Received: 7 December 2020 | Accepted: 28 December 2020 DOI: 10.1111/1365-2435.13758 RESEARCH ARTICLE Multitrophic richness enhances ecosystem multifunctionality of tropical shallow lakes Dieison A. Moi1 Roger P. Mormul1 | Gustavo Q. Romero2 | Pablo A. P. Antiqueira2 | 3 1 | Franco Teixeira de Mello | Claudia C. Bonecker 1 Graduate Program in Ecology of Inland Water Ecosystems, Department of Biology, State University of Maringá, Maringá, Brazil 2 Laboratory of Multitrophic Interactions and Biodiversity, Department of Animal Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil 3 Departamento de Ecología y Gestión Ambiental CURE, Universidad de la República, Maldonado, Uruguay Correspondence Dieison A. Moi Email: dieisonandrebv@outlook.com Funding information Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundacão de Amparo à Pesquisa do Estado de São Paulo; São Paulo Research Foundation, Grant/Award Number: 2017/09052-4 and 2018/12225-0; Royal Society, Newton Advanced Fellowship, Grant/Award Number: NAF/R2/180791; ANII National System of Researchers (SNI); PEDECIBA-Geociencias Handling Editor: Oscar Godoy Abstract 1. The role of multitrophic diversity in sustaining multiple functions simultaneously (multifunctionality) is still poorly understood in natural communities, especially in highly diverse aquatic ecosystems. Existing studies have focused on the effect of single trophic group on ecosystem function and on individual ecosystem functions, neglecting the fact that multiple trophic groups are needed to maintain ecosystem multifunctionality. 2. Here, using a 16-year database from tropical shallow lakes, we combined species richness of nine single trophic group into a unique measurement of multitrophic richness. We then investigated the influence of the richness within separate single trophic group and in a multitrophic context on ecosystem multifunctionality. We also investigated how removal of any single trophic group influence the effect of the multitrophic richness on multifunctionality; and how the interactions among multiple single trophic group affect multifunctionality. 3. We showed that the multitrophic richness had a stronger positive effect on multifunctionality than the richness of single trophic group. The removal of each single trophic group decreased the effect of the multitrophic richness on multifunctionality. The larger predatory vertebrates and primary producers had stronger positive effects on multifunctionality, but the richness of basal trophic groups fuelled the large-sized predators, thus indirectly contributing to increase multifunctionality. 4. Our study highlights the need for preserving multiple trophic groups to sustain multifunctionality in highly diverse aquatic ecosystems; thus, trophic degradation of the ecosystems should strongly impair their functioning. KEYWORDS biodiversity, complementarity, ecosystem functioning, freshwater ecosystems, multifunctionality, trophic groups 1 | I NTRO D U C TI O N in prehistoric times (Ceballos et al., 2015). Biodiversity loss could profoundly impact human society, since biodiversity is neces- Biodiversity conservation is a major challenge to humanity, as an- sary to sustain many services that humans depend on (Balvanera thropogenic activities have accelerated species extinction at unprec- et al., 2006; Hooper et al., 2012; Isbell et al., 2011). Because dif- edented rates (Hooper et al., 2012; Sala et al., 2000). Consequently, ferent species often have different functions, biodiversity is in- the current global extinctions are comparable with mass extinctions creasingly recognized as pivotal for the simultaneous maintenance 942 | © 2021 British Ecological Society wileyonlinelibrary.com/journal/fec Functional Ecology. 2021;35:942–954. Functional Ecology MOI et al. | 943 of multiple ecosystem functions, that is, multifunctionality (Hector experimental studies are limited that capture these interactions, and & Bagchi, 2007; Lefcheck et al., 2015). Experimental studies have therefore the magnitude of the B-EMF effect that occurs in the real shown that more diverse communities can maintain consistently world (Duffy et al., 2017). higher multifunctionality over time due to turnover between com- Large-sized vertebrates (predators, omnivores, herbivores and plementary species, with different species promoting functions in detritivores) may substantially influence ecosystem functioning, different years (Allan et al., 2011), or through the presence of cer- such as primary production, decomposition and nutrient cycling, tain influential species (Loreau & Hector, 2001). These two biological directly via nutrient provisioning through faeces and carcass depo- mechanisms are often known as complementary and selection effects sition, or indirectly, by altering the diversity of other trophic groups respectively. The complementary effect occurs by niche partition- (Atkinson et al., 2017; McIntyre et al., 2007). Invertebrate omni- ing and facilitative interactions between species, which improve the vores (such as macroinvertebrates) are responsible for processing community's efficiency in sustaining multiple ecosystem functions large amounts of plant and animal detritus (Tamura & Kagaya, 2019), (Balvanera et al., 2006; Hector & Bagchi, 2007). The selection ef- which may determine the amount of resources and functional rates fect occurs by the dominance of highly productive species (Loreau in the aquatic food web, influencing decomposition rates and nu- & Hector, 2001). However, the understanding of biodiversity–­ trient cycling. The diversity of small invertebrates (such as small- multifunctionality relationship (B-EMF) has been biased towards sized filter feeders, small omnivores and carnivores, or phagotrophic terrestrial ecosystems (e.g. Maestre et al., 2012; Meyer et al., 2018), protists) is fundamental to maintain multiple functions and energy while freshwater ecosystems remain largely unexplored. flow within the aquatic food webs. Likewise, primary producers One of the main challenges when studying B-EMF is that ex- directly and indirectly influence ecosystem functioning by increas- isting studies focus on the diversity of single trophic group (e.g. ing the biomass of several trophic groups, besides affecting nutri- Eisenhauer et al., 2013; Hector & Bagchi, 2007), ignoring that the ent cycling and primary productivity (Engelhardt & Ritchie, 2001; functional role of any trophic group may depend on the diversity Scherber et al., 