Significance
Current models for biological invasions are predominantly based on macroorganisms. Few invasion model assumptions have been validated for microbial systems. Further research on microbial invasion dynamics is required to determine whether macrobial models are appropriate for microbes, as well as to understand present and future distributions of invasive microorganisms, particularly in the face of contemporary environmental changes. We studied the establishment of an invasive protist in natural microbial assemblages in replicate experimental microcosms and found that, under adequate environmental conditions, invasion success was determined by the number of invading propagules rather than resource availability and the diversity of the invaded communities. This study is among the first to test invasibility hypotheses using an actual invasive microbial species in natural communities.
Keywords: microbial ecology, diversity, invasion resistance, propagule pressure, Prymnesium
Abstract
The ecological dynamics underlying species invasions have been a major focus of research in macroorganisms for the last five decades. However, we still know little about the processes behind invasion by unicellular organisms. To expand our knowledge of microbial invasions, we studied the roles of propagule pressure, nutrient supply, and biotic resistance in the invasion success of a freshwater invasive alga, Prymnesium parvum, using microcosms containing natural freshwater microbial assemblages. Microcosms were subjected to a factorial design with two levels of nutrient-induced diversity and three levels of propagule pressure, and incubated for 7 d, during which P. parvum densities and microbial community composition were tracked. Successful invasion occurred in microcosms receiving high propagule pressure whereas nutrients or community diversity played no role in invasion success. Invaded communities experienced distinctive changes in composition compared with communities where the invasion was unsuccessful. Successfully invaded microbial communities had an increased abundance of fungi and ciliates, and decreased abundances of diatoms and cercozoans. Many of these changes mirrored the microbial community changes detected during a natural P. parvum bloom in the source system. This role of propagule pressure is particularly relevant for P. parvum in the reservoir-dominated southern United States because this species can form large, sustained blooms that can generate intense propagule pressures for downstream sites. Human impact and global climate change are currently causing widespread environmental changes in most southern US freshwater systems that may facilitate P. parvum establishment and, when coupled with strong propagule pressure, could put many more systems at risk for invasion.
Microbial species invasions, thought to occur worldwide in terrestrial and aquatic systems and involve both pathogenic and free-living taxa, represent an emerging challenge to our understanding of the interplay between biodiversity and ecosystem function, particularly under pressures of global environmental change (1). Despite assertions of limitless dispersal capability, sensu Baas-Becking (2), many microorganisms seem not to be cosmopolitan, and biogeographic studies suggest a significant effect of ecological drift and dispersal limitation in their distributions (3, 4).
Invasions of microbial species are hard to track because small, inconspicuous species are routinely overlooked in most assessments of invasive species (5) and are detected only when they have conspicuous impacts, such as the formation of blooms. Invasions from pathogenic microbes have been relatively well-studied because they are comparatively easy to track, in part, due to strong, observable impacts (1). A particularly well-studied example is the invasion of the human gut microbiota by pathogenic bacteria (6, 7). We know, however, much less about invasion by nonpathogenic microbes, even though we have evidence that they do occur in nature. For example, in the last two decades, a number of invasions by aquatic microbial species have been documented (8–11). Although there are some studies on the ecological impacts of these invasions (8), we still know little about their potential consequences.
Traditional macroorganism-based ecological theory suggests that invasion success tends to be highest in exotic species characterized by high dispersal abilities, growth rates, and resource efficiencies (12), and in native communities characterized by low species diversity and high disturbance levels, including fluctuation of resources (13). Once an invasion occurs, it can induce significant change in the invaded community, including modification of community structure and loss of species and ecosystem function (13).
Although the applicability of macrobial principles to microbial systems is debatable (1, 4), they constitute a general framework of study and readily testable hypotheses, such as whether microbial invasions are limited by dispersal or whether high diversity in native communities provides resistance to invasion by exotics. Indeed, few available experimental studies of microbial invasions have been designed to directly test such hypotheses, but, interestingly, they tend to suggest that interspecific interactions, rather than diversity per se, play critical roles in invasion resistance (14–16), although nutrient supply (17) and propagule pressure (18) have also been shown to facilitate microbial invasions.
Unfortunately, there are limitations in these previous studies, such as the use of artificial communities with very low species numbers. By not taking into account the enormous diversity, variability, and stochasticity inherent to natural communities, which can have thousands of interacting bacterial and protistan species, as well as viruses, it is unclear how applicable these earlier experimental studies are with respect to actual invasion dynamics. Additionally, these studies have used arbitrarily selected invaders that might lack the specific ecological and life history traits that are thought to facilitate microbial invasion (12).
A potential model organism for studying these processes is the golden alga, Prymnesium parvum. Despite being of marine origin, P. parvum has apparently invaded and successfully established in US freshwater ecosystems in the last 30 y and is currently present in inland systems in up to 20 states (19). This species is remarkably adaptable, being capable of growing in a wide range of temperature and salinity conditions (20). It is a mixotroph, acting both as a competitor of other algal species, and as a predator of bacteria (21) and other protists (22). It also produces toxic metabolites, which are thought to provide an advantage against competing species of algae (ref. 23; but see ref. 24). The generalist lifestyle, metabolic flexibility, and toxicity of P. parvum are among the main characteristics expected of an invasive species (1).
Also contributing to the use of P. parvum as a model for studying microbial invasions is a substantial body of literature dealing primarily with ecotoxicological aspects of P. parvum (25, 26). However, there are several recent observational (27–29) and experimental field studies (30, 31) examining the potential roles of immigration and nutrient availability in population dynamics and impacts of P. parvum. Although they provide good first steps in addressing the recent P. parvum range expansion, these earlier studies are limited in design. The field studies are correlational and provide good characterizations of P. parvum’s niche space, but they do not allow for inference into mechanisms or impacts of P. parvum population establishment. Background presence of P. parvum and unknown initial microbial community diversity in the field experiments prohibit isolation and identification of possible propagule pressure effects. Additionally, the field experiments do not address the response of invaded communities to successful invasion, nor are these studies placed within the broader framework of ecological and invasion theory.
In this study, we used a robust, replicated experiment comprising natural aquatic microbial communities to isolate the roles of propagule pressure and community resistance to invasion in the establishment success of P. parvum in an otherwise environmentally suitable habitat. We also characterized the response of the invaded microbial community. We hypothesized that (i) microcosms with high nutrients and low diversity would be more easily invaded, having more resources available and potentially a lower biotic resistance, (ii) increased propagule pressure would facilitate invasion, and (iii) successful invasion of P. parvum would cause distinctive changes in the richness and diversity of invaded microbial communities.
