Introduction

Studies conducted in the past decades have revealed the browning of surface waters followed by significant increases in organic carbon concentration (Roulet and Moore 2006). These trends in water colour result from changes in land management (Meyer-Jacob et al. 2015) coupled with climate changes (Larsen et al. 2011), which produce increasing amounts of dissolved organic matter of terrestrial origin being delivered to aquatic ecosystems. This type of organic matter is a mixture of aromatic and aliphatic hydrocarbon structures, with various attached functional groups, that create heterogeneous aggregates weakly biodegradable in water (Thurman 1985). The current rate of browning of freshwater ecosystems is without precedent due to climatic perturbations in temperature and precipitation that have implications for terrestrial productivity and control the hydrological transport of organic matter within lake catchments (Weyhenmeyer et al. 2016). Observed lake browning processes are expected to have global effects, such as changes in the carbon cycle (Battin et al. 2009), deterioration of drinking water quality caused by elevated concentrations of organic matter (Dadi et al. 2015), and increasing costs of water treatment because of the formation of toxic byproducts during disinfection (Galapate et al. 2001). The other effects of lake browning stem from the influence of organic matter concentrations on the abiotic conditions of aquatic ecosystems. The easily-observed effects result from light attenuation caused by humic compounds. Thus, the occurrence of elevated concentrations of organic matter hampers photosynthetic processes (Karlsson et al. 2009) and consequently reduces lakes’ primary production (Jones 1992) as well as triggers the expansion of trophically related communities utilizing organic matter as the primary source of energy (Jansson et al. 2007). Moreover, elevated concentrations of organic matter stimulate oxygen depletion (Brothers et al. 2014) as a result of higher respiration rates, the promotion of photo-oxidation, and the suppression of released photosynthetic oxygen (Lindell et al. 2000). Oxygen depletion in freshwaters typically has biogeochemical consequences, including internal phosphorus loading (Mortimer 1942) and reduced organic matter mineralization (Fortino et al. 2014), as well as significant ecological consequences, including decreases in the biological richness and diversity of an ecosystem (Townsend et al. 1992). Other aspects of the influence of tDOM on aquatic ecosystems stem from the biochemical characteristics of its components. Some papers report that the presence of humic substances exert chemical and oxidative stress on aquatic animals (Meems et al. 2004; Timofeyev et al. 2004), they may also induce hormone-like effects manifested in the dysfunctional growth and reproduction of invertebrates and vertebrates (Höss et al. 2002; Lutz et al. 2005).

Although numerous laboratory experiments have demonstrated that the addition of dissolved organic matter alters the physiological traits and life histories of invertebrates (Minguez et al. 2020; Nova et al. 2019), the influence of browning on the abundance dynamics of invertebrate populations and communities is rarely observed in field studies. Observation of freshwater ecosystems suggests that browning might have a neutral or slightly positive influence on the population sizes of common species of invertebrates (Kelly et al. 2016), whereas its influence on ecosystem service delivery is negligible (Williamson et al. 1999). To explain the above discrepancies, the individual-, population-, and community-response of freshwater invertebrates to concentration of tDOM were investigated. Although such multidimensional research into species’ response might lead to better comprehension of the consequences of the relationships between organisms and their environments (i.e. Adamczuk and Mieczan 2015), they are actually quite rare. For the experiment, two taxa of common cladocerans were selected: Chydorus sphaericus (O. F. Müller, 1776) emend. Frey 1980 and Acroperus harpae (Baird, 1834). These species are representative of the Chydoridae family and occur across a broad geographical range. A. harpae is widely distributed in Europe and C. sphaericus represents a complex of cryptic species (Belayeva and Taylor 2009) widely distributed in the Palearctic, some regions of Africa, Australia, and South America (Smirnov 1996). A. harpae and C. sphaericus often co-occur in the regions of their geographical coverage. Acroperus’ distribution is related to pH and potassium concentration (Nevalainen et al. 2011a) and is reported to occur in less productive and well oxygenized lakes (Bos and Cumming 2003). C. sphaericus is able to live and reproduce across a wide pH spectrum and occurs under a wide range of environmental conditions, as it settles in water bodies with different levels of calcification, salination, and nutrients concentration and is able to survive low concentrations of dissolved oxygen (Brucet et al. 2009); thus, it is characterized as eurytopic, pioneering, and highly adaptive (Smirnov 1996). Both species are cyclic parthenogens, able to produce a maximum of two parthenogenetic eggs (Smirnov 1996), and demonstrate environmental sex determination (Nevalainen et al. 2011b). It may be hypothesized that the species display disparate responses to browning due to their different ranges of ecological tolerance, however both species experience costs of mitigating the effect of tDOM at the level of the individual organism. The research question is what are these costs at individual level and how are they manifested at higher levels of organisation (population and community). Answering to the above question may provide a more nuanced view of the response of invertebrates to the browning of freshwater ecosystems.