2010). Furthermore, direct and indirect vertical of others (Jing et al., 2015). Additionally, different trophic groups interactions between trophic groups influence ecosystem-level may have complementary or opposite effects on ecosystem func- processes (Schuldt et al., 2018), and usually the effect of higher tioning. For example, the diversity of plants and microbes has com- trophic levels (apex consumers) on ecosystem functioning depends plementary effects on nutrient cycling (Jing et al., 2015), and plant on the availability of lower trophic levels (trophic cascading effect; diversity increases the diversity of consumer communities (Ebeling Carpenter et al., 2001; Rasher et al., 2013). For instance, the effect et al., 2018; Eisenhauer et al., 2013). Conversely, plant and herbi- of vertebrate predators on ecosystem functioning depends on the vore diversity exerts opposing effects on biomass stocks (Duffy availability of small omnivorous vertebrates. Similarly, the functional et al., 2007). Experimental evidence suggests that changes at more effect of small omnivorous vertebrates depends on the availability than one trophic level (vertical diversity) interactively affect indi- of omnivorous invertebrates and small omnivores and carnivores vidual functions of ecosystems, such as animal biomass production (Duffy et al., 2017; Rasher et al., 2013). Primary producers increase and primary productivity (Franco et al., 2019; Handa et al., 2014; niche availability, and affect interactions between multiple trophic Seabloom et al., 2017). Understanding how biodiversity affects the groups, often affecting adjacent trophic levels and reverberate up multifunctionality of ecosystems requires analysing diversity within to higher trophic levels (Scherber et al., 2010). Thus, incorporating (horizontal diversity) and across trophic levels (vertical diversity; multitrophic and multifunctionality from the perspective of explicit Duffy et al., 2007). Multitrophic communities are especially import- trophic interaction could provide robust insights into underlying ant in maintaining multiple ecosystem functions. For example, in mechanisms in which biodiversity simultaneously sustain multifunc- Mongolian grasslands, diversifying livestock by mixing sheep and tionality in freshwater ecosystems. cattle increased the diversity of plants, insects, soil microbes and We used a multitrophic approach to evaluate the B-EMF in tropical nematodes, thus indirectly increasing the ecosystem multifunc- shallow lakes using a 16-year database. The richness was measured tionality (Wang et al., 2019). Recent studies found that high multi- for nine trophic groups from different taxa, including apex piscivorous trophic richness had stronger positive effects on multifunctionality (vertebrate predators), herbivorous, detritivorous and omnivorous fish; than richness in any single group (Delgado-Baquerizo et al., 2020; primary producers (vascular plants and bryophytes); small-sized filter Soliveres et al., 2016). Thus, synthesizing multiple trophic groups feeders (rotifers and nauplii); small omnivores and carnivores (micro- under a unique multitrophic measure could be more advantageous crustaceans); phagotrophic protists (Amoeba Testacea); and omnivo- to predict the real B-EMF. rous insect larvae (Chironomidae). We combined the richness of these Biodiversity loss occurs across many trophic groups (Allan groups into a unique multitrophic measure (Allan et al., 2014). In ad- et al., 2014), nevertheless, in freshwater ecosystems, it is not dition, a set of 14 variables was obtained related to key components known how a change in biodiversity across multiple trophic groups of ecosystem functioning, including nutrient cycling (P and N), decom- affect multifunctionality. This limits our ability to predict how position (oxygen and organic matter), primary productivity (chloro- multitrophic biodiversity loss affect the functioning of these eco- phyll a of phytoplankton and periphyton) and secondary production systems. Moreover, in natural ecosystems, complex biotic interac- (animal biomass production and abundance), and synthesized these tions between trophic groups are assumed to affect B-EMF, and functions under multifunctional indexes. Three basic approaches were 944 | Functional Ecology MOI et al. employed to explore the B-EMF. First, the averaging approach synthe- macrophytes, including vascular plants and bryophytes), small-sized sizes multifunctionality into a single metric that estimates the average filter feeders (rotifers and nauplii), small omnivores–carnivores (mi- value of multiple functions achieved in a given assemblage. Second, crocrustaceans), phagotrophic protists (Amoeba Testacea) and in- the multi-threshold approach estimates the number of functions that vertebrate omnivores (omnivorous macroinvertebrate larvae). Fish exceed some pre-defined threshold of ‘functionality’ in a given as- were caught using two gear types (seines and gillnets). Two stand- semblage. Third, the turnover approach tests whether different species ard gillnets, which had 10-m long each and 11 mesh sizes (2.4, 3, 4, within each trophic group promote different functions and quantifies 5, 6, 7, 8, 10, 12, 14 and 16 cm from knot to knot) were attached the fraction of species within trophic groups that contribute to one or together making a 20-m long set. We set up this 20-m long set (i.e. more functions. A detailed review of these approaches can be found the two gillnets of 10-m long attached together) in the ‘middle’ zone in Byrnes et al. (2014). We then investigated the causal relationships of each lake, which was deployed for a 24-hr period. Simultaneously of each trophic group as well as multitrophic richness with multifunc- to standard gillnets, we used a seine with 20-m long and mesh size tionality. We also fitted a structural equation model to evaluate how of 0.5 cm, which was always operated in the littoral zone of the single trophic group and their interactions affect direct and indirectly lakes, and also for a 24-hr period. The samplings were always re- the ecosystem multifunctionality (Figure S1). As different trophic alized in the exact same locations over time. The used these two groups may affect different ecosystem functions (Delgado-Baquerizo methods provide a representative sample of most species present et al., 2020; Soliveres et al., 2016), we predict that (a) multitrophic rich- in lake compartments. ness may be more important in driving multifunctionality