendre P, Shipley B (2014) FD: measuring functional diversity from multiple traits, and other tools for functional ecology. 1–29
Landeiro VL, Franz B, Heino J et al (2018) Species-poor and low-lying sites are more ecologically unique in a hyperdiverse Amazon region: Evidence from multiple taxonomic groups. Divers Distrib 24:966–977. https://doi.org/10.1111/ddi.12734
Langeani F, Castro RMC, Oyakawa OT et al (2007) Diversidade da ictiofauna do Alto Rio Paraná: composição atual e perspectivas futuras. Biota Neotrop 7:181–197. https://doi.org/10.1590/S1676-06032007000300020
Langer TA, Cooper MJ, Reisinger LS et al (2018) Water depth and lake-wide water level fluctuation in fluence on α- and β-diversity of coastal wetland fish communities. J Great Lakes Res 44:1–7. https://doi.org/10.1016/j.jglr.2017.11.001
Lansac-Tôha FM, Heino J, Quirino BA et al (2019) Differently dispersing organism groups show contrasting beta diversity patterns in a dammed subtropical river basin. Sci Total Environ 691:1271–1281. https://doi.org/10.1016/j.scitotenv.2019.07.236
Lechêne A, Lobry J, Boët P, Laffaille P (2018) Change in fish functional diversity and assembly rules in the course of tidal marsh restoration. PLoS ONE 13:1–27. https://doi.org/10.1371/journal.pone.0209025
Legendre P (2014) Interpreting the replacement and richness difference components of beta diversity. Glob Ecol Biogeogr 23:1324–1334. https://doi.org/10.1111/geb.12207
Legendre P, Anderson MJ (1999) Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol Monogr 69:1–24
Legendre P, De Cáceres M (2013) Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol Lett 16:951–963. https://doi.org/10.1111/ele.12141
Leibold MA, Holyoak M, Mouquet N et al (2004) The metacommunity concept: a framework for multi-scale community ecology. Ecol Lett 7:601–613. https://doi.org/10.1111/j.1461-0248.2004.00608.x
Levine JM, Vilà M, D’Antonio CM et al (2003) Mechanisms underlying the impacts of exotic plant invasions. Proc R Soc B Biol Sci 270:775–781. https://doi.org/10.1098/rspb.2003.2327
Liu X, Wang H (2010) Estimation of minimum area requirement of river-connected lakes for fish diversity conservation in the Yangtze River floodplain. Divers Distrib 16:932–940. https://doi.org/10.1111/j.1472-4642.2010.00706.x
Lopes TM, Muniz CM, Schmitz MH et al (2022) Drivers of fish trophic guild composition in lakes of the Upper Paraná River floodplain. Aquat Sci 84:1–13. https://doi.org/10.1007/s00027-022-00860-9
López-Delgado EO, Winemiller KO, Villa-Navarro FA (2020) Local environmental factors influence beta-diversity patterns of tropical fish assemblages more than spatial factors. Ecology 101:1–12. https://doi.org/10.1002/ecy.2940
Moi DA, Alves DC, Figueiredo BRS et al (2021) Non-native fishes homogenize native fish communities and reduce ecosystem multifunctionality in tropical lakes over 16 years. Sci Total Environ 769. https://doi.org/10.1016/j.scitotenv.2020.144524
Mori AS, Isbell F, Seidl R (2018) β-Diversity, community assembly, and ecosystem functioning. Trends Ecol Evol 33:549–564. https://doi.org/10.1016/j.tree.2018.04.012
Muniz CM, García-Berthou E, Ganassin MJM et al (2021) Alien fish in Neotropical reservoirs: Assessing multiple hypotheses in invasion biology. Ecol Indic 121:107034. https://doi.org/10.1016/j.ecolind.2020.107034
Myers N, Mittermeier RA, Mittermeier CG et al (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858. https://doi.org/10.1038/35002501
Oksanen J, Blanchet FG, Friendly M et al (2019) vegan: Community Ecology Package