Materials and methods

Experimental system

tDOM was obtained from extracts of bluegrass, Poa pratensis L. This organic matter from a terrestrial plant species was used to produce a mixture of organic compounds similar to those entering water ecosystems. Bluegrass leaves were ground into coarse fragments, saturated with distilled water, and constantly aerated for 3 months at 25 °C. To detach the dissolved organic matter, i.e. the mixture of organic compounds smaller than 0.45 µm in diameter (Thurman 1985), leachate was extracted sequentially through a series of sieves and filters, finally passing through a 0.45 µm membrane. The extract was stored in sterile bottles at 4 °C and re-filtered through a 0.45 µm membrane before use. Concentration of tDOM was expressed as the concentration of organic carbon (Leenheer and Croué 2003) determined in the filtered extract using a PASTEL UV spectrophotometer (Secomam, France). The experiments were based on treatments with six different concentrations of tDOM in the experiments: 0, 5, 10, 15, 25, and 50 mg/L. An appropriate volume of leaf extract was added to COMBO medium (Kilham et al. 1998) in order to obtain the above concentrations of tDOM. As addition of organic matter may cause oxygen depletion, oxygen concentration in each treatment was checked to design whether experimental treatments need to be aerated throughout the experiment. Eventually, the treatments were not aerated, since they displayed similar oxygen concentration suggesting that amount of oxygen involved in mineralization of organic matter was passively replenished from the air. Both A. harpae and C. sphaericus were cultured at 22 °C, in 16 h light and 8 h dark photoperiod, fed copiously with instant algae, and transferred to fresh medium every 4 days.

Experimental environment

Prior to adding the animals, physical and chemical analysis of the treatment was conducted to determine how the addition of tDOM impacts the medium. The concentration of total suspended solids (TSS), total dissolved solids (TDS) and surfactants (SUR), chemical oxygen demand (COD), biochemical oxygen demand (BOD), pH, and electric conductivity (EC) were measured 24 h after tDOM was added. All measurements were made using a PASTEL UV spectrophotometer (Secomam, France) and a YSI 556 MPS multiparametric probe. Linear regression was used to explore the relationships between the above parameters and tDOM concentration.

Response of individuals to tDOM concentrations

The aim of this experiment was to assess how the addition of tDOM affects invertebrates’ individual traits. Thus, 30 mothers with asexual eggs were randomly separated from the laboratory culture. Each mother was transferred to a 50 mL jar. After she had released the offspring, the mother was removed from the jar and one neonate was selected for the experiment, transferred to new media and reared separately (one individual per 50 mL jar). The rest of the neonates (< 12 h) from each brood were anaesthetised and measured to assess individual somatic growth. Individuals were observed daily to assess survival and production of broods. These observations were continued until their natural death. The outcomes of the observations were used to construct an integral projection model (IPM) describing alterations in individual traits under different tDOM concentrations. This model was selected because it is widely employed to forecast population dynamics and is applied to populations in which an individual trait depends on a continuous variable (Easterling et al. 2000). The general equation of the model:

$$ n_{t \, + \, 1} \left( {x^{\prime}} \right) = \int_{{_{\Omega } }}^{{}} {K\left( {x^{\prime},x} \right) \, n_{t} \left( x \right) \, dx^{\prime}} $$