Results
Community Diversity Manipulation.
Microbial assemblages were created from composite mixtures of lake water collected from three different sites in Lake Texoma (Oklahoma and Texas, United States) during the summer non-P. parvum bloom season. Salinity and temperature were adjusted to 2.3 parts per thousand (ppt) and 15 °C to simulate the ambient winter lake conditions conducive to P. parvum blooms (29). Three days later, community resistance to invasion was manipulated in half of the microcosms by supplementing nitrogen and phosphorus levels to reduce community diversity and increase resource availability.
Nutrient manipulation resulted in clear differences in experimental microcosms after 5 d (day 8) of incubation (Table 1). Total chlorophyll was higher [generalized linear model (GLM), F-test, P < 0.001] whereas richness and alpha diversity of eukaryotic communities were lower (GLM, F-test, P = 0.008 and P < 0.001, respectively) (Table S1) in microcosms that received nutrient additions. Communities contained 6,543 bacterial and 1,079 eukaryote operational taxonomic units (OTUs, here defined as rRNA sequences with a 97% similarity), with community composition significantly affected by nutrients [permutational multivariate analysis of variance (PERMANOVA), F-test, P < 0.012 for both communities] (Table S2). Nonmetric multidimensional scaling (NMDS) ordination showed that microcosm assemblages for both eukaryotes and bacteria clustered by nutrient treatment (Fig. S1, blue symbols). It was under these conditions in which P. parvum was inoculated into the microcosms (day 8).
Table 1.
Chlorophyll and diversity conditions for microcosms before inoculation of propagule treatments
| Indicator | Low nutrient treatment | High nutrient treatment | P |
| Chlorophyll a | 32.2 ± 2.84 | 98.3 ± 7.02 | <0.001 |
| Eukaryotic richness | 703 ± 12.4 | 593 ± 37.2 | 0.008 |
| Eukaryotic alpha diversity | 60.1 ± 4.49 | 28.4 ± 4.34 | <0.001 |
| Bacterial richness | 4162 ± 197 | 3892 ± 489 | 0.425 |
| Bacterial alpha diversity | 479 ± 35.6 | 408 ± 85.6 | 0.259 |
Values are mean ± SD; n = 9 per treatment for chlorophyll (µg/L), n = 3 per treatment for richness (Chao1) diversity (inverse Simpson) estimates. Bold font indicates a significant difference between low and high nutrient treatments; for more detail on statistical analyses, consult Table S1.
Table S1.
Results for the generalized linear models (GLMs) testing the effects of nutrient treatments in the richness, alpha diversity, and chlorophyll content of microcosms at day 8, before inoculation of propagule treatments
| Treatment | df | Deviance | Resid. df | Resid. deviance | F-value | P |
| GLM—Effects of nutrients on bacterial richness | ||||||
| Total model | — | — | 5 | 665,166 | — | — |
| Nutrients | 1 | 109,441 | 4 | 555,724 | 0.788 | 0.425 |
| GLM—Effects of nutrients on bacterial alpha diversity | ||||||
| Total model | — | — | 5 | 24,646 | — | — |
| Nutrients | 1 | 7,432.3 | 4 | 17,213 | 1.727 | 0.259 |
| GLM—Effects of nutrients on eukaryotic richness | ||||||
| Total model | — | — | 5 | 21,126.4 | — | — |
| Nutrients | 1 | 18,057 | 4 | 3,069.3 | 23.532 | 0.008 ↓ |
| GLM—Effects of nutrients on eukaryotic alpha diversity | ||||||
| Total model | — | — | 5 | 1,589.82 | — | — |
| Nutrients | 1 | 1,511.8 | 4 | 78.07 | 77.458 | <0.001 ↓ |
| GLM—Effects of nutrients on chlorophyll concentration | ||||||
| Total model | — | — | 20 | 138.427 | — | — |
| Nutrients | 1 | 135.52 | 19 | 2.907 | 885.85 | <0.001 ↑ |
Bold font indicates significant results. Arrows indicate whether the measured indicator increased (up arrow) or decreased (down arrow) with the nutrient treatment (high nutrients, compared with low nutrients). Dashes represent values that cannot be assigned to that component. Resid. df, Residual df; Resid. deviance, Residual deviance.
Table S2.
Results for the permutational multivariate analysis of variance (PERMANOVA) testing the effects of nutrient treatments in the composition of bacterial and eukaryotic communities at day 8, before inoculation of propagule treatments
| Effects of nutrients in: | df | SS | MS | F-value | R2 | P |
| Bacterial communities | ||||||
| Nutrients | 1 | 0.193 | 0.193 | 1.439 | 0.171 | <0.001 |
| Residuals | 7 | 0.940 | 0.134 | — | 0.830 | — |
| Total | 8 | 1.133 | — | — | 1 | — |
| Eukaryotic communities | ||||||
| Nutrients | 1 | 0.185 | 0.185 | 5.169 | 0.425 | <0.001 |
| Residuals | 7 | 0.251 | 0.036 | — | 0.575 | — |
| Total | 8 | 0.436 | — | — | 1 | — |
Community composition was calculated using 97%-similarity OTUs. Bold font indicates significant results. Dashes represent values that cannot be assigned to that component. MS, Mean of sum of Squares; SS, Sum of Squares. F-values obtained by permutation.
Fig. S1.
Composition of (A) eukaryotic and (B) bacterial communities for microcosms at the time of Prymnesium parvum invasion (day 8) and at the end of the experiment (day 15), visualized using NMDS ordination. Shape of dots indicates nutrient treatment whereas colors indicate day, as indicated in the legend. NMDS stress values are 0.043 for bacteria and 0.119 for eukaryotes.
Invasion Success.
Propagule pressure was manipulated by adding P. parvum from laboratory cultures at three invading population sizes: 12,860,000 cells (6,430 cells per mL), 1,286,000 cells (643 cells per mL), and 128,000 cells (64 cells per mL). P. parvum establishment success was unaffected by the diversity and richness of the receiving microbial eukaryote community but was directly proportional to propagule pressure (P < 0.001) (Fig. 1 and Table S3). After addition of the highest propagule pressure, P. parvum population mean (±SD) density was 7,764 ± 1,616 cells per mL on day 15. In the medium propagule treatment, P. parvum was still present in three microcosms at very low densities (mean = 333 ± 422 cells per mL) on day 11, but remained in only one microcosm on day 15 (mean = 14 ± 34 cells per mL). In the low propagule pressure treatment, P. parvum was detected in one microcosm on day 11 but, by day 15, had become undetectable in all six microcosms. Therefore, high propagule pressure overwhelmed any effects of reduced resistance in the form of increased nutrients and lower community diversity.