than richness Macroinvertebrate larvae were sampled using Petersen sampler of single trophic group; (b) multitrophic richness effects on ecosystem (0.0345 m2), and samples were collected three times at both sides and multifunctionality might be stronger as more trophic groups are con- in the centre of each lake (Moi, Alves, et al., 2020). Rotifers, nauplii, mi- sidered; thus (c) the loss of any trophic groups should reduce the effect crocrustaceans and Amoeba Testacea were sampled at the subsurface of the multitrophic richness on ecosystem multifunctionality. in the pelagic zone using a motorized pump and a plankton net (68 µm) that filtered 1,000 L water per sample. Aquatic macrophyte richness 2 | M ATE R I A L S A N D M E TH O DS 2.1 | Study site was estimated by a plot (0.25 m2) in two transects, which were positioned perpendicularly to each other, dividing each lake into four quadrants of similar area. The square was positioned at every metre along the two transects to the end of the plant cover and all plant species inside the square were identified. To survey submerged macrophyte, The study was conducted in the Upper Parana floodplain (20°40′– we used a fork (0.2 m × 0.2 m) with aluminium stick to drag the plants 22°50′S; 53°10′–53°24′W), Brazil. This area is part of a large tropical from the underwater area of each square. All trophic groups were sam- floodplain and presents a mean annual temperature and precipita- pled four times a year in each lake during the 16 years (except 2003, tion of 22°C and 1,500 mm respectively. Data used in the study are 2016, 2017 and 2018), totalling 183 samplings (61 samplings in each part of a ‘long-term ecological research project’ (PELD-Sitio PIAP) lake) between 2002 and 2018. that has been conducted in this floodplain since 2000. Data were Overall, we found 46 vertebrate predator species, 66 vertebrate collected for 16 years (2002–2018) in three independent shallow omnivore species, 194 vertebrate detritivore species and 10 ver- lakes (Fechada lake, Garças lake and Ventura lake). These three lakes tebrate herbivore species. We also collected 79 primary producer are adjacent to three floodplain rivers: the Baia river, Parana river species, 137 phagotrophic protist species, 332 small-sized filter and Ivinhema river respectively (Figure S2). See Supplementary feeder species, 175 small omnivore–carnivore species and 54 inver- Material: Methods for detailed description of the study sites. tebrate omnivorous species. Trophic position of all groups was determined from our own feeding trials and from the literature (Graça 2.2 | Sampling design Our sampling was explicitly designed to assess aquatic biodiversity and ecosystem functions in shallow lakes. For 16 years, four & Pavanelli, 2007; Hahn et al., 2004; Mormul et al., 2012; Weisse et al., 2016). 2.3 | Diversity measures annual samples (conducted in the four seasons of the year) were taken in each lake, except for the years of 2003, 2016, 2017 and All sampled taxa in our study were identified to species level, ex- 2018 when only two annual samples were realized. The sampling cept the Chironomidae family, where organisms were identified to includes the entire compartment and depth (i.e. sediment, pelagic morphospecies level. Before calculations of the richness of trophic zone and littoral zone) of all the lakes. We collected several aquatic groups (explained below), the abundance matrix of the groups taxa, which were subsequently classified into nine trophic groups: was rarefied according to smaller abundance values observed to vertebrate predators (apex piscivorous fish), vertebrate omnivores each group in the sampled period (Figure S3). Note that not all (omnivorous fish), vertebrate detritivores (detritivorous fish), ver- the trophic groups passed our rarefaction cut-off. For instance, tebrate herbivores (herbivorous fish), primary producers (aquatic primary producers were not rarefied because these trophic group Functional Ecology MOI et al. | 945 data were represented as presence–absence. We used rarefac- aquatic ecosystem functions. For example, dissolved oxygen is one tion because observed species richness may be affected by sam- of the main indicators of aquatic ecosystem metabolism (Solomon pling effort in different periods, which could bias our data (Chao et al., 2013), and its levels indicate organic matter oxidation, respira- et al., 2014). We then measured the species richness (rarefied tion and production of aquatic organisms. Dissolved organic matter richness) of different trophic groups (i.e. the number of species is an important indicator of aquatic detritus decomposition, which within each trophic group) present in the lakes in each sampled pe- includes living and non-living animal and vegetal forms (Moore riod. We used richness as a metric of aquatic biodiversity, because et al., 2004). Dissolved organic matter also may influence light avail- richness is the most used and the simplest metric of biodiversity ability in aquatic ecosystems, thus indirectly impacting primary (Balvanera et al., 2006; Isbell et al., 2011; Lefcheck et al., 2015). producers. P and N are the two most used nutrients by primary pro- Additionally, richness of all groups was highly correlated with ducers in aquatic ecosystems, thus their availability may influence Shannon diversity (Pearson r = 0.80, p < 0.001). This indicates whole-ecosystem primary production (Elser et al., 2009). Periphyton that the choice of rarefied richness as a diversity metric does not and phytoplankton are the two most important producers in aquatic alter our results. ecosystems, and their production may correspond to approximately half of the primary biosphere production (Field et al., 1998). Finally, 2.4 | Multitrophic richness fish, macroinvertebrates and small planktonic invertebrates are the most abundant animals in aquatic ecosystems, and their production (biomass and abundance) may represent secondary production of We calculated the overall multitrophic richness based on multidi- the system (Scheffer, 2004). Detailed descriptions of the sampling versity index (Allan et al., 2014) using the average rarefied species of each ecosystem variables are provided in the Methods section of richness across nine trophic groups. Prior to the analyses, species Supplementary Material. richness values were standardized for each trophic group by scaling them to the maximum observed value. We did not sum species richness values to calculate overall