In an effort to compare relative susceptibility of the Carmel and SNI populations without the newly observed RLOv, we repeated the above study using the remaining control abalone and donor abalone from SNI, where the RLOv has not been observed (Friedman and Crosson, 2012). The trial was conducted in the same manner as above with the exception that duplicate tanks holding five animals each were used from each population under the same control and experimental conditions as outlined above. No abalone were sampled during the study except for moribund

**FIGURE 1 | Exposure system used in challenge Trials 1 and 2.** Abalone from SNI (black circles) and GC (gray circles) were held in replicate tanks (n = 5 in Trial 1 and 2 in Trial 2). The white circles represent tank of uninfected red abalone to test for tank independence in the recirculation system.

Seawater flowed from the head tanks (H) holding either infected or uninfected abalone into the exposure tanks. Subsequently seawater flowed into the square sumps and through a series to filters, UV-sterilization and heating systems prior to being returned to the head tank.

or dead individuals. All remaining animals were sacrificed and sampled as above.

### *Trial 3: Single RLO infections (WS-RLO)*

We re-analyzed data from Friedman et al. (2002) in which black abalone from Año Nuevo Island (37.1086◦N, 122.3378◦W) were exposed to the WS-RLO via cohabitation under the same temperature conditions as in Trials 1 and 2 (18◦C) but were held in flowing seawater in lieu of recirculating seawater systems. Abalone used in Trial 3 were larger (mean size = >100 mm) than those used in Trials 1 and 2 (mean size = 49 and 57 mm, respectively). Moribund and surviving abalone were sampled for histology as described above. New analyses of histology and survival data were conducted and are outlined below.

### **SEA SURFACE TEMPERATURE DATA**

Our primary source for sea surface temperature (SST) data at SNI was waverider data buoy 46219 (33.221◦N, 119.882◦W), moored 32.4 km west of SNI site 8 and operated by the US National Oceanic and Atmospheric Administration (NOAA). Buoy 46219 is also identified as buoy 067 of the Coastal Data Information Program, Scripps Institution of Oceanography, La Jolla, CA, USA. Between September 2004 and December 2012 a total of 42,677 publically available SST measurements were logged hourly, distributed across 46 different months at the buoy site. To verify that data from the buoy site represented SST conditions at SNI, we compared SST data collected in 2007 in the intertidal zone at SNI site 8 with data from the buoy. SNI data were collected between February 1 and December 31 using "TidbiT®" SST loggers (Onset® Computer Corporation, Bourne, MA, USA) cemented to rocky substrata. The data sets included 106 records of paired (within 1 h of one another) SST measurements from the two locations that were logged within 1 h of high tide at SNI. Times of high tides were determined with Nobeltec® Tides and CurrentsTM software, version 3.5 (Jeppesen Marine, Portland, OR, USA). The requirement for observations at high tide ensured that the data loggers at SNI were fully immersed when SST records were obtained.

### **STATISTICAL ANALYSES**

Histology was used as the primary determinant of infection status. Abalone that were not exposed, but showed signs of infection (*n* = 2 in Trial 2), were discarded from analyses because these animals were most likely contaminated with RLO during the end of the trials. We did not include data from animals that were sampled for histology midway through the trials, but rather used data only from abalone that died or survived until the end of the trials. Our measure of phage infection (RLOv) was either present or absent or ranked density in histological preparations, depending on the analysis. Histological scores for foot condition (atrophy) and RLO density were assumed to be continuous variables on a log scale. Mortality rates of control abalone differed among the trials and this variation in background mortality was controlled for in analyses by nesting the effect of infection within trial. WS-RLO gene copies based on qPCR analyses were included as an additional estimate and acted as a proxy for RLO load (Friedman et al., 2014). To simplify the analyses, we pooled abalone across trials into two groups based on exposure history of disease-selected and naïve. Abalone from SNI were progeny of abalone selected for disease resistance ("selected"), while those from Carmel, where losses due to WS have not been observed (Miner et al., 2006) were considered "naïve." Our survivorship models considered infected (vs uninfected) nested within trial, the interaction between infection status and exposure history, and the interaction between infection status and presence of phage. Here, the interaction terms were the main predictions of our hypotheses, i.e., that the mortality rate of infected abalone relative to control abalone is affected by phage or exposure history to RLOs. We used a proportional hazards model to determine the effect of exposure history and RLOv infection on the relative mortality rates of experimental abalone (for convenience, plots were made with parametric survivorship analysis using a Weibull distribution – results were consistent between the two approaches).

We used logistic stepwise regression to consider a chain of causal mechanisms leading to abalone mortality and pathological indices. For all models, we nested the main effect within trial. Least squares regression was used for all tests except for a nominal regression to contrast mortalities versus survivors. We assumed that all indices were continuous variables due to the presence of several fractional values. Treating these as ordinal variables did not alter the nature of the results. For the linear models, the normality of the residuals was inspected using normal

quantile plots to insure that the assumptions of the test were met. All statistical analyses were run using JMP 10.0 (SAS, Cary, NC, USA).

We calculated Pearson's *r* value to assess the strength of the correlation in SST data between NOAA data buoy 46219 and SNI site 8, using paired data consistent with selection criteria as described above.

### **RESULTS**

### **SURVIVAL**

In the proportional hazards survivorship analysis (78 deaths, 69 survivors), infected abalone had significantly higher mortality rates than control abalone (infection nested in site, χ2 = 29.4, df = 3, *P* < 0.0001). The relative mortality rate of phage-infected abalone was significantly lower than abalone without phage (interaction between infection and presence of phage, χ2 = 19.0, df = 1, *P* < 0.0001). There was a near-significant (two-tailed) effect of exposure history (interaction between infection and history of exposure, χ2 = 19.0, df = 1, *P* = 0.066), with naïve Carmel abalone suffering slightly higher mortality rates than abalone from disease-selected SNI populations, particularly in Trial 1. To illustrate the magnitude of these effects, **Figure 2** shows mortality curves for four categories of infected abalone. Here, the projected effect of phage (RLOv) was to extend the mean (with 95% confidence) time until 50% mortality in infected abalone from 189 (169–211) days (naïve) or 211 (175–251) (selected) days to 399 (352–450) days (naïve) or 443 (386–514) days (selected; **Figure 3**).

### **HISTOLOGY**

Abalone mortality significantly increased with pedal atrophy (withered foot) index (nested within trial, χ2 = 65.4, df = 3, *n* = 136), *P* < 0.0001). Pedal atrophy index significantly increased with DG-RLO index (nested within trial, *F*-ratio = 23.8, df = 3, *n* = 133, *P* < 0.0001). DG-RLO index significantly increased with metaplasia index (nested within trial, *F*-ratio = 67.6, df = 3, *n* = 143, *P* < 0.0001). Metaplasia index significantly increased with PE-RLO index (nested within trial, *F*-ratio = 67.6, df = 3, *n* = 143, *P* < 0.0001). PE-RLO index in infected abalone was not affected by exposure history (nested within trial, *F*-ratio = 0.03, df = 2, *n* = 93, *P* = 0.97). However, in Trial 1, more than half of the PE-RLOs were RLOv (phage-infected WS-RLO), preventing them from contributing to metaplasia and thereby reducing DG-RLO loads as evidenced by reduced DG-RLO indices in these animals (**Table 1**).

Given that exposure history did not influence infection potential based on similar PE-RLO between groups, we conducted a specific test to see if pathological changes due to infection varied between animals that survived and those that died in the three trials. We also examined if exposure history affected other aspects of pathology. Overall, animals that survived had lower levels of metaplasia (index of metaplasia = 0.56) than those that died (index = 1.37; *F*-ratio = 5.5, df = 7, *n* = 144, *P* < 0.01). Within each trial, metaplasia indices of survivors were lower than those that died, with index values of 0.39 and 1.35, respectively, in Trial 1 (*F*-ratio = 6.9, df = 3, *n* = 75, *P* < 0.001), 1.46 and 2.21, respectively, in Trial 2 (*F*-ratio = 1.98, df = 3, *n* = 27, *P* = 0.052), and 0.27 and 0.79, respectively, in Trial 3 (*F*-ratio = 4.5, df = 1, *n* = 40, *P* < 0.05). Presence of the phage (RLOv) reduced both metaplasia (index = 1.12 with phage and 1.68 without phage, *F*-ratio = 4.41, df = 1, *n* = 95, *P* = 0.039) and DG-RLO load (index = 1.38 with phage vs 1.90 without phage, *F*-ratio = 8.99, df = 1, *n* = 95, *P* = 0.0035). The effect of exposure history on infected animals was demonstrated in Trial 1 by the observation of reduced metaplasia upon infection in selected (index = 0.81) relative to naïve animals (index = 1.54; *F*-ratio = 5.30, df = 1, *n* = 54, *P* < 0.05). However, mean metaplasia index was similar between naïve (2.23) and selected (2.00) animals in Trial 2 (*F*-ratio = 0.307, df = 1, *n* = 21, *P* = 0.59; **Table 1**; **Figure 4**). Overall, exposure history affected metaplasia but the effect was not consistent across trials, suggesting, perhaps, that not all the exposed abalone had been under selection for increased resistance at SNI.

### **qPCR**

Naïve infected abalone from Carmel contained ∼ 2.5 times more copies of the WS-RLO rDNA gene than did those from disease-selected WS-RLO-infected abalone from SNI [3.82 × 104 (SE = 6.62 × 103) vs 1.47 × 104 (SE = 8.30 × 103) copies per milligram tissue, respectively; *F*-ratio = 4.90, df = 1, *n* = 71, *P* = 0.03]. Abalone that died also contained a mean of 50% more copies of the WS-RLO DNA gene than did survivors. However, these differences were not significantly different from one another [4.28 × 104 (SE = 6.85 × 103) vs 1.28 × 104 (SE = 8. × 103) copies per milligram tissue, respectively; *F*-ratio = 2.10, df = 3, *n* = 71, *P* = 0.22]. Although infected Carmel abalone excreted 12 times more copies of RLO DNA per nanogram fecal DNA than

**FIGURE 3 | Kaplan–Meier survivorship curves for (A) RLO-exposed and (B) control (unexposed) black abalone from Trials 1–3 in which abalone from Carmel and San Nicholas Island (SNI) were exposed to three RLOs (WS-RLO, ST-RLO, and RLOv) in Trial 1, two RLOs**

**(WS-RLO and ST-RLO) in Trial 2, or one RLO (WS-RLO) in Trial 3.** Survival trends for abalone from the three collection sites are represented by dotted lines (SNI), solid lines (Carmel) and dashed lines (Año Nuevo Island).

**Table 1 | Histology trends among groups sampled as mortalities relative to those that survived within each trial.**

RLO infection intensities were scored by estimating the number of rickettsial colonies per 20× field of view: (0) no infection, (1) 1–10, (2) 11–100, and (3) >100 (Friedman et al., 2002). Tissue changes (metaplasia or foot atrophy) were similarly scored: 0 represented normal tissue, 1 indicated <10% change, 2 indicated 11–25% change, and 3 indicated >25% change. Bold indicates significant differences in measured response of exposed abalone between sampling periods (mortality or survivor) within a collection site. Italics indicate nearly significant differences between mortalities and survivors within a collection site (P = 0.052–0.069). Underlined data indicate significant differences in measured response between abalone with different exposure histories (collection site) and whether they were animals that died (mortality, M) or survived (S) to be sampled. 1San Nicolas Island. 2Año Nuevo Island.

did infected abalone from SNI [1.9 × 104 (SE = 9.73 × 103) vs 0.16×104 (SE=9.73×103) copies per nanogram genomic DNA], these differences were not significant (*F*-ratio = 1.60, df = 1, *n* = 17, *P* = 0.22).

### **SEA SURFACE TEMPERATURE**

We found that 17.7% of SST records from NOAA buoy 46219 were ≥17◦C and 5.3% ≥18◦C (**Table 2**). During the period of record many events of ≥4 h duration occurred in which 17.0 ≤ SST < 18.0◦C, and with SST > 18.0◦C (**Table 2**). The correlation between paired SST records from the buoy and from SST loggers at SNI site 8 was significant (Pearson's *r* = 0.71, df = 104, *P* < 0.001).

### **DISCUSSION**

Our findings suggest that the increased survivorship seen at SNI was due to reduced metaplasia and WS-RLO loads in the DG. While we found some evidence for our hypothesis of the evolution of resistance in black abalone following mass mortality, the significance of these results varied among trials. It is possible that selection for resistance varied among the samples we used in our experiments (which were taken at different times). Furthermore, sample sizes were small in Trial 2, which combined with a shorter trial duration may have influenced our ability to detect differences in measured metrics between groups. Trial 2 (lacking phage) was terminated after day 85 (when no abalone deaths had occurred for 2 weeks). Although the slopes of the mortality curves

the WS-RLO to invade and infect this organ, which together result in

**Table 2 | Frequency and duration of occurrence of sea surface temperatures (SSTs) sufficiently high to increase the risk of black abalone to effects of withering syndrome at San Nicolas Island (SNI), California USA.**

**(E)** 400×.

Data are publically available and were collected hourly by NOAA waverider buoy 46219, located 32.4 km west of black abalone study site 8 at SNI. Data collection was intermittent from September 2004 through December 2012. Minimum episode duration was set at 4 h. Data from the buoy were significantly correlated with temporally paired SST data obtained during high tide in black abalone habitat at site 8 in 2007.

for exposed animals in Trial 2 were similar, ∼2× more SNI abalone survived than those from Carmel (**Figure 3A**). Thus, had the study continued longer, we might have seen differences in survival between exposure history arise as seen in trail one. Despite inferential constraints imposed by sample size limitation, differences in mortality of naïve relative to disease-selected abalone when exposed to the WS-RLO were consistent with effects observed in Trial 1.