Fig. 1.
Population densities of P. parvum in microcosms during the experiment in the three invading propagule pressure treatments (high, 6,430 cells per mL, red stars; medium, 640 cells per mL, blue squares; and low, 64 cells per mL, gray circles). Points for days 11 and 15 are offset for clarity.
Table S3.
Results for the generalized linear model (GLM) testing the effects of nutrient and propagule pressure in the final concentration of P. parvum cells at the end of the experiment (i.e., day 15)
| Treatment | df | Deviance | Resid. df | Resid. deviance | P (>Chi) |
| Total model | — | — | 17 | 103,235 | — |
| Propagule pressure | 2 | 101,316 | 15 | 1,919 | <0.001 |
| Nutrients | 1 | 0 | 14 | 1,919 | 1 |
Bold font indicates significant results. Dashes represent values that cannot be assigned to that component. Resid. df, Residual df; Resid. deviance, Residual deviance.
Effects of Invasion in the Microbial Community.
The microbial communities at the end of the experiment (day 15) for all microcosms combined contained 7,691 bacteria and 1,064 eukaryote OTUs. Although the nutrient treatments still were evident (Fig. 2 and Table S4) (linear mixed-effects model, nutrient effect, P < 0.02 for bacterial diversity and eukaryote richness and diversity), taxonomic richness and diversity were substantially lower on day 15 compared with day 8 across all propagule pressure and nutrient treatments (compare Table 1 and Fig. 2; Table S4) (time effect, all P ≤ 0.02). There was no effect of propagule pressure nor any interaction between propagule pressure and nutrient treatment on the diversity or richness of any of the communities (Table S4) (propagule effect, all P > 0.05). Communities on days 8 and 15 were clearly different, not only in diversity values, but also in community composition, as visualized by NMDS ordination (Fig. S1), regardless of treatment.
Fig. 2.
Community conditions of experimental microcosms at the end of the experiment (day 15) showing (A) taxonomic richness and (B) alpha diversity for eukaryotes (blue symbols) and bacteria (red symbols) for three propagule pressure treatments: low (circles), medium (triangles), and high (crosses). Richness (Chao1) and diversity (Simpson’s reciprocal) of microbial communities were calculated using 97%-similarity OTU abundances.
Table S4.
Results for the linear mixed-effects models (LMEMs) testing the effects of time, nutrient, and propagule pressure treatments and their interactions in the richness, alpha diversity, and chlorophyll content of microcosms at the end of the experiment (i.e., day 15)
| Effects of day, nutrients, and propagule pressure on: | Num. df | Den. df | F-value | P |
| Eukaryotic richness | ||||
| (Intercept) | 1 | 19 | 770.196 | — |
| Time | 1 | 2 | 306.31 | 0.003 ↓ |
| Nutrients | 1 | 19 | 7.154 | 0.015 ↓ |
| Propagule pressure | 3 | 2 | 4.292 | 0.195 |
| Nutrients × propagule pressure | 3 | 2 | 0.731 | 0.622 |
| Eukaryotic alpha diversity | ||||
| (Intercept) | 1 | 19 | 939.275 | — |
| Time | 1 | 2 | 366.806 | 0.003 ↓ |
| Nutrients | 1 | 19 | 49.113 | <0.001 ↓ |
| Propagule pressure | 3 | 2 | 1.934 | 0.359 |
| Nutrients × propagule pressure | 3 | 2 | 16.837 | 0.057 |
| Bacterial richness | ||||
| (Intercept) | 1 | 19 | 3,514.135 | — |
| Time | 1 | 2 | 55.211 | 0.018 ↓ |
| Nutrients | 1 | 19 | 0.551 | 0.467 |
| Propagule pressure | 3 | 2 | 1.585 | 0.409 |
| Nutrients × propagule pressure | 3 | 2 | 1.342 | 0.454 |
| Bacterial alpha diversity | ||||
| (Intercept) | 1 | 19 | 1,441.212 | — |
| Time | 1 | 2 | 93.929 | 0.011 ↓ |
| Nutrients | 1 | 19 | 8.456 | 0.009 ↓ |
| Propagule pressure | 3 | 2 | 1.123 | 0.503 |
| Nutrients × propagule pressure | 3 | 2 | 1.091 | 0.511 |
Bold font indicates significant results. Arrows indicate whether the measured indicator increased (up arrow) or decreased (down arrow) with time (at day 15, compared with day 8) and with the nutrient treatment (high nutrients, compared with low nutrients). Dashes represent probability values that cannot be assigned to that component. Denom. df, denominator df; Num. df, Numerator df.
Day-15 eukaryote and bacteria communities were different across nutrient and propagule pressure treatments (Fig. 3 and Table S5) (PERMANOVA, F-test, all P < 0.013), but no interaction between treatments was detected. Among the propagule pressure treatments, the successfully invaded microcosms (i.e., the high propagule pressure treatment) clustered separately from all other treatments (gray ellipses in Fig. 3) (Table S6) (PERMANOVA post hoc analysis, P ≤ 0.047 for all pairwise comparisons with the high propagule pressure treatment). Control communities, which received no P. parvum inocula or nutrient additions, did not differ in composition from noninvaded communities (Fig. 3, orange squares). In the case of the eukaryotic communities, the inoculation of P. parvum cells as part of the propagule pressure treatment was not the source of significant differences. Analysis of community composition after removing all OTUs identified from the order Prymnesiales revealed no differences from analysis of the complete communities in terms of NMDS ordination or grouping.
Fig. 3.
Composition of (A) eukaryotic and (B) bacterial communities in microcosms at the end of the experiment (day 15) visualized using NMDS ordination. Colors indicate low (blue), medium (green), or high (yellow) propagule pressure; shape indicates either low (triangles) or high (circle) nutrient treatments. Communities from control bottles appear as orange squares. High (High PP) and all remaining (Non-high PP) propagule pressure treatments are surrounded by 95% confidence dispersion ellipses. NMDS stress values are 0.145 for bacteria and 0.160 for eukaryotes.
Table S5.
