Shifts in assembly rules and loss of zooplankton functional diversity across hypereutrophic fishponds

Shifts in assembly rules and loss of zooplankton functional diversity across hypereutrophic fishponds | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology Letters This is a preprint and has not been peer reviewed. Data may be preliminary. 24 September 2025 V1 Latest version Share on Shifts in assembly rules and loss of zooplankton functional diversity across hypereutrophic fishponds Authors : Cihelio Amorim 0000-0002-7171-7450 [email protected] and Martin Kainz Authors Info & Affiliations https://doi.org/10.22541/au.175872988.86764576/v1 Published Ecology Letters Version of record Peer review timeline 426 views 262 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Freshwater species are facing massive declines, often driven by eutrophication. Identifying which facets of biodiversity are sensitive is crucial, as species loss does not always translate to reduced ecosystem functioning and functional diversity. We examined how assembly rules shape zooplankton functional diversity in hypereutrophic fishponds. Higher eutrophication was hypothesized to cause functional homogenization through reduced functional diversity, habitat filtering, and trait convergence. Higher eutrophication indeed reduced functional diversity metrics, while species richness was kept stable. Functional richness, dispersion, and dissimilarity shifted from limiting similarity, where niche partitioning and competition shape community structure, to random (incidence data) and habitat filtering (biomass) with increasing eutrophication. Functional divergence transitioned from random to habitat filtering, while redundancy increased at higher trophic states. Trait convergence was the dominant process, with the environment selecting species with similar traits. Biodiversity assessments and managers should consider how functional diversity and ecosystem functions respond to anthropogenic and environmental changes. Shifts in assembly rules and loss of zooplankton functional diversity across hypereutrophic fishponds Running title: Biodiversity loss caused by eutrophication Cihelio A. Amorim a,b * & Martin J. Kainz a,b a WasserCluster Lunz – Biologische Station GmbH, Dr. Carl Kupelwieser Promenade 5, 3293, Lunz am See, Austria. b Research Lab for Aquatic Ecosystem Research and Health, University for Continuing Education – Danube University Krems, Krems, Austria Authors’ information: *CAA – ORCID 0000-0002-7171-7450; Email: [email protected] , [email protected] (corresponding author) MJK – ORCID 0000-0002-2388-1504; Email: [email protected] Authors’ contributions CAA contributed to conceptualization, methodology, validation, data curation, visualization, formal analysis, and wrote the first draft of the manuscript. MJK contributed to methodology, resource acquisition, and project administration. CAA and MJK contributed substantially to revisions and approved the submission. Keywords: Assembly rules; Competition; Eutrophication; Functional homogenization; Habitat filtering; Limiting similarity; Pond biodiversity; Stress-dominance hypothesis; Threats to biodiversity; Trait convergence. Data availability The associated data and codes used in this manuscript are openly available for Editors and Reviewers in the figshare repository, which will be made available to the public under the doi 10.6084/m9.figshare.29400842 upon acceptance of the manuscript. Number of words in the abstract: 150 Number of words in the main text: 4953 Number of figures: 5 Number of references: 67 Abstract Freshwater species are facing massive declines, often driven by eutrophication. Identifying which facets of biodiversity are sensitive is crucial, as species loss does not always translate to reduced ecosystem functioning and functional diversity. We examined how assembly rules shape zooplankton functional diversity in hypereutrophic fishponds. Higher eutrophication was hypothesized to cause functional homogenization through reduced functional diversity, habitat filtering, and trait convergence. Higher eutrophication indeed reduced functional diversity metrics, while species richness was kept stable. Functional richness, dispersion, and dissimilarity shifted from limiting similarity, where niche partitioning and competition shape community structure, to random (incidence data) and habitat filtering (biomass) with increasing eutrophication. Functional divergence transitioned from random to habitat filtering, while redundancy increased at higher trophic states. Trait convergence was the dominant process, with the environment selecting species with similar traits. Biodiversity assessments and managers should consider how functional diversity and ecosystem functions respond to anthropogenic and environmental changes. Graphical abstract Functional diversity of zooplankton declined sharply with increasing eutrophication in hypereutrophic fishponds, despite stable species richness. Community assembly shifted from competition to habitat filtering, leading to functional homogenization (redundancy) and altered interactions among functional guilds. Ponds with total phosphorus below 300 µg/L maintained higher diversity with early signs of disrupted functional diversity, ecosystem functioning, and stability. Introduction Freshwater biodiversity is facing major threats, often attributed to pollution and eutrophication (Dudgeon 2019; Sayer et al. 2025). This biodiversity decline has severe implications for ecosystem functioning and services (Cardinale et al. 2012). Excessive nutrient enrichment (e.g., nitrogen and