describes the changes in population size distribution, n, from time t to t + 1 using a population projection kernel, K (x, xʹ), integrated over the set of all possible sizes, Ω. The IPM was constructed to display transitions from a daily census (t). The four kernels describing all daily transitions were survivability, S (x), growth, G (xʹ│x), reproduction, R (x), and an offspring size distribution r (xʹ│x). Survivability (S) described the probability that an individual with size x survived from time t to time t + 1. Survivability data were binomial with possible outcomes of either death (0) or survival (1), thus they were modelled using a logistic regression. Daily growth (G) described the probability that an individual grew from size x at time t to size xʹ at time t + 1. Reproduction (R) was defined as the product of the probability of carrying eggs (p), and brood size (ƒ), by individuals of size x at time t. The probability of carrying eggs (p) was estimated by dichotomizing the presence of eggs in the brood pouch into zeroes (0 eggs) and ones (> 0 eggs) and was modelled with a logistic regression. Brood size (ƒ), the total number of new-born individuals reproduced by individuals of size x at time t, was obtained during the experiment and was fitted using linear regression. Offspring size distribution r (xʹ│x) described the probability of obtaining offspring with size xʹ at day t + 1, given the maternal size at day t, and was fitted using linear regression. The procedures for each kernel are explained in detail in Supplemental Material Appendix S1. The resulting final equation of the model was:

$$ n_{{t + 1}} \left( {x^{\prime}} \right) = \int_{\Omega }^{{}} {\left[ {S{\text{ }}\left( x \right) + G{\text{ }}\left( {x^{\prime}x} \right) + p{\text{ }}\left( x \right) + f\left( x \right) + ~r\left( {x^{\prime}x} \right)} \right]\left( {n_{t} \left( x \right)dx^{\prime}} \right.} $$

All models were tested for each individual trait, including the additive effects of tDOM concentration (M). The effects of age structure (A) on individual traits were also explored (Fig. S1, S2 in Supplemental Material Appendix S2). However, size and age showed a significant correlation (C. sphaericus: r2x-A = 0.706; A. harpae: r2x-A = 0.722), thus A in interaction with x was included in the model. As a result, a composite IPM was created, whereby the kernels (survival probability S, growth G, probability of carrying eggs p, brood size ƒ, and offspring size distribution r) were functions of body size (x), size and age (x:A), and tDOM (M), after it had been confirmed that M was only weakly correlated with x and A (C. sphaericus: r2x-M = 0.012, r2A-M = 0.017; A. harpae: r2x-M = 0.008, r2A-M = 0.022). Finally, interactions between M and x, and M, x, and A were also included. The most complex model was thus:

$$ y \, \left( {x, \, xA, \, M} \right) = \beta_{0} + \beta_{1} x \, + \beta_{2} M \, + \beta_{3} x:A \, + \beta_{4} M:x + \beta_{5} M:x:A \, + \varepsilon_{res} $$

and 96 models nested within this model were fitted. To reduce the risk of finding spurious effects and improve prediction accuracy, the averaged model was built on the basis of the best models (Raftery et al. 1997; Yang 2007). The Akaike information criterion (AIC) was applied to select the best models, i.e. models fulfilling the criterion of delta AIC ≤ 2 (Table S1–S5 in Supplemental Material Appendix S3). A conservative zero method was used to build the averaged model; in this method, zero was assigned to the parameters not present in the model (Grueber et al. 2011). It was possible to use the conservative zero method since the differences between the log and the logit link function were negligible in the IPM model.

Response at population and community level to tDOM concentrations

Experimental populations were established to find the population response, expressed by changes in population size and reproduction, to the range of tDOM concentrations. The initial population was 20 individuals of each species. The populations were cultured together in 700 mL glass jars containing COMBO media enriched with tDOM, with the addition of 1-hexadecanol to prevent animals becoming trapped in the water surface film (Desmarais 1997). Each experimental treatment was established in three replicates. The experiment lasted for 100 days and population measurements were made every 4 days. Prior to measurements being taken, each experimental culture was bubbled for several seconds to prevent patchy distribution of animals in the jars. Then, 100 ml of the experimental culture was passed through a 30 µm sieve to separate the animals. Separated animals were anaesthetized with carbonated water to prevent egg rejection and then preserved with Lugol’s solution. The size of each population (expressed as the number of individuals per treatment) was estimated using a stereomicroscope. The number of asexual and sexual females in reproduction was counted to obtain the reproduction (Fasex, Fsex) value of each population. Fasex and Fsex were estimated as the percentage quotient of the number of either embryo-bearing parthenogenetic individuals or ephippial females and the total number of individuals. Differences in population sizes, Fasex and Fsex across treatments were determined by repeated measures analysis of variance with tDOM concentration as within-subject factor and time of the experiment (25 measures between 4 and 100th day of the experiment) as level of repeated measures.