Results for the permutational multivariate analysis of variance (PERMANOVA) testing the effects of nutrient and propagule pressure treatments in the composition of bacterial and eukaryotic communities at the end of the experiment (i.e., day 15)
| Effects of nutrients in: | df | SS | MS | F. model | R2 | P |
| Bacterial communities | ||||||
| Propagule pressure | 3 | 0.468 | 0.156 | 1.561 | 0.205 | <0.001 |
| Nutrients | 1 | 0.229 | 0.229 | 2.289 | 0.100 | <0.001 |
| Propagule pressure × nutrients | 2 | 0.191 | 0.095 | 0.954 | 0.083 | 0.596 |
| Residuals | 14 | 1.399 | 0.100 | — | 0.612 | — |
| Total | 20 | 2.286 | — | — | 1 | — |
| Eukaryotic communities | ||||||
| Propagule pressure | 3 | 1.052 | 0.351 | 4.573 | 0.415 | <0.001 |
| Nutrients | 1 | 0.231 | 0.231 | 3.009 | 0.091 | 0.013 |
| Propagule pressure × nutrients | 2 | 0.182 | 0.091 | 1.186 | 0.072 | 0.271 |
| Residuals | 14 | 1.074 | 0.077 | — | 0.423 | — |
| Total | 20 | 2.539 | — | — | 1 | — |
Community composition was calculated using 97%-similarity OTUs. Bold font indicates significant results. Dashes represent values that cannot be assigned to that component.
Table S6.
Post hoc comparison of the composition of bacterial and eukaryotic communities between different propagule pressure treatments at the end of the experiment (i.e., day 15), using permutational multivariate analysis of variance (PERMANOVA)
| Group 1 | Group 2 | F-Model | R2 | P | Adjusted P |
| PERMANOVA—Post hoc comparison of bacterial communities | |||||
| Control | Low | 0.955 | 0.120 | 0.595 | 0.678 |
| Control | Med | 1.111 | 0.137 | 0.226 | 0.678 |
| Control | High | 1.795 | 0.204 | 0.012 | 0.047 |
| Low | Med | 1.043 | 0.094 | 0.276 | 0.678 |
| Low | High | 1.995 | 0.166 | 0.002 | 0.015 |
| Med | High | 1.720 | 0.147 | 0.002 | 0.015 |
| PERMANOVA—Post hoc comparison of eukaryotic communities | |||||
| Control | Low | 1.672 | 0.193 | 0.048 | 0.144 |
| Control | Med | 1.837 | 0.208 | 0.071 | 0.144 |
| Control | High | 4.617 | 0.397 | 0.011 | 0.045 |
| Low | Med | 1.793 | 0.152 | 0.063 | 0.144 |
| Low | High | 8.793 | 0.468 | 0.002 | 0.013 |
| Med | High | 4.754 | 0.322 | 0.004 | 0.020 |
Community composition was calculated using 97%-similarity OTUs. P values were adjusted for multiple comparisons using a Holm–Bonferroni correction. Bold font indicates significant results.
Characteristic Species.
Regarding community composition, changes in the abundances of eukaryotic phyla (rank 3) or bacterial classes (Fig. S2) were limited to higher proportions of fungi and ciliate OTUs in invaded microcosms and more diatom and cercozoan OTUs in the noninvaded microcosms. However, indicator value analysis (32) revealed that the primary distinction between invaded and noninvaded communities was driven by changes in abundances of taxa at lower taxonomic levels and specific OTUs (Tables S7 and S8). Noninvaded communities were characterized by 26 eukaryotic and 14 bacterial OTUs. The most abundant indicator eukaryotes in noninvaded communities were diatoms (seven OTUs) comprising mostly centric diatoms from the subphyla Bacillariophytina and Coscinodiscophytina. Ciliates, chlorophytes, and dinoflagellates were also present (six, four, and four OTUs, respectively). Remaining groups in noninvaded communities included cercozoans, centrohelids, chrysophytes, and an unknown eukaryotic OTU. Indicator bacterial taxa were predominantly from the phylum Actinobacteria (10 OTUs), mostly from the order Actinomycetales. Other indicator bacterial OTUs were from the phyla Cyanobacteria, Planctomycetes, and Chloroflexi.
Fig. S2.
Composition of (A) eukaryotic and (B) bacterial communities for high and non-high propagule pressure (PP) treatments, using 97%-similarity OTUs. Eukaryotes are shown at the level of rank 3 (previously phylum) and bacteria are shown at the level of class. OTUs that constituted less than 0.2% of each microcosm library have been omitted for clarity.
Table S7.
Indicative eukaryotic OTUs, as determined using the Dufrene–Legendre indicator species analysis
| OTU | Rank 1 | Rank 2 | Rank 3 | Rank 4 | Rank 5 | Rank 6 | Rank 7 | Indicator value | Adjusted P value |
| Non-high propagule pressure treatments | |||||||||
| Denovo2228 | Archaeplastida | Chloroplastida | Chlorophyta | Chlorophyceae | Carteria | — | — | 0.9382716 | 0.009 |
| Denovo167 | Archaeplastida | Chloroplastida | Chlorophyta | Chlorophyceae | Carteria | — | — | 0.7678487 | 0.043 |
| Denovo196 | Archaeplastida | Chloroplastida | Chlorophyta | Trebouxiophyceae | - | — | — | 0.797411 | 0.042 |
| Denovo2920 | Archaeplastida | Chloroplastida | Chlorophyta | Trebouxiophyceae | Parachlorella | — | — | 0.7509579 | 0.027 |
| Denovo925 | Centrohelida | M1-18D08 | — | — | — | — | — | 0.9651972 | 0.009 |
| Denovo919 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | Peritrichia | 0.8937785 | 0.014 |
| Denovo3439 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | Peritrichia | 0.8002665 | 0.042 |