The ability for marine mollusks to evolve disease resistance has been investigated in a number of species with varying results. A viral disease of abalone (abalone viral ganglioneuritis, AVG) in Australia caused high mortality along the coast of Victoria in 2005–2006 and surviving abalone tested 5–6 years later remained susceptible to the virus suggesting a lack of evolved resistance (Crane et al., 2013). The ephemeral nature of AVG and possibly insufficient time for significant recruitment post-selection may explain the apparent lack of developed resistance to this disease. The development of disease resistance has been investigated in oysters infected with protistan and viral pathogens.

After four generations of selection beginning with American oyster *Crassostrea virginica* broodstock that had survived disease pressure from two protistan pathogens for 2–5 years, cumulative survival more than doubled when exposed to the protists (Calvo et al., 2003) illustrating the development of resistance over time. Resistance in populations has also been observed in Pacific oysters *Crassostrea gigas* after several generations of selection to a herpes virus (ostreid herpesvirus-1); heritability of traits was also high (e.g., h2 = 0.61–0.95; Dégremont et al., 2010). These examples illustrate a genetic basis for resistance in marine mollusks and that rates of selection for resistance vary among species. The black abalone from SNI were likely first or second generation progeny of those that survived the initial losses of ≥95% due to WS given their observation 10 years after the first observation of WS on SNI in 1992 and ∼5–6 years after ∼90% mortality (VanBlaricom et al., 1993; Crosson et al., 2014). It is possible that abalone have limited ability to evolve resistance to novel pathogens like AVG and WS-RLO; however our data suggest that over time enhanced resistance to the

WS-RLO is likely to increase in populations experiencing disease pressure.

Given the known association of WS-induced mortality rates with warm-water events such as ENSO episodes, it is plausible that the recently observed global-scale hiatus in sea surface warming (e.g., Meehl et al., 2011; Kosaka and Xie, 2013) may have mitigated detrimental effects of WS on black abalone populations in southern California. However, we are skeptical that ocean temperature trends alone can explain observed positive trends in black abalone population densities at SNI and elsewhere in the southern California Islands for two reasons. First, our evaluation of relevant SST data indicated that periods of SST high enough to enhance vulnerability of black abalone to WS at SNI were frequent and often of substantial duration (continuous for up to 30 days), despite the hiatus in ocean warming. Second, we would have expected a regional climate signal to have led to signs of incipient population-scale recovery at more than just a small number of sites.

Our finding that the phage effectively eliminates the ability of infected WS-RLOs to cause disease was unexpected. Seasonal losses due to WS in California farms have lessened over the past several years since the phage was observed in farmed animals suggesting that natural phage-therapy operates under farm conditions (R. Fields, personal communication), and this effect may also apply to the field. However, whether or not phage commonly infects wild abalone remains to be seen.

Viroplankton (especially bacteriophages) are the most abundant biological elements in the marine environment and play key roles in driving host population dynamics (see review by Suttle, 2005). The observation of a phage hyperparasite in the WS-RLO is therefore not surprising. A number of phages have been reported infecting a variety of marine bacteria including RLOs (see detailed list in Friedman and Crosson, 2012). Hyperparasites have been shown to influence primary host–parasite relationships and recent renewed interest in phage therapy has highlighted the importance of the interplay among host immune response, primary pathogen and hyperparasite (Sabelis et al., 2002). Given the intracellular nature of the RLOs and their location in the digestive epithelium, the host immune response may play less of a role in WS than in bacterial septicemia. Infection of a prokaryote by a phage results in death of the host cell by lysis (lytic cycle; see reviews byYoung,1992, 2002) or by induced changes in physiology through co-opting of the host cell to produce more phages, and by lysogeny (Kourilsky, 1973; Weinbauer, 2004; Bobay et al., 2013). Lytic phages have been used as a therapeutic alternative to antibiotics in many systems including those of bacterial pathogens in marine species. Bacteriophages were recently reported as an alternative control for *Vibrio anguillarum* infecting Atlantic salmon (Higuera et al., 2013) and *V. parahaemolyticus* infections in brine shrimp (Martínez-Díaz and Hipólito-Morales, 2013). In both cases, efficacy of the phage therapy was based on phage-induced lysis of its bacterial host. Some phages integrate into the host genome (lysogenize) and are replicated and passed on to the next generation via host cell division (Kourilsky, 1973; Golais et al., 2013). Lysogeny has been shown to increase the virulence and pathogenicity of bacterial pathogens including those infecting marine species such as *V. harveyi* in shrimp (Ruangpan et al., 1999; Khemayan et al., 2006). Shrimp infected with phage-lysogenized *V. harveyi* experienced over 100 times the death rate of those lacking infection with the phage *V. harveyi* siphophage 1 (VHS1; Intaraprasong et al., 2009).

The phage infecting the WS-RLO appears unique in its ability to alter the course of infection, not by lysis of its bacterial host but apparently by effectively eliminating normal function of the WS-RLO. This is evidenced by a reduction in the effect of PE infection on the DG and resulting reduced mortality of the abalone host and functioning as a natural phage therapy. Approximately half of the WS-RLO colonies were phage infected (RLOv), thereby effectively reducing the pathogenic infection load by half. Increased host tolerance to WS-RLO infection in the PE may also contribute to the observed reduction in metaplasia and DG-RLO loads. Lysis of RLOv cells has not been observed by electron microscopic examination (Friedman and Crosson, 2012), but presumably occurs at some level in order to effect phage transmission. Alternatively, after its initial infection of the WS-RLO, transmission of the phage may rely primarily on lysis of the host abalone gastrointestinal epithelial cells by the RLOv followed by infection of new host cells and or hosts. Lysis of WS-RLO infected gastrointestinal cells has been observed by electron microscopy (Moore and Friedman, unpublished data). A lack of RLOv lysis suggests that the phage may be defective or that signals to induce host cell lysis are rare (Young, 1992, 2002; Golais et al., 2013).

### **CONCLUDING REMARKS**

It is encouraging that black abalone are surviving longer and recruiting to SNI. This does not seem to be fully explainable by stabilized SSTs in years since the original mass mortality. While our results suggest that some abalone at SNI have evolved increased resistance to the RLO, evidence for an evolved response was not strong. Differences in survival between exposure histories and tolerance to infection were enhanced with phage presence. The phage seen in our experiments substantially increased the longevity of infected abalone and, if prevalent in the wild, might help this endangered species recover from the brink of extinction.

### **AUTHOR CONTRIBUTIONS**

All authors helped write and edit the manuscript with Carolyn S. Friedman as the primary author. Carolyn S. Friedman also designed the experiments, read slides, analyzed data, made and or edited tables/figures. Kevin D. Lafferty analyzed data, created **Figure 2**, and designed **Figure 3**. Nathan Wight and Lisa M. Crosson conducted Trials 1 and 2, extracted DNA, sampled animals, and conducted qPCR assays. Glenn R. VanBlaricom collected abalone for experiments, conducted temperature analyses, and created **Table 2**.

### **ACKNOWLEDGMENTS**

This research was funded, in part, by grants from the National Sea Grant College Program, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, under project numbers R/F-200B and R/FISH-208 through the California Sea Grant Program, the School of Aquatic and Fishery Sciences at the University of Washington, and the California Department of Fish and Game. We thank Ray Fields, Don Rothaus, Jim Moore, Ian Taniguchi, and Melissa Neuman for assistance with animal collection and Robyn Strenge for assistance with animal maintenance. We thank Colleen Burge for her editorial comments. The views expressed herein are those of the authors and do not necessarily reflect the views of NOAA or any of its sub-agencies. Any use of trade product or firm name herein is for descriptive purposes only and does not imply endorsement by the US Government. The U.S. government is authorized to reproduce and distribute this paper for governmental purposes.

### **REFERENCES**

*Special Publication of Fisheries and Aquatic Sciences*, ed. A. Campbell (Ottawa, ON: NRC Research Press), 11–24.

*Infectious Disease: In Pursuit of Virulence Management*, eds U. Dieckmann, J. A. J. Metz, M. W. Sabelis, and K. Sigmund (Cambridge: Cambridge University Press), 297–321.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 10 December 2013; paper pending published: 21 January 2014; accepted: 12 February 2014; published online: 18 March 2014.*

*Citation: Friedman CS, Wight N, Crosson LM, VanBlaricom GR and Lafferty KD (2014) Reduced disease in black abalone following mass mortality: phage therapy and natural selection. Front. Microbiol. 5:78. doi: 10.3389/fmicb.2014.00078*

*This article was submitted to Aquatic Microbiology, a section of the journal Frontiers in Microbiology.*

*Copyright © 2014 Friedman, Wight, Crosson, VanBlaricom and Lafferty. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Mycoloop: chytrids in aquatic food webs

#### *Maiko Kagami 1 \*, Takeshi Miki 2 and Gaku Takimoto1*

1 Faculty of Science, Toho University, Funabashi, Japan

2 Institute of Oceanography, National Taiwan University, Taipei, Taiwan

#### *Edited by:*

Télesphore Sime-Ngando, Centre National de la Recherche Scientifique, France

#### *Reviewed by:*

Nathalie Niquil, Centre National de la Recherche Scientifique, France Bas Ibelings, University of Geneva, Switzerland

#### *\*Correspondence:*

Maiko Kagami, Department of Environmental Science, Faculty of Science, Toho University, 2-2-1 Miyama, Funabashi, Chiba 274-8510, Japan

e-mail<EMAIL_ADDRESS>
Parasites are ecologically significant in various ecosystems through their role in shaping food web structure, facilitating energy transfer, and controlling disease. Here in this review, we mainly focus on parasitic chytrids, the dominant parasites in aquatic ecosystems, and explain their roles in aquatic food webs, particularly as prey for zooplankton. Chytrids have a free-living zoosporic stage, during which they actively search for new hosts. Zoospores are excellent food for zooplankton in terms of size, shape, and nutritional quality. In the field, densities of chytrids can be high, ranging from 101 to 109 spores L−1. When large inedible phytoplankton species are infected by chytrids, nutrients within host cells are transferred to zooplankton via the zoospores of parasitic chytrids. This new pathway, the "mycoloop," may play an important role in shaping aquatic ecosystems, by altering sinking fluxes or determining system stability. The grazing of zoospores by zooplankton may also suppress outbreaks of parasitic chytrids. A food web model demonstrated that the contribution of the mycoloop to zooplankton production increased with nutrient availability and was also dependent on the stability of the system. Further studies with advanced molecular tools are likely to discover greater chytrid diversity and evidence of additional mycoloops in lakes and oceans.

**Keywords: parasitic fungi, chytridiomycota, diatom, daphnia, mycoloop, indirect mutualism, stability, trophic transfer**

### **ECOLOGICAL SIGNIFICANCE OF PARASITES**

Parasites are important components of ecological communities (Thomas et al., 2005; Hatcher and Dunn, 2011). They have the potential to regulate host populations, mediate interspecific competition between hosts and other species, maintain genetic polymorphism and biodiversity, and affect community structure. Nevertheless, the effects of parasites and diseases on food webs and ecosystem dynamics have been neglected until recently (Polis and Strong, 1996; Marcogliese and Cone, 1997). New research suggests that parasites have the potential to alter food-web topology, stability, interaction strength and energy flow (Lafferty, 2006; Kuris et al., 2008; Lafferty et al., 2008).

Parasites commonly function as prey within ecosystems (Johnson et al., 2010; Thieltges et al., 2013). There are two main ways in which parasites become prey. Predators can either consume the infected hosts of parasites (concomitant predation) or their free-swimming life stage (Johnson et al., 2010). Many aquatic parasites including viruses, chytrids, trematodes, and nematodes, have a free-swimming stage that may be subject to predation (Gonzalez and Suttle, 1993; Kagami et al., 2004; Kuris et al., 2008; Johnson et al., 2010). The Chytridiomycota (chytrids) are one of the dominant groups of parasites in aquatic ecosystems. The free-living zoosporic stage of chytrids actively searches for and infects host cells, extracting nutrients and developing into mature sporangia that release new zoospores (Canter, 1967; **Figure 1**). There are more than 700 species of chytrids known to infect phytoplankton, zooplankton, fungi, plants, and invertebrate animals (Sparrow, 1960; Gleason et al., 2008). Here in this review, we mainly focus on parasitic chytrids that infect phytoplankton, and explain their roles in aquatic food webs as prey for zooplankton through the "mycoloop" pathway (Kagami et al., 2007a).

### **CHYTRIDS ZOOSPORES AS PREY FOR ZOOPLANKTON: FOOD QUALITY AND QUANTITY, AND THE MYCOLOOP**

Predation of parasites can be beneficial to predators if they can gain energy and nutrition from parasites. Chytrid zoospores are a good food source for zooplankton in terms of size and shape (Kagami et al., 2004). In addition, zoospores are rich in polyunsaturated fatty acids (PUFAs) and cholesterol, which are essential for the growth of crustaceans (Kagami et al., 2007b). Zooplankton, such as cladocerans (*Daphnia*) and copepods, are able to grow by acquiring important supplementary nutrients from a diet of zoospores (Kagami et al., 2007b, 2011). Thus, chytrids may improve zooplankton production and enhance trophic transfer.

Many recent studies suggest that parasite biomass is not negligible, and may in fact be often significantly high (Kuris et al., 2008). The abundance of chytrids in aquatic systems has been found to be much higher than traditionally thought. Because morphological identification of chytrid zoospores is difficult, attempts have been made to use the fluorescent stains to count the density of chytrid zoospores in lakes (Kudoh, 1990). Recently, molecular techniques, such as CARD-FISH (Jobard et al., 2010) and Real-Time qPCR (Lefèvre et al., 2010) have been applied to estimate zoospore abundance in field samples and have detected zoospore concentrations of 101–106 spores L−1 (**Table 1**).

Compared to quantifying the abundance of zoospores, sporangia are much easier to count because they are attached to host phytoplankton cells. Using prevalence of infection and host cell

density data from the literature, we determined the density of sporangia to be 101–108 spores L−1 in field surveys (**Table 1**). The result indicated that direct counts of zoospores in the field may underestimate the real densities. By using the zoospore per sporangia conversion factors determined by previous studies (13–28 zoospores per sporangium, Sen, 1988; 4–25 zoospores per sporangium, Bruning, 1991), or the zoospore per sporangium biovolume conversion (0.166 perμm3 empty sporangium volume, Bruning, 1991), we can roughly estimate that zoospore abundance could actually reach more than 109 zoospores L−1in the field. It should be noted that lowest abundance of zoospore can be 10 spores L−1, or even zero. This indicates that the potential importance of mycoloop may vary with seasons and lakes.