To establish the community response to tDOM concentrations, the functional stability of the community was calculated for each treatment. Functional stability was expressed in two ways: resilience and temporal stability. Resilience (R) was measured as the slope of the log-transformed difference between the community reared in the treatment with additional tDOM (5–50 mg/L) and the community reared in the environment without tDOM. Temporal stability (D) was calculated as the inverse of the standard deviation of the residuals around the resilience (Dakos et al. 2012). Resilience and temporal stability were calculated from community level data. Rs and Ds of C. sphaericus and A. harpae populations were also calculated to find the relationships between each population and community across tDOM concentrations. These relationships were produced using Spearman’s rank correlation. Differences in Rs and Ds of communities and populations were determined by ANOVA, followed by pairwise comparisons using Tukey’s HSD. Rs and Ds were correlated with each other (Spearman’s rank correlation) to estimate differences in the dimensionalities of functional stability.

Results

Experimental environment

The addition of tDOM not only increased the concentration of organic carbon but also significantly altered the chemical characteristics of experimental treatments. Significant increases in TDS, TSS, SUR, COD, BOD, and EC were observed associated with tDOM concentration, and the linear regression showed strong correlations between the values of the above variables and amounts of tDOM (r2 > 0.90, p < 0.001 for each correlation, Fig. 1a–e). Addition of tDOM did not affect pH (r2 = 0.06, p > 0.1), and this variable was quite stable in all the experimental environments (Fig. 1f).

Fig. 1
figure 1

Linear relationships between tDOM concentration and total suspended solids (TSS), total dissolved solids (TDS), and surfactant (SUR) concentrations, chemical and biochemical oxygen demands (COD, BOD), electric conductivity (EC), and pH in the experimental treatments

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Response of individuals to tDOM concentrations

The best models and the averaged model showed that survival probability of C. sphaericus significantly decreased along with size (β1) (Table 1, Fig. S3–S6 and Table S6 in Supplemental Material Appendix S4). Although the best three models (16–42% of the weight) showed that higher concentrations of tDOM promoted the survivability of larger and older animals (Table S6 in Supplemental Material Appendix S4), the averaged model showed that tDOM neither considered as a separate variable (β2) nor in interaction with size and age (β3–β5) had significant effect on survival of C. sphaericus (Table 1). Daily survival of A. harpae decreased with the size and age of this species (β3), whereas tDOM had a positive influence on its survivability (β2, Table 1, Table S6 in Supplemental Material Appendix S4).

Table 1 Averaged coefficients and standard errors for each of the individual traits, daily survival probability S, daily growth G, probability of carrying eggs p, brood-size f, and neonate body size at birth r as single and interacted effect of size distribution x, age structure A and tDOM concentration M
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Daily growth was on average 1.23∙10–5 mm for C. sphaericus and 1.94∙10–5 mm for A. harpae and decreased with body size and age in both species (Fig. S7–S10 in Supplemental Material Appendix S4). The best models, describing the daily growth of C. sphaericus included body size (β1) and interaction between size and age (β3) as well as tDOM concentration in interaction with size as well as size and age (β4, β5, Table S7 in Supplemental Material Appendix S4), however the averaged model showed the significance of β1, β3, and β5 (Table 1). Thus, negative correlations between daily growth and body size in C. sphaericus were stronger in the group containing the oldest animals, and this effect was reinforced by tDOM concentration. For A. harpae, both the best models and the averaged model included the effects of body size (β1), as well as size:age interaction (β3), and these variables had a negative influence on daily growth rate (Table 1, Table S7 in Supplemental Material Appendix S4).

The averaged and best models describing the probability of carrying eggs for both C. sphaericus and A. harpae included body size (β1), tDOM concentration (β2), and tDOM:size interactions (β4, Table 1, Table S8 in Supplemental Material Appendix S4). The probability of carrying eggs in C. sphaericus decreased with size (β1), but increased with tDOM concentration (β2), such that larger individuals were less likely to be carrying eggs at higher tDOM concentrations (β4, Table 1, Fig. S11–S12 in Supplemental Material Appendix S4). For A. harpae, the opposite phenomenon was observed; the probability of this species carrying eggs increased with body size (β1) and decreased with tDOM concentration (β2), thus larger individuals living under higher tDOM concentrations displayed a higher probability of carrying eggs than their equivalents living under lower tDOM concentrations (β4, Table 1, Figs. S13–S14 in Supplemental Material Appendix S4).