| Denovo1651 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Prostomatea | Coleps | 0.7443182 | 0.038 |
| Denovo3009 | SAR | Alveolata | Ciliophora | Intramacronucleata | Spirotrichea | Choreotrichia | Tintinnopsis | 0.8 | 0.038 |
| Denovo326 | SAR | Alveolata | Ciliophora | Intramacronucleata | Spirotrichea | Hypotrichia | Halteria | 0.869808 | 0.024 |
| Denovo1650 | SAR | Alveolata | Ciliophora | Intramacronucleata | Spirotrichea | Hypotrichia | Halteria | 0.857233 | 0.027 |
| Denovo1369 | SAR | Alveolata | Dinoflagellata | Dinophyceae | Peridiniphycidae | Gonyaulacales | Ceratium | 0.9333333 | 0.009 |
| Denovo3588 | SAR | Alveolata | Dinoflagellata | Dinophyceae | Peridiniphycidae | Gonyaulacales | Ceratium | 0.9333333 | 0.009 |
| Denovo1335 | SAR | Alveolata | Dinoflagellata | Dinophyceae | Peridiniphycidae | Gonyaulacales | Ceratium | 0.8666667 | 0.014 |
| Denovo3366 | SAR | Alveolata | Dinoflagellata | Dinophyceae | Peridiniphycidae | Gonyaulacales | Ceratium | 0.8 | 0.027 |
| Denovo806 | SAR | Rhizaria | Cercozoa | — | — | — | — | 0.986124 | 0.009 |
| Denovo1201 | SAR | Rhizaria | Cercozoa | Vampyrellidae | Vampyrella | — | — | 0.9025506 | 0.041 |
| Denovo2148 | SAR | Stramenopiles | Chrysophyceae | LG01-09 | — | — | — | 0.7834646 | 0.038 |
| Denovo354 | SAR | Stramenopiles | Diatomea | — | — | — | — | 0.6281194 | 0.019 |
| Denovo432 | SAR | Stramenopiles | Diatomea | — | — | — | — | 0.821684 | 0.042 |
| Denovo1498 | SAR | Stramenopiles | Diatomea | Bacillariophytina | Mediophyceae | — | — | 0.9083779 | 0.016 |
| Denovo343 | SAR | Stramenopiles | Diatomea | Bacillariophytina | Mediophyceae | Skeletonema | — | 0.8666667 | 0.014 |
| Denovo1271 | SAR | Stramenopiles | Diatomea | Bacillariophytina | Mediophyceae | Thalassiosira | — | 0.6878613 | 0.010 |
| Denovo1216 | SAR | Stramenopiles | Diatomea | Coscinodiscophytina | Fragilariales | Synedra | — | 0.7738693 | 0.042 |
| Denovo3801 | SAR | Stramenopiles | Diatomea | Coscinodiscophytina | Fragilariales | Synedra | — | 0.8136752 | 0.043 |
| Denovo673 | Unknown eukaryote | — | — | — | — | — | — | 0.8923679 | 0.009 |
| High propagule pressure treatments | |||||||||
| Denovo1076 | SAR | Alveolata | Apicomplexa | Conoidasida | Coccidia | Adeleorina | — | 0.9126984 | 0.014 |
| Denovo2321 | SAR | Alveolata | Apicomplexa | Conoidasida | Cryptosporida | Cryptosporidium | — | 0.8823529 | 0.029 |
| Denovo1859 | SAR | Alveolata | Ciliophora | — | — | — | — | 0.8612135 | 0.038 |
| Denovo2968 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | Scuticociliatia | 0.8012821 | 0.010 |
| Denovo1373 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | Scuticociliatia | 0.9016393 | 0.014 |
| Denovo2923 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | Scuticociliatia | 0.7936508 | 0.024 |
| Denovo2150 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | Scuticociliatia | 0.7768362 | 0.025 |
| Denovo2657 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Oligohymenophorea | CV1-2A-17 | 0.6382979 | 0.043 |
| Denovo2394 | SAR | Alveolata | Ciliophora | Intramacronucleata | Conthreep | Prostomatea | Cryptocaryon | 0.8012821 | 0.014 |
| Denovo3365 | SAR | Alveolata | Ciliophora | Postciliodesmatophora | Heterotrichea | — | — | 0.6432749 | 0.042 |
| Denovo677 | SAR | Alveolata | Ciliophora | Postciliodesmatophora | Heterotrichea | Spirostomum | — | 0.7978723 | 0.014 |
| Denovo2602 | SAR | Alveolata | Ciliophora | Postciliodesmatophora | Heterotrichea | Spirostomum | — | 0.8592133 | 0.024 |
| Denovo2544 | SAR | Alveolata | Ciliophora | Postciliodesmatophora | Heterotrichea | Spirostomum | — | 0.883758 | 0.041 |
| Denovo2863 | SAR | Stramenopiles | Chrysophyceae | — | — | — | — | 0.7383966 | 0.043 |
| Denovo3633 | SAR | Stramenopiles | Chrysophyceae | Ochromonadales | Ochromonas | Ochromonas | — | 0.6666667 | 0.030 |
| Denovo152 | SAR | Stramenopiles | Chrysophyceae | Ochromonadales | Paraphysomonas | — | — | 0.84 | 0.014 |
| Denovo3735 | SAR | Stramenopiles | Chrysophyceae | Ochromonadales | Paraphysomonas | — | — | 0.8632287 | 0.014 |
| Denovo3289 | SAR | Stramenopiles | Chrysophyceae | Ochromonadales | Paraphysomonas | — | — | 0.617284 | 0.044 |
| Denovo1095 | SAR | Stramenopiles | Chrysophyceae | Ochromonadales | Paraphysomonas | — | — | 0.6666667 | 0.027 |
| Denovo3070 | SAR | Stramenopiles | Pirsonida | Pirsonia | — | — | — | 0.7801418 | 0.042 |
The indicator value of each OTU is determined by the product of the relative frequency and relative average abundance in each propagule pressure treatment. The P value is the false discovery rate (FDR)-adjusted probability of obtaining as high an indicator value as observed over 1,000 iterations of the analysis. Low-abundance OTUs (those having fewer than 10 sequences among all microcosms) were removed from the analysis. Dashes represent taxonomic ranks where no identification consensus could be reached for the OTU.
Table S8.