phosphorus), intensified by agriculture, growing population, and climate change, is expected to increase further during the 21st century (Sinha et al. 2017). These processes create severe damage to freshwater ecosystems and biota, such as algal blooms, oxygen depletion, reduced water transparency, and loss of biodiversity (Smith & Schindler 2009; Amorim & Moura 2021). Equally important, several ecosystem services are affected, including food security, warning for needed reductions in external loadings of nitrogen and phosphorus (Jeppesen et al. 2025). Ponds and small water bodies harbor high biodiversity and serve as critical sites for biogeochemical cycling and food web interactions (Céréghino et al. 2014). Even highly eutrophic fishponds may support high taxonomic richness (Wezel et al. 2014), which are less impacted by eutrophication (Rosset et al. 2014). Greater biodiversity improves ecosystem functioning and stability (Tilman et al. 2006; Pennekamp et al. 2018). However, the diversity–stability relationship is multifaceted, involving complex interactions between species richness, functional traits, and phylogenetic diversity (Craven et al. 2018). Although most studies have focused on taxonomic diversity (e.g., Cardinale et al. 2012), evidence suggests that phylogenetic (Cadotte et al. 2012) and functional (Van Der Plas 2019) diversity metrics are better predictors of ecosystem functioning. The mechanisms underlying the assembly of biological communities have been a central focus in ecology for many decades (e.g., Hutchinson, 1961). Assembly rules determine the mechanisms that drive species distributions, coexistence, and community composition (Götzenberger et al. 2012). They predict the critical role of traits and environmental filters in shaping communities, as only the species with traits compatible with the habitat’s conditions will survive and thrive (Weiher & Keddy 1995). The main approaches to studying ecological assembly rules include a) species co-occurrence, through competitive exclusion; b) niche limitation, where limited niches restrict coexistence; c) guild proportionality, where competition or environmental filters act differently in the guilds; and d) limiting similarity, where traits become dissimilar to avoid competitive exclusion (Götzenberger et al. 2012). Trait divergence, a tool to detect limiting similarity, predicts that coexisting species present different traits to better exploit available niches; while trait convergence, applied to identify habitat filtering, predicts that species are functionally redundant as the restricted number of niches selects species with a specific set of traits (Grime 2006). Assembly rules have largely been developed and implemented for plant and bird assemblages (Götzenberger et al. 2012), while attempts to study the assembly rules of plankton communities using trait-based approaches remain relatively scarce (e.g., Amorim and Moura, 2022; Borics et al., 2020; Klais et al., 2017), with even fewer efforts focusing on zooplankton (e.g., Vogt et al. 2013). Besides being key consumers and conveyors of dietary energy within the planktonic food web, zooplankton possess diverse functional attributes and ecological strategies (Litchman et al. 2013). Despite their fundamental role in trait-based ecology, there have been contrasting results on how eutrophication influences zooplankton assembly rules. For example, research has shown a decrease in the functional diversity (FD) of zooplankton caused by eutrophication (e.g., Moody & Wilkinson 2019; Fernández-Aláez et al. 2025), while FD in Canadian lakes correlated positively with lake productivity (Vogt et al. 2013). These results point to the relevance of exploring zooplankton assembly rules in the face of the alarming rates of extinction caused by eutrophication (Sayer et al. 2025). Productive terrestrial ecosystems are known to promote trait divergence, while nutrient-limited environments are stressful for the biotic communities and lead to trait convergence (”stress-dominance hypothesis”) (Coyle et al. 2014) (Figure 1a). Phytoplankton communities may exhibit trait convergence and divergence simultaneously, and increased productivity (e.g., eutrophication) drives trait divergence, supporting the stress-dominance hypothesis (Borics et al. 2020). Despite this, most studies presume that productive environments benefit communities due to higher nutrient and food availability, often overlooking the detrimental effects of extreme eutrophication on aquatic systems (Jeppesen et al. 2025). In this study, we investigated how pond eutrophication influences FD and assembly rules of zooplankton. We answered the following questions: (a) how does eutrophication affect FD and functional guilds; and (b) which assembly rule (e.g., habitat filtering or limiting similarity) governs zooplankton communities under hypereutrophic conditions? We hypothesized that higher eutrophication (i.e., harsher environments) leads to functional homogenization through trait convergence, with habitat filtering consequently becoming the dominant assembly process (Figure 1b). To the best of our knowledge, this is the first study that documents non-random assembly processes in zooplankton assemblages. We have advanced the research on community assembly rules by addressing classic gaps that have persisted in ecological studies (Weiher & Keddy 1995; Shinohara et al. 2023). We have described and explained assembly rules with a focus on how functional traits and species diversity control the patterns along environmental gradients such as eutrophication. Figure 1 | Conceptual diagram showing the