Olden JD, Kennard MJ, Leprieur F et al (2010) Conservation biogeography of freshwater fishes: Recent progress and future challenges. Divers Distrib 16:496–513. https://doi.org/10.1111/j.1472-4642.2010.00655.x
Olden JD, Rooney TP (2006) On defining and quantifying biotic homogenization. Glob Ecol Biogeogr 15:113–120. https://doi.org/10.1111/j.1466-822X.2006.00214.x
Ortega JCG, Agostinho AA, Santos NCL et al (2018) Similarities in correlates of native and introduced fish species richness distribution in Brazilian reservoirs. Hydrobiologia 817:167–177. https://doi.org/10.1007/s10750-018-3508-0
Ota RR, Deprá GC, Graça WJ et al (2018) Peixes da planície de inundação do alto rio Paraná e áreas adjacentes: revised, annotated and updated. Neotrop Ichthyol 16:1–111. https://doi.org/10.1590/1982-0224-20170094
Petsch DK (2016) Causes and consequences of biotic homogenization in freshwater ecosystems. Int Rev Hydrobiol 101:113–122. https://doi.org/10.1002/iroh.201601850
Petsch DK, Bertoncin APS, Ortega JCG, Thomaz SM (2022a) Non-native species drive biotic homogenization, but it depends on the realm, beta diversity facet and study design: a meta-analytic systematic review. Oikos 1–11. https://doi.org/10.1111/oik.08768
Petsch DK, Cionek VM, Thomaz SM, Santos NCL (2022b) Ecosystem services provided by river–floodplain ecosystems. Hydrobiologia. https://doi.org/10.1007/s10750-022-04916-7
Pinheiro J, Bates D, R Core Team (2022) Linear and Nonlinear Mixed Effects Models. 1–338
Podani J, Ricotta C, Schmera D (2013) A general framework for analyzing beta diversity, nestedness and related community-level phenomena based on abundance data. Ecol Complex 15:52–61. https://doi.org/10.1016/j.ecocom.2013.03.002
Podani J, Schmera D (2011) A new conceptual and methodological framework for exploring and explaining pattern in presence-absence data. Oikos 120:1625–1638. https://doi.org/10.1111/j.1600-0706.2011.19451.x
Quirino BA, Carniatto N, Guglielmetti R, Fugi R (2017) Changes in diet and niche breadth of a small fish species in response to the flood pulse in a Neotropical floodplain lake. Limnologica 62:126–131. https://doi.org/10.1016/j.limno.2016.10.005
Quirino BA, Lansac-Tôha FM, Thomaz SM et al (2021) Macrophyte stand complexity explains the functional α and β diversity of fish in a tropical river-floodplain. Aquat Sci 83:1–12. https://doi.org/10.1007/s00027-020-00768-2
R Core Team (2020) R: A Language and Environment for Statistical Computing
Rahel FJ (2007) Biogeographic barriers, connectivity and homogenization of freshwater faunas: It’s a small world after all. Freshw Biol 52:696–710. https://doi.org/10.1111/j.1365-2427.2006.01708.x
Roberto MC, Santana NF, Thomaz SM (2009) Limnology in the Upper Paraná River floodplain: large-scale spatial and temporal patterns, and the influence of reservoirs. Brazilian J Biol 69:717–725. https://doi.org/10.1590/S1519-69842009000300025
Santos DA, Hoeinghaus DJ, Gomes LC (2018) Spatial scales and the invasion paradox: a test using fish assemblages in a Neotropical floodplain. Hydrobiologia 817:121–131. https://doi.org/10.1007/s10750-018-3531-1
Simberloff D (2009) The role of propagule pressure in biological invasions. Annu Rev Ecol Evol Syst 40:81–102. https://doi.org/10.1146/annurev.ecolsys.110308.120304
Simberloff D, Martin JL, Genovesi P et al (2013) Impacts of biological invasions: What’s what and the way forward. Trends Ecol Evol 28:58–66. https://doi.org/10.1016/j.tree.2012.07.013
Socolar JB, Gilroy JJ, Kunin WE, Edwards DP (2016) How should beta-diversity inform biodiversity conservation? Trends Ecol Evol 31:67–80