Indicative bacterial OTUs, as determined using the Dufrene–Legendre indicator species analysis
| OTU | Phylum | Class | Order | Family | Genus | Indicator value | Adj. P value |
| Non-high propagule pressure treatments | |||||||
| Denovo32742 | Actinobacteria | Acidimicrobiia | Acidimicrobiales | C111 | — | 0.8580645 | 0.024 |
| Denovo33315 | Actinobacteria | Actinobacteria | Actinomycetales | — | — | 0.8200717 | 0.034 |
| Denovo4218 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.9112426 | 0.006 |
| Denovo11127 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.8666667 | 0.010 |
| Denovo20884 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.9135802 | 0.016 |
| Denovo14048 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.8148148 | 0.024 |
| Denovo19018 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.9107143 | 0.024 |
| Denovo23164 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.8657005 | 0.024 |
| Denovo28240 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.8514851 | 0.024 |
| Denovo1320 | Actinobacteria | Actinobacteria | Actinomycetales | ACK-M1 | — | 0.8307692 | 0.034 |
| Denovo21463 | Chloroflexi | Anaerolineae | Caldilineales | Caldilineaceae | — | 0.6564885 | 0.043 |
| Denovo32663 | Cyanobacteria | Oscillatoriophycideae | Oscillatoriales | Phormidiaceae | Planktothrix | 0.6548507 | 0.015 |
| Denovo795 | Cyanobacteria | Synechococcophycideae | Pseudanabaenales | Pseudanabaenaceae | — | 0.6902655 | 0.016 |
| Denovo33080 | Planctomycetes | Planctomycetia | Planctomycetales | Planctomycetaceae | Planctomyces | 0.9107143 | 0.010 |
| High propagule pressure treatments | |||||||
| Denovo33043 | Bacteroidetes | Flavobacteriia | Flavobacteriales | Flavobacteriaceae | — | 1 | 0.006 |
| Denovo9881 | Bacteroidetes | Flavobacteriia | Flavobacteriales | Flavobacteriaceae | — | 0.8333333 | 0.014 |
| Denovo29919 | Bacteroidetes | Flavobacteriia | Flavobacteriales | Flavobacteriaceae | — | 0.8259912 | 0.015 |
| Denovo2352 | Bacteroidetes | Flavobacteriia | Flavobacteriales | Flavobacteriaceae | — | 0.8078231 | 0.016 |
| Denovo5439 | Bacteroidetes | Flavobacteriia | Flavobacteriales | Flavobacteriaceae | Winogradskyella | 0.6666667 | 0.044 |
| Denovo219 | Proteobacteria | Alphaproteobacteria | Caulobacterales | Caulobacteraceae | — | 0.9677419 | 0.006 |
| Denovo32145 | Proteobacteria | Alphaproteobacteria | Caulobacterales | Caulobacteraceae | — | 0.9668508 | 0.006 |
| Denovo6254 | Proteobacteria | Alphaproteobacteria | Caulobacterales | Caulobacteraceae | — | 0.9574468 | 0.006 |
| Denovo27327 | Proteobacteria | Alphaproteobacteria | Rhizobiales | — | — | 0.9863946 | 0.006 |
| Denovo9454 | Proteobacteria | Alphaproteobacteria | Rhizobiales | — | — | 0.9817352 | 0.006 |
| Denovo32791 | Proteobacteria | Alphaproteobacteria | Rhodobacterales | Rhodobacteraceae | — | 1 | 0.006 |
| Denovo11300 | Proteobacteria | Alphaproteobacteria | Rhodobacterales | Rhodobacteraceae | — | 0.7838284 | 0.034 |
| Denovo8546 | Proteobacteria | Alphaproteobacteria | Rickettsiales | Rickettsiaceae | — | 0.9922179 | 0.006 |
| Denovo15444 | Proteobacteria | Alphaproteobacteria | Rickettsiales | Rickettsiaceae | — | 0.8248731 | 0.015 |
| Denovo4158 | Proteobacteria | Alphaproteobacteria | Rickettsiales | Rickettsiaceae | — | 0.8333333 | 0.024 |
| Denovo11138 | Proteobacteria | Deltaproteobacteria | Bdellovibrionales | Bacteriovoracaceae | — | 0.8961474 | 0.006 |
| Denovo32148 | Proteobacteria | Deltaproteobacteria | Bdellovibrionales | Bacteriovoracaceae | — | 0.9375 | 0.006 |
| Denovo27723 | Proteobacteria | Deltaproteobacteria | Bdellovibrionales | Bacteriovoracaceae | — | 0.8995816 | 0.024 |
The indicator value of each OTU is determined by the product of the relative frequency and relative average abundance in each propagule pressure treatment. The P value is the false discovery rate (FDR)-adjusted probability of obtaining as high an indicator value as observed over 1,000 iterations of the analysis. Low-abundance OTUs (those having fewer than 20 sequences among all microcosms) were removed from the analysis. Dashes represent taxonomic ranks where no identification consensus could be reached for the OTU.
By contrast, invaded communities were characterized by 20 eukaryotic and 18 bacterial OTUs. Indicator eukaryotes in invaded communities included 11 ciliate OTUs from diverse families, chrysophytes from the genera Paraphysomonas and Ochromonas (six OTUs), one pirsonid, and two apicomplexans. Bacterial OTUs included the phyla Proteobacteria (13 OTUs from the classes Alphaproteobacteria and Deltraproteobacteria) and Bacteroidetes (five OTUs from the class Flavobacteriales).
Discussion
We studied an experimental P. parvum invasion in natural microbial assemblages from Lake Texoma, a reservoir with a history of seasonal P. parvum blooms. To characterize the parameters that drive the successful establishment of this invasive species, we manipulated nutrient supply and propagule pressure, allowing us to elucidate the intrinsic importance of each of these factors and their potential interactions as they pertain to the invasion process.
Within experimental microcosms, invasion success was determined exclusively by propagule pressure, with successful invasions occurring only in high propagule pressure microcosms, which had increased P. parvum cell densities at the end of the experiment (day 15). Conversely, microcosms with failed invasions showed no presence of P. parvum cells, meaning that the alga was either absent or present at abundances below our detection limit of ∼200 cells per mL. In these cases, it seems that resistance to invasion of the receiving community was strong enough to prevent establishment and growth of the initial inoculum.
In contrast to our initial hypotheses and general invasion theory, nutrient treatments had no effect on invasion success, even though this disturbance increased the gross resource supply and yielded a reduced number and diversity of species, both factors that are thought to facilitate invasions (1, 13). This lack of an effect of increased resource availability may reflect the fact that our system is eutrophic and that ambient nutrient availability was already high. Alternatively, it might be attributed to the metabolic flexibility of P. parvum, which can obtain energy both autotrophically and heterotrophically from a wide variety of sources (22).
Bottle effects may have altered available niche space and influenced the response of the communities to treatments (as evidenced by the effect of time on community composition in Fig. S1), possibly causing the observed dissociation between diversity and invasibility. However, previous experiments on P. parvum invasion processes showed that communities in large (1,570-L) enclosures and small (2-L) microcosms presented no significant differences in plankton dynamics for longer periods than those encompassed by our experiment (30). Furthermore, the difference between days was probably also caused by medium-term responses of the community to initial environmental changes because stabilization of communities in response to disturbances is a continuous process that can extend for longer periods than those covered by our incubation period (33).
