Causal networks of phytoplankton diversity and biomass are modulated by environmental context

Quantification of causal networks

We first compared the relative strengths of causal links across systems (Supplementary Fig. S3). Phytoplankton species richness was the major controlling factor for phytoplankton biomass (significant in 16 of 19 sites, Fig. 2a) in these diverse aquatic systems, consistent with experimental studies¹⁷. However, the averaged linkage strength for this effect was not significantly different from that of NO₃ (i.e., BD → EF vs. NO₃ → EF; permutation test P = 0.501), highlighting that nitrogen availability was equally important in affecting phytoplankton biomass in natural systems.

In the opposite direction, phytoplankton biomass was a significant driver of phytoplankton species richness in most ecosystems (15 of 19 sites, Fig. 2b). However, NO₃ more often had a stronger effect, appearing as the most important driver in 11 of 19 sites compared to phytoplankton biomass (4 of 19 sites) (Fig. 2b). Although the difference in effect strength was not significant (permutation test, P = 0.162), these results implicated nitrogen availability as an essential determinant affecting both phytoplankton diversity and biomass. As a sensitivity test, we also examined the effects of Shannon diversity. The results suggest that the importance of nutrients is robust to the use of other diversity indexes (e.g., Shannon diversity in Supplementary Fig. S4), although the causal effects from phytoplankton biomass became relatively more important compared to biomass effects on species richness (Fig. 2b). Based on these findings, we inferred that processes influencing nutrients (e.g., external loadings and internal cycling³⁸) need to be considered when investigating aquatic biodiversity. Changes in those processes (e.g., climatic³⁹ or anthropogenic⁴⁰ driven nutrient changes) may indeed substantially impact phytoplankton biodiversity, and subsequent ecosystem functioning.

The importance of NO₃ uncovered in our analyses might not be a counter-intuitive result, as many systems analyzed in this study were P-rich. For instance, the average phosphate concentration was 57.5 and 41.7 μgP/L for Lake Mendota (Me) and Lake Monona (Mo) (Supplementary Table S1), respectively. In addition, there were also high total phosphorus (TP) concentrations in shallow lake systems, e.g., average TP was 106.1, 112.5, and 126.4 μgP/L in Lake Inba (Ib), Lake Kasumigaura (Ks), and Müggelsee (Mu), respectively. Phosphorus was not always a limiting factor in eutrophic and mesotrophic systems, e.g., Lake Kasumigaura⁴¹ and Lake Geneva (Gv)⁴². In addition, nitrogen was deficient and limited cyanobacteria bloom in Müggelsee (Mu)⁴³. Nonetheless, we cannot exclude the possibility of colimitation⁴⁴ in N and P and the possibility that P availability also depends on N⁴⁵, which warrants further investigation.

Apart from nutrients and temperature, the causal effects of other important drivers on phytoplankton biomass and diversity were also examined, though not in all 19 systems due to data limitation. The causal effects of physical environmental factors, such as irradiance and water column stability, were presented in Supplementary Fig. S5; the results indicated that the quantified causal strengths on average were not as strong as the effects of diversity and nutrients. Moreover, the effects of consumers (e.g., zooplankton), which have been suggested as important drivers affecting species diversity of phytoplankton communities⁴⁶, were also examined. Based on our analysis of zooplankton, the causal effects of herbivorous crustaceans on phytoplankton biomass and diversity were significant in most of the analyzed systems. However, these effects were on average not as strong as the effects of phytoplankton diversity and nutrients, respectively (Supplementary Fig. S6). Nonetheless, these findings were not generalized to all 19 systems due to a lack of complete datasets as shown in Supplementary Table S3, and thus warrant more detailed investigation in future studies.

In addition to individual causal effects, we investigated feedbacks across systems. Pairwise feedbacks (e.g., BD ↔ EF and NO₃ ↔ EF) were common (Fig. 2c). However, the averaged linkage strength was often stronger in one direction when involving BD (Fig. 3). Specifically, the average strength of BD → EF was stronger than for the opposite direction of EF → BD (permutation test P = 0.015); BD → EF was stronger than EF → BD in 14 of the 19 systems (Fig. 3). In addition, biodiversity effects on nutrients (BD → NO₃ and BD → PO₄) were also stronger than their reversed effects (NO₃ → BD and PO₄ → BD) in 12 and 13 systems, respectively. In comparison, the interactions between nutrients and productivity were more symmetrical: nutrient effects on biomass (NO₃ → EF and PO₄ → EF) were stronger than biomass effects on nutrients (EF → NO₃ and EF → PO₄) in only 9 and 8 of 19 systems, respectively. These results supported the previous findings⁸ that biodiversity effects more often operate at short-term scales, which makes effects more observable in our monthly-scale analyses than feedback effects on diversity, which are expected to occur on a more prolonged timescale, e.g., through slowly changing nutrient cycling³¹ or decomposition⁴⁷. Nevertheless, the timescale dependence of causal interactions in ecosystem networks is a topic that needs further study.