predictions of the ”stress-dominance hypothesis” (a) and the hypothesis tested herein (b). The stress-dominance hypothesis posits an increased role of trait convergence (TC) and habitat filtering (HF) in harsh, less productive environments, alongside a reduction in trait divergence (TD) and limiting similarity (LS) (a). Extreme productivity levels (here, hypereutrophic environments) may also be stressful for aquatic communities (e.g., zooplankton), resulting in greater trait convergence and habitat filtering, while decreasing trait divergence and limiting similarity (b). Materials and methods Study sites The study was conducted in nine shallow fishponds (maximum depth from March to September 2024. The surroundings of the ponds consist primarily of forests, agricultural lands, and urban areas (Figure S1). The ponds are primarily stocked with Common Carp ( Cyprinus carpio ) and occasionally supplemented with lake whitefish ( Coregonus sp.) and tench ( Tinca tinca ). The fish are harvested in autumn by draining the ponds, with restocking occurring in late winter or early spring. In 2024, the ponds were similarly stocked with 3- to 4-year-old carp at a density of 350–450 carp ha -1 ; therefore, predation effects on zooplankton were similar across all ponds. The fish primarily feed on pond zooplankton, supplemented with fish feed. Sampling and analysis Pond sampling was carried out monthly from March (after ice melt) to September 2024 (before pond drainage) to assess basic physicochemical parameters, and zooplankton diversity and biomass, covering the entire zooplankton growing season. Water temperature, pH, and electrical conductivity were measured in situ with a multiparameter probe (OTT Hydrolab HL4, Berlin, Germany), and water transparency was estimated using a Secchi disk. Water samples were collected with a Schindler trap (5 L). Total phosphorus (TP) and soluble reactive phosphorus (SRP) were determined following Hansen and Koroleff (1999). Nitrate (NO 3 ), nitrite (NO 2 ), and ammonia (NH 4 ) were analyzed on a continuous-flow analyzer (Alliance Instruments GmbH, Flowsys EC, Salzburg, Austria), and dissolved organic carbon (DOC) on an elemental analyzer (Thermo Fischer Scientific, Flash 2000 – HT Plus, Waltham, USA). Dissolved inorganic nitrogen (DIN) was calculated as the sum of NO 3 , NO 2 , and NH 4 . Qualitative samples were collected from March to September ( n =63) while quantitative samples were collected from July to September ( n =27). The entire qualitative sample was checked for the composition and species richness of rotifers, cladocerans, and copepods, identified to species level. Quantitative integrated zooplankton samples (20 L) were collected with a Schindler trap, pooled from different depths, filtered through a plankton net (55 µm mesh size), and immediately preserved with Ethanol (96%). At least 300 individuals of the most abundant and 50 of the less abundant species were counted (modified from Mack et al., 2012) in at least three 2.5-mL aliquots under an inverted microscope. Zooplankton density (ind. L -1 ) was converted to biomass (µg L -1 ) using length-dry weight regressions (e.g., Dumont et al. 1975; Ejsmont-Karabin 1998). Species richness was calculated as the total number of species per sample in the incidence matrix, and Simpson’s diversity was calculated in the R package ”vegan” using the biomass matrix. Four functional traits were estimated for each species based on their morphology or using data from the literature (e.g., Barnett et al. 2007; Obertegger & Flaim 2015): size classes (morphological; ordinal), feeding type (behavioral and physiological; categorical), trophic group (behavioral and physiological; categorical), and habitat (behavioral; categorical). These traits are translated into resource acquisition, growth, reproduction, and survival ecosystem functions (Martini et al. 2021). Functional diversity, community-weighted means, and null models Functional trait, incidence, and biomass matrices of pond zooplankton communities were used to calculate FD indices. Biomass was transformed using the Hellinger transformation, and the trait matrices were converted into a Gower’s dissimilarity matrix (Podani 1999). Functional richness (FRic), evenness (FEve), divergence (FDiv), dispersion (FDis), and dissimilarity (RaoQ: Rao’s quadratic entropy) were calculated in the R package ”FD” (Laliberte & Legendre 2010). Functional redundancy was computed by dividing the trait dissimilarity (RaoQ) by Simpson’s index (D) (FRed=1–RaoQ/D) (Ricotta et al. 2016). We calculated FRic, FDis, and RaoQ using the incidence data because those metrics depend solely on the convex hull volume in trait space (Laliberté & Legendre 2010). Community-weighted means (CWM) were calculated as the relative abundance of all species possessing a specific trait using the biomass matrix. The CWM was determined for size classes (<200 µm, 200–600 µm, and rotifers (Rotifera), stationary suspension (Calanoida), tactile-raptorial (Cyclopoida, Harpacticoida, and Leptodoridae), D- (Daphniidae), B- (Bosminidae), C- (Chydoridae), and S- (Sididae) filtration types), trophic groups (herbivorous, omnivorous, carnivorous, and detritivorous), and habitat (littoral and pelagic). Trait convergence and divergence, the assembly rules habitat filtering, and limiting similarity were estimated by comparing observed values of CWM and FD indices with random expectations through null models (Gotelli 2000). For that, we created 1000 randomly assembled communities through the randomization of species abundances in all communities, keeping species frequencies and total abundances