Molecular studies have also revealed that chytrid zoospore may often be miscounted as small heterotrophic nano-flagellates (HNF), due to similar forms and sizes (Sime-Ngando et al., 2011). A significant portion of small eukaryotes (0.6–5μm) was recently found to be chytrid zoospores in freshwater lakes (11–23%, Lefèvre et al., 2007; 30% Lepère et al., 2008). In addition, CARD-FISH identified 5–60% of unknown flagellates as chytrids zoospores (Jobard et al., 2010). In aquatic ecosystems, most small heterotrophic eukaryotes (*<*5μm) are considered to play a role in microbial food webs by acting as predators of bacteria and bacterium-sized phytoplankton (Sherr and Sherr, 1983). In contrast, chytrids consume phytoplankton directly as parasites, and they do not eat bacteria. These findings require that we should revise our understanding of microbial food webs.

Zoospores may become particularly important to *Daphnia* when large inedible phytoplankton species, such as the diatom *Asterionella*, dominate the phytoplankton community. Large phytoplankton species are quite resistant to grazing by zooplankton such as *Daphnia* (Knisely and Geller, 1986; Kagami et al., 2002). However, if large inedible phytoplankton species are infected by chytrids, then nutrients within host cells are consumed by chytrids and can be grazed by *Daphnia*. This new pathway has been dubbed the "mycoloop" since nutrients from large inedible algae are transferred to zooplankton via the zoospores of parasitic chytrids (Kagami et al., 2007a).

Trophic transfer efficiency from host algae to chytrids is an essential parameter to examine the importance of mycoloop in the field. The transfer efficiencies of carbon, nitrogen, and phosphorus (CNP) from host *Asterionella* populations to freeswimming zoospores were estimated to be 6–9% in the laboratory experiment, when the prevalence of infection was about 60% (Kagami et al., 2007b). Those efficiencies were population based, and may become even higher if the prevalence of infection may exceed 90%. While, a single zoospore can use the host tissues quite efficiently because chytrid can directly consume host nutrients by entering through a germ tube (Van Donk and Ringelberg, 1983). CNP concentrations in a single zoospore (10.7 pg C, 0.6 pg N, 2.4 pg P per zoospore, Kagami et al., 2007b) are comparable to 20% of those in single host cell, indicating just five zoospores may be enough to exploit all algal tissues. 20% must be overestimated, if we consider the range of zoospores per sporangia (4–25 zoospores per sporangium, Bruning, 1991). We need to measure the CNP concentrations of zoospores and host cells, and number of zoospores per sporangium accurately with different species and conditions. From these estimates, we can examine how important chytrid zoospores may be as a food source for zooplankton in the field, in comparison to other possible food sources. In addition, such estimates are also crucial for modeling approaches, to predict the roles of chytrids in altering the network structure and stability (Niquil et al., 2011), and in determining the zooplankton production (Miki et al., 2011).

#### **Table 1 | Density of zoospores or sporangia of chytrids in lakes.**

(Continued)

Minimum densities during the presence of chytrids were shown if available. The density of sporangia was estimated from the prevalence of infection and host cell density data from the literature (shown as "Host density × %").

CFW, calcofluor white; OM, oligo-mesotrophic; M, mesotrophic; E, eutrophic.

\*Uncertain identification based on morphology or phylogeny.

aAsterionella, Fragilaria, Synedra, Staurastrum, Oocystis.

bAsterionella, Synedra, Staurastrum, Cyclotella, Fragilaria, Ankira, Melosira, Starodesmus, Chodatella, Ankystrodesms, Cylindrospermum, Oocystis.

cAsterionella, Synedra, Staurastrum, Cyclotella, Fragilaria, Ankira, Melosira, Oscillatoria, Microcystis, Fragilaria, Gomphosphaeria, Anabaena.

The "mycoloop" may occasionally play an important role in shaping aquatic systems, by altering the material flow (**Figure 1**). Traditionally, large inedible phytoplankton species are believed to be lost by sinking from the euphotic zone (Malone, 1981). However, if large phytoplankton are parasitized, then nutrients within host cells are instead consumed by chytrids, and can, in turn, be grazed by *Daphnia* through the mycoloop (Kagami et al., 2007a). In addition, large inedible colonies of phytoplankton may be fragmented into smaller pieces due to chytrid infections, making them more edible to zooplankton (**Figure 1**, Sime-Ngando, 2012). The trophic transfer efficiency from large phytoplankton to *Daphnia* would not change largely even if the heavily infected colonies are fragmented and grazed (i.e., after most of the host cells are consumed by chytrid). It would change, however, if the lightly infected host colonies are fragmented and grazed (i.e., before most of the host cells are consumed by chytrid) (Sime-Ngando, 2012). In this way, nutrients in host phytoplankton cells are partly incorporated into the food web in the euphotic zone, instead of sinking.

On the other hand, some of the infected host colonies may remain inedible for *Daphnia*, or even become less edible due to the aggregation of cells (Kagami et al., 2005, **Figure 1**) and may sink faster than single colonies. In addition, frustules of previously infected cells may sink faster than living cells (Kagami et al., 2006). In this way, sinking of frustules and aggregates of host cells may actually be facilitated by chytrid infections.

### **PREDATION ON CHYTRIDS MAY SUPPRESS OUTBREAKS OF CHYTRIDS**

Predation on the free-living stages of parasites may result in reduced disease risk for hosts (Packer et al., 2003). Indeed, the presence of *Daphnia* can decrease chytrid infection intensity on phytoplankton (Kagami et al., 2004). Recent studies also revealed that *Daphnia* grazing on free-swimming zoospores of *Batrachochytrium dendrobatidis* can decrease the disease chytridiomycosis of amphibians (Hamilton et al., 2012; Searle et al., 2013).

### **PHYTOPLANKTON—CHYTRID—ZOOPLANKTON INTERACTIONS**

Although a short-term experiment demonstrated that the direct trophic link from chytrid fungus to zooplankton (F-Z link) increased zooplankton growth (Kagami et al., 2007b), the effects of the F-Z link on food web dynamics is not easily predictable.

Considering that chytrid infections are common in large inedible phytoplankton species (Sommer, 1987; Kagami et al., 2007a), fungal parasitism may indirectly increase the abundance of small edible phytoplankton by altering resource competition. This may in turn enhance zooplankton production through grazing pathways via an "indirect mutualism" (Levine, 1976; Vandermeer, 1980). If the F-Z link then decreases the abundance of fungal zoospores (or fungal parasitism), it weakens the indirect mutualism between fungi and zooplankton, and will then decrease material transfer from small phytoplankton to zooplankton (indirect effect).

Therefore, the F-Z link may enhance zooplankton production through the mycoloop (direct effects), while it may also decrease zooplankton production by weakening indirect mutualism (indirect effect). By using a simple food web model, we successfully evaluated the effects of parasitic chytrids (fungal parasitism) and the F-Z link (both direct and indirect effects) on food web dynamics (Miki et al., 2011). In summary, presence of the F-Z link caused unexpected indirect effects in the food web, and was an important determinant for the stability of the system (see the following section for more detail). The model indicated that the high growth efficiency and high nutritional quality of fungi were crucial for the F-Z link to increase zooplankton production. The model also indicated that the contribution of the mycoloop (material transfer via the F-Z link) to zooplankton production increased with nutrient availability and depended on the system stability. This implies that neglecting the dynamical aspect of the system will lead to inaccurate estimates of material and energy fluxes. In the following section, we will review the theoretical approaches in detail to evaluate the roles of parasitic fungi in aquatic food webs.

### **MULTIFACETED IMPACTS OF FUNGUS-ZOOPLANKTON INTERACTIONS ON FOOD WEB DYNAMICS: LESSONS FROM DYNAMICAL MODELS**

There are two modeling approaches for describing the structure, dynamics, and fluxes of material and energy in food webs and ecosystems: steady state models and dynamical models. The steady state model (or linear model) is a powerful tool in ecosystem sciences to quantitatively estimate material fluxes with limited observations (Vezina, 1989). For example, it has been used to estimate the impacts on carbon fluxes in aquatic ecosystems, of the microbial loop (e.g., Anderson and Ducklow, 2001; Anderson and Tang, 2010), bacteriophage (Fuhrman, 1999; Motegi et al., 2009), and the food web structure (Niquil et al., 2006). Recent studies also quantified the impact of chytrid fungi in lake carbon fluxes using this modeling approach with inverse estimates of fluxes (Grami et al., 2011; Niquil et al., 2011). On the other hand, the dynamical model, which often requires a larger number of parameters and more specific mathematical formulations for inter-compartment interactions (e.g., trophic and competitive interactions), can provide information about both the steady state structure and non-steady state dynamics of the food web. Here, we would like to highlight the three major impacts of the F-Z link on the food web; (1) effect on food web structure and zooplankton production, (2) influence on system stability, and (3) contribution to material fluxes (mycoloop), elucidated by dynamical food web models (Miki et al., 2011; Gerla et al., 2013). Since conclusions are often different between steady state models and dynamic models, we will compare these two modeling approaches.

1. *Effects of the F-Z link on food web structure and zooplankton production:* The dynamical model can predict the unexpected consequences of nonlinear effects of adding or removing a specific trophic linkage or a specific player in the structure of the food web (Pimm, 1991). In our case, we added/removed two trophic linkages; fungal parasitism and the F-Z link (Miki et al., 2011) for a detailed comparison among three scenarios: the system without parasitic fungi, the system with fungal parasitism but without the F-Z link, and the system with both fungal parasitism and the F-Z link. The dynamical model predicts that the F-Z link indirectly lowers the abundance of small phytoplankton, altering the food web structure (Miki et al., 2011). This prediction agrees with the predicted decline in picophytoplankton production in Lake Pavin steady state model (Grami et al., 2011). Similarly, both models predict the positive impact of fungal parasitism and F-Z link on zooplankton production and biomass. This implies robust positive impacts of fungi on trophic transfer to higher trophic levels. Some scenarios in the dynamical model have not been explored in the framework of the steady state model. The dynamical model predicted that the F-Z link can unexpectedly reduce the production and biomass of zooplankton compared to a system with fungal parasitism but without an active F-Z link (Miki et al., 2011). This is unexpected considering the apparent (direct) benefit of the F-Z link to zooplankton. This occurs through an indirect effect; the F-Z link increases the host population (large inedible phytoplankton) via a top-down cascade, which in turn decreases the population of non-host, small edible phytoplankton through intensified resource competition (**Figure 2**). In particular, when the growth efficiency of parasitic fungi on host tissues and the nutrient quality of zoospores for zooplankton are not large enough (i.e., the metabolic loss through these trophic interactions is large) or productivity (nutrient availability) in the system is low, then indirect negative effects are greater than positive direct benefits and the F-Z link then causes a reduction in the zooplankton biomass and production (compare A vs. B in **Figure 2**). In order to better quantify the role of fungal parasitism and the F-Z link separately, three scenarios (the system without fungi, the system with fungal parasitism but without trophic transfer from fungi to zooplankton, and the system with both fungal parasitism and F-Z link) should be compared even with the steady state model approach.

**zooplankton.** The net effects of the fungus–zooplankton link on zooplankton biomass production depends on three conditions: the growth efficiency of fungi on the host, the growth efficiency of zooplankton consuming fungi, and the nutrient supply in the system. *N*, inorganic nutrient; *L*, large phytoplankton; *S*, small phytoplankton; *F*, chytrid fungi; and *Z*, zooplankton. **(A)** When these growth efficiencies are high, in

zooplankton is low, or the system productivity is high, the F-Z link increases zooplankton biomass production, compared to the system with fungal parasitism only. **(B)** When metabolic losses are high or the system productivity is low, the F-Z link decreases zooplankton biomass production compared to the system with fungal parasitism only. More quantitative results are shown in Miki et al. (2011).

state assumption). However, the steady state assumption tends to significantly overestimate (a factor of 2–10) the annual averaged contribution of the F-Z link under seasonal fluctuations (Miki et al., 2011; **Figure 3**). More specifically, the predicted relative contribution of fungi to zooplankton production from the dynamical model under a stable environment is 38.6% when the growth efficiency of fungi on host phytoplankton is assumed to be 75% and the total phosphorus is 100μgPL−1 (**Figure 3**). This prediction is comparable to the estimate of the contribution of fungal zoospores in the total diet of microzooplankton in ologimesotrophic Lake Pavin (38%) (Grami et al., 2011). However, an introduction of seasonality into the dynamical model lowers the contribution of fungi to 19.9%. Such an overestimate is a general feature in nonlinear systems. When the trophic flux (*F*) is proportional to the abundance of resources (*R*) and consumers (*C*): *F* = *aRC* where *a* is the consumption coefficient, then the average flux - *F*(*t*) is not equivalent to the product of the averages *R*(*t*) and *C*(*t*). Instead, we

have *F*(*t*) = *a R*(*t*) · *C*(*t*) + Cov *(R*(*t*)*,C*(*t*)*)* , implying that neglecting the impacts of asynchronous population dynamics of resources and consumers (Cov(*R*,*C*) *<* 0) is the source of the overestimation with a steady-state assumption.

Combination of steady state models and dynamical models are a promising approach to greatly improve our understanding of the roles of parasitic fungi.