Subsequently, we quantified the strengths of pairwise feedbacks as the geometric mean of the linkage strengths in each direction, following a previous study⁹ (see more details in Methods). Among these feedbacks (Fig. 2c and Supplementary Fig. S7), BD ↔ NO₃ had the highest median and average strength (0.78 and 0.68, respectively) across systems. However, strengths of BD ↔ NO₃ were highly variable among systems (large interquartile range in Fig. 2c), and thus were only significant in 11 of 19 systems, compared to BD ↔ EF (15 of 19 systems). These findings reinforced the importance of nutrients as key determinants for aquatic biodiversity and implied that nutrient effects are context-dependent. In other words, BD ↔ NO₃ was less common than BD ↔ EF across systems, despite its stronger average strength. The prevalence of BD ↔ EF indicated a need for more long-term experiments and process-based/theoretical modeling accounting for bidirectional interactions between diversity and biomass¹⁶, because bidirectional interactions and feedbacks may challenge our simple predictions for ecosystem dynamics, based on knowledge of unidirectional interactions³⁰.

Quantification of the causal network also allowed us to analyze triangular feedbacks. Within the conceptual framework of Fig. 1b, there are four kinds of triangular feedbacks involving biodiversity, ecosystem functioning, and either nitrate or phosphate (Type I: BD → EF → NO₃ and BD → EF → PO₄; Type II: EF → BD → NO₃ and EF → BD → PO₄). There was at least one significant triangular feedback in 14 of 19 sites (Fig. 2d). More specifically, NO₃-associated feedbacks (Type I-N and Type II-N) were usually stronger than PO₄-associated feedbacks (Type I-P and Type II-P) (Fig. 2d), although the difference in strength among the four types of feedbacks was not significant (Fig. 2d; Kruskal–Wallis test, P = 0.59). The dominance of NO₃-associated feedbacks in our study was attributed to many of the sites being marine and eutrophic lakes, which are likely to be N-limited due to an imbalance in external loadings⁴⁸ or strong denitrification⁴⁹. Among both NO₃– and PO₄-associated feedbacks, there were no significant differences in strength between Type I and Type II feedbacks (Supplementary Fig. S7), suggesting that biodiversity can directly influence biomass (Type I), as well as through a pathway that involves endogenous nutrient variables (Type II) and eventually feeds back on itself.

Causal networks under environmental contexts

Our empirical analyses revealed state dependency of the causal links and feedbacks among biodiversity, biomass, and environmental factors in natural systems; that is, their strengths were highly dependent on the state of other variables. Based on a cross-system comparison (Methods), strengths of individual links (e.g., BD → EF), pairwise feedbacks (e.g., BD ↔ EF), and triangular feedbacks (e.g., BD → EF → NO₃ → BD) varied systematically, depending on environmental characteristics (Fig. 4 and Supplementary Fig. S8). Ecosystems with higher species diversity (long-term average species richness) and lower average PO₄ concentrations had stronger BD → EF links (Fig. 4a; correlation coefficient r = 0.600 and −0.513; P = 0.007 and 0.025 for species diversity and PO₄, respectively). These results were further confirmed by stepwise regression, indicating that the ecosystems characterized by higher diversity, lower average temperature, and oligotrophic conditions had stronger BD → EF (best-fit regression model: BD → EF strength = 0.663 + 0.171*BD − 0.139*T − 0.096*PO₄; F_{3, 15} = 9.958 and P < 0.001). In contrast, temperature and PO₄ effects on phytoplankton biomass (i.e., T → EF and PO₄ → EF) were negatively associated with long-term average species diversity, but positively associated with average PO₄ (Fig. 4a). Therefore, we inferred that phytoplankton biomass in P-rich systems was more sensitive to warming (due to strong T → EF). This synergistic effect of warming and eutrophication on biomass has been reported in other aquatic ecosystems⁵⁰; in this study, this synergistic effect was weaker when species diversity was higher and BD → EF was stronger (Fig. 4a). Perhaps greater diversity and its effects mitigate adverse impacts of global warming⁹, although warming may also weaken biodiversity effects on ecosystem functioning due to strong interspecific competitions under high temperatures⁵¹.