multitrophic richness because this 2.6 | Ecosystem multifunctionality would have given higher weighting to species-rich trophic groups. For example, small-sized filter feeder group had richness values of To obtain a quantitative multifunctionality index, we used the fol- 332 species while vertebrate omnivores had only 10 species. The lowing three independent multifunctionality approaches: (a) the overall multitrophic richness varies between 0 and 1 and unify the averaging multifunctionality index, (b) the multiple threshold mul- richness of all nine trophic groups under a single multitrophic meas- tifunctionality index and (c) turnover approach (Byrnes et al., 2014). ure (i.e. whole-ecosystem multitrophic richness). Simultaneously Pairwise correlations between functions vary randomly from nega- to overall multitrophic richness, we calculated multitrophic rich- tive (−0.25) to positive (0.62) with an average correlation close to ness by removing each trophic group at each step. This comparison 0 (Figure S4). To calculate the averaging multifunctionality index, estimates how each trophic group contributes to the effect of the we first standardized each of the 14 individual ecosystem variables multitrophic richness on multifunctionality. This approach also indi- (phosphorous, nitrogen, dissolved organic matter, percentage of cates likely keystone trophic groups (i.e. trophic groups that contrib- dissolved oxygen, chlorophyll a of phytoplankton, chlorophyll a of ute disproportionately to the effect of the multitrophic richness on periphyton, piscivorous biomass, omnivorous biomass, detritivorous multifunctionality), similar to keystone species in metacommunity biomass, herbivorous biomass, rotifera abundance, microcrusta- theory (Mouquet et al., 2013). cean abundance, Amoeba Testacea abundance and Chironomidae abundance) using the maximum transformation as follows: f(x) = xi/ 2.5 | Ecosystem functions max(x), in which each transformed variable had a minimum value of 0 and a maximum of 1. These standardized ecosystem variables were then averaged to obtain a multifunctionality index. This Simultaneously to trophic groups sampling, we measured 14 vari- index is widely used in the multifunctionality literature (Lefcheck ables that represent ecosystem functions, which may be influenced et al., 2015; Maestre et al., 2012). For multiple thresholds, we calcu- by aquatic organisms: (a) nutrient cycling [total Phosphorous (P) lated the number of functions for five thresholds (10%, 25%, 50%, and nitrogen (N) available in water]; (b) decomposition (dissolved 75% and 90%; Byrnes et al., 2014). Because the choice of any thresh- organic matter and percentage of dissolved oxygen available); (c) old is likely to be arbitrary, the use of multiple thresholds is recom- primary production (chlorophyll a of phytoplankton and chlorophyll mended to better understand the role of biodiversity in affecting a of periphyton); and (d) secondary production (piscivorous bio- multifunctionality. Our thresholds chose are similar to those used in mass, omnivorous biomass, detritivorous biomass, herbivorous bio- previous (Soliveres et al., 2016; Wang et al., 2019); thus, they provide mass, macroinvertebrate abundance, rotifera abundance, Amoeba high applicability to a wide range of different ecosystems. Testacea abundance and microcrustacean abundance). All vari- To evaluate whether different sets of species within each trophic ables were also measured 183 times, as well as the trophic groups. group affected different individual ecosystem variables, we used a These measured variables in our study may be used as a proxy of turnover approach. This metric provides a straightforward measure 946 | Functional Ecology MOI et al. that identifies what species have positive effects for each individual trophic group plays a significant role on the effect of multitrophic variable and tests whether the species within a trophic group dif- richness on ecosystem multifunctionality. Model fits, marginal R 2 fers among functions they affect (Byrnes et al., 2014). For this, we and AICc values for overall and simplified multitrophic richness applied a stepwise AIC model selection to fit linear models to each measures on average multifunctionality, and each of the five multi- function to obtain the minimally adequate set of species within each functional thresholds, are reported in Tables S6 and S7. trophic group affecting each function (Hector & Bagchi, 2007). We Furthermore, we evaluated the relationship of each trophic group then assess the relationship between the number of functions and (rarefied richness) with the different multifunctionality indexes, also the cumulative fraction of the species within trophic group that had using LMEs. We log-transformed the rarefied richness values to a positive effect on those functions. The average multifunctional- achieve normality in the residuals. The models of each trophic group ity, multiple threshold multifunctionality and turnover approaches were compared with the full model (overall multitrophic richness) were calculated using the ‘multifunc’ package (Byrnes et al., 2014). using stepwise selection by AICc, also presenting the marginal R2. Our We calculated the correlation strength among ecosystem variables data met the assumptions of normality and variance homogeneity for to down-weight highly correlated functions. the LMEs, as indicated by graphical analyses of residuals. To assess the relationship of each single trophic group with each of the ecosys- 2.7 | Statistical analysis tem variables, we conducted a Spearman matrix correlation between the richness of single trophic group and 14 individual ecosystem variables. Model fits, marginal R 2 and AICc values for each single trophic We investigated the relationships of the overall multitrophic richness group; multitrophic richness on average multifunctionality; and each (all trophic groups) and simplified multitrophic richness (excluding of five multifunctional thresholds are reported in Tables S8 and S9. each trophic group at each step) with the different multifunction- Piecewise structural equation modelling (pSEM; Lefcheck, 2016) ality indexes (averaging multifunctionality and five multifunctional was employed to evaluate the causal relationships among richness thresholds) using linear mixed-effect models (LMEs) available in the of the trophic groups and their direct and indirect effects on mul- package nlme (Pinheiro et al., 2013). As our data had a long time