Bayesian SSU rDNA phylogeny of <i>Reticulamoeba</i> isolates (Lineages 1–4) and other Granofilosea in a cercozoan context.

<p>Support values shown when above the following thresholds: Bayesian posterior probabilities >75; ML bootstrap support >50%; or lower when of particular interest for the branching order within Granofilosea. Black filled circles indicate support of >95% bootstrap and 0.95 posterior probability. Gran- and Endo- clade designations refer to Bass et al. (2009).</p

Different forms and growth stages of <i>Reticulamoeba minor</i> (Lineage 1; Isolates 1–3).

<p><b>1A</b>–<b>1D</b>. Mature amoeboid cells with granular reticulopodia emerging from and radiating around cells. In <b>1B</b> and <b>1D</b> potential food sources (diatoms and bacteria) are associated with the <i>Reticulamoeba</i> cells. <b>1E</b>–<b>1H</b>. Swimming/gliding flagellate stages. <b>A</b>, <b>B</b>, <b>D</b>, <b>E</b> = Isolate 1; <b>C</b> = Isolate 3; Isolates 1–3 were phenotypically indistinguishable. Scale bars (A-D)  = 10 µm; (E-H)  = 5 µm.</p

Amoeboid form of <i>Reticulamoeba</i> Isolate 4 (Lineage 2).

<p> Flagellate forms not shown but very similar to <i>R. minor</i> and <i>R. gemmipara</i>. The cells of this isolate were always strongly associated with diatoms, as in these photos. Scale bar  = 10 µm.</p

Different forms and growth stages of <i>Reticulamoeba gemmipara</i> (Lineage 4; Isolates 6 & 7).

<p><b>2A</b>–<b>2C</b>. Mature amoeboid cells with forming ‘daughter’ cells. <b>2D</b>–<b>2G</b>. Earlier stage cells, including (<b>2E</b>, <b>2F</b>) cells formed within an hour of flagellate forms settling. <b>2H</b>–<b>2I</b>. Swimming/gliding flagellate stages. All images of Isolate 6, except D (Isolate 7). Scale bar  = 10 µm.</p

Maximum Likelihood (RAxML) SSU rDNA phylogeny of <i>Reticulamoeba</i> isolates 1–7 and the six main lineages identified in this study.

<p>58 sequences, 1067 positions. ML bootstrap values shown when >70%.</p

Maximum Likelihood (RAxML) SSU rDNA tree including sequences from BioMarKs.

<p>The BioMarKs (V4 region) sequences were generated using eukaryote-wide primers, and are labelled ‘BioMarKs: …’. The two such lineages shown were the only sequences in the whole of the BioMarKs data that were related to <i>Reticulamoeba</i>. 738 positions used for analysis.</p

Table_1_Optimization of environmental DNA analysis using pumped deep-sea water for the monitoring of fish biodiversity.xlsx

Deep-sea ecosystems present difficulties in surveying and continuous monitoring of the biodiversity of deep-sea ecosystems because of the logistical constraints, high cost, and limited opportunities for sampling. Environmental DNA (eDNA) metabarcoding analysis provides a useful method for estimating the biodiversity in aquatic ecosystems but has rarely been applied to the study of deep-sea fish communities. In this study, we utilized pumped deep-sea water for the continuous monitoring of deep-sea fish communities by eDNA metabarcoding. In order to develop an optimum method for continuous monitoring of deep-sea fish biodiversity by eDNA metabarcoding, we determined the appropriate amount of pumped deep-sea water to be filtered and the practical number of filtered sample replicates required for biodiversity monitoring of deep-sea fish communities. Pumped deep-sea water samples were filtered in various volumes (5–53 L) at two sites (Akazawa: pumping depth 800 m, and Yaizu: pumping depth 400 m, Shizuoka, Japan) of deep-sea water pumping facilities. Based on the result of evaluations of filtration time, efficiency of PCR amplification, and number of detected fish reads, the filtration of 20 L of pumped deep-sea water from Akazawa and filtration of 10 L from Yaizu were demonstrated to be suitable filtration volumes for the present study. Fish biodiversity obtained by the eDNA metabarcoding analyses showed a clear difference between the Akazawa and Yaizu samples. We also evaluated the effect of the number of filter replicates on the species richness detected by eDNA metabarcoding from the pumped deep-sea water. At both sites, more than 10 sample replicates were required for the detection of commonly occurring fish species. Our optimized method using pumped deep-sea water and eDNA metabarcoding can be applied to eDNA-based continuous biodiversity monitoring of deep-sea fish to better understand the effects of climate change on deep-sea ecosystems.</p

DataSheet_1_Optimization of environmental DNA analysis using pumped deep-sea water for the monitoring of fish biodiversity.pdf

Deep-sea ecosystems present difficulties in surveying and continuous monitoring of the biodiversity of deep-sea ecosystems because of the logistical constraints, high cost, and limited opportunities for sampling. Environmental DNA (eDNA) metabarcoding analysis provides a useful method for estimating the biodiversity in aquatic ecosystems but has rarely been applied to the study of deep-sea fish communities. In this study, we utilized pumped deep-sea water for the continuous monitoring of deep-sea fish communities by eDNA metabarcoding. In order to develop an optimum method for continuous monitoring of deep-sea fish biodiversity by eDNA metabarcoding, we determined the appropriate amount of pumped deep-sea water to be filtered and the practical number of filtered sample replicates required for biodiversity monitoring of deep-sea fish communities. Pumped deep-sea water samples were filtered in various volumes (5–53 L) at two sites (Akazawa: pumping depth 800 m, and Yaizu: pumping depth 400 m, Shizuoka, Japan) of deep-sea water pumping facilities. Based on the result of evaluations of filtration time, efficiency of PCR amplification, and number of detected fish reads, the filtration of 20 L of pumped deep-sea water from Akazawa and filtration of 10 L from Yaizu were demonstrated to be suitable filtration volumes for the present study. Fish biodiversity obtained by the eDNA metabarcoding analyses showed a clear difference between the Akazawa and Yaizu samples. We also evaluated the effect of the number of filter replicates on the species richness detected by eDNA metabarcoding from the pumped deep-sea water. At both sites, more than 10 sample replicates were required for the detection of commonly occurring fish species. Our optimized method using pumped deep-sea water and eDNA metabarcoding can be applied to eDNA-based continuous biodiversity monitoring of deep-sea fish to better understand the effects of climate change on deep-sea ecosystems.</p

Data Sources

Fasta files with assembled transcriptomes of Carpediemonas-like organism

Protein alignments

Alignments of organellar protein