Both C. sphaericus and A. harpae carried a maximum of two eggs (Figs. S15–S18 in Supplemental Material Appendix S4) and their brood size significantly increased with body size (β1, Table 1). All the best models describing the brood size of C. sphaericus also included the effect of the interaction between tDOM concentration and size (β4), suggesting that the correlation between brood size and body size was enhanced by tDOM (Table S9 in Supplemental Material Appendix S4), and both β1 and β4 were significant in the averaged model describing brood size of C. sphaericus (Table 1). The best models and the averaged model describing brood size of A. harpae indicated a significant influence of body size (β1) and tDOM concentration (β2), suggesting that while larger individuals carried bigger broods, tDOM concentration had a significant negative effect on A. harpae brood size (Table 1, Table S9 in Supplemental Material Appendix S4).

Neonate size was negatively correlated with tDOM concentration for C. sphaericus (β2, Figs. S19–S20 in Supplemental Material Appendix S4) and this negative effect was significantly higher for larger and older maternal individuals (β5, Table 1). The effects of the interaction between tDOM, body size, and age of mothers (β5) was significant in all the best models describing neonate body size for C. sphaericus (Table S10 in Supplemental Material Appendix S4). However, tDOM concentration had no effect on neonate size of A. harpae (Fig. S21–S22 in Supplemental Material Appendix S4). Larger individuals of this species produced bigger neonates (β1), but this trend was reduced in the group of the oldest animals (β3, Table 1). The influence of β1 was significant in both the averaged and best models (Table 1, Table S10 in Supplemental Material Appendix S4).

Response at population and community level to tDOM concentrations

Although populations of C. sphaericus significantly increased throughout experiment, tDOM concentration had no apparent effect on the changes population size of C. sphaericus, since similar increases in population size were observed in all the treatments throughout the experiment (Table 2, Fig. 2a, Fig. S23 in Supplemental Material Appendix S5). A. harpae populations, however, displayed significant differences in size throughout experiment associated with tDOM concentration levels (Table 2). In environments with tDOM concentrations of 0–10 mg/L, a steady increase in population size was observed, under tDOM concentrations of 15–25 mg/L populations displayed a weak increase, whereas with 50 mg/L of tDOM, population density oscillated around the initial number of individuals and, after 44 days of the experiment, a steady decrease in population size was observed (Fig. 2b, Fig. S24 in Supplemental Material Appendix S5).

Table 2 The results of repeated measures ANOVA showing differences in population responses to tDOM concentration throughout the experiment
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Fig. 2
figure 2

Population sizes of C. sphaericus (a) and A. harpae (b) under different tDOM concentrations

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Similar patterns were found in asexual reproduction. Fasex of C. sphaericus ranged between 5.2 and 6.3% and was affected by neither tDOM concentration nor time of the experiment (Table 2). Despite insignificant differences, however, large fluctuations in Fasex were observed in each treatment throughout the experiment, and the ranges of these fluctuations increased with tDOM concentration (Fig. 3a, Fig. S25 in Supplemental Material Appendix S5). Fasex in A. harpae, conversely, differed significantly among treatments (Table 2). With tDOM concentrations of 0–10 mg/L, Fasex ranged between 5.7% and 6.4%, but with tDOM concentrations of 15 mg/L, Fasex values achieved 4.7–6% during the first 16 days of the experiment and then decreased to 1.3–2.3% during the remainder of the experiment. Low values of Fasex (1.1–1.4%) in A. harpae populations were also observed in treatments with tDOM concentrations of 25 − 50 mg/L (Fig. 3b, Fig. S26 in Supplemental Material Appendix S5).