se- tifunctionality. Because the effects of the trophic groups on multi- ries (61 sampling periods for each lake over time), samplings closer functionality were similar in all three independent multifunctionality in time are likely to be more similar than those that are farther apart. approaches, we decide to use only the averaging multifunctionality To correct this potential temporal bias, we addressed any potential index in the pSEM. In contrast to the single regression model, pSEM temporal autocorrelation by modelling a correlation among sam- offers the ability to evaluate multiple pathways by which each trophic pling periods using a continuous autoregressive 1 autocorrelation group could influence multifunctionality. To carry out the pSEM, we structure from the CAR1 function available in the nlme package. We specified an a priori model of causal relationships among all trophic then compared the LMEs incorporating the CAR1 autocorrelation levels based on our ecological knowledge and literature appointments structure with those without autocorrelation using AICc; and found (Figure S1), see Carpenter et al. (2001), Scheffer, 2004, Scherber that models without incorporating CAR1 autocorrelation fitted bet- et al. (2010), Atkinson et al. (2017), and Moi, Alves, et al. (2020). The ter than models controlling temporal autocorrelation (ΔAIC > 2; relationships between trophic groups were specified with base on Tables S1–S4). This shows that our data had no time autocorrelation what commonly occurs in natural systems (Figure S1). For instance, issues, and then we decided to use the models without incorporating primary producers provide shelter and habitats for invertebrate and the temporal autocorrelation. In addition, to account for potential vertebrate trophic groups (Moi, Alves, et al., 2020; Scheffer, 2004). non-independence of seasons, and to account for the effect of lakes Vertebrate omnivores, detritivores and herbivores offer food sources identity, we nested the seasons within year in each lake as a ran- for large vertebrate predators (Scheffer, 2004). Likewise, inverte- dom structure. Thus, we allowed the intercept to vary in each season brates, such as small omnivores–invertivores and chironomids, pro- within year independently for each lake. Importantly, our study is vide food sources for vertebrate omnivores, such as small omnivorous conducted in a floodplain system, where during floods the environ- fish (Carpenter et al., 2001; Scheffer, 2004). The interactions be- ments, such as lakes and rivers are connected. The connection of tween multiple trophic groups play a key role in influencing the func- the lakes with rivers causes the exchange of species, especially in tioning of lakes, such as productivity and nutrient cycling (Atkinson the lakes (Agostinho et al., 2004; Moi, Alves, et al., 2020; Thomaz et al., 2017; Carpenter & Kitchell, 1993; Carpenter et al., 1987). et al., 2004). Thus, the temporal samples realized in our study may be We tested multicollinearity for each trophic group by calculating considered independent, since species and water of lakes changes the variance inflation factor (VIF). VIF > 3 indicates possible collin- between sample periods. We compared the complete model (overall earity, which was not observed in our model. As we had many trophic multitrophic richness) with those simplified (excluding richness of groups, we reduced the number of trophic groups in the pSEM using each trophic group at each step) using stepwise selection by AICc. A Akaike information criteria corrected for small sample size (AICc), model with lower AICc is the model with lower predictive error, that which is implemented in the piecewiseSEM package (Lefcheck, 2016). is, the best performing model. We expected that the models without This model selection resulted in more straightforward and more ro- any trophic group had higher AICc and lower slope and marginal R 2 bust models to test the interactions between trophic groups and how than the complete model, which would indicate that the removed they affect ecosystem multifunctionality. The full model (including all Functional Ecology MOI et al. | 947 trophic groups) was compared with the reduced model (without some and multifunctional thresholds) compared to multitrophic richness trophic groups) using AICc (AICfullmodel – AICreducedmodel; Table S5). We measures without inclusion of trophic groups (i.e. selected by AICc; used lack of effect on multifunctionality as a criterion to remove any Tables S6 and S7). Moreover, the estimated effect size of the multi- trophic group from the model. We considered ΔAICc > 2 units to trophic richness on multifunctionality decreased when each trophic distinguish the complete from the reduced models. Notably, the full group was removed (Figure S5). In addition, changes in overall mul- and reduced final models differed in at least ΔAICc = 72.75 units titrophic richness predicted multifunctionality better than changes (Table S5). The pSEM was fitted using a linear mixed-effect model in in the richness of any single group, according to explained variances the package, with seasons nested within years within (i.e. marginal R 2; Figure 1a). The overall multitrophic richness had a each lake as random factor. We present the standardized coefficient stronger positive effect on average multifunctionality (Figure 1b), for each path and estimated the indirect effects by coefficient multi- and on all multifunctional thresholds (Figure 1c) than the richness plication. Path significance was obtained by maximum likelihood and of any single trophic group (Figure 2; Tables S8 and S9). The overall model fit was evaluated using Shipley's test of d-separation through multitrophic richness was the best performing model (by AICc model Fisher's C statistic (p > 0.05 indicates adequate model). Our analyses selection) to predict the variation in multifunctionality in both av- were conducted using R language. eraging and multithreshold approaches (Tables S8 and S9) and was piecewiseSEM positively correlated with most individual functions (Figure S7). 