In the opposite direction, stronger EF → BD was associated with lower temperature and PO₄ concentrations, as well as shallower depths (Fig. 4b; best-fit regression model: EF → BD strength = 0.572 − 0.170*PO₄ − 0.101*Depth − 0.096*T; F_{3, 15} = 9.800 and P < 0.001). In shallower systems, which are better mixed and less vertically heterogeneous, impacts of species competition on diversity may be more influential⁵². In contrast, the effects of the most important driver on diversity, NO₃ → BD, exhibited an opposite response to temperature (Fig. 4b), suggesting that the dominant determinants of aquatic biodiversity varied along a temperature gradient.

Water temperature and phytoplankton biomass (Chla as a proxy) also critically determine strengths of various pairwise feedbacks. Stronger PO₄-mediated feedbacks (BD ↔ PO₄ and EF ↔ PO₄ in Fig. 4c) were usually more associated with cold (r = −0.247 and −0.329, respectively) and less productive systems (r = −0.421 and −0.527, respectively); this contrasted with BD ↔ NO₃, which was more associated with warm (Fig. 4c; r = 0.503) and productive environments (r = 0.571). This finding was consistent with the notion that N is more often a limiting element for phytoplankton growth in warm, tropical/subtropical systems than in cold, temperate systems⁵³. However, EF ↔ PO₄ and BD ↔ EF had no clear relationship with temperature (r = 0.167 and −0.177, respectively) or biomass (r = 0.284 and 0.163, respectively). Thus, we speculated that climate warming will shift aquatic ecosystems towards stronger coupling in biodiversity-NO₃ feedback than biodiversity–biomass feedback.

Our analyses improved understanding of how more complex regulations varied with environmental characteristics. In our study, strengths of triangular feedbacks, regardless of direction, were positively associated with high diversity environments. Of the four triangular feedbacks, both type I-N feedbacks (BD → EF → NO₃) as well as the type II-N feedback (EF → BD → NO₃), were positively associated with high-diversity environments (Fig. 4d, r = 0.531 and 0.561, respectively). Therefore, we inferred that the dynamics of biodiversity, biomass, and endogenous variables were tightly coupled in high-diversity systems; that is, dynamics of one component were more responsive to changes in other parts of the feedback. Our findings contrasted with the prevailing view that ecosystem functioning is insensitive to changes in diversity at high levels of diversity (i.e., the redundancy concept¹⁸), prompting a further investigation to clarify the role of biodiversity in regulating ecosystem dynamics⁹.

Interestingly, triangular feedbacks in different directions (i.e., Type I versus II) had distinct responses to biomass levels. The strength of Type I feedbacks had no statistical association with biomass, implying that diversity effects on biomass (i.e., BD → EF) can propagate to nutrients (i.e., EF → nutrients) and then to diversity itself (i.e., nutrients → BD), irrespective of biomass levels. In contrast, Type II feedbacks had associations with biomass; the latter was positively associated with strengths of Type II-NO₃ feedbacks (r = 0.567; P = 0.011), but negatively associated with strengths of Type II-PO₄ feedbacks (r = −0.430; P = 0.066). This highlighted the importance of considering linkage directionality when studying these regulatory feedbacks under various environmental contexts. For example, even when the same components were considered, two causal interactions in the opposite direction (e.g., BD → EF or EF → BD) responded to environmental factors differently (e.g., Fig. 4a, b, respectively).

Our network-based approach enabled us to describe how responses of triangular feedbacks to environmental changes differed from responses of individual links or pairwise feedbacks. Different regulatory feedbacks were associated with distinctive environmental characteristics (Fig. 4d) and not necessarily similar to that of individual links involved. For instance, EF → BD-driven triangular feedbacks (Type II) were associated with average phytoplankton biomass (Fig. 4d), but phytoplankton biomass was associated with neither the EF → BD nor BD ↔ EF, individually. Indeed, the cross-system patterns of triangular feedbacks were statistically distinguishable from that of pairwise feedbacks (Supplementary Fig. S9), implying unique responses of complex feedbacks to environmental gradients. Therefore, predicting ecosystem responses to environmental changes is challenging, even if responses of individual links or of pairwise feedbacks can be elucidated, because quantification of individual causal links in isolation might fail to recover more complex network modules. As feedbacks and other network modules⁵⁴ are critical for stabilizing/destabilizing ecosystem dynamics^5,55, it is becoming apparent that studying interdependencies among key ecological processes from a holistic network view is needed for predicting ecosystem dynamics under environmental changes.