Fig. 3
figure 3

Asexual reproduction of C. sphaericus (a) and A. harpae (b) under different tDOM concentrations. Reproductive effort is expressed as the percentage quotient of the number of embryo-bearing parthenogenetic females to total number of individuals

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For both species, significant differences in sexual reproduction in relation to concentrations of tDOM were observed (Table 2). In the case of C. sphaericus, Fsex was significantly higher in tDOM concentrations ≥ 15 mg/L (0.6% − 0.8% vs 0.3% − 0.5% at ≤ 10 mg tDOM/L, Fig. 4a, Fig. S27 in Supplemental Material Appendix S5). Fsex of A. harpae ranged between 0.1 and 0.2% in environments with tDOM concentrations of 0 − 5 mg/L, and between 2 and 5% in environments with 10–50 mg/L of tDOM. Moreover, experimental populations differed significantly in Fsex values throughout experiment (Table 2, Fig. 4b, Fig. S28 in Supplemental Material Appendix S5).

Fig. 4
figure 4

Sexual reproduction of C. sphaericus (a) and A. harpae (b) under different tDOM concentrations. Reproductive effort is expressed as percentage quotient of ephippial females to total number of individuals

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The resilience of invertebrate communities (R) ranged on average between R =  − 0.24 and R =  − 0.70, and differences in R values were insignificant at all tDOM concentrations (ANOVA, F4,10 = 0.514, p > 0.01, Fig. 5a). In comparison to the communities, the Rs of species’ populations differed significantly across tDOM concentrations. The resilience of the A. harpae population in the treatment with 5 mg/L of tDOM was significantly higher than the resilience of the other A. harpae populations (ANOVA, F4,10 = 15,160, p < 0.01; Tukey HSD, p < 0.01, Fig. 5a). Populations of C. sphaericus, on the other hand, demonstrated significantly higher Rs in treatments with tDOM concentration ≥ 25 mg/L (ANOVA, F4,10 = 11.248, p < 0.05; Tukey HSD, p < 0.05, Fig. 5a). The resilience of the invertebrate communities showed significant correlation with C. sphaericus populations (Spearman’s rank correlation, r = 0.43, padj < 0.05, Fig. S29 in Supplemental Material Appendix S6) and did not correlate significantly with the resilience of A. harpae populations (Spearman’s rank correlation, r = 0.21, padj > 0.05, Fig. S30 in Supplemental Material Appendix S6). The temporal stability (D) of the invertebrate communities significantly correlated with the temporal stabilities of C. sphaericus populations (Spearman’s rank correlation, r = 0.62, padj < 0.05, Fig. S31 in Supplemental Material Appendix S6). Both invertebrate communities and C. sphaericus populations showed high temporal stability across tDOM concentrations, since mean values of D ranged between1.56 and 1.92 for communities and between 1.36 and 1.70 for C. sphaericus, and differences between values of Ds were insignificant across treatments (ANOVA, F4,10 = 0.589, p > 0.05 for the communities; F4,10 = 0.553, p > 0.05 for C. sphaericus, Fig. 5b). The mean values of temporal stability of A. harpae populations, however, showed a significant decrease along with increasing tDOM concentrations (D = 0.40–1.79; ANOVA, F4,10 = 17.508, p < 0.01; Tukey HSD, p < 0.05, Fig. 5b) and was uncorrelated with the D of the communities (Spearman’s rank correlation, r = 0.07, padj > 0.05, Fig. S32 in Supplemental Material Appendix S6). The relationships between the two aspects of functional stability produced an insignificant correlation for the C. sphaericus (Spearman’s rank correlation, r = 0.03, padj < 0.05, Fig. S33 in Supplemental Material Appendix S6) and a significant positive correlation for A. harpae (Spearman’s rank correlation, r = 0.77, padj > 0.05, Fig. S34 in Supplemental Material Appendix S6) and invertebrate communities (Spearman’s rank correlation, r = 0.65, padj > 0.05, Fig. S35 in Supplemental Material Appendix S6).

Fig. 5
figure 5

The functional stability, expressed as resilience (a) and temporal stability (b), of C. sphaericus and A. harpae populations as well as invertebrate communities across tDOM concentrations