3 | R E S U LT S We found that increasing the richness of single trophic group significantly increased average multifunctionality except for phagotrophic protists and small-sized filter feeders (Figure 2; Table S8). The overall multitrophic richness (including all trophic groups) was a Increased richness of single trophic group also increased multifunc- better predictor of the ecosystem multifunctionality (both average tionality in the multiple threshold approach (Figure S6; Table S9). (a) (b) (c) F I G U R E 1 Multitrophic-multifunctionality relationship, (a) variance explained by R2 marginal of the models for all models (for each individual trophic group and for multitrophic richness including all trophic groups) and for averaging multifunctionality and multithreshold, (b) averaging multifunctionality and (c) number of functions above multiple thresholds (10%, 25%, 50%, 75%, 90%). Results of models are provided in Tables S8 and S9 948 | Functional Ecology MOI et al. F I G U R E 2 Relationships between multifunctionality (average) and aquatic biodiversity. The linear relationship between multifunctionality and richness of each individual trophic group. Statistical analysis was performed using linear mixed-effects models. Results are provided in Table S8. Only significant fitted lines are displayed on the graphs The richness of single groups was positively correlated with multi- groups, particularly, overlaps lower than 10% for eight of the ple different individual variables, related to provisioning (second- nine trophic groups was observed (Table S10). The richness of ary productivity) and supporting functions (primary productivity, apex predators, omnivorous, detritivorous and herbivorous ver- decomposition and nutrient cycling; Figure S7). Importantly, the tebrates; omnivorous invertebrates; small omnivores–carnivores; effect of aquatic biodiversity on individual and multiple functions and primary producers was consistently correlated positively with varied among trophic groups. In general, the richness of trophic several individual functions, including nutrient cycling as well as groups comprised of large-sized species (e.g. vertebrates) was primary and secondary production (Figure S7). The richness of more important to ecosystem functioning than the richness of tro- these trophic groups strongly increased the average multifunc- phic groups containing small-sized species (e.g. small-sized filter tionality and most multifunctional thresholds (10%, 25%, 50%, feeders and phagotrophic protists). The turnover analysis revealed 75% and 90%; Figure 2; Figure S6). Small-sized filter feeders and that when all 14 ecosystem functions were considered, significant phagotrophic protists were weakly correlated with few individual positive effect was found for roughly 77% to 83% of the species functions (Figure S7) and weakly affected (or not affected) the av- among vertebrate predators, invertebrate omnivores, primary erage multifunctionality and multifunctional thresholds (Figure 2; producers, vertebrate omnivores, vertebrate detritivores and ver- Figure S6). tebrate herbivores for the set of functions (Figure 3). Conversely, Structural equation modelling fit the data well (Fisher's C = 7.83, only 52% to 64% of the species of small omnivores–carnivores, AICc = 131.41, p = 0.450) and revealed positive direct and indirect small-sized filter feeders and phagotrophic protists significantly interactions among trophic groups in the food chain, which resulted promoted the set of 14 ecosystem functions (Figure 3). We found in an explanation of 50% of the multifunctionality variance (Figure 4; a low functional overlap between species within different trophic Table S11). For instance, primary producers increased the richness of MOI et al. Functional Ecology | 949 F I G U R E 3 Proportion of species within each trophic group that contributes positively to functioning when an increasing number of ecosystem functions are analysed simultaneously. The number of species varies within each trophic group (Vertebrate predators = 46, Vertebrate omnivores = 66, Vertebrate detritivores = 19, Vertebrate herbivores = 14, Primary producers = 79, Invertebrate omnivores = 54, Small omnivores–carnivores = 175, Small-sized filter feeders = 332 and Phagotrophic protists = 137 species), and in total, 14 functions were considered. Each plot shows the average proportion, with quartiles, the 1.5 times interquartile range as whiskers and outliers. Points have been jittered along the x-axis so that combinations with overlapping values can be seen F I G U R E 4 Structural equation model of causal relationships within and among multiple trophic groups and their cascading effects on multifunctionality. Solid black arrows represent significant positive paths (p ≤ 0.05 piecewise SEM). Arrows for non-significant paths (p ≥ 0.05) are in light grey. The thickness of the significant paths represents the magnitude of the standardized regression coefficient or effect sizes, given on the arrows. R2s for component models are given in the boxes of variables. Doubleheaded arrows indicate correlations between error terms. Results are provided Table S11 small omnivores–carnivores indirectly favouring the richness of ver- which increased the ecosystem multifunctionality (Figure 4, indi- tebrate omnivores (Figure 4, indirect effect = 0.040). The vertebrate rect effect = 0.052). Likewise, the vertebrate detritivores also indi- omnivore, in turn, increased the richness of vertebrate predators, rectly increased the multifunctionality by increasing the richness of 950 | Functional Ecology MOI et al. vertebrate predators (Figure 4, indirect effect = 0.036). Moreover, the conservation of shallow lakes facing the current global biodiver- primary producers directly increased the richness of vertebrate de- sity crisis: first, it indicates that species loss from any trophic group tritivores and predators (Figure 4). Primary producers also indirectly should lead to loss of functions in such aquatic systems. Also, as low increased multifunctionality by increasing the richness of small om- functional redundancy may imply higher ecosystem vulnerability nivores–carnivores (indirect effect = 0.027) and invertebrate om- (Biggs et al., 2020), our results indicate that shallow lakes can be nivores (indirect effect = 0.077; Figure 4). Finally, according to the very vulnerable ecosystems. Furthermore, the high complementar- pSEM model, the top predatory vertebrates was the trophic group ity of the trophic groups suggests that a trophic group complements with the strongest positive effect on multifunctionality (β = 0.265), the positive effects of other trophic groups in the ecosystem. Thus, followed by primary producers (β = 0.257), invertebrate omnivores preserving a high multitrophic richness is vital for the ecosystems to (β = 0.187) and small omnivores–carnivores (β = 0.172). Omnivorous maintain multiple functions simultaneously. In this sense, the high and detritivorous vertebrates did not directly affect the multifunc- trophic downgrading caused by human actions (Estes et al., 2011) tionality (Figure 4); small-sized filter feeders, phagotrophic protists could have more negative impacts on the aquatic ecosystems than and vertebrate herbivores were removed from the final model by previously thought, specially through the loss or simplification of AICc selection (Table S5). trophic groups. Trophic groups comprised of large-sized species, such top pred- 4 | D I S CU S S I O N ators, omnivorous and detritivorous vertebrates, along with primary producers, were essential for maintaining multifunctionality. These groups had positive effects on average multifunctionality and mul- Our results demonstrated that the increase in the multitrophic rich- tifunctional thresholds, and their effects on multifunctionality in- ness strongly increased multifunctionality. The multitrophic effect creased as more functions were included. This may be explained by on multifunctionality was stronger than the effect of any single the fact that the studied lakes are shallow and small, and in these trophic group, thus supporting our first prediction; this indicates lakes, large-sized species may affect multiple functions through path- that richness across multiple trophic groups is more suitable to pre- ways such as (a) bioturbation (increasing resuspension of organic dict B-EMF than focusing on single groups. Complementing this, matter and nutrient); (b) providing carcasses and faeces (increasing the removal of any trophic group reduced the multitrophic richness decomposition and productivity); (c) translocating nutrient among effect size on multifunctionality, which also agree with our second lake compartments; and (d) indirectly by trophic cascading, preying on expectation; thus, indicating that all trophic groups were important small organisms (zooplankton) and favouring algal growth (Atkinson to sustaining the multifunctionality. Together, these two results evi- et al., 2017; Carpenter et al., 2001; Schmitz et al., 2010). Additionally, dences that preserving multiple trophic groups is fundamental to large-sized vertebrates and primary producers may change the entire sustaining ecosystems functioning (Soliveres et al., 2016), and loss stability of shallow lakes, triggering shifts towards alternative stable of any trophic group could disrupt the multitrophic richness ability states (Moi, Alves, et al., 2020; Mormul et al., 2012). Top predators in maintaining the ecosystem functioning. Our results are similar are particularly important, as they are key components in aquatic to those found in terrestrial ecosystems, such as grasslands (Wang ecosystems and may promote primary and secondary productivity by et al., 2019) and subtropical forests (Schuldt et al., 2018; Sobral direct pathways via increasing nutrient levels or by indirect pathways et al., 2017), as well as marine ecosystems (Lefcheck et al., 2015), via structuring the food chains and controlling interactions between indicating a high degree of generality and consistence between mul- lower trophic groups and consequently their effects on multifunc- titrophic richness and ecosystem multifunctionality across aquatic tionality (Antiqueira et al., 2018; Schmitz et al., 2010). and terrestrial ecosystems. In addition, our results go beyond, show- Although all trophic groups played a crucial role in maintaining ing that the interactions between multiple trophic groups richness ecosystem functioning, large-sized trophic groups were the most im- accounted for a relatively large fraction (50%) of the observed varia- portant. Large predator vertebrate was the single trophic group with tion in multifunctionality of aquatic ecosystems. stronger effect size on average multifunctionality and most multifunc- The strong positive effect of the multiple trophic groups on tional thresholds (Figure S6; Table S8 and S9). Moreover, the removal ecosystem multifunctionality suggests that species within trophic of large predatory vertebrate strongly reduced the effect size of the groups are highly complementary (Barnes et al., 2018), which be- multitrophic richness on multifunctionality (Figure S5). Such results comes more evident as more functions are considered (Lefcheck may have critical implications in the face of current global changes et al., 2015). We found an increase in the proportion of species because the largest predators seem to be the most sensitive trophic within each trophic group promoting ecosystem functioning as group to multiple anthropogenic impacts (Estes et al., 2011; Ripple more functions were considered. Moreover, the low functional et al., 2014). Global warming has been strongly related to reducing overlaps (<10%) of the trophic groups evidence a low redundancy, mean body size of species in aquatic ecosystems, leading to a dom- implying that different functions were performed by different spe- inance of small-sized species and extirpation of large-sized species cies, that is, no single species promoted all functions, but instead, (Daufresne et al., 2009). This reduction in body size of species and multiple species were necessary to sustain the multifunctionality consequent loss of large-sized vertebrates should strongly impair (Barnes et al., 2018). These results have important implications for the functioning these systems. Furthermore, large-sized predatory Functional Ecology MOI et al. | 951 vertebrate in aquatic ecosystems has faced other human threats, predators) mechanisms of trophic diversity acted in concert, maxi- such as the construction of large hydroelectric plants. For instance, mizing the ecosystem functioning in studied lakes. in our study site, several hydroelectric plants have been built in the In conclusion, our study evidenced that multitrophic richness is a last 50 years (Agostinho et al., 2004). These ventures cause the im- key driver of multifunctionality in hyperdiverse tropical shallow lakes. poundment of the rivers, hindering the migration of large vertebrates Our findings demonstrate the importance of considering the multi- (especially those large-sized predators). Since large-sized predators in trophic richness to understand B-EMF across a wide range of ecosys- floodplains need to migrate to reproduce, these species could disap- tems, and suggest that investigations of multifunctionality focused