### **FUTURE PERSPECTIVES**

### **MOLECULAR TOOLS**

Recent advances in molecular methods enable us to investigate the species composition of microorganisms. Indeed, several methods, such as PCR-DGGE, clone libraries, FISH, and qPCR have been applied to describe the species composition and biomass of certain species of parasitic chytrids (Jobard et al., 2010; Lefèvre et al., 2010; Kagami et al., 2012; Marano et al., 2012, Maier et al., under revision). In addition, next generation sequencing

#### **FIGURE 3 | Comparison of the contribution of the mycoloop pathway to zooplankton production under constant and seasonal**

**environments.** The relative percent contribution of the mycoloop pathway (large phytoplankton → parasitic fungi → zooplankton) to total zooplankton biomass production under constant and seasonal environmental conditions was calculated as the ratio of the F-Z link nutrient flux to zooplankton compared to the total nutrient flux to zooplankton (nutrient flux from the F-Z link plus nutrient flux from small phytoplankton) (see also **Figure 2**). The ratios of the contribution of the mycoloop under constant environmental conditions compared to that under seasonal environmental conditions was calculated for concentrations of total phosphorus (TP) = 25.0, 50.0, 100, 150μgP L−1 to be 10.3, 2.30, 1.94, 1.78. TP in the model ecosystem was calculated by the average phosphorus supply I0 divided by the turnover rate of the system (0.05/day). The daily fluctuation in the phosphorus supply I(t) is given by I(t) = I0 1*.*0 + 0*.*5 sin (2πt*/*365) for the seasonal environment; the maximum deviation from average is ±50%. Modified from Miki et al. (2011).

will be beneficial for the analysis of fungal community structures. However, since the DNA database of aquatic fungi is scarce, especially for the parasitic fungi (chytrids), it is difficult to determine species composition and function (e.g., parasitic or saprotrophic) by analyzing environmental DNA alone. In addition, choosing the right primer sets are essentially important when examining the species composition by molecular tools (Wurzbacher et al., 2010, Ishii et al., in review). Therefore, prior to applying advanced molecular tools, we first need to build a robust database, especially for the parasitic chytrids. Culturing, single cell PCR methods, and whole genome sequencing will aid in having a better understanding of the community structure and function of parasitic fungi wide-ranging ecosystems.

### **OTHER POSSIBLE MYCOLOOPS**

In addition to parasitic chytrids, saprotrophic chytrids may also play important roles in aquatic food webs. For instance, pollen deposition into lakes may not be utilized directly by zooplankton, but can be decomposed/consumed by saprotrophic chytrids (Masclaux et al., 2011, 2013). Grazing of zoospores released from pollen may then function as another "mycoloop" (**Figure 4**).

Recently discovered fungi, the Cryptomycota, exhibit a similar life cycle to chytrids including a free-swimming stage, and are also known to infect phytoplankton (Jones et al., 2011). In marine environments, some Chytridiomycota or zoosporic fungal-like protists such as Labyrinthulomycota are also known to infect marine phytoplankton (Raghukumar, 2002; Gleason et al., 2011), and may play important roles in marine food web dynamics (Raghukumar, 2002). These results indicate the existence of

other possible mycoloops in freshwater and marine ecosystems via the route of free-swimming zoospores of newly discovered Chytridiomycota, Cryptomycota, or Labyrinthulomycota.

### **ACKNOWLEDGMENTS**

We are grateful to Prof. Hans-Peter Grossart for his valuable discussions and encouragements. We thank M. Maier for correcting English. This study was supported by a research fellowship from the Japan Society of the Promotion of Science.

### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 24 January 2014; accepted: 27 March 2014; published online: 22 April 2014. Citation: Kagami M, Miki T and Takimoto G (2014) Mycoloop: chytrids in aquatic food webs. Front. Microbiol. 5:166. doi: 10.3389/fmicb.2014.00166*

*This article was submitted to Aquatic Microbiology, a section of the journal Frontiers in Microbiology.*

*Copyright © 2014 Kagami, Miki and Takimoto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Parasitic chytrids sustain zooplankton growth during inedible algal bloom

#### *Serena Rasconi 1 \*†‡, Boutheina Grami 1†‡, Nathalie Niquil 1†, Marlène Jobard2 and Télesphore Sime-Ngando2*

1 CNRS UMR 6250, UMRi 7266, LIENSs, Université de La Rochelle, La Rochelle, France 2 CNRS UMR 6023, LMGE, Clermont Université, Aubière Cedex, France

#### *Edited by:*

Kevin D. Lafferty, US Geological Survey, USA

#### *Reviewed by:*

Frank H. Gleason, University of Sydney, Australia Maiko Kagami, Toho University, Japan

#### *\*Correspondence:*

Serena Rasconi, WasserCluster – Biologische Station Lunz, Dr. Carl Kupelwieser Promenade 5, A-3293 Lunz am See, Austria e-mail<EMAIL_ADDRESS>
#### *†Present address:*

Serena Rasconi, WasserCluster – Biologische Station Lunz, Lunz am See, Austria Boutheina Grami, Laboratoire de phytoplanctonologie, Université de Carthage, Bizerte, Tunisie Nathalie Niquil, CNRS UMR 7208, BOREA, Institut de Biologie Fondamentale et Appliquée, Université de Caen Basse-Normandie, Caen Cedex, France ‡These authors have contributed equally to this work.

### **INTRODUCTION**

Parasites are known to be ubiquitous in their environments. Although they have been considered as important forcing factors for ecological processes (Hudson et al., 2006), they have only recently been included in food web studies. In aquatic ecosystems, freshwater parasites are especially common in the form of "zoosporic" fungi (i.e., chytrids). The life cycle of these parasites is characterized by dispersal forms, uniflagellated zoospores, and sporangia attached to the host cells. Microscopic observations provided evidence for the presence of both forms in freshwater ecosystems (Rasconi et al., 2009; Jobard et al., 2010). These parasites mostly affect primary producers (Canter, 1950; Sparrow, 1960), food web dynamics (Mccallum et al., 2004; Sime-Ngando, 2012) and ecological processes (Hudson et al., 2006). Many phytoplankton species are sensitive to chytrid parasites and the related ecological implications are important (Canter and Lund, 1948; Niquil et al., 2011; Sime-Ngando, 2012). Chytrid infections have been linked to mass mortalities of host organisms, to suppression or postponement of phytoplankton blooms, and have selective effects on host species composition and successions (Van Donk and Ringelberg, 1983; Bruning et al., 1992;

This study assesses the quantitative impact of parasitic chytrids on the planktonic food web of two contrasting freshwater lakes during different algal bloom situations. Carbon-based food web models were used to investigate the effects of chytrids during the spring diatom bloom in Lake Pavin (oligo-mesotrophic) and the autumn cyanobacteria bloom in Lake Aydat (eutrophic). Linear inverse modeling was employed to estimate undetermined flows in both lakes. The Monte Carlo Markov chain linear inverse modeling procedure provided estimates of the ranges of model-derived fluxes. Model results confirm recent theories on the impact of parasites on food web function through grazers and recyclers. During blooms of "inedible" algae (unexploited by planktonic herbivores), the epidemic growth of chytrids channeled 19–20% of the primary production in both lakes through the production of grazer exploitable zoospores. The parasitic throughput represented 50% and 57% of the zooplankton diet, respectively, in the oligo-mesotrophic and in the eutrophic lakes. Parasites also affected ecological network properties such as longer carbon path lengths and loop strength, and contributed to increase the stability of the aquatic food web, notably in the oligo-mesotrophic Lake Pavin.

#### **Keywords: fungal parasites, bloom, stability, inverse modeling, ecological network analysis**

Kagami et al., 2007; Rasconi et al., 2009). Unstable ecosystems seem to favor the opportunistic behavior of parasites (Rasconi et al., 2012), where their activity represents an important but as yet overlooked ecological driving force in food web dynamics. During blooms, parasites can optimize their virulence, as the host population is genetically more uniform (Brown et al., 2002) and bloom-forming species exhibit short generation time. Some fungal parasites seem to be most common in large and bloom forming algae that are fairly resistant to grazing by zooplankton (Sommer, 1987; Kagami et al., 2007; Rasconi et al., 2012), including diatoms and cyanobacteria. Recent studies have suggested a role for fungal parasites in destroying large filamentous phytoplankton, which are considered important for seasonal pelagic succession (Rasconi et al., 2012; Gerphagnon et al., 2013). This finding raises the hypothesis that parasites may play important roles during monospecific blooms of inedible algae because they can release dissolved substrates for microbial processes through host destruction, and provide energetic particles as zoospores for higher trophic levels (Kagami et al., 2007; Grami et al., 2011).

Diatoms traditionally form large spring blooms in temperate lakes, providing fuel for planktonic community development at the start of the growing season. However, some of the diatom blooms are not grazed by filter-feeding zooplankton due to their large size, and this biomass is believed to be lost by sinking from the euphotic zone instead of being grazed. Recently there has also been an increasing awareness of food quality as a limiting factor for zooplankton growth (Brett and Muller Navarra, 1997; Sterner and Elser, 2009; Kagami et al., 2011). Laboratory studies suggest that a diatom monodiet lacks or is deficient in some essential component required for copepod egg development and may have a harmful effect on the success of egg hatching. Some authors have also highlighted the toxicity of these algae as food and presented evidence showing that the hatching success of wild copepods feeding on a diatom-dominated bloom is heavily compromised (Miralto et al., 1999). If diatoms have a deleterious effect, high diatom abundance could limit secondary production and affect fish production (Irigoien et al., 2002). However, diatoms have traditionally been regarded as providing the bulk of the food that sustains the planktonic seasonal succession and the food chain to top consumers (Sommer et al., 1986). Additionally, spring diatom proliferations are generally followed by a rapid increase in zooplankton. Diatoms are well known as preferential hosts for chytrid epidemics in the plankton (Ibelings et al., 2004; Kagami et al., 2007) and chytrid zoospores were experimentally demonstrated to be efficiently grazed and be able to sustain *Daphnia* growth in *Asterionella* cultures (Kagami et al., 2007). This implies that during fungal epidemics abundant zoospores may become a food source for some grazers. When fungi infect these large inedible phytoplankton species, they consume nutrients within these cells to produce zoospores, some of which are grazed by zooplankton with important consequences for the recycling of the organic matter in the pelagic food web (Kagami et al., 2007; Grami et al., 2011; Niquil et al., 2011).

Cyanobacteria, the most ancient phytoplankton on the planet, have been an important element for forming the earth's oxygen atmosphere. They frequently form blooms and dominate phytoplankton communities in warm, stratified and nutrient-enriched waters. In lakes, they form the basis of the food chain and function as nitrogen fixers. Proliferation events seem to have increased substantially during recent decades, likely as a result of eutrophication and temperature increase; they may have a large impact on water quality and biological communities. Many genera of cyanobacteria are known to produce a wide variety of toxins and bioactive compounds (Sivonen and Jones, 1999), which are a health risk to both animals and humans. From the perspective of aquatic food webs, cyanobacterial blooms can noticeably decrease the efficiency of the energy transfer from primary producers to primary consumers (Lurling and Roessink, 2006). However, filamentous cyanobacteria are known to be the target of different chytrid species (Canter, 1972). A recent hypothesis has been proposed stating that proliferation events may not always represent trophic bottlenecks, since eukaryotic parasites provide energetic particles as zoospores for higher trophic levels (Rasconi et al., 2012; Gerphagnon et al., 2013). Parasitism helps release dissolved substrates for microbial processes through host destruction.

Despite current evidence that bloom situations are considered deleterious for the ecosystem due to harmful and toxic species, blooms can constitute ecologically important events. They contribute to the natural processes of a lake and in some cases provide important benefits by boosting primary productivity and influence the energetics and population dynamics of consumer organisms. Considering the widespread occurrence of parasites during such situations, their consumption likely represents a far more important trophic link than previously recognized. Eukaryotic parasites as consumers drain energy throughout the food web and provide energetic particles for the grazers by predation on free living stages (Kagami et al., 2007; Grami et al., 2011; Miki et al., 2011). Parasitic activity was estimated to constitute between 36% and 44% of observed trophic links in a marine food web (Lafferty et al., 2006). In this paper we analyzed the role of parasitic chytrids and how they affect organic matter transfer in two different algal proliferation contexts: a spring diatom proliferation in an oligo-mesotrophic deep lake, and a late summer cyanobacterial bloom in a eutrophic shallow lake. The aim was to investigate how parasites drive energy and nutrients from their hosts to zooplankton in these two different situations to corroborate our recent findings that the activity of parasites and grazing on parasitic zoospores might sustain the growth of zooplankton through releasing nutrients bound in inedible algae and thus represent an important alternative carbon pathway in pelagic environments (Kagami et al., 2007; Grami et al., 2011; Miki et al., 2011).

Moreover, since we have demonstrated in a previous work (Grami et al., 2011) that parasites drive an increase in species richness, trophic level, connectance, and trophic chain length of the food web, we wanted also to establish the effects of parasites on the ecosystem properties linked to stability during monospecific algal proliferations. In this context of infections of primary producers, predation on parasites occurs at low trophic levels, which is considered a top down effect that reduce loop weight and increase the strength of links (Neutel et al., 2002). Weak to intermediate strength links are important as they can decrease complex oscillatory food-web dynamics and promote community persistence and stability (Mccann et al., 1998). Moreover, food-web stability is linked to species number and connectance, which is enhanced when species at high trophic levels feed on multiple prey or when species at intermediate trophic levels are fed on by multiple predator species (Gross et al., 2009). Parasites can alter topological properties of the network such as patterns of biodiversity, linkage between density and loop strengths, with implications for changing interactive networks and network stability (Lafferty et al., 2008). The significance of such observations is only beginning to be appreciated; integration of parasites has the potential to alter our understanding of food web structure and theory. The impact that parasites have on food web properties like stability and resilience has been previously overlooked and will need to be measured to ascertain its importance in the food web. From a theoretical perspective, predation on algal parasites helps to unite two emerging concepts in plankton ecology: the energetics of the overlooked parasite-grazer system flow during algal biomass proliferation and the impact of this link on the structural and functional properties of the ecosystem.