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Discussion

Increased amount of organic matter in browning freshwaters correlates with a decrease in the biomass of edible phytoplankton and oxygen depletion (Carpenter et al. 1998), and through decreased concentration of oxygen and biomass of phytoplankton it influences species structure and abundance of invertebrates (Brothers et al. 2014). In this experiment, invertebrates were systematically fed on algae, and oxygen availability was passively replenished from the air. Thus, the any alterations in observed traits were the result of above mechanism of tDOM influence on experimental populations. Terrestrial organic matter is a mixture of organic compounds (Vitale and Di Guardio 2020) and the addition of tDOM significantly altered the chemical characteristics of treatments in this experiment. Values of TSS, TDS, SUR, and EC that increased with tDOM concentration showed that tDOM was a rich source of colloidal and ionic salts. The responses of C. sphaericus and A. harpae individuals to the above alterations were reflected in the daily changes in their individual traits. These rates combined in the IPM model revealed a set of differences between the two species. In this model, body size was used as a continuous state variable in discrete time. Cladocerans grow in length throughout their life (Lynch 1980), thus the body size of individuals is often used to estimate their relative age. Daily observations of C. sphaericus and A. harpae from their birth to death allowed to estimate their actual age in this experiment. The application of size and size-age relationships as independent variables in the IPM allowed to discriminate between changes in individual traits that resulted from the size and age of individuals from those that were caused by tDOM concentration. The results of the IPM model indicated that individual traits altered along with body size of C. sphaericus, whereas alterations in individual traits of A. harpae were connected mainly with age. This observation may suggest that C. sphaericus are more able to respond flexibly to environmental change, since interplays between body size and individual traits (especially these resulting from fecundity) are important indicators of plasticity of life histories (Stearns 1989) that directly translate to population size. Moreover, the implementation of combinations of tDOM, size, and age (β4, β5) as equivalent variables revealed lifetime changes in response to tDOM. C. sphaericus showed stronger responses to tDOM than A. harpae in terms of size (β4) and age (β5). This may suggest more efficient energy allocation during the lifetime of C. sphaericus in order to reduce tDOM effects. The higher flexibility in response to tDOM demonstrated by C. sphaericus was also seen in growth rate. Body growth of both species was negatively correlated with body size and age (β1, β3), which means that the growth dynamic was slower in larger and older individuals, however in the case of C. sphaericus this negative correlation was significantly stronger at higher concentration of tDOM. Many studies have revealed trade-offs between growth, survivability, and reproduction (Roff 2002). These trade-offs among individual traits were clearer for C. sphaericus. Decreased growth in larger and older individuals associated with tDOM concentration (β5) correlated with decreased probability of carrying eggs and increased brood size. Trade-offs among individual traits of A. harpae displayed the opposite pattern. Increasing concentrations of tDOM (β2) correlated with increased survival probability and decreased probability of carrying eggs and smaller brood size. Life histories of cladocerans often show trade-off between brood size and neonate size; this trade-off is most often observed in food-limited environments where food availability correlates positively with brood size and negatively with neonate size (Guinnee et al. 2004). In this experiment, the neonate size of C. sphaericus was negatively affected by tDOM concentration (β2) and showed negative correlation with the probability of carrying eggs. This trade-off is in accordance with some observations that females produce smaller offspring when their reproductive value in the next time step is large relative to current reproductive prospects (Kindsvater and Otto 2014). tDOM concentration had no influence on neonate size of A. harpae, which was affected exclusively by the size and age of individuals (β1, β3), similarly to the survival probability of C. sphaericus. These findings might suggest some individual traits are neutral, developing regardless of the impact of stressful factors. It is also apparent that neither species performed maximally across all the individual traits in response to increased tDOM concentrations.