Sensitivity analysis using composition-converted biomass measure

Our conclusions were robust to the use of alternative biomass measure. Specifically, the main findings (Figs. 2 and 4) based on the analysis of phytoplankton biomass inferred by Chla were qualitatively similar with findings based on composition-converted biomass (Supplementary Figs. S10 and 11). Although the relationship between Chla and true phytoplankton biomass varies with environmental conditions (e.g., light), it remains an effective functional index inferring phytoplankton stock with more emphasis on photosynthesis capacity (i.e., biomass of photosynthetic machines). In contrast, composition-converted biomass, though has similar meaning with overall phytoplankton biomass, contains high uncertainty by assuming species-specific conversion factors, especially when the measurement of individual cells size was lacking. In addition, these conversion factors were determined by various geometrical models, which differ among systems; this is in contrast to the standard chemical approach used in determining Chla, which makes Chla more suitable to reveal cross-system variations in causal strengths along environmental gradients (Fig. 4 and Supplementary Fig. S11e–h). However, Chla integrates all kinds of photoautotrophs that might not be fully included in counting data (e.g., picoplankton). Thus, investigating the diversity effects of more complete phytoplankton groups requires novel techniques (e.g., metagenomics⁵⁶). Nonetheless, it remains an open question about how the presented causal links and feedbacks change when considering various types of functional indices and diversity measures.

Caveats for the reconstruction of causal networks in natural phytoplankton communities

Several issues warrant further studies in aquatic ecosystem networks involving phytoplankton diversity and biomass. Firstly, the number of marine sites was limited, hindering comparisons of marine versus freshwater systems. Furthermore, our analyses based on CCM cannot access the sign of feedbacks (i.e., positive or negative), although it is known that the sign is important in determining the response of feedbacks to external perturbations (e.g., amplified or dampened). Although methods to estimate the sign of interactions were proposed (e.g., S-map^22,57), the robustness of these methods has not been thoroughly examined⁵⁸. Lastly, due to limitations of data availability, our analysis only quantified causal strength across systems at a consensus monthly scale, acknowledging that state-space reconstruction methods (e.g., CCM) are scale-dependent⁵⁹, e.g., one causal driver dominated monthly might not necessarily dominate at other time scales. Therefore, exploring causal feedbacks at other time scales needs further investigations by including more datasets with high temporal resolution and long-duration monitoring.

Final remarks

Our findings bridged two popular and contrasting research directions: whereas many studies consider diversity effects on ecosystem functioning^8,60, other studies aim to identify determinants of species diversity⁶¹, which can be traced to Hutchinson’s seminal question about species coexistence⁶². Our findings highlighted that these two ecological processes are interdependent and embedded in a complex network. Thus, these ecological processes should not be investigated in isolation^10,12,30, but instead be examined in integrated feedbacks, especially for rapid turnover systems (e.g., plankton or microbial communities in aquatic systems) in which feedbacks from biomass to diversity can operate quickly through light shading²⁹ or exploitation on nutrients⁶³. It is noteworthy that our proposed methodological framework can be applied to explore in more detail causal feedbacks or paths if precise mechanistic measures (e.g., nutrient recycling rate rather than nutrient stock) can be monitored over time. For instance, biodiversity was suggested to influence ecosystem functioning via species complementarity or selection effects. Measuring complementarity or selection effects is available for experimental data⁶⁴, but remains a challenging task for observational data. Thus, the incorporation of these detailed mechanistic measures in the causal networks is an important future research topic. More comprehensive surveys are required in future ecological monitoring to improve our understanding of causal mechanisms embedded in causal networks.

Our analyses quantified diversity-associated causal networks among various natural aquatic ecosystems. The selected long-term datasets from various aquatic ecosystems represented a reasonable parallel to long-term biodiversity experiments conducted in terrestrial grassland ecosystems⁶⁵. Revealing the dominance of diversity effects on biomass in these systems (Fig. 2) was enabled by using methods for nonlinear dynamical systems, in lieu of linear statistical analyses^19,25. Indeed, when linear analyses were applied in our datasets (Supplementary Fig. S12), the importance of diversity effects on biomass (BD → EF) and nitrate effects on diversity (NO₃ → BD) were not clearly identified as that shown in the nonlinear CCM analysis (Fig. 2a, b).

Through cross-system comparison, the strength of causal relationships associated with species diversity varied with nutrient levels and time-averaged mean levels of diversity in aquatic systems (Fig. 4a). Associations with environmental gradients were also present in other network modules (i.e., feedbacks; Fig. 4c, d). These statistical associations revealed the macroecological relationships of how the strength of biodiversity effects and related feedbacks varied with environmental gradients. Moreover, the unveiled macroecological patterns also improved our understanding of how causes and effects of biodiversity are modulated by biotic and abiotic contexts. Although the proposed relationships need to be examined through more long-term experiments, our quantitative and empirical framework for constructing causal networks provided a foundation for better predicting the consequences of biodiversity loss across ecosystems.

Source: Ecology - nature.com