To investigate parasite-related flows, ecosystem properties and ecological theories, we applied mathematical tools such as linear inverse models for trophic network representation through carbon flows. For the first time we evaluate and compare the impact of zoosporic parasites ("chytrids," class Chytridiomycetes, families Rhizophidiaceae and Chytridiaceae) on the functioning of a planktonic ecosystem using field data collected from the euphotic zone of two different lake ecosystems in the Massif Central region of France: the oligo-mesotrophic Lake Pavin and the eutrophic Lake Aydat. We compared carbon flows between the complete food web including parasitic chytrids during spring diatom bloom peak in Lake Pavin (April 2007 from 4th to 18th, PavDiat), with the model representative for the fall cyanobacteria bloom in Lake Aydat (from September 24th to October 10th 2007, AydCyan) and quantified the amount of primary production channeled through the food web. These models were built using the Linear Inverse Modeling procedure (LIM, Vezina and Platt, 1988) recently modified into the LIM-Monte Carlo Markov Chain (LIM-MCMC, Van Den Meersche et al., 2009). This method allows reconstruction of missing flow values and alleviates the problem of under-sampling, using the principle of conservation of mass (Vezina and Platt, 1988). The flows obtained from the models were used for calculations of Ecological Network Analysis indices that characterize the structure and functioning properties of the food web, and help reveal emergent properties (Ulanowicz, 1986, 1997; Ulanowicz et al., 2009).

LIM-MCMC and ecological network analysis were used to reveal overlooked trophic links in two contrasted freshwater ecosystems (Pavin and Aydat Lakes) where parasites of microalgae were integrated during bloom periods. Our objectives were to identify and compare (1) the carbon flows involved in the chytrid-parasitism pathway during monospecific algal proliferations in both lakes, (2) the emergent properties of different planktonic food webs during different algal blooms in which parasites are integrated, and (3) the structural and functional properties of two different ecosystems containing different parasite-host associations.

### **MATERIALS AND METHODS STUDY SITE AND SAMPLING**

Samples were collected in two freshwater lakes with different trophic status located in the French Massif Central. Lake Pavin (45◦ 29 41 N, 002◦ 53 12 E) is an oligo-mesotrophic, deep volcanic mountain lake (*Z*max = 92 m), with a permanent anoxic monimolimnion from 60 m depth downwards. This site has a small surface area (44 ha), about equal to the drainage basin area (50 ha) and offers a unique environment with low human influences, and consistent annual seasonal dynamics in the water column (Lefêvre et al., 2007, 2008; Rasconi et al., 2012). Lake Aydat (45◦ 39 48 N, 02◦ 59 04 E) is a small eutrophic lake (*Z*max = 15 m, surface area = 60 ha). Compared to the surface of the lake, the catchment area (3 × 104 ha) is very large and contains intensive agricultural lands.

Samples were collected fortnightly in a central location of each lake by simple capillarity as described by Sime-Ngando and Hartmann (1991). This method allowed collecting integrated samples (21 L) representative of the euphotic layers (0– 20 m for Lake Pavin and 0–5 m for Lake Aydat). Samples from Lake Pavin were collected between 4 and 18 April 2007 (diatom bloom), while those considered for the Lake Aydat were collected between 24 September and 10 October 2007 (cyanobacteria bloom). Samples were pre-filtered on 150µm pore size nylon filter (except for metazooplankton samples) for the elimination of metazoan zooplankton and taken to the laboratory for immediate analysis.

### **ABUNDANCE AND BIOMASS OF PLANKTONIC ORGANISMS**

Sub-samples were processed for identification and quantification of picoplankton, heterotrophic nanoflagellates, phytoplankton, zooplankton and the two life stages of microphytoplankton fungal parasites (Chytridiales). Details on the material and methods are available in Grami et al. (2011).

### *Bacteria*

Sample aliquots were fixed with glutaraldehyde before counting of heterotrophic and autotrophic picoplankton by a flow cytometer (BD system). Carbon conversion factors of 0.35 pg C µm−3 and 0.22 pg C µm−3 were used for conversion of biovolumes to carbon biomasses of heterotrophic bacteria (*bac*) (Bjornsen, 1986) and picophytoplankton (*ph1*, 0.2–2µm) (Mullin et al., 1966; Sondergaard et al., 1991), respectively.

### *Heterotrophic nanoflagellates (hnf, 2–20 µm)*

Sub-samples (15 ml) were fixed and handled according to Caron (1983) for quantification of heterotrophic nanoflagellates. Counts were performed using an inverted epifluorescent microscope (Leica DMIRB). Mean cell biovolumes were estimated for each sample by measuring the linear dimension of at least 50 cells and equating shapes to standard geometric forms. Carbon biomass was calculated using a conversion factor of 0.22 pg C µm−3 (Børsheim and Bratbak, 1987).

### *Nano- and microphytoplankton (ph2, 2–20 µm and ph3, 20–150 µm, respectively)*

Sub-samples (200 ml) were fixed with alkaline Lugol solution (1% v/v) and cells were counted and identified using the Utermohl method (1931) under an inverted microscope (WILD—M40). Cell biovolumes were estimated by measuring the linear dimension of at least 100 cells and equating shapes to standard geometric forms. The resulting volumes were transformed into organic carbon values using the conversion equation of Menden-Deuer and Lessard (2000) (pgC cell−1 = 0*.*288 × Vol(0*,*811) for diatoms and pgC cell−1 = 0*.*216× Vol(0*,*939) for the other autotrophic genera).

### *Ciliates (mic, 20–150 µm)*

Sub-samples (200 ml) were fixed with alkaline Lugol solution (5% v/v) and ciliates were counted and identified using the same method as for microphytoplankton. For carbon biomasses of ciliates, biovolumes were converted into organic carbon using conversion factors of 0.19 pg C µm−3 (Putt and Stoecker, 1989).

### *Metazooplankton (mes)*

The metazooplankton was collected by filtering raw samples from the euphotic layer (0–20 m) through a 50µm pore-size mesh. Retained animals were preserved in 4% formalin-sucrose (Prepas, 1978). Identification and counting, after addition of few drops of rose Bengal to improve detection, were conducted under a binocular microscope (Wild M3Z) using Dolfuss chambers (Dussart, 1967). The carbon biomass of each metazoan group was estimated by multiplying the individual carbon contents by the corresponding abundances. For Copepods the dry weight (DW, mg) was calculated as 22.5% of wet weight (Riemann et al., 1990; Gradinger et al., 1999) and C content (mg) was estimated as 40% of DW (Feller and Warwick, 1988). For Cladocera the length (L, mm) of each organism was used to determine its carbon content (Cclad) as: µg C ind−1 = 5*.*24× L - 1.08 (Kankaala and Johansson, 1986). For rotifers, wet weights were converted to dry weight according to Pace and Orcutt (1981) and Mccauley (1984). Dry weights were converted to carbon biomass using carbon: DW ratio of 0.48 (Andersen and Hessen, 1991).

### *Chytrid parasites*

Sub-samples were handled for chytrid parasites counting based on a size fraction approach and the use of the fluorochrome calcofluor white (CFW) for diagnosing, staining and counting chitinaceous fungal parasites (i.e., sporangia of chytrids) of microphytoplankton (Rasconi et al., 2009). 20 L of the integrated samples were passed through a 25µm pore size nylon filter. Large phytoplankton cells in the *>*25µm size fraction were collected and fixed with formaldehyde (2% final conc.) before staining and analysis.

Nanoplanktonic cells in the *<*25µm size-fraction were concentrated by ultrafiltration and 180 ml of the ultrafiltrate retentate was fixed with formaldehyde (2% final conc.), before staining and analysis. Aliquots (150µl) of each fraction were stained by CFW (1% v/v) and drops (10µl) of stained samples were mounted between glass slides and cover slips for observation and counting under an inverted epifluorescent microscope (more details are available in Rasconi et al., 2009). Identification of chytrids was based on phenotypic keys known from classical manuals, primarily those in Canter (1950); Canter and Lund (1951), and Sparrow (1960). The prevalence of infection was estimated as the percentage of infection in the host population according to Bush et al. (1997), i.e., Pr (%) = [(Ni/N) × 100], where Ni is the number of infected host cells, and N is the total number of host cells. Carbon biomass of sporangia attached to the host cells (*spg*) was estimated using a conversion factor of 10.7 pg C cell−1 (Kagami et al., 2007).

For zoospore (*zsp*) counting, sub-samples were processed using the CARD-FISH method of Not et al. (2002), recently modified by Jobard et al. (2010). The number of zoospores produced by sporangia was considered and carbon biomass of zoospores was estimated using a conversion factor of 10.7 pg C cell−1 (Kagami et al., 2007).

### **MODEL CONSTRUCTION**

Data from the field were used to construct pelagic food web models that quantitatively illustrate carbon pathways in Lake Pavin during diatom spring bloom and Lake Aydat during cyanobacteria autumn bloom, in the presence of chytrids (both sporangia and zoospore stages in the life cycle). Since the unknown flows far outnumbered the known flows, we adopted the LIM-MCMC method (Van Den Meersche et al., 2009), derived from the LIM of Vezina and Platt (1988) to reconstruct trophic flows through the pelagic food web. The approach is based on four steps described with more details in Grami et al. (2011).

### *Compartments and a priori model*

The first step consists in constructing a conceptual model including all possible flows between compartments and between compartments and the outside. We represented the pelagic food web and carbon pathways in the mycoloop, we thus did not include compartments for fishes and macrophytes. Living compartments included three phytoplankton compartments, three grazer compartments, one compartment for heterotrophic bacteria and two compartments for fungal parasites of microphytoplankton. We divided the phytoplankton into picophytoplankton (*ph1*: 0.2–2µm); nanophytoplankton (*ph2*: 2–20µm; principally Cryptophyta as *Rhodomonas* sp. in both lakes and Chlorophyta as *Ankistrodesmus sp.* and *Ankyra sp*. in Lake Pavin) and microphytoplankton (*ph3*: 20–150µm; essentially large and filamentous Bacillariophyceae, as *Synedra* sp., *Melosira* sp., and *Asterionella* sp. in Lake Pavin and filamentous cyanobacteria as *Anabaena* sp. and *Oscillatoria* sp. in Lake Aydat). Grazer compartments were the heterotrophic nanoflagellates (*hnf* : 2–20µm), microzooplankton (*mic*; 20–150µm: Ciliates and small Rotifera) and mesozooplankton (*mes*; *>*150 mm; Cladocera, Copepoda and some large Rotifera). Phytoplankton fungal parasites compartments included sporangia attached to the host cells (*spg*) and free zoospores (*zsp*). Non-living compartments were dissolved organic carbon (*doc*) and detritus (*det*).

The food web contained 53 carbon flows for the model of Lake Pavin and 54 for the model of Lake Aydat (a flow was added allowing the microzooplankton to consume detrital material). The sole carbon inputs were gross primary production by each phytoplankton size fraction. Carbon output from the network was driven by respiration of all living compartment and carbon loss by sinking from *ph2*, *ph3*, *spg*, *mic*, *mes,* and *det* compartments. Mesozooplankton contribution to the carbon output flow considers their consumption by higher trophic level and their production of sinking fecal pellets. All living compartments except fungal parasites contribute to the DOC production that was taken up by bacteria. In addition to *ph2*, *ph3*, *mic,* and *mes* contribution to detritus production, we considered the existence of a carbon flow from bacteria and heterotrophic nanoflagellates to detritus.

Attached bacteria were identified on TEP—Transparent Exopolymer Particles, associated with vertical flows of carbon in Lake Pavin during spring (Carrias et al., 1998), and then a flow of bacteria to detritus was considered. The bacteria to detritus flow was calculated using data on bacteria attached to TEP sedimenting in Lake Pavin during spring (Lemarchand et al., 2006). These attached bacteria are known to constitute preferential prey for heterotrophic nanoflagellates (Arnous et al., 2010). Detritus production of sporangia was due to chitinaceous wall dissolution or break-up during zoospore discharge (Sparrow, 1960). Moreover, zoospores were considered as contributing to detritus production by the loss of their flagellum when they found a host to fix on. The carbon flow from microphytoplankton to sporangia represented carbon pumped from diatom cells to sporangia and the carbon flow from sporangia to zoospores was considered as the zoospores produced by sporangia. Grazing relationships were defined by considering size and preferential ingestion of each identified grazer. Heterotrophic flagellates grazed on *bac* and *ph1*, microzooplankton grazed on *bac*, *ph1*, *ph2*, *ph3*, *hnf, zsp,* and *det* (this latter only in Lake Aydat) and mesozooplankton grazed on *bac*, *ph1*, *ph2*, *ph3*, *hnf*, *mic*, *zsp,* and *det*.

### *Equalities*

The second step was setting equations (equalities) to constrain the mass balance of the system and to impose measured flows. The mass balance equations for all compartments are given in the first 11 lines of the **Table 1**. Some of the estimated flows were measured during previous studies that focused on spring blooms in Lake Pavin and are introduced as additional equations (lines 12–15 of **Table 1**); these include values for total gross and net primary production (Devaux, 1980; Bettarel et al., 2003), bacterial production (Bettarel et al., 2003) and viral lysis of bacteria (Bettarel et al., 2003) considered as the value of the flux from bacteria to DOC. Some other equalities were introduced for the Aydat model and values of total gross primary production (Aleya et al., 1988), bacterivory by heterotrophic nanoflagellates and microzooplankton (Bettarel et al., 2004) were considered (lines 16–18 of **Table 1**). Primary production values used for Pavin and Aydat were measured from 14C uptake according to Steemann-Nielsen (1952). Bacterial production was determined by measuring the uptake of tritiated thymidine into bacterial DNA (Petit et al., 1999), after incubating the samples for 45 min. Viral lysis of bacteria was considered as the value of the flux from bacteria to DOC. The fraction of bacterial mortality from viral lysis was related to the calculated frequency of visibly infected cells, calculations are detailed in Bettarel et al. (2003). Details on the method are available in Grami et al. (2011) and cited references there in. Values of protozoan bacterivory were calculated using tracer particles and epifluorescence microscopy (EM) following (Pace and Bailiff, 1987) method modified by Carrias et al. (1996), more details are available in Bettarel et al. (2004).

Gpp-compartment A: gross primary production of compartment A.