constant, while allowing for changes in species richness, using the ” c0 ” (for the incidence matrix) and ” c0_samp ” (for the biomass matrix) algorithms in the ”vegan” R package (Gotelli 2000). Standardized effect sizes (SES) were calculated by dividing the difference between observed and random mean values by the standard deviation of null models (de Bello 2012). For CWM, positive SES values represent trait divergence and negative values represent trait convergence (de Bello 2012). For FD, positive SES values suggest limiting similarity, and negative SES values indicate habitat filtering (Mouchet et al. 2010). Given the strong linear dependence of SES on observed FD (de Bello 2012), shifts in SES values may be interpreted as mirroring the patterns of observed FD metrics. Data analysis All statistical analyses were conducted using statistical software R 4.5.1, with the significance level set at p <0.05. The associated datasets and R codes used in this study are available at https://figshare.com/s/607e6f95ccb69d66a5db (Amorim & Kainz 2025). A Principal Component Analysis (PCA) was applied to summarize environmental variables among fishponds, followed by a PERMANOVA to test for multivariate differences across trophic states (”vegan” and ”pairwiseAdonis” packages). To account for non-linear patterns, generalized additive mixed models (GAMM, package ”mgcv”) (Wood 2017), were employed to predict the impacts of eutrophication (log-transformed TP) on the response variables. Pond location and sampling time (month) were treated as random factors to account for possible spatial and temporal autocorrelation. Fish predation was not a contributing random factor, as all the ponds had similar stocks. Appropriate families and link functions were chosen depending on the distribution of the response variables (QQ plots and histogram of residuals) and Akaike Information Criterion (AIC) values. To represent different levels of eutrophication, the plots show three trophic state divisions: eutrophic (40–100 µg L -1 ), hypereutrophic (100–300 µg L -1 ), and highly hypereutrophic (>300 µg L -1 ) levels (adapted from Nürnberg 1996 and Meyer et al. 2025). This splitting is intended to highlight variation in the response variables along the eutrophication gradient and not between the levels. One-sample t-tests or Wilcoxon signed-rank tests confirmed whether SES values were different from zero, for the entire dataset or trophic states, depending on the normality of the data, tested with Shapiro tests. Two piecewise structural equation models (pSEM, package ”piecewiseSEM”) (Lefcheck 2016) were fitted using generalized linear models to test for direct and indirect impacts of eutrophication (log-transformed TP), functional traits (CWM), and taxonomic diversity (log-transformed species richness and Simpson’s index) on FD metrics (FRic, FDiv, FEve, FRed, and the latent variable FD, calculated by averaging FDis and RaoQ due to their strong correlation: r=0.97). The first model used the incidence matrix and species richness of relevant functional traits, while the second used the biomass matrix and CWM of the traits. The goodness-of-fit was evaluated through Fisher’s C statistic and its p -value. The AIC was adjusted for the low sample size using the C Information Criterion (AIC C ). Correlation among explanatory variables was assessed using Spearman tests. Robust bootstraps were employed to define uncertainty in path coefficients and to reduce Type I error due to model choice and sample size. The data were resampled with replacement 999 times, and the pSEM model was reestimated for each bootstrap. Paths were considered truly significant if their 95% bootstrap CIs did not include zero (Thulin 2024). A Monte Carlo power analysis estimated the adequacy of sample size for the pSEM model using the ”mvrnorm” function from the ”MASS” package. 999 datasets were simulated with preserved empirical means, variances, and correlations. The full pSEM was refitted, and statistical power for each path was estimated using the proportion of simulations in which each path’s effect was significant (Thulin 2024). Results Environmental conditions and zooplankton community responses to eutrophication All fishponds were classified as hypereutrophic (except Asang pond), with average total phosphorus (TP) concentrations exceeding 100 µg L -1 . Großer Harabruck and Pilz fishponds were the most hypereutrophic. The ponds were acidic to neutral and presented low values of electrical conductivity and SRP. Temperature, TP, DOC, and electrical conductivity increased from March to August, while DIN and transparency showed an opposite trend. Aside from TP, no other variable showed consistent differences among the ponds (Figure S2). Multivariate differences in physicochemical parameters were observed across the three trophic state levels (PERMANOVA, F=9.63, p <0.001, Figure S3). A total of 59 zooplankton species were identified, distributed in rotifers (30 spp.), cladocerans (19 spp.), calanoid copepods (2 spp.), cyclopoid copepods (7 spp.), and harpacticoid copepods (1 spp.). The calanoid copepod Acanthodiaptomus denticornis (Wierzejski 1887) was dominant in Asang fishpond (relative abundance >50%); the cladoceran Daphnia galeata Sars, 1863 in Asang and Großer Harabruck fishponds; copepod nauplii and the cladoceran Daphnia curvirostris Eylmann, 1887 in Pilz pond; the cyclopoid copepod Acanthocyclops americanus (Marsh, 1892) in Gebharts, Großer Harabruck, Schandachen, and Winkelauer fishponds; and the cyclopoid copepod Mesocyclops leuckarti (Claus, 1857) was dominant in Haslawer fishpond. Species richness was not