The individual traits of both species were reflected in their population traits. Although species may respond differently to allochthonous inputs (Adamczuk et al. 2019) there are some reports that moderate concentrations of organic carbon may correlate positively with the abundance of planktonic animals (e.g. Kelly et al. 2016). A similar phenomenon was observed in this experiment, since both the species developed well under tDOM concentration ≤ 10 mg/L, and the populations of C. sphaericus also increased in size under higher concentrations of tDOM. For C. sphaericus the consequences of increased tDOM observed at population level were higher fluctuations in Fasex and increased Fsex, but these changes appeared to have no influence on population size. Populations of A. harpae responded more strongly to tDOM. Populations of this species in treatments with tDOM concentrations of ≥ 15 mg/L showed decreases in abundance as well as a decrease in asexual reproduction in favour of sexual reproduction. Both species, similarly to other cladocerans, are facultative sexual organisms (Gilbert and Williamson 1983; Nevalainen et al. 2011b) although asexual reproduction is dominant and enables the species to expand a population more rapidly. Sexual reproduction provides a temporary escape from unfavourable conditions through diapause, and an increase in population fitness due to the greater genetic variability of the following generation (Hurst and Peck 1996). However, populations that trigger sexual reproduction bear costs comprising the production of males, melanisation of the ephippia, and dormancy of ephippial eggs. Thus, although a strategy of sexual reproduction allows cladocerans to survive harsh conditions, it may reduce the growth rate of the population. The decreases observed in the A. harpae population size could be an effect of reduced asexual reproduction in favour of sexual reproduction that did not provide an equivalent number of offspring to sustain the population. Trade-offs among the individual traits of C. sphaericus that resulted in population growth are in the accordance of assumptions of life-history hypothesis (McNamara and Houston 1996), whereas the strategy adopted by A. harpae, observed at individual level, was clear only in the context of the population dynamics of this species. At population level, A. harpae constrained asexual reproduction and invested in sexually reproducing individuals to escape from unfavourable conditions temporarily through diapausing eggs. Contemporaneously, at individual level, organisms prolonged survivability at the cost of reproduction. Diminishing reproduction to save energy for the physiological processes involved in neutralizing the effects of physiological stress is a strategy that has been observed in organisms exposed to detrimental factors (Tuljapurkar 1990). Thus, a decline in asexual reproduction might be considered to be the direct effect of a harmful influence, but investment in sexual reproduction at population level, as well as low parthenogenetic reproduction and high survivability at individual level, may, in combination, be a direct mitigating choice for A. harpae to overcome adverse conditions.

Alterations in individual traits and promotion of either asexual or sexual reproduction had consequences reflected in functional stability, expressed as the resilience and temporal stability of populations. Both aspects of functional stability decreased along with increased concentrations of tDOM in the A. harpae populations, whereas in the C. sphaericus populations the contrary trend was observed. Interestingly, the resilience and temporal stability of C. sphaericus were highest in those environments with tDOM concentrations ≥ 25 mg/L, which may suggest that C. sphaericus prefers higher tDOM concentrations. However, in regard to the observed alterations in individuals’ traits, the high resilience of C. sphaericus reared in treatments with high concentration of tDOM results from overcompensation stemming from high offspring production at the cost of individual body growth. In the case of A. harpae populations, the decreased resilience and temporal stability associated with increased tDOM concentrations were the result of a mitigating strategy consisting of investing in resting eggs at the cost of the development of the existing population. The impact of environmental variables is usually considered in regard to the response of entire aquatic communities. Such an approach is as appropriate as communities show more stable development over time despite changes in species composition (Cottingham et al. 2001). In such communities the effect of species loss does not decrease the potential to drive ecosystem service delivery (Winfree et al. 2015). This experiment showed that even in a two-species community a decrease in abundance of one component could be compensated by the abundance of the other; thus, any decrease in abundance would not be observed at community level. The resilience (R) and temporal stability (D) of the community showed a strong positive correlation with the R and D of the C. sphaericus population, whereas the A. harpae population did not have a significant influence on the functional stability of the entire community. This is in agreement with other studies showing that dominant species make the major contribution to community stability (Dangles and Malmqvist 2004). However, although community stability was shaped by C. sphaericus, the relationships between the two aspects of the community functional stability were similar to these observed for A. harpae population, showing a significant positive correlation between resilience and temporal stability; such a correlation suggests there is a one-dimensional response to the environment. The resilience and temporal stability of the C. sphaericus population were, on the other hand, uncorrelated, suggesting non-dimensionality in functional stability. Moreover, both the populations showed significant differences in functional stability, whereas the functional stability of the community did not differ significantly among tDOM concentrations. This is evidence that invertebrate communities might be able to maintain their function in ecosystem service delivery in browning freshwaters due to species compensation. Thus, the effects of browning might be missed at community level.

Conclusions

Many freshwater systems have experienced a gradual, long-term increase in dissolved organic matter concentrations in recent decades, and the consequences of this phenomenon for the diversity and functioning of invertebrates remain poorly understood and hotly debated. The present study shows that observation of the reactions of invertebrates at several levels of organisation gives a more complete assessment of the risk of browning freshwater ecosystems. Moreover, the results of this study suggest that conclusions drawn from research on populations or communities omitting individual life histories might be misinterpreted. Thus, the linking of alterations at individual level with population and community responses to concentrations of dissolved organic matter is the ultimate target to adequately predict the response of aquatic communities to the browning of freshwater.