Souza-Filho E, Rocha P, Comunello E, Stevaux J (2004) Efects of the Porto Primavera dam on the physical environment of the downstream foodplain. In: Thomaz SM, Agostinho AA, Hahn NS (eds) The Upper Paraná River and its Floodplain: Physical aspects, Ecology and Conservation, 1st edn. Backhuys Publishers, Leiden, pp 55–74
Stohlgren TJ, Jarnevich C, Chong GW, Evangelista PH (2006) Scale and plant invasions: A theory of biotic acceptance. Preslia 78:405–426
Thomaz SM, Bini LM, Bozelli RL (2007) Floods increase similarity among aquatic habitats in river-floodplain systems. Hydrobiologia 579:1–13. https://doi.org/10.1007/s10750-006-0285-y
Thomaz SM, Dibble ED, Evangelista LR et al (2008) Influence of aquatic macrophyte habitat complexity on invertebrate abundance and richness in tropical lagoons. Freshw Biol 53:358–367. https://doi.org/10.1111/j.1365-2427.2007.01898.x
Thomaz SM, Kovalenko KE, Havel JE, Kats LB (2015) Aquatic invasive species: general trends in the literature and introduction to the special issue. Hydrobiologia 746:1–12. https://doi.org/10.1007/s10750-014-2150-8
Toussaint A, Charpin N, Brosse S, Villéger S (2016) Global functional diversity of freshwater fish is concentrated in the Neotropics while functional vulnerability is widespread. Sci Rep 6:1–9. https://doi.org/10.1038/srep22125
Violle C, Navas M, Vile D et al (2007) Let the concept of trait be functional! Oikos 116:882–892. https://doi.org/10.1111/j.2007.0030-1299.15559.x
Vitule JRS, Agostinho AA, Azevedo-Santos VM et al (2017) We need better understanding about functional diversity and vulnerability of tropical freshwater fishes. Biodivers Conserv 26:757–762. https://doi.org/10.1007/s10531-016-1258-8
Vitule JRS, Skóra F, Abilhoa V (2012) Homogenization of freshwater fish faunas after the elimination of a natural barrier by a dam in Neotropics. Divers Distrib 18:111–120. https://doi.org/10.1111/j.1472-4642.2011.00821.x
Whittaker RH (1960) Vegetation of the Siskiyou Mountains, Oregon and California. Ecol Monogr 30:279–338. https://doi.org/10.2307/1943563
Wickham H (2016) ggplot2: Elegant Graphics for Data Analysis. Springer, New York
Winemiller KO, Fitzgerald DB, Bower LM, Pianka ER (2015) Functional traits, convergent evolution, and periodic tables of niches. Ecol Lett 18:737–751. https://doi.org/10.1111/ele.12462
Winemiller KO, McIntyre PB, Castello L et al (2016) Balancing hydropower and biodiversity in the Amazon, Congo, and Mekong. Scie 351:128–129. https://doi.org/10.1126/science.aac7082
Zandonadi L, Acquaotta F, Fratianni S, Zavattini JA (2016) Changes in precipitation extremes in Brazil (Paraná River Basin). Theor Appl Climatol 123:741–756. https://doi.org/10.1007/s00704-015-1391-4
Zarabska-Bożejewicz D, Kujawa K (2018) The effect of land use on taxonomical and functional diversity of lichens in an agricultural landscape. Fungal Ecol 33:72–79. https://doi.org/10.1016/j.funeco.2018.01.005

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Influence of species invasion, seasonality, and connectivity on fish functional and taxonomic beta-diversity in a Neotropical floodplain

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Abstract

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1. Introduction

2. Methodology

2.1 Study area

2.2 Sampling

2.3 Functional characterization

2.4 Statistical Analysis

3. Results

3.1 Characterization of the fish community

3.2 Taxonomic and functional beta-diversity

3.3 Distance-based redundancy analysis

3.4 Taxonomic and Functional LCBD

4. Discussion

5. Conclusion

Declarations

References

Supplementary Files

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