influenced by total phosphorus but showed significant differences across months (random effects). Simpson’s diversity index was significantly lower at highly hypereutrophic conditions. Lastly, total biomass did not respond to the eutrophication gradient, but it was influenced by pond identity (random effects) (Figure 2; Table S2). Figure 2 | Eutrophication Simpson’s diversity despite stable species richness and biomass. Effects of eutrophication (log-transformed TP) on species richness (a), Simpson’s diversity (b), and total biomass (c) of zooplankton in the studied fishponds. Models were fitted using generalized additive mixed models (GAMM), accounting for spatial and temporal autocorrelation by adding pond location and sampling time (month) as random factors. Solid red lines represent the significant effects of TP. Vertical dashed lines separate the TP gradient into eutrophic (40–100 µg L -1 ), hypereutrophic (100–300 µg L -1 ), and highly hypereutrophic (>300 µg L -1 ) levels. ns non-significant, * p <0.05, ** p <0.01, *** p <0.001. Functional diversity and assembly rules: shifts from limiting similarity to habitat filtering FRic, FDis, RaoQ (from both incidence and biomass matrices), FEve, and FDiv significantly decreased with eutrophication. FRic and FEve were further influenced by ponds (random effects). FRed was high (>0.7), but did not significantly respond to the eutrophication gradient (Figure S4; Table S1). SES values comparing observed and expected values under null models for the incidence data revealed major shifts from limiting similarity at eutrophic conditions to random assembly patterns for FRic, FDis, and RaoQ at hypereutrophic and highly hypereutrophic states, with significant declines along the eutrophication gradient. Biomass-based SES for FRic declined with eutrophication but remained positive, approaching random assembly at higher eutrophication levels. FDiv patterns were generally driven by habitat filtering processes (negative SES), mainly at hypereutrophic levels, while SES for FDis and RaoQ shifted from random to habitat filtering at highly hypereutrophic states, facing strong negative effects of TP. SES for FEve was governed by random processes but declined with TP; meanwhile, SES for FRed increased with eutrophication, reaching values higher than expected under null models at highly hypereutrophic ponds (Figure 3; Tables S2-S3). Figure 3 | Sharp declines in functional diversity driven by eutrophication. Changes in standardized effect sizes (SES) from the incidence data: functional richness (FRic, a), dispersion (FDis, b), and trait dissimilarity (RaoQ, c); and from the biomass data: functional richness (FRic, d), dispersion (FDis, e), trait dissimilarity (RaoQ, f), evenness (FEve, g), divergence (FDiv, h), and redundancy (FRed, i) along the eutrophication gradient (log-transformed total phosphorus, TP). Values below zero represent habitat filtering, and values above zero indicate limiting similarity. Models were fitted using generalized additive mixed models (GAMM), accounting for spatial and temporal autocorrelation by adding pond location and sampling time (month) as random factors. Solid red lines represent the significant effects of TP. Vertical dashed lines separate the TP gradient into eutrophic (40–100 µg L -1 ), hypereutrophic (100–300 µg L -1 ), and highly hypereutrophic (>300 µg L -1 ) levels. ns non-significant, * p Impacts of eutrophication on trait convergence and divergence The zooplankton community in the studied fishponds mainly consisted of organisms larger than 600 µm, with a median relative abundance (CWM) of 82%, followed by medium-sized (200–600 µm, 11%), and small-sized (<200 µm, 5%) organisms. The most common feeding types were tactile raptorial (Cyclopoida, Harpacticoida, and Leptodora , 78%), D-filtration (Daphniidae, 13%), and stationary suspension (Calanoida, 2%). Rotifers with microphagous and raptorial feeding types, along with cladocerans with B-, C-, and S-filtration types, made up less than 2% of the total biomass. Medium-sized organisms (200–600 µm) were positively influenced by eutrophication until hypereutrophic levels, then declined at TP levels above 300 µg L -1 . Suspension feeders and D-filtration feeders were negatively impacted by eutrophication, showing significant decreases in their CWM values as TP increased. Tactile raptorial feeders thrived with eutrophication until TP reached 300 µg L -1 . Regarding trophic groups, herbivores and omnivores had median CWMs of 22% and 56%, respectively, while carnivores and detritivores contributed less than 2%. Eutrophication negatively affected herbivores and had a positive impact on omnivores (Figure S6; Table S1). Most functional traits were generally driven by trait convergence (SES B-, C-, and S-filtration feeders, detritivores, and littoral species), compared to trait divergence (SES >0; e.g., pelagic species). The SES for the size class 200–600 µm significantly increased with eutrophication until hypereutrophic levels, followed by declines. The SES for organisms >600 µm shifted from trait divergence to random at hypereutrophic levels. The SES for stationary suspension, D-filtration feeders, and herbivores significantly decreased with eutrophication, while the SES for tactile-raptorial feeders and omnivores was positively affected by TP (moving from trait convergence to random together with carnivores). Stationary suspension and herbivores shifted from trait divergence in eutrophic ponds, to random in hypereutrophic, and further to trait convergence under highly hypereutrophic conditions. Littoral species moved from random to