on pear, which can profoundly alter the multitrophic diversity and, conse- single trophic group might underestimate the importance of biodiver- quently, ecosystem functioning (Pelicice et al., 2017). sity for multifunctionality across the whole food web. Our study has Small-sized trophic groups (such as invertebrate omnivores important novel insights and implications for the conservation and and small omnivores–carnivores) also were important to sustain management of shallow lakes. First, we show that the species richness the multifunctionality (although at a lower intensity compared to of multiple trophic groups, such as consumers and primary producers, large-sized trophic groups). The pSEM revealed that these small- are crucial moderators in driving ecosystem multifunctionality, and sized trophic groups were important to sustain the richness of loss of any trophic group could negatively impact the ecosystem func- large-sized trophic groups, indirectly increasing the multifunc- tioning. Second, we provide the first evidence that bottom-up and tionality. This result demonstrates that small-sized trophic groups top-down mechanisms can act simultaneously in increasing multiple indirectly play a key role in maintaining ecosystem multifunction- functions in shallow lakes. Third, the large-sized trophic groups seem ality by fuelling large-sized trophic groups. Small-sized trophic to exert a stronger direct effect on multifunctionality, and small-sized groups are intermediate energy carriers; thus, they play a key role trophic groups are important to fuel these large-sized groups, thus in- in transferring energy of primary producers to large vertebrates. directly influencing the ecosystem multifunctionality. Therefore, the These groups are fundamental to energy and matter flow across loss of any group may trigger cascading effects, which might disrupt the food web, and indirectly cause impact on the ecosystem mul- food webs of whole ecosystem, with consequent negative effects on tifunctionality (Zhao et al., 2019). Our results are supported by the multifunctionality of lakes that sustain services to humanity such previous studies (Schuldt et al., 2018; Zhao et al., 2019), which as fish biomass production. In summary, our study evidences that is demonstrated the importance of evaluating direct and indirect vital preserve a high diversity of multiple trophic groups, which must effects among multiple trophic groups to understand how bio- include primary producers to top predators, because these groups are diversity affects ecosystem functioning (Duffy et al., 2007). complementary in sustaining ecosystem multifunctionality. Importantly, human actions have led to trophic degradation (i.e. species loss across multiple trophic groups) across marine, ter- AC K N OW L E D G E M E N T S restrial and freshwater ecosystems (Estes et al., 2011). Thus, the The authors thank Sidinei Magela Thomaz for valuable comments loss of any trophic group, even the apparent less important one, on the first version of the manuscript. This study was funded by could result in irreversible effects on ecosystem functioning, such the Brazilian Long-Term Ecological Research Program (PELD/CNPq). as the breakdown of energy transfer across the food web and re- D.A.M. received a scholarship from the Brazilian National Council ducing the multitrophic richness ability to maintain the ecosystem for Scientific and Technological Development (CNPq). P.A.P.A. multifunctionality. received a postdoc scholarship from the Fundacão de Amparo à Primary producers were positively associated with most individ- Pesquisa do Estado de São Paulo (FAPESP; Proc. No. 2017/26243-8). ual trophic groups (except herbivores) and had a direct strong posi- G.Q.R. acknowledges financial support for research provided by tive effect on ecosystem multifunctionality. The primary producers the São Paulo Research Foundation (FAPESP: grants 2017/09052-4 considered in our study are aquatic macrophytes, which increase en- and 2018/12225-0) and by the Royal Society, Newton Advanced vironmental heterogeneity, providing space that allow species coex- Fellowship (grant no. NAF/R2/180791). F.T.d.M. received financial istence, thus increasing biodiversity (Carpenter et al., 2001). Usually, support from the ANII National System of Researchers (SNI) and enhancing macrophyte richness leads to increasing host species PEDECIBA-Geociencias. G.Q.R., R.P.M. and C.C.B. received CNPq- (Carpenter & Lodge, 1986). We found that the increase in macro- Brazil productivity research grants. The authors declare that there phyte richness was positively related to increases in the richness of are no conflict of interests. multiple heterotrophic groups, indicating that these primary producers increased niche availability in the studied ecosystems (Moi, AU T H O R S ' C O N T R I B U T I O N S Alves, et al., 2020), and consequently the multitrophic diversity. The D.A.M. designed the study, performed the research, analysed the positive effect of primary producer on small-sized trophic groups data and wrote the first version of the manuscript. G.Q.R., P.A.P.A., cascaded to large-sized trophic groups, which indirectly increased R.P.M., F.T.d.M. and C.C.B. wrote the manuscript. multifunctionality (Figure 4). In addition, our finding demonstrated that both primary producers and top predator's richness strongly DATA AVA I L A B I L I T Y S TAT E M E N T increased the ecosystem multifunctionality. This highlights that Data are available on Dryad Digital Repository https://doi.org/ combined bottom-up (via primary producers) and top-down (via top 10.5061/dryad.fttdz0 8rc (Moi, Romero, et al., 2020). 952 | Functional Ecology MOI et al. ORCID Dieison A. Moi https://orcid.org/0000-0002-7946-9260 https://orcid.org/0000-0003-3736-4759 Gustavo Q. Romero Pablo A. P. Antiqueira Roger P. Mormul Franco Teixeira de Mello Claudia C. Bonecker https://orcid.org/0000-0002-1118-8796 https://orcid.org/0000-0001-9020-4784 https://orcid.org/0000-0003-4904-6985 https://orcid.org/0000-0003-4338-9012 REFERENCES Agostinho, A. A., Gomes, L. C., Veríssimo, S., & Okada, E. K. (2004). Food regime, dam regulation and fish in the Upper Paraná River: Effects on assemblage attributes, reproduction and recruitment. Reviews in Fish Biology and Fisheries, 14, 11–19. Allan, E., Bossdorf, O., Dormann, C. F., Prati, D., Gossner, M. 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Funct Ecol. 2021;35:942–954. https://doi. org/10.1111/1365-2435.13758