Compartment A-Compartment B (e.g., Bac-hnf) represent the carbon flowing from compartment A to compartment B.

Compartment A-res: respiration of compartment A.

Compartment A-los: carbon loss by sedimentation of compartment A.

Bac, bacteria; Ph1, picophytoplankton; Ph2, nanophytoplankton; Ph3, microphytoplankton; Hnf, heterotrophic nanoflagellate; Mic, microzooplankton; Mes, mesozooplankton; Spg, sporangia; Zsp, zoospores; Det, detritus; Doc, dissolved organic carbon.

\*values are in mgC m−2 d−1.

\*\*det-mic was only considered for Aydat model.

### *Constraints*

The third step consisted of imposing ecological limits (maximum and/or minimum) for each unknown flow, which means a linear system of inequalities: 76 inequalities where provided for Lake Pavin and 78 inequalities for Lake Aydat. The latter is presented, explained and referenced in **Table 2**. Details about these inequalities are given in Grami et al. (2011).

### *Solutions*

The last step of the inverse analysis was the calculation of flows. A range of possible values was given by the method of the Monte Carlo Markov Chain joined to the mirror technique (Van Den Meersche et al., 2009). A jump value of 10 mgC m−2d−1 and 100,000 iterations were used to cover all possible solutions. More details on this method are available in Van Den Meersche et al. (2009) and Niquil et al. (2012).

### **ECOLOGICAL NETWORK ANALYSIS**

The resulting flows issued from inverse analysis, together with estimated biomasses, were used for calculating Ecological Network Analysis indices in order to describe the emergent properties of the ecosystem.

*Total system throughput* (TST) represents a measure of the total system activity and is the sum of all the flows through all compartments (Kay et al., 1989).

*Average path length* (APL) is the average number of compartments crossed by a unit of carbon from its entry to the system to its leaving. It represents a measure of the system retention (Kay et al., 1989).

*System ascendency* (A) is a measure of the system size and organization. It is the product of the TST and the average mutual information (AMI: degree of specialization of flows in the network) (Ulanowicz, 1986). This value is more informative about the organization of the system when it is expressed in relation to development capacity and considered as its maximum value (A/DC). It defines the ecosystem degree of development. High Relative ascendency indicates more specialized and less redundant pathways. The structure of energy flows can be related to the concept of structural asymmetry and ecosystem stability (Rooney et al., 2006), because the pattern of asymmetric channel flow enhances the equilibrium stability of an ecosystem. The difference between the development capacity and the ascendency is called redundancy (R), which is a quantification of the multiplicity of parallel flows. The relative redundancy (R/DC, %) is a measure of the ecosystem degree of information loss due to parallel pathways. The system would be redundant when the ascendency is low.

*Development capacity* (DC) is calculated as the product of TST and the upper limit of AMI, corresponding to the maximum potential ascendancy and to a food web with maximum specialization. The development capacity is the sum of ascendency, redundancy and information loss related to external exchanges.

In this study, as suggested by Ulanowicz (1986), growth and development were characterized by indices calculated over only internal exchanges. We consider the internal capacity of ecosystem development (DCi), i.e., the sum of internal ascendency (Ai) and internal redundancy (Ri). The internal relative ascendency Ai/DCi could point to a strong dependency of an ecosystem on external inputs (Baird and Heymans, 1996) in case it decreases in relation to the A/DC ratio. However, as pointed out by Baird et al. (1991), Ai/DCi ratio could be an aspect of a highly organized ecosystem. The internal relative redundancy (Ri/DCi) is considered as a measure of ecosystem stability by many authors (Rutledge et al., 1976; Baird et al., 1998, 2004).

*Finn Cycling Index* (FCI) is the ratio of carbon flowing in loops (the carbon comes back to the compartment it left) to the sum of all carbon flows. I.e., it is the fraction of all flows involved in recycling (Finn, 1976) and can also be considered a measure of the retentiveness of a system.

*Connectance* measures the trophic connections within a food web. The overall connectance includes the effects of all transfers (exogenous and endogenous exchanges). The intercompartmental connectance characterizes only the endogenous exchanges. The food-web connectance pertains only to transfers among the living compartments (Ulanowicz, 2003).

*Comprehensive Cycling Index* (CCI) was proposed as a new index that gives the real importance of cycling after added corrections to the FCI. This new index considers four types of pathways that energy and matter can follow to join one compartment to another (Allesina and Ulanowicz, 2004).

*Trophic analysis* maps the complex network of trophic transfers as a linear food chain (called Lindeman spine, Ulanowicz and Kemp, 1979) based on the trophic concept of Lindeman (Lindeman, 1942). The Lindeman spine allows calculation of the trophic efficiency for each level, also called transfer efficiency (Ulanowicz and Wulff, 1991). The global trophic efficiency (Geff) is computed as the logarithmic mean of all the trophic level efficiencies. Two more indices were derived from the Lindeman spine: the grazing chain efficiency and the percentage of detritivory. The mean values of the 100,000 set of flows resulting from the LIM-MCMC analysis were used to build the Lindeman spine using the WAND software.

*Detritivory/Herbivory* ratio (D/H) is the sum of consumption of non-living material (detritus or DOC) divided by the sum of flows of consumption of autotrophic organisms. Parasitism was included in herbivory flow.

For these calculations, we used an algorithm written for Matlab© by Carole Lebreton and Markus Schartau (pers. comm.) to calculate the first set of indices: TST, APL, A, DC, R, Ri and connectance. 100,000 iterations of these ecological indices were computed allowing statistical analysis of the difference between those calculated for Lake Pavin and Lake Aydat. Recycling, connectance and trophic efficiencies indices were calculated using the ecological network analysis package WAND© by Allesina and Bondavalli (2004) available at http://www*.*dsa*.*unipr*.* it/netanalysis/?Software*.* Only one value index was obtained for these indices, based on the average flow value for each flow of the food-web, preventing us from statistically testing the difference between the two blooms.

### **DATA ANALYSIS**

The statistical comparison between the two situations described, concerning the set of indices calculated under Matlab (TST, APL, A, DC, R, Ri and connectance) was tested with two tailed Student (t) tests using XLSTAT©.

#### **Table 2 | Constraints used on different planktonic food web processes.**

(Continued)

(Continued)

### **RESULTS**

### **FLOW ANALYSIS**

The values for overall flows for both models are given in **Table 3**. Carbon input into the lakes was only due to primary production. No allochthonous detritus input was considered due to the small catchment area (50 ha) of Lake Pavin and the low rainfall during the study period in Lake Aydat. Due to blooms of large algae in Lake Pavin (*Synedra* sp., *Melosira* sp., *Cyclotella* sp., and *Asterionella* sp.) and filamentous cyanobacteria in Lake Aydat (*Anabaena* sp. and *Oscillatoria* sp.) the major contribution to the gross primary production was provided by microphytoplankton (75% and 89% in Lake Pavin and Aydat, respectively). Calculated carbon throughput (T) of microphytoplankton was higher in Lake Aydat (2243 mgCm−2d−1). Consequently, all throughputs (and primarily those directly affected by primary production) were more important in Lake Aydat (**Figure 1**). According to the trophic status of the lakes, the contribution of picophytoplankton production to total gross primary production was higher in the oligo-mesotrophic Lake Pavin (14.7%) compared to the eutrophic Lake Aydat (1%).

The flows calculated in mgC m−2d−1 by the LIM—MCMC method directly involved in fungal compartments are detailed in **Figure 2**. In Lake Pavin, 9.6% of total system throughput was channeled to the sporangia parasitic compartment, which corresponded to 36.8% of microphytoplankton production. The sporangia compartment channeled 75% of its carbon input to the zoospore compartment. In Lake Aydat, the calculated percentage of the total system throughput values channeled to the sporangia compartment was slightly higher than in Lake Pavin and represented 11.7% (33% of *ph3* gross primary production). However, the sporangia compartment channeled less carbon to the zoospores compartment (67.6%). This was linked to a major sporangia loss flow in Lake Aydat (22.4% of total sporangia throughput) compared to Lake Pavin (only 5.5%).

Carbon flows involved in parasitism were of major importance for grazer compartments (i.e., micro- and mesozooplankton). Major zoospore throughput was channeled to the microzooplankton compartment (62.4% and 83.7%, respectively for Pavin and Aydat) and only 14.2% and 5% of zoospore throughput was ingested by the mesozooplankton compartment (**Figure 2**). Zoospores constituted the major nutritional resource for microzooplankton in Lake Pavin (50.5%) and in Lake Aydat (57.7%) (**Figure 3**). However, since microzooplankton represented the main ingested prey by mesozooplankton in both lakes (about 40–41%), carbon that originated from microphytoplankton parasitism was indirectly channeled to mesozooplankton through its ingestion of major microzooplankton throughput (**Figure 2**).

Moreover, carbon indirectly channeled from microphytoplankton to grazers through ingestion of zoospores produced by parasites' sporangia, represented 19–21% of microphytoplankton gross primary production in both lakes (**Figure 2**). This corresponded to 57–60% of sporangia throughput transferred to grazer compartments.

Sedimentation flows were lower in Lake Pavin compared to Lake Aydat, and represented 25% and 61.7% of the total system throughput, respectively. The largest contribution to this loss flow in Lake Aydat originated from microphytoplankton (57.7%, **Table 4**) while it originated from mesozooplankton (46.5% of total system loss, **Table 4**) in Lake Pavin. Detritus production originated mainly from microphytoplankton in the Aydat ecosystem (60% of total detritus throughput, **Table 4**) while in Lake Pavin zooplankton was the main contributor (48.2%, **Table 4**).

### **ECOLOGICAL NETWORK ANALYSIS**

The LIM-MCMC derived flow results of each model were used to calculate the ecological network indices. **Table 5** provides comparisons of the most relevant ecological network analysis indices for both lakes. Significant differences (*P <* 0*.*05) in network properties of the two considered webs were observed for TST, DC, A, A/DC, DCi, Ai and Ri. No relevant significant difference was detected between both lakes for APL, AMI, R/DC, Ai/DCi and Ri/DCi.

The total system throughput (TST, **Table 5**) was much lower in Lake Pavin compared to Lake Aydat due to higher primary production and carbon input during the cyanobacteria bloom. The calculated averages were 2625 and 8889 mgCm−2d−1, respectively, for Pavin and Aydat.

The average mutual information (AMI) values gives information about flow specialization. Values of AMI were almost the same for Aydat and Pavin (1.94 for Aydat and 1.96 for Pavin).

**Table 3 | Flow description, name and corresponding value (mg C m−2 d−1) of steady state models of the pelagic food web of Lake Pavin and Lake Aydat during each lake bloom period.**

Italic values indicate flows that were estimated or derived from processes determined in situ. The rest are values constrained by one or two inequations and estimated by the LIM-MCMC method.

The average pathway length (APL), a measure of the system retention capacity, was not significantly higher in Lake Pavin (**Table 5**). The calculated mean number of compartments through which each inflow passes was 2.9 in Lake Pavin and 2.5 in Lake Aydat.

The topological indices, which characterize the ecosystem structure, were affected by the different carbon input in different trophic situations of the two lakes and were lower in Lake Pavin compared to Lake Aydat except for redundancy. The development capacity was higher for Lake Aydat (**Table 5**) following the pattern of higher TST due to cyanobacteria bloom. Relative ascendency (A/DC%) also was affected by higher TST in the eutrophic lake, being around 66% in Aydat and 61% in Pavin. System inefficiency due to internal parallel pathways, termed relative redundancy (R/DC), was higher, but not significantly, in Lake Pavin during the spring bloom (**Table 5**). Internal indices calculated over only internal exchanges showed higher internal ascendency (Ai) and internal redundancy (Ri) for the Lake Aydat, due to the presence of populations more adapted to rapid changes and the effect of parasites helping to restablish broken pathways more easily. But, relative internal or functional redundancy (Ri/DCi) was slightly more important for Lake Pavin (56%) due to the similar effect of parasites over the internal exchanges.

The overall connectance, number of trophic connections, within each food web including exogenous transfers, was higher for Lake Pavin (2.2 vs. 1.9) due to the effect of parasites in reducing the losses of organic matter and enhancing the transfers through the mycoloop. Even when only based on endogenous exchanges, the amount of trophic connections given by the intercompartmental connectance index was also higher in the Lake Pavin ecosystem (2.3 vs. 1.9). The same was true for food web connectance, which pertains only to transfers among living compartments (1.8 vs. 1.5).

The Finn Cycling Index (FCI), showed higher recycling activity through the Lake Pavin (6%) than the Lake Aydat (3%) food web due to the higher exploitation of zoospores energetic by higher trophic levels and stronger loops. CCI, an update of the FCI index, also showed a higher recycling for Lake Pavin compared to Lake Aydat (7% vs. 4%).

All trophic level efficiencies derived from the Lindeman spine analysis were higher in Lake Pavin with the exception of trophic level III, which was more efficient in carbon transfer in Lake Aydat (66.10%) than Lake Pavin (48.33%) (**Table 6**). Trophic Level III was represented by zoospores, micro- and mesozooplankton. The global trophic efficiency (Geff) index confirmed that carbon was better transferred from the lowest to the highest trophic levels in Lake Pavin (47.09%) (**Table 6**). The Geff index is linked to grazing chain efficiency.

The Lake Pavin network exhibited a lower rate of detritivory (219 vs. 576 mgC m−2d−1) and circulation within the detrital pool (42.98 vs. 144.68 mgC m−2d−1) compared to Lake Aydat. However, the D/H ratio was higher in Lake Pavin than in Lake Aydat (**Table 6**).