trait convergence, and pelagic from random to trait divergence, under highly hypereutrophic states (Figure 4; Tables S2-S3). Impacts of eutrophication and traits on zooplankton diversity Complex direct and indirect effects mediated the impacts of eutrophication on FD through interactions with functional traits and taxonomic diversity. The pSEM model based on the incidence data (Fischer’s C=1.299, p =0.522, AIC C =–649.7, Figure 5a) revealed that total phosphorus negatively impacted FRic and FD (FDis + RaoQ). The richness of herbivores, omnivores, and carnivores contributed positively to the overall species richness, which in turn, boosted FD. Herbivore richness had a positive influence on FRic and a negative influence on FD. Both functional diversities were positively correlated with each other. TP increased carnivore richness, which, in turn, positively correlated with omnivore and herbivore richness. The model based on biomass data (Fischer’s C=18.427, p =0.68, AIC C =–462.5, Figure 5b) displayed more complex interactions. TP confirmed its negative influence on FRic, FDiv, and Simpson’s diversity, while omnivore CWM hindered FRic, and herbivores negatively impacted Simpson’s diversity. Species richness and Simpson’s diversity boosted FD, while they had opposite effects on FRed (richness influenced negatively and Simpson’s diversity positively); FRed, in turn, diminished FD, revealing direct and indirect effects of taxonomic diversity. FEve boosted FDiv. Total phosphorus harmed herbivores but favored omnivores, and these two were negatively correlated with each other. Both bootstrap and Monte Carlo tests supported that all but one path estimate had sufficient statistical power for sample size sufficiency and adequate estimation of p -values. The path FEve~species richness in the biomass-based pSEM (estimate=0.42, p =0.032) did not persist after empirical bootstrap testing ( p =0.104) (Figures S6-S9). Figure 4 | Eutrophication causes trait convergence with contrasting effects on opposing traits. Changes in standardized effect sizes (SES) for community-weighted means (CWM) of 17 zooplankton functional trait categories along the eutrophication gradient. Values below zero represent trait convergence driven by habitat filtering, and values above zero indicate trait divergence driven by limiting similarity (competition). Models were fitted using generalized additive mixed models (GAMM), accounting for spatial and temporal autocorrelation by adding pond location and sampling time (month) as random factors. Solid red lines represent the significant effects of TP. Vertical dashed lines separate the TP gradient into eutrophic (40–100 µg L -1 ), hypereutrophic (100–300 µg L -1 ), and highly hypereutrophic (>300 µg L -1 ) levels. ns non-significant, * p Figure 5 | Functional diversity and assembly rules of zooplankton are driven by complex interactions between eutrophication and traits. Piecewise structural equation models (pSEM) identifying direct and indirect effects of eutrophication (log-transformed TP), relevant functional traits (Omni: Omnivores; Herb: Herbivores; Carn: Carnivores), and taxonomic diversity (S: log-transformed species richness; D: Simpson’s diversity index) on functional diversity indices (FD: FDis + RaoQ, latent variable; FRic; FEve; FDiv; FRed). a) pSEM model using incidence data from March to September 2024 (Omni, Herb, and Carn represent the species richness of those traits in each sample); b) pSEM model using biomass data from July to September 2024 (Omni and Herb represent the community weighted means of those traits in each sample). Only significant paths are shown: * p <0.05, ** p Discussion This study used taxonomic and trait-based approaches to evaluate how eutrophication influences zooplankton community assembly in fishponds. We found that extreme eutrophication led to declines in FD and shifts in assembly rules from limiting similarity to habitat filtering, while keeping species richness stable. Trait convergence, recorded for most functional trait categories, suggests strong habitat filtering so that only species with traits adapted for high nutrient availability are retained, reducing overall FD. Alternatively, the divergence shown especially among tactile-raptorial feeders and omnivores can perhaps indicate niche differentiation and competition between those functional guilds. Robust relationships between functional traits, diversity, and TP levels in our fishponds suggest strong links between environmental stressors and zooplankton assembly mechanisms. The observed reduced Simpson’s diversity suggests the dominance of species or functional groups with high biomass production in highly eutrophic environments. The taxonomic and FD of zooplankton was shown to exhibit contrasting responses to eutrophication, with some studies reporting declines (Jeppesen et al. 2000; Moody & Wilkinson 2019) or even increases (Vogt et al. 2013). These variations highlight the importance of considering community responses across different eutrophication levels and scales. Studies in temperate lakes have shown that zooplankton taxonomic diversity declines are often accompanied by increases in biomass, particularly dominated by small cladocerans and cyclopoid copepods under hypereutrophic conditions (Jeppesen et al. 2000). The response of taxonomic diversity to eutrophication varies with scale and diversity indices used. Although all the study ponds were hypereutrophic, sharp declines in Simpson’s diversity occurred at highly hypereutrophic conditions, indicating an imbalance in species’ relative biomasses. Species richness, however, exhibited