Our results also are at odds with those of previous experiments that found that P. parvum abundances were correlated with nutrient concentrations, but not with additions of propagules (30). In that experiment, however, initial communities had differing background levels of P. parvum cells, and the number of introduced cells was low, equivalent to our low propagule pressure treatment, which, as shown here, did not exert enough propagule pressure to become established in the community. Thus, the main differences between treatments in that experiment were likely driven by the response of the background levels of P. parvum to the nutrient additions.
Studies in macroorganisms have predicted that one of the consequences of invasion is species loss (13), a phenomenon not observed in our microcosms, where diversity and richness were not different across propagule pressure treatments on day 15. However, we observed a strong effect of propagule pressure in the community composition. The use of next generation sequencing allowed us to characterize the effects of this community response with greater precision and less bias than previous studies, which measured community composition using either microscope identification (34) or quantification of photosynthetic pigments (30, 35). Both of these methods yield a skewed version of community composition, with a bias toward large, well-characterized taxa clustered into broad taxonomic categories (e.g., nanoflagellates). Compared with these studies, effects that we observed were subtler, with primary effects in the abundances of specific OTUs rather than in abundances of entire higher level taxonomic groups.
Analysis of indicator species showed that a wide diversity of freshwater microbial taxa responded to invasion. Most of these changes were in agreement with previous observations in P. parvum invasions, such as the reduction in abundances of diatoms, cercozoans, and chlorophytes (30, 34, 36, 37) and the increased abundances of chrysophytes (37). We also observed a general increase in fungal OTU number, which has also been observed in P. parvum blooms in Lake Texoma (37). Ciliates can be both prey and predators of P. parvum (38), and the presence of distinctive but closely related ciliate OTUs in both invaded and noninvaded microcosms suggests that species within this group can have opposite responses to the community changes induced by the invasion of P. parvum.
Although there are few previous studies on microbial communities associated with P. parvum blooms, the most abundant indicator species for high propagule pressure communities were members of Alphaproteobacteria and Bacteroidetes, groups that are predominant in the bacterial communities attached to algal cells (39). Non-high propagule pressure communities were enriched with the order Actinomycetales, a ubiquitous and abundant group of limnetic bacteria (40), whose abundance also was found to decrease during P. parvum blooms (37).
Overall, even if none of our microcosms reached the high densities that characterize the dense nearly monospecific blooms that are the usual focus of P. parvum studies (25), the observed community changes detected agree with previous observational research in Lake Texoma. There, a year-long study found that a P. parvum bloom was associated with a drastic disruption in the normal pattern of microbial community seasonal succession and with community changes in microbial components of the planktonic assemblage (27, 37) that mirror those seen in this study.
At first glance, our results seem to contradict previous findings of a primary role for environmental conditions in P. parvum blooms in Lake Texoma, regardless of dispersal of propagules (29). In this reservoir, winter blooms of P. parvum have occurred almost yearly since 2003, but only in the western arm of the reservoir, characterized by relatively high salinities (28, 29). Although still presumably subjected to downstream transport of propagules from these large, sustained blooms, which can reach densities over 2 × 105 cells per mL, the rest of the lake has not experienced any establishment of P. parvum (defined as recurrent winter blooms). However, it is important to keep in mind that, before propagule exposure, we manipulated our microcosms’ salinity levels to mimic those thought to be conducive to P. parvum establishment: i.e., ∼2 ppt (29, 41), effectively creating a niche space for P. parvum. The role of salinity is particularly important, P. parvum being a marine species that is already at the limit of its salinity tolerance in this reservoir (41) and with most recorded blooms occurring when salinity exceeds 1.7 practical salinity units (psu) (29).
Based on our studies, it is clear that environmental conditions are primary factors determining P. parvum distributions and bloom formation. However, once these conditions are met, the intensity of dispersal (measured as propagule pressure) becomes a determining factor in establishment success and biogeographic patterns of this invasive species. It is important to note that the temperature, nutrient, and salinity conditions present in our microcosms fall well within the ranges found naturally in Lake Texoma and other freshwater systems in the southern United States and that these particular conditions are projected to increase in frequency in the coming decades (42, 43), creating situations that, in tandem with the already existing high propagule supply, could drive further range expansions and blooms of P. parvum.
Our results failed to support several of our initial hypotheses and seemingly contradict common assumptions about invasions in natural ecosystems. Increases in nutrient supply and decreases in community diversity did not facilitate P. parvum invasions and, at least within the ranges used in this experiment, had no influence in invasion success. Instead of resource availability, invasibility was determined by propagule pressure, and microcosms within the same propagule treatment presented similar outcomes in terms of establishment success. Although the important role of propagule pressure is in agreement with previous observations in terrestrial and aquatic ecosystems (44), the absence of any observed interaction between nutrient availability, biotic resistance, and propagule pressure as proposed in conceptual models (45) was unexpected.
These results expand our knowledge of the general processes behind microbial invasions of planktonic communities and validate results gleaned from synthetic ecosystems, such as the important role of propagule pressure for invasion success (18) and the lack of an intrinsic biotic resistance by more diverse communities (14, 15). Our experiment also reveals possible mechanisms behind the observed increase in the range of P. parvum populations within the United States and may provide a means of identifying systems with high risk of invasion. P. parvum blooms can maintain densities over 1.5 × 105 cells per mL for prolonged periods of time (27), and many of these blooms are terminated through hydraulic flushing during periods of high inflow (27, 46), potentially exerting significant propagule pressure to downstream reservoirs and lakes (29, 47). Nevertheless, propagule supply remains an unknown factor in natural systems, even as efficient tools to measure P. parvum cell densities along lake and riparian systems have been developed (48). We believe that the implementation of routine sampling programs is a feasible goal that not only will serve as an early-warning system for the formation of P. parvum blooms but also may validate the role of propagule pressure in ecosystem invasibility and provide information for implementing accurate monitoring and successful management strategies.
Materials and Methods
Experimental Design.