### **DISCUSSION**

### **PARASITES AND ALGAL BLOOMS**

The planktonic food webs modeled in our study are considered to be representative of intensive algal proliferation (bloom) situations in two contrasting freshwater ecosystems. Lake Pavin is characterized by the dominance of diatoms during spring blooms while Lake Aydat shows a dominance of cyanobacteria during autumn blooms. In the classical food web version with no parasites, phytoplankton biomass produced during such proliferation events were considered to be lost by sinking (Malone, 1980; Legendre and Le Fevre, 1991; Kiorboe, 1993), and thus not able to sustain higher trophic levels. Some blooming species are even considered to be toxic, leading to reproductive failure in marine and freshwater invertebrates, interfering with zooplankton feeding (Rohrlack et al., 1999) and limiting their distribution (Ianora and Miralto, 2010). A study on the feeding behavior of

copepods during phytoplankton spring blooms showed that nonphytoplankton prey supported 40–71% of the copepod carbon requirement (Kobari et al., 2010).

However, it has been shown that some mesozooplankton (i.e., Cladocera) coexist with blooming filamentous cyanobacteria (Epp, 1996), and phytoplankton parasites can constitute an important food source during such situations. Indeed, fungal life stages could represent key intermediates in the pelagic food chain (Gleason et al., 2009). Chytrid zoospores were found to be efficiently grazed by mesozooplankton (Kagami et al., 2004) and could even sustain *Daphnia* growth cultured *in vitro* (Kagami et al., 2007). There has been evidence for higher instances of parasitism leading to less ecosystem reliance on detritus consumption and its related recycling (Niquil et al., 2011).

Our study focused on the algal bloom peak episodes in Lakes Pavin (from 4 to 18 April 2007) and Aydat (from September 24 to October 10 2007) and complements previous work conducted during the spring epidemic of chytrid parasites (whole spring diatom growth, from March to June 2007) in Lake Pavin (Grami et al., 2011), which found that parasitism indirectly channels primary produced carbon to grazers and that this process is an important part of the food web functioning. We were able to show the importance of parasites as trophic links, the importance of parasitic zoospores as nutritional sources able to sustain zooplankton diet, and their role in supporting upper trophic levels and recycling algal biomass.

### **CARBON FLOWS INVOLVED IN PARASITISM**

Parasitism affected the flow of carbon during the diatom peak bloom in Lake Pavin (676.25 mgC m−2 d−1) and the cyanobacteria peak bloom in Lake Aydat (2520 mgC m−2 d−1). Here, total gross primary production in both lakes was higher than reported in the previous model constructed for Lake Pavin during a longer period (Grami et al., 2011; 360.54 mgC m−2 d−1, March to June 2007). These values corroborate previous findings on the trophic status of both an oligo-mesotrophic lake (Amblard et al., 1992) and a eutrophic lake (Aleya et al., 1988). These values are consistent with gross primary production measured during a study conducted during spring phytoplankton proliferation in the north basin of the mesotrophic Japanese Lake Biwa (1639 mgC m−2 d−1; Yoshimizu et al., 2001).

A large amount of the dominant primary producer's carbon (microphytoplankton) was involved in sporangia development (37% and 33%, respectively, in Lake Pavin and Lake Aydat); representing 28% and 29% of their total primary production. During both blooms the planktonic community was characterized by large inedible algae, high biomass and high gross primary production (*ph3* GPP). Under these circumstances, the channeling of primary production through the food web should be highly impacted by the presence of chytrids. Values of the highest possible impact of chytrids given by model simulations of different rates of parasitism on microphytoplankton tested in the Lake Biwa by Niquil et al. (2011), were around 35% of microphytoplankton GPP.

Grazers were found to be indirectly sustained by primary production through the consumption of parasitic zoospores. Indeed, a large percentage of the throughput originating mainly from sporangia was channeled to microzooplankton (76% and 83% for Lake Pavin and Lake Aydat, respectively). Values based on the mean spring model of Lake Pavin (Grami et al., 2011), were lower: only 21% of the microphytoplankton production was channeled through chytrids sustaining 38% of microzooplankton diet. Considering the higher primary production input during the bloom period, we compared our results with those obtained from a modeling study of Lake Biwa (Niquil et al., 2011). The primary production rate was around 1580 mgC m−2 d−1 and the estimated chytrid contribution to the diets of microzooplankton was only 10%; bacterial prey were the main carbon source (70% of

dissolved organic carbon.

**FIGURE 3 | Diet composition of each grazer for each Lake.** Bac, bacteria; Ph1, picophytoplankton; Ph2, nanophytoplankton; Ph3, microphytoplankton; Hnf, heterotrophic nanoflagellate; Mic, microzooplankton; Mes, mesozooplankton; Spg, sporangia; Zsp, zoospores; Det, detritus; Doc, dissolved organic carbon.

**Table 4 | Percentage of total carbon throughput loosed by sedimentation and contribution to detritus throughput of each compartment and for both Lakes.**

Bold indicates higher values.

their diet, Niquil et al., 2011). In a previous study, Nakano et al. (1998) reported a low ingestion rate of HNF during summertime. This confirms our hypothesis that chytrid spores could have been previously misidentified in the flagellate community and therefore have a high potential as a trophic link. During the bloom they are able to replace bacterivory and lead a higher recycling of large phytoplankton biomass.

Estimation of microbial food web efficiency (microzooplankton efficiency; 36.7% vs. 14.4%) and the microbial link (percentage of mesozooplankton demand; 53% vs. 50%), showed higher values for Lake Pavin compared to Lake Aydat. Microzooplankton efficiency, which was significantly higher for Lake Pavin, represents carbon transfer efficiency from microto mesozooplankton (Gaedke and Straile, 1994) calculated as microzooplankton ingestion by mesozooplankton divided by microzooplankton throughput. Based on this percentage, we could conclude that Lake Pavin, due to its higher primary production, transferred to sporangia *via* parasitism (36.8% vs. 33.25%) then to zoospores (74.86% vs. 67.6%) possessed a higher microbial food web efficiency than Lake Aydat. Comparing our models with models from the literature reveals that Lake Pavin, which is characterized by large inedible algae with high parasitism of the dominant species, has similar values to environments characterized by primary production, mainly from edible picophytoplankton (as estimated by Niquil et al., 2001 in the Takapoto atoll). Therefore, based on microzooplankton efficiency and microbial link values (**Table 7**), parasites are able to replace inedible phytoplankton, thus providing edible resources that appear to be sufficient to meet zooplankton energy requirements and sustain the carbon demand of higher trophic levels.

Parasitism could allow a better carbon transfer from primary producers to zooplankton. This pathway (producers *>* parasites *>* grazers), called the "Mycoloop" by Kagami et al. (2007), was quantified in this study during the particularly large algae proliferation. The presence of parasites allowed not only a recycling of the high input of phytoplankton biomass through an increase of energy transfer from primary producers to consumers, but also a consequent overcoming of the trophic bottleneck created by the large amount of inedible biomass in the lake.

The mycoloop may also have an impact on sedimentation rates, which are usually very important during an inedible algal bloom. When considered for the first time in a pelagic food web model, parasitism induced less carbon loss from the pelagic zone by reducing direct sedimentation of large phytoplankton species (21–10% of *ph3* GPP) and their detritus production (11–3% of their *ph3* GPP) (Pavin spring model, Grami et al., 2011). During the two bloom situations, Lake Pavin had a lower percentage of carbon loss by sedimentation from the microphytoplankton compartment (9.8% vs. 40% of microphytoplankton GPP), lower detritus production by *ph3* (3% vs. 10.2% of microphytoplankton GPP) and better carbon transfer from microphytoplankton to

**Table 5 | Topological indices of pelagic food webs of Lake Pavin Spring bloom and Lake Aydat Autumn bloom; and** *t* **test results.**

Bold indicates higher values. \*Alpha *<* 0.05; \*\*Alpha *<* 0.01.

#### **Table 6 | Indices derived from the Lindeman spine.**

Bold indicates higher values.

sporangia (36.8% vs. 33.3%). Some studies observed that during a bloom 40–60% of the cells reaching the hypolimnion were still viable (Amblard and Bourdier, 1990). It would therefore appear that in the absence of parasites the majority of algal production is lost by sinking and is unavailable to support higher trophic levels. In the eutrophic Lake Aydat, the cyanobacteria production was so high that even parasitism could not help channel high rates of this carbon to higher trophic levels (i.e., zooplankton) and the carbon loss due to sedimentation of microphytoplankton still accounted for 57.7% of the total. In oligo-mesotrophic Lake Pavin, parasitism helped to reduce carbon sedimentation loss to only 9.8%, during the peak of spring diatoms, 20% of algal production was channeled to higher trophic levels and lost afterwards by sinking of the large grazers.

### **ECOSYSTEM EMERGENT PROPERTIES**

This paper sets out, for the first time, a model that explicitly investigates the influence of parasitism on pelagic food web properties under algal bloom situations in two different lake ecosystems. Indeed, using the LIM-MCMC method, we quantified and compared the amount of carbon that reached high trophic levels *via* the mycoloop.

 If high relative ascendency (A/DC) ratios reflect high degrees of organization (Ulanowicz, 1986), then, in the presence of parasites, Lake Aydat is slightly more organized than Lake Pavin (66% vs. 61%, significant difference at *P <* 0*.*05). However, due to redundancy, the ecosystem structure can evolve to counter the effects of external perturbations (Ulanowicz, 2003). In this case, Lake Pavin should be only slightly more resistant to stress than Lake Aydat. Ulanowicz et al. (2009) stated that the balance between the organized and non-organized parts of ecosystems increases resistance and stability; Heymans et al. (2002) suggested that redundancy should increase ecosystem resilience. If so, Lake Pavin and Lake Aydat should be equally stable and resilient.

Ascendency combines the total system activity (TST), with the organization by which the component processes are linked (AMI). It gauges how well the system is performing at processing the given medium. Chytrids were found to allow a higher fraction of the total system throughput to pass along specialized pathways (Grami et al., 2011). The degree of specialization measured by the AMI index (Ulanowicz, 1997) gave almost no difference between Lake Aydat and Lake Pavin during blooms, suggesting that both lakes have equally specialized pathways.

Baird and Ulanowicz (1993) considered the internal ascendency indices (Ai/DCi), calculated over only internal exchanges, as an aspect of a highly organized ecosystem. Given that, Lake Aydat and Lake Pavin, with almost equal Ai/DCi index, might have the same tendency to internalize their activity. Since a relevant decrease was observed for the Ai/DCi ratio in relation to the A/DC ratio for both lakes, it could point to a strong dependency of Lake Pavin and Lake Aydat on external inputs (Baird and Heymans, 1996). However, Rutledge et al. (1976) and Baird et al. (1998, 2004) considered the internal redundancy (Ri/DCi) as a measure of ecosystem stability. If so, with no significant difference between values of Ri/DCi given for Lake Pavin and Lake Aydat,

**Table 7 | Comparison of some food web indicators (main contributors to PP, Microbial food web efficiency, Microbial Link and trophic efficiency at Level II) calculated for Lake Pavin and Lake Aydat and other ecosystems.**

it seems that broken pathways might be equally re-established for both, which would result in the same level of stability and resilience.

Parasites are known to add links to food webs (Lafferty et al., 2008), as observed in estuarine (Lafferty et al., 2005; Thompson et al., 2005; Kuris et al., 2008) and freshwater (Kudoh and Tokahashi, 1990; Sukhdeo, 2010; Amundsen et al., 2013) ecosystems. Even if this seems logical, as for the addition of any species to a food web, this is especially true for parasites that have a freeliving stage that can interact with other compartments and that may be eaten (Gross et al., 2009), such as chytrids zoospores. Parasitism also enhances the number of flows (Grami et al., 2011) and the number of compartments through which each inflow passes. Lake Pavin, with a slightly higher average path length (APL) value than Aydat (**Table 5**), has a longer chain length. This important structural property defines the number of links in a food chain and the energy transfer through its components (Jordan et al., 2003). According to Neutel et al. (2002), parasites with their complex life cycles, have more carbon loops with low interactions, which can offset the destabilizing effect of higher connectance. Lafferty et al. (2008) found that parasitism enhances connectance two fold. Chytrids in the Lake Pavin increased slightly the number of links and enhanced especially the inter-compartmental connectance compared to Lake Aydat, which represents the robustness of interactions inside an ecosystem, a measure of its organization. Robustness, indeed, is considered as a parameter linked to the stability of an ecosystem.

Cycling helps ecosystems optimize exploitation of resources by allowing a better use of energy and matter introduced into the system. In an ecosystem with low cycling (calculated by FCI), loss of carbon is thought to be higher, such as in the case of Lake Aydat compared to Lake Pavin. When perturbations occur, cycling reduces impacts to the ecosystem by acting as a buffer against large changes and can increase the ability of the ecosystem to resist changes. Then, the higher FCI in Lake Pavin increases the residence time of carbon within the ecosystem, making it more resistant to change. Another simple and effective measure of the quantitative importance of cycling in ecosystems is the comprehensive cycling index (CCI), which is one of the principal ways that ecosystem complexity can enhance stability (Allesina and Ulanowicz, 2004). CCI was also higher in the Lake Pavin ecosystem. Odum (1969) had identified the amount of cycling as one of his 24 criteria for "mature" or developed ecosystems. Wulff and Ulanowicz (1989) suggested that the increased amount of cycling is a homeostatic response of the ecosystem to stress.

### **CONCLUSIONS**

To our knowledge, field data on pelagic ecosystem trophodynamics during bloom events with and without parasites do not yet exist. Most models use the basic tenet "zooplankton eat phytoplankton" and parasites have long been considered as playing an insignificant role in ecosystem steady-state functioning. Our models document the carbon transfer channeled by parasites from primary producers to consumers in two ecosystems dominated by inedible phytoplankton biomass and production. We provided quantitative estimates of the importance of parasitism as an indirect pathway channeling primary production contained in "inedible algae" and showed that this primary-produced carbon is efficiently conveyed to grazers and able to sustain zooplankton during inedible algal blooms, especially in the oligomesotrophic Lake Pavin. By considering parasites, our study indicates that the ecosystem becomes more efficient and specialized, and less reliant on detritivory, which is consistent with various suggestions about the influence of parasitism on ecosystem properties. In short, parasitism on phytoplankton stabilizes the system significantly during inedible algal blooms, especially in the case of oligo-mesotrophic lakes. Ecological Network Analysis indices proved to be suitable tools for evaluating ecosystem properties linked to ecosystem stability (as resistance or resilience) in different trophic status environments. An interesting possibility would be to consider using these indices as ecological status indicators for climate change effect studies.