weak and non-significant variations. Research on large lakes shows a unimodal pattern in species richness, with greater diversity at moderate productivity levels but being impaired under extreme conditions (Dodson et al. 2000). Shallow lakes and ponds are inherently more productive and have variable richness responses based on geographic scale, taxa, and productivity level, while eutrophic and hypereutrophic fishponds show weak species richness declines (Rosset et al. 2014). Kuczyńska-Kippen & Pronin (2018) demonstrated that fishponds, with lower zooplankton diversity than fishless ponds, still included unique and rare species, indicating the greatest biodiversity potential of small water bodies even under eutrophication. This supports the exceptional conservation value of small ponds as sources for aquatic biodiversity. Nonetheless, small shifts in taxonomic diversity may hide significant reductions in FD, as species richness alone might not be a proper surrogate for biodiversity changes, requiring the evaluation of multiple facets. Functional diversity is widely regarded as a key predictor of ecosystem stability and functioning (Van Der Plas 2019). In our study, all FD indices (FRic, FEve, FDiv, FDis, and RaoQ) declined with increasing eutrophication, indicating losses of ecosystem functions in hypereutrophic fishponds. Those metrics are known to strongly correlate with assembly rules, with high FD representing limiting similarity, and low diversity indicating habitat filtering (Mouchet et al. 2010). FRed remained consistently high across all eutrophication levels, but SES for FRed consistently increased under highly hypereutrophic conditions. This pattern is due to the exclusion of traits sensitive to higher eutrophication, leaving unexplored niches for tolerant species belonging to a few traits (redundancy) (Mouchet et al. 2010). Similar patterns have been reported in other freshwater communities, including benthic cyanobacteria (Silva et al. 2025), submerged macrophytes (Cheng et al. 2023), and fish (Feng et al. 2023). For zooplankton, FD indices declined with eutrophication in agricultural and urban shallow ponds (Duré et al. 2021; Fernández-Aláez et al. 2025). The consistently high FRed suggests that despite declines in diversity, all ponds maintained high trait redundancies, with the remaining species performing similar ecosystem functions. Functional homogenization, driven by dominant generalist species, prevented the establishment of rare specialists, as observed elsewhere (Lengyel et al. 2023). While some ecosystems, like coral reefs (Mouillot et al. 2014), exhibit high biodiversity, taxonomic diversity does not always correlate with functional heterogeneity. High species richness may buffer biodiversity loss, but functional over-redundancy may threaten some underrepresented functions under disturbances (Mouillot et al. 2014). Nevertheless, redundant groups can be more resilient to species loss, as the remaining species compensate for extinct ones (Biggs et al. 2020). In less eutrophic environments, the lower redundancy of traits makes the ecosystem vulnerable to species loss, with negative consequences for ecosystem stability (Silva et al. 2025). Functional diversity indices are valuable tools for understanding community assembly patterns (Mouchet et al. 2010). Our results reveal a shift from limiting similarity under eutrophic states (e.g., FRic, FDis, RaoQ based on incidence) to random and then to niche filtering (e.g., FDiv, FDis, RaoQ based on biomass) in hypereutrophic and highly hypereutrophic conditions. This implies that eutrophication reduces FD, lowers interspecific competition, and favors groups adapted to high TP levels (e.g., cyclopoid copepods). Notably, only (Vogt et al. 2013) studied the assembly rules of zooplankton to date, showing patterns dominated by randomness (>85% of lakes with TP 600 µm), herbivores, stationary suspension feeders, and D-filtration feeders are seemingly less tolerant at hypereutrophic levels in fishponds, perhaps because of reduced habitat quality or altered food quality. In contrast, medium-sized taxa (200–600 µm), omnivorous feeders, and tactile-raptorial positively respond to increasing total phosphorus at eutrophic and hypereutrophic levels, potentially taking advantage of increased resources, but decline at highly hypereutrophic conditions (>300 µg L -1 TP), suggesting threshold responses. This pattern has been noted elsewhere, with calanoids (stationary suspension herbivores) declining and cyclopoids (tactile-raptorial omnivores) increasing with eutrophication (Sommer & Stibor 2002). The slow growth and reproduction rates of calanoids make their larval stages vulnerable to grazing by cyclopoids (Adrian 1997). Daphniids, with faster metabolic and reproductive rates (Sommer & Stibor 2002) rapidly evolve and grow faster under hypereutrophic conditions (Frisch et al. 2014). Those patterns were accompanied by reduced FD at extreme nutrient enrichment, favoring functionally redundant species and homogenizing zooplankton communities in the studied fishponds. Trait convergence governed most traits, while divergence was limited to large taxa at less eutrophic levels (e.g., organisms >600 µm, stationary suspension feeders, and herbivores). This suggests that severe eutrophication promotes a few traits best suited to the conditions found in such fishponds (trait convergence and habitat filtering). Historically, ecologists argued that closely related species are more susceptible to competition, limiting coexistence and facilitating colonization by distantly related taxa (i.e., trait divergence and