Experiments were conducted with natural freshwater microbial communities in microcosms. Samples from surface waters of three sites in Lake Texoma (sampling stations: L2, frequent winter P. parvum blooms; L4, P. parvum present in winter; and L6, P. parvum rarely observed) (28) were retrieved, prefiltered with a 63-μm mesh to remove large zooplankton, mixed, and distributed into 21 2.5-L experimental microcosms. The absence (here defined as below the detection limit of 200 cells per mL) of P. parvum was confirmed by counting 10 hemocytometer fields at 200× magnification. Microcosms were incubated under artificial light in a 12-h light:12-h dark cycle on a bottle roller at 15 °C for 3 d, and then salinity was manipulated (day 3) by additions of synthetic sea salt (Instant Ocean; Spectrum Brands; final salinity = 2.3 ppt) to simulate ambient winter lake conditions, which are conducive to P. parvum blooms (29). Nutrients were added to nine microcosms on day 3 to generate high nutrient (0.199 mg/L phosphorus, 1.25 mg/L nitrogen; n = 9) and low (ambient) nutrient (0.069 mg/L phosphorus, 0.79 mg/L nitrogen; n = 9) treatments. We also maintained a control treatment (n = 3) in which neither nutrients nor propagule pressure were manipulated. All microcosms were then incubated for 5 d under the same late winter and early spring temperature and light conditions to allow microbial communities to respond to nutrients.
On day 8, bottles were divided into low, medium, and high propagule pressure treatments by adding cells from cultures of P. parvum isolated from Lake Texoma (strain UOBS-LP0109). Culture cell densities were estimated using the hemocytometer method described above, and samples were diluted to achieve the desired cell concentrations. Each nutrient × propagule pressure treatment combination was triplicated.
At the beginning of the invasion experiment (day 8), a 200-mL sample was taken from three low nutrient and three high nutrient microcosms for genetic material extraction and sequencing. Afterward, microcosms were incubated for 7 d, at the end of which (day 15) 250-mL samples were taken from all microcosms for genetic material extraction and sequencing. Chlorophyll a was measured on days 8 and 15, with a TD 700 bench-top fluorometer.
DNA Extraction and Sequencing.
DNA was extracted using a standard phenol/chloroform extraction protocol. Sequences encompassing variable regions of the ribosomal SSU gene were used as genetic markers for microbial communities. For bacteria, we amplified the SSU v6 region using the 967F and 1064R primers (49, 50), and, for eukaryotes, we amplified the SSU v9 region with the 1380F and 1510R primers (51). Amplicon sequencing was done in a 454 GS FLX Titanium system in the University of Oklahoma Advanced Center for Genome Technology (ACGT) (52, 53). Sequences obtained were quality checked; sequences with divergences from the primers or with an average quality <25 in a 50-bp sliding window were discarded. Minimum length for trimmed sequences was >50 bp for bacteria and >120 bp for eukaryotes. Chimeric sequences were detected using the ChimeraSlayer algorithm of the QIIME 1.8.0 software (54) and removed from the database. Sequences were uploaded to the NCBI Sequence Read Archive, under BioProject PRJNA271537 and Biosamples SAMN03274828–SAMN03274857.
Community Analyses.
Communities were analyzed using QIIME; OTUs were determined de novo using a 97% similarity threshold. Taxonomic assignment of bacterial OTUs was performed using the assign_taxonomy pipeline from QIIME, using the Greengenes database trees from August 2012 (55). Unclassified OTUs, singletons, and OTUs classified as archaea, eukaryotes, chloroplasts, or mitochondria were removed from the dataset. Taxonomic assignment of eukaryotic OTUs was performed using the Silva NGS online platform (56) with default parameters. Because some remaining OTUs had high abundances in our dataset, they were manually checked using BLASTN (57) searches and assigned the best consensus classification based on similarity to available 18S sequences. Unclassified, bacterial, archaeal, and metazoan OTUs and singletons were removed from the dataset.
Community data were exported as a BIOM file, imported into R 3.1.0 (58), and analyzed using phyloseq v1.8.1 (59). Before community analyses, all libraries were normalized to the size of the smallest library using random sampling without replacement; a random number generator seed was selected a priori to ensure reproducibility.
Alpha diversity on days 8 and 15 was measured using the reciprocal Simpson's index whereas richness was estimated using Chao1. Differences between community composition of each microcosm were visualized using nonmetric multidimensional scaling (NMDS) with phyloseq. Beta diversity was calculated with the Bray–Curtis index using vegan v2.0–10 (60). Detection and measurement of P. parvum cells in the microcosms were done at the end of the experiment, using hemocytometer-based microscopy as described above.
Statistical Analyses.
All statistical analysis were conducted in R. To analyze the differences in diversity, richness, and chlorophyll in microcosms on day 8 (the beginning of the invasion experiment), we used a generalized linear model with a Gaussian distribution, using nutrient treatment as the independent variable. At the end of the experiment (day 15), we analyzed the same diversity estimators for all microcosms using a linear mixed-effects model with day, nutrient, propagule pressure, and the interaction between nutrients and propagule pressure as fixed factors, and microcosm identity as a random factor; both analyses were done with nlme v3.1–117 (61).
To test for differences in P. parvum cell concentrations at the end of the experiment, we used a generalized linear model, using a quasi-poisson distribution to account for overdispersion (62). Assumptions of normality and heterogeneity of variance for the residuals were checked using R plotting functions. The effects of propagule pressure and nutrient supply on microbial community composition were tested with a permutational multivariate analysis of variance (PERMANOVA) using Bray–Curtis distance matrices with vegan. Post hoc comparisons between high and non-high propagule pressure communities were done using pairwise PERMANOVAs with Holm–Bonferroni-adjusted probability values.
For determination of indicator species, low-abundance OTUs with <10 sequences for eukaryotes and <20 sequences for bacteria were removed because they were unlikely to contribute to significant differences between the different groups. Using these modified OTU libraries, we calculated the indicator value d of each OTU as the product of the relative frequency and relative average abundance in groups, using labdsv v.1.6–1 (63), and adjusted P values for multiple comparisons using the false discovery rate control.
Acknowledgments
We thank J. D. Easton, A. C. Easton, K. L. Glenn, and J. E. Beyer (Plankton Ecology and Limnology Laboratory) and Y. Xing, L. Zhou, K. Wang, and H. Lau (Advanced Center for Genome Technology) for technical assistance, and A. C. Jones and D. A. Caron for helpful discussions during the design and implementation of the experiment. Comments from two anonymous reviewers significantly improved this paper. Financial assistance for this project was provided by grants from the National Science Foundation (Grant DEB-1011454 to R.M.Z. and K.D.H.), the Oklahoma Department of Wildlife Conservation (through the Sport Fish Restoration Program) (Grant F-61-R to K.D.H.), the Oklahoma Water Resources Research Institute (to K.D.H.), and the University of Oklahoma Office of the Vice President for Research (to K.D.H. and B.A.R.).
Footnotes
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Data deposition: The sequences reported in this paper have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive under BioProject PRJNA271537 and Biosamples SAMN03274828–SAMN03274857.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1505204112/-/DCSupplemental.
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