### **ACKNOWLEDGMENTS**

This study was supported by a grant from two French ANR Programme Blanc: DREP (Diversite et Roles des Eumycetes dans le Pelagos) and ROME (Rare and Overlooked Microbial Eukaryotes)—Coordinator TSN—and by the CPER, Region Poitou-Charente. We thank David Murray and Stephen Pacella for help with English language editing.

### **REFERENCES**

Biwa with special reference to trophic interactions with *Daphnia galeata*. *Arch. Hydrobiol*. 142, 21–34.

food web in the Gulf of St. Lawrence. Part 1. Seasonal variations. *Deep Sea Res. II* 47, 585–607. doi: 10.1016/S0967-0645(99)00120-4

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 January 2014; paper pending published: 29 January 2014; accepted: 29 April 2014; published online: 23 May 2014.*

*Citation: Rasconi S, Grami B, Niquil N, Jobard M and Sime-Ngando T (2014) Parasitic chytrids sustain zooplankton growth during inedible algal bloom. Front. Microbiol. 5:229. doi: 10.3389/fmicb.2014.00229*

*This article was submitted to Aquatic Microbiology, a section of the journal Frontiers in Microbiology.*

*Copyright © 2014 Rasconi, Grami, Niquil, Jobard and Sime-Ngando. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

## Morphology, phylogeny, and ecology of the aphelids (Aphelidea, Opisthokonta) and proposal for the new superphylum Opisthosporidia

### *Sergey A. Karpov1,2 \*, Maria A. Mamkaeva2 , Vladimir V. Aleoshin3 , Elena Nassonova2,4 , Osu Lilje5 and Frank H. Gleason5*

1 Zoological Institute, Russian Academy of Sciences, St. Petersburg, Russian Federation

2 St. Petersburg State University, St. Petersburg, Russian Federation

3 A. N. Belozersky Institute for Physico-Chemical Biology, Lononosov Moscow State University, Moscow, Russian Federation

4 Institute of Cytology, Russian Academy of Sciences, St. Petersburg, Russian Federation

5 School of Biological Sciences F07, University of Sydney, Sydney, NSW, Australia

#### *Edited by:*

Télesphore Sime-Ngando, Centre National de la Recherche, France

#### *Reviewed by:*

Sigrid Neuhauser, Natural History Museum London, UK Jean-François Mangot, University of Oslo, Norway

#### *\*Correspondence:*

Sergey A. Karpov, Zoological Institute, Russian Academy of Sciences, St. Petersburg 199034, Russian Federation e-mail<EMAIL_ADDRESS>
The aphelids are a small group of intracellular parasitoids of common species of eukaryotic phytoplankton with three known genera Aphelidium, Amoeboaphelidium, and Pseudaphelidium, and 10 valid species, which form along with related environmental sequences a very diversified group. The phyla Microsporidia and Cryptomycota, and the class Aphelidea have recently been considered to be a deep branch of the Holomycota lineage forming the so called the ARM-clade which is sister to the fungi. In this review we reorganize the taxonomy of ARM-clade, and establish a new superphylum the Opisthosporidia with three phyla: Aphelida phyl. nov., Cryptomycota and Microsporidia. We discuss here all aspects of aphelid investigations: history of our knowledge, life cycle peculiarities, the morphology (including the ultrastructure), molecular phylogeny, ecology, and provide a taxonomic revision of the phylum supplied with a list of species. We compare the aphelids with their nearest relatives, the species of Rozella, and improve the diagnosis of the phylum Cryptomycota.

**Keywords: Opisthosporidia, Aphelida, Cryptomycota, Microsporidia,** *Rozella***, ultrustucture, molecular phylogeny, ecology**

### **INTRODUCTION**

The aphelids are a small group of intracellular parasitoids of algae, which are currently placed in the class Aphelidea (Gromov, 2000). The class Aphelidea includes two freshwater genera, *Aphelidium* and *Amoeboaphelidium,* and a marine genus, *Pseudaphelidium.* Although for a long time members of the class Aphelidea had uncertain affinities, the phylogenetic position of this class has been recently clarified by molecular phylogenetic analyses of *Amoeboaphelidium protococcarum* (Karpov et al., 2013). The aphelids belong to the supergroup Opisthokonta, which includes multicellular animals and fungi, and a variety of unicellular organisms, which over the past decade the molecular phylogeny has been tied to each of the two following major clades (Paps et al., 2013). The Metazoa, Choanoflagellata, and Mesomycetozoea now form the Holozoa (Torruella et al., 2012), whereas nucleariid amoebae, fungi, rozellids (Cryptomycota), aphelids, and microsporidia form the Holomycota (Liu et al., 2009; Lara et al., 2010; Jones et al., 2011a; Karpov et al., 2013; Letcher et al., 2013). Recent molecular phylogeny analyses show that the class Aphelidea is sister to both Microsporidia and Cryptomycota (Karpov et al., 2013). This strongly argues in favor of the re-classification of Aphelidea at the phylum rank. All the three phyla form a separate branch sister to classical ("true") fungi, which include Dikarya (Ascomycota and Basidiomycota), paraphyletic Zygomycota, and Chytridiomycota *sensu lato* (Voigt et al., 2013).

This review focuses on the aphelids and discusses their phylogeny, life cycle, morphology, ecology, and their taxonomy. Because of their close phylogenetic relationship and life cycle similarity (in contrast with the fast-evolving Microsporidia), we often consider them in comparison with Cryptomycota. Indeed, the aphelids have a life cycle similar to that of the Cryptomycota, but are parasitoids of algae, and not of zoosporic fungi and Oomycetes as are the known species of *Rozella*.

### **APHELID RELATIONSHIPS: HISTORICAL SKETCH**

Historical interpretations of the phylogenetic affinities of the aphelids were thoroughly discussed by Gromov (2000). Therefore, we only highlight here some of the important points. The research on aphelids began in the 19th century when the genus *Aphelidium* Zopf was first described (Zopf, 1885). 40 years later in 1925,*Amoeboaphelidium* Scherffel was described, and both these organisms were treated as the Cienkowski's "Monadinea" group, comprised of the extremely divergent "fungal animals" – organisms with a fungal-like life cycle, but having an amoeboid trophic stage (ref. in Gromov, 2000). In the 1950–1960s, the aphelids were included in the order Proteomyxida or subclass Proteomyxidia within the class Rhizopoda (Hall, 1953; Honigberg et al., 1964; Kudo, 1966). However, subsequently these protists were completely forgotten in classifications in later years (Levine et al., 1980; Karpov, 1990; Page and Siemensma, 1991; Cavalier-Smith, 1993, 1996/1997). This is difficult to understand because during the 1960s and 1970s a number of articles were written on *Aphelidium* and *Amoeboaphelidium* by Schnepf et al. (1971) and Gromov et al. (ref. in Gromov, 2000). By the end of the last century we knew much more about the life cycles, ultrastructure and biological peculiarities of several species of aphelids. Gromov (2000)reviewed this material and established a new class Aphelidea for *Aphelidium* Zopf, 1885, *Amoeboaphelidium* Scherffel, 1925, and *Pseudaphelidium* Schweikert et Schnepf, 1996.

Until recently, the relationship between the aphelids and fungi was unclear. Cavalier-Smith (1998) suggested that the genus *Aphelidium* belongs to the opisthokonts because of their posteriorly directed uniflagellate zoospores and flat mitochondrial cristae. Gromov (2000) placed the class Aphelidea in the phylum Rhizopoda *sensu lato* on the basis of the amoeboid nature of the trophozoite stage, despite an unpublished 18S rRNA partial sequence of *Amoeboaphelidium protococcarum* which suggested a relationship with Choanozoa (Pinevich et al., 1997). Later Karpov (Adl et al., 2005) transferred the class Aphelidea into the phylum Mezomycetozoea based on the preliminary 18S rRNA molecular phylogeny of *Aphelidium*. In addition to these classifications based on the 18S rRNA marker, aphelids were placed within Ichthyosporea based on their parasitic nature (Shalchian-Tabrizi et al., 2008), and then, based on their morphology and lifestyle, as a "new" order Aphelidida, class Rozellidea, in the new subphylum Paramycia Cavalier-Smith (2013) of the phylum Choanozoa Cavalier-Smith, 1981 (Cavalier-Smith, 2013). The creation of a "new"Aphelidida order was unjustified, since that order had already been established by Gromov (2000) earlier.

Previous classifications attempts based on 18S rRNA partial gene sequences were affected by the limited resolution of this marker for the eukaryotic tree. Only recently the molecular phylogeny of *Amoeboaphelidium protococcarum* (strain x-5 CALU), based on five genes (RPB1, RPB2, 18S, 28S, and 5.8S rRNA), unambiguously showed that the aphelids branch together with Cryptomycota (*Rozella* + related environmental sequences) and microsporidia forming the ARM (Aphelidea + *Rozella* + Microsporidia) branch (Karpov et al., 2013; **Figure 1**). Letcher et al. (2013) confirmed the phylogenetic position of *Amoeboaphelidium* by isolating one more strain (FD01) of *Amoeboaphelidium protococcarum* and studying its ultrastructure and molecular phylogeny based on 18S, 5.8S, and 28S rRNA gene sequences.

The fact that the aphelids form a monophyletic group with cryptomycota/rozellids and microsporidia to the exclusion of Chytridiomycota and other fungi is also reflected in the possession of a distinctive morphological feature. According to Gromov (2000) a unique characteristic of aphelids is the intracellular trophic stage which is amoeboid and which engulfs the host cell contents. A similar stage is found in *Rozella* (Powell, 1984), but is absent in fast evolving and highly derived Microsporidia (Vávra and Lukeš, 2013). This characteristic strongly differentiates the aphelids and *Rozella* from Chytridiomycota and other fungi, and is unambiguously supported by molecular phylogeny of *Rozella allomycis* and two strains of *Amoeboaphelidium protococcarum* together with their related environmental sequences (James et al., 2013; Karpov et al., 2013; Letcher et al., 2013).

### **LIFE CYCLES**

The complex life cycles of *Aphelidium, Amoeboaphelidium,* and *Pseudaphelidium* appear to be very similar to each other (**Figure 2**) and superficially similar to those of many species of Chytridiomycota with endobiotic development in their algal host cells (Gromov, 2000).

As an example, the *Aphelidium* opisthokont zoospore attaches to the host algae and encysts while losing its flagellum (**Figure 3A**). A cyst germinates and penetrates the host cell wall with an infection tube. The posterior vacuole appears in the cyst, enlarges, and then pushes the contents of the cyst into the interior of the host cell through the infection tube (**Figures 2** and **3A**). The parasitoid becomes the intracellular phagotrophic amoeba which engulfs the host cytoplasm with pseudopodia and transports the food into a central digestive vacuole. The parasitoid grows forming an endobiotic plasmodium with residual body while it totally consumes the cytoplasm of the host cell (**Figures 2** and **3B**). A multinucleate plasmodium is formed with a large central vacuole and a residual excretory body. The parasitoid does not form its own sporangium wall; rather it uses the host cell walls as the sporangium wall (**Figure 3C**). The mature plasmodium then divides into a number of uninucleated cells (**Figures 2** and **3C,D)**. After maturation, the uniflagellated zoospores of *Aphelidium* are released from the empty host cell through the hole made previously by the infection tube and infect other algae (**Figures 2** and **3D,E)**.

All known aphelids can produce multiple infections (**Figures 2** and **3**). In the case of *Amoeboaphelidium,* several amoebae grow separately in the infected alga but later fuse into a multinucleate cell, forming a single plasmodium in each infected cell (Gromov and Mamkaeva, 1968).

Sometimes giant multinucleate amoebae are released along with the uninucleate amoebae from the sporangium (Gromov and Mamkaeva, 1968). The origin of these giant amoebae is not clear, but they might result from the incomplete divisions of the mature plasmodium (Gromov and Mamkaeva, 1968). Most of these giants died after a short period of activity in culture.

Originally, amoeboid zoospores without any traces of flagella were described for *Amoeboaphelidium* (Scherffel, 1925; Gromov and Mamkaeva, 1968; Gromov, 2000). Further investigations of *Amoeboaphelidium protococcarum* (strain x-5 CALU) showed that amoebae actually have a posterior immobile pseudocilium (**Figure 4** – see discussion below). Gromov (2000) assumed that the type of propagule produced is genus specific. Indeed, the *Aphelidium* produces zoospores with a posterior flagellum (**Figures 2** and **3**), *Amoeboaphelidium* produces amoeboid zoospores with posterior pseudocilium (**Figures 2** and **4**), and *Pseudaphelidium* produces amoebae, which encyst. The cysts release one, two or, more often, four uniflagellated zoospores after germination (**Figure 2**). *Pseudaphelidium* zoospores lack the refractive globule, characteristic of *Aphelidium*.

Some aphelid species produce dormant or resting spores which are thought to be resistant to environmental extremes, and/or the result of sexual reproduction (Gromov andMamkaeva,1968,1975; Letcher et al., 2013). In this case, the plasmodium ejects the residual body into the space between the cell wall of the alga and the

**FIGURE 1 | Position of** *Amoeboaphelidium protococcarum* **on the tree inferred from rDNA analyses by Bayesian and ML methods (after: Karpov et al., 2013).** PhyloBayes tree topology was calculated from an alignment of 144 sequences and 4,384 nucleotide characters. Node support values are given as follows: Bayesian posterior probabilities (PhyloBayes/MrBayes) followed by bootstrap values (RAxML). We used the GTR + CAT model without partition by genes for PhyloBayes

calculations and the same model with partition by genes for RAxML calculations. The GTR + I + -12 model was used for MrBayes calculations. Filled circles indicate that all support values are above 98%; empty circles indicate that at least one support value is above 98%. Shown in gray are support values for clades not included in the consensus trees. Symbols in the inset indicate habitats for environmental sequences. Scale bar indicates substitutions per site.