limiting similarity) (Grime 2006). Recent findings have indicated that this is rather the exception than the rule, with the need to identify relevant traits capable of distinguishing niche- or competition-driven patterns (Mayfield & Levine 2010). Trait convergence is more common, as also confirmed in our study, influenced by trait similarity across different habitat gradients (Winemiller et al. 2015). Eutrophication influenced FD by complex direct and indirect effects that involve interactions between traits and taxonomic diversity. Those complex interactions are proven to have direct influences on ecosystem functioning and stability (Sperandii et al. 2025). The pSEM revealed that while it boosted the abundance of omnivores, eutrophication negatively impacted herbivores, taxonomic, and FD indices. Trophic groups indirectly influenced FD through subsequent decreases in taxonomic diversity (Simpson’s index) upon dominance. The dominance of omnivores, one of the groups most influenced by eutrophication, led to declines in FRic, since they were among the only ones with traits to tolerate highly hypereutrophic conditions (redundancy). Despite being positively influenced by the taxonomic diversity, FD was hindered by functional redundancy from the loss of unique functions. When the habitat selects species with similar traits, habitat filtering assembly rules result in lower-than-expected FD, thus enhancing the functional redundancy of the remaining traits (de Bello et al. 2009). These results suggest that maintaining larger organisms (e.g., Calanoid copepods and Daphniids) benefits FD through the occupancy of empty niches. Our study integrates null models with pSEM to uncover the ecological mechanisms influencing zooplankton community assembly. Null models allowed us to rigorously test assumptions against random community assembly processes, providing deeper insights into FD patterns and the distinct roles of habitat filtering versus limiting similarity. Given the linear dependency of SES derived from null models on observed FD and species pools (de Bello 2012), our findings must be applied with caution in similar small and anthropogenically disturbed habitats with similar zooplankton compositions, such as shallow lakes and ponds. pSEM enabled us to disentangle the complex relationships between eutrophication, traits, taxonomic and FD, fostering robust causal inferences while reducing the risks of spurious correlations. Although the need for higher seasonal resolution has long been recognized as a critical gap in assembly rule studies (Weiher & Keddy 1995; Shinohara et al. 2023) and explored herein, long-term patterns of community assembly for freshwater communities are still understudied (e.g., Kuczynski & Grenouillet 2018). Among the limitations of our study is its narrow spatial resolution and heterogeneity of the habitats studied. Since our study was confined to eutrophic and hypereutrophic systems with identical results to other anthropogenically impacted ponds (Fernandez-Fournier & Avilés 2018; Kuczyńska-Kippen & Pronin 2018), the lower end of the eutrophication gradient was not explored. Thus, further studies at larger temporal and spatial scales are necessary, as community assembly rules and responses in oligotrophic and mesotrophic environments may differ from our findings, and metacommunities might evolve over time. Conclusions In summary, this research recorded sharp declines of zooplankton FD under severe eutrophication in fishponds. Community assembly rules were largely regulated by trait convergence and habitat filtering, confirming our hypothesis. The findings present a new perspective on the stress-dominance hypothesis originally developed for vegetation and suggest that poor nutrient and stressful conditions impair communities by inducing trait convergence through habitat filtering (Coyle et al. 2014). By applying this hypothesis to freshwater zooplankton, we showed that habitat filtering and trait convergence continued to rule zooplankton assemblages under extreme conditions, though from a reverse point of view, connecting excessive nutrient supply, as contrasted with limitation, to environmental stress. The highly hypereutrophic conditions, the extreme end of the productivity gradient in aquatic ecosystems, strongly harmed some functional groups, but favored other traits (e.g., tactile-raptorial feeders and omnivores). Freshwater ponds are undoubtedly hotspots for biodiversity (Céréghino et al. 2014). Our findings indicate that even extensively managed hypereutrophic fishponds can maintain stable and high zooplankton richness, especially if total phosphorus levels remain below 300 µg L -1 . However, this could cover up the detrimental effects of eutrophication on other aspects of biodiversity, for example, FD. The functional homogenization triggered by eutrophication alerts to the need for multidisciplinary approaches in assessing species response to environmental stressors beyond species inventories. To ensure FD and stability in fishponds, pond managers should implement measures to promote sustainable fish production and conservation of biodiversity, such as reducing nutrient input from adjacent agriculture and fish feeds. Declarations Declaration of conflicts of interest: none. Acknowledgments This research was funded by the Lower Austrian Government (project K3-F-913/004-2023; ’TeichFit’). 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DOI: https://doi.org/10.22541/au.175872988.86764576/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Cihelio A. Amorim, Martin J. 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