Cocos

Ophiuroid species names were collected from the literature and entered into the online World Ophiuroidea Database [1] , part of the World Register of Marine Species (WoRMS) [6] . The current taxonomic status of the about 3000 nominal species and over 4000 names (including new combinations) was assessed and recorded in the database. Then these data were used to assemble Table 1, numbers of species and genera per family. The systematics largely follows Smith et al. [46] , except where more recent information is available. Ophiocanopidae was removed by Stöhr et al. [61] and its only genus Ophiocanops is included in Ophiomyxidae. The genera Ophiomoeris and Ophiochondrus, formerly placed in Hemieuryalidae, have recently been transferred to Ophiacanthidae [47] . The systematics of the Euryalida has been revised recently and the family Asteroschematidae has been lowered to subfamilial rank within Euryalidae [51] (link).
A biogeographic analysis of the world's extant ophiuroid species was performed by extracting a list of described species from the World Ophiuroidea Database [1] . Distributional data was obtained from a global database of museum catalogue sample data [62] (link), supplemented by additional records from the taxonomic literature to ensure a coverage of all species. We selected this database, because the World Ophiuroidea Database is complete with regard to taxonomic information, but still lacking in distributional data. Other possible databases that collect distribution data are the Encyclopedia of Life (EoL), the Global Biodiversity Information Facility (GBIF) and the Ocean Biogeographic Information System (OBIS), but none of these has yet sufficient amounts of data. The imprecise nature of the data contained in older taxonomic literature did not permit a quantitative approach to defining biogeographical regions. Instead, the world's marine environment was divided into 12 a priori large-scale regions based on available information (Figure 6, see below) and four depth strata: shelf (0–200 m), bathyal (200–3500 m), abyssal (3500–6500 m) and hadal (below 6500 m) [63] . The aerial extent of these regions and depth strata was calculated from the ETOPO bathymetric dataset [64] . Equatorial regions were defined as being bounded by the 30° latitude in both hemispheres, the approximate boundary of tropical shallow-water coral-reef distributions [65] and the bathyal tropical-temperate transition in the Indo-Pacific [46] , [59] . Polar regions were bounded by 60° latitudes, thus separating the Antarctic continent from most of the subantarctic islands [66] . Temperate/boreal regions were defined as falling between these zones, 30–60° in each hemisphere. Longitudinal boundaries were set for the equatorial and southern temperate regions in mid-ocean reflecting the faunal relationship between offshore areas and nearby continental margins. The Indian Ocean boundary was set at 90°E, placing the Chagos and St Paul/Amsterdam islands in the Indian and South Africa regions respectively, and the Christmas/Cocos Islands and Indo-Malay archipelago in the Indo-Pacific region. The Atlantic regions were broadly separated by the Mid-Atlantic Ridge. The boundary in the Pacific Ocean was placed between the eastern Pacific islands of Juan Fernandez-Galapagos-Clipperton and the Indo-Pacific Hawaii-Pitcairn-Easter Islands. These regions reflect our knowledge of the fauna at shelf and upper bathyal depths, however, we have adopted the same regions for deeper areas to facilitate inter-depth comparisons. In reality, species ranges will not be exactly congruent and adjacent biogeographic regions or depth strata are likely to form broad transition zones, making it problematic to define precise biogeographical boundaries [62] (link). The temperate regions in particular contain enhanced species turnover between tropical, temperate and polar faunas [62] (link). The lack of quantitative location data from the older taxonomic literature also precludes the adjustment of regional species richness by sampling effort [67] . Despite these limitations, we believe that the data are useful for a first approximation of global ophiuroid biogeography.

Mussels were collected from the Main Beach site of Tatoosh Island (48.32°N, 124.74°W), located in the eastern Pacific 0.7 km off the northwestern tip of Washington State, USA. Six mussel shells were collected from among 6 tidepools, while 6 more were collected at a distance of approximately 5 m apart on an adjacent exposed bench on 10 April 2008 and immediately cleaned of all soft tissue. The shells (mean length 4.47 cm and 4.42 cm for tidepool and bench mussels respectively) were put on ice and brought to Argonne National Labs.
DNA was extracted and purified using Ultraclean Mega Prep Soil DNA Isolation Kit and following directions therein (MO BIO Laboratories,Inc.) and the two extractions are referred to as tidepool versus bench mussels. The tidepool sample yielded 4320 ng in 108 µL (Invitrogen Qubit fluorometer dsDNA HS Kit), while bench mussels had 168 ng in 350 µL and required use of the GenomiPhi V2 DNA Amplification Kit (GE Healthcare). We followed the Roche GS-FLX (454) shotgun library preparation protocol; the tidepool sample used 2.4 µg and the bench sample used 5.0 µg for library preparation. Both samples had a mean fragment size of 750 bp after library preparation. All sequencing was performed with the 454 GS-FLX instrument and LR70 sequencing chemistry (Roche Applied Science).
We analyzed the taxonomic composition of our two metagenome sample sets with the MG-RAST server [26] (link) using similarity to a large non-redundant protein database. Using the same non-redundant database, we also tested the affinities of our sequences for known metabolic function against both SEED subsystems [27] (link) and KEGG metabolic pathways [28] using a maximum e-value of e<10⁻⁵. Although there are a number of metabolic functions that can be tested, our specific interest in microbial contributions to the nitrogen cycle focused our efforts on both nitrogen metabolism and carbon dioxide fixation. Thus, we probed particularly for enzymes related to the components of nitrogen and CO₂ use.
In addition to describing the taxonomic and metabolic features of this microbial community on mussels, we also tested the similarity and differences with other recently described marine microbial assemblages that are public, including those of coastal Georgia [29] (link), 4 tropical Pacific Ocean seawater samples in the Line Islands [19] , and the extensive Global Ocean Sampling Expedition [13] (link). For the latter, we chose for comparison 4 coastal locales that that spanned a wide geography and sampled surface waters, including the Gulf of Maine (GS002, MG-RAST id #4441579.3), Nag's Head, NC (GS013, 4441585.3), Cocos Island, Costa Rica (GS025, 4441593.3), and an upwelling zone off of Fernandina, Galapagos (GS031, 4441597.3). We excluded marine metagenome analyses that had selectively filtered and extracted samples to isolate viruses. We focused our analyses on nitrogen metabolism and CO₂ fixation to test the similarities and differences of our mussel-associated microbes. Given the abundance of nitrogen in our mussel-associated waters, we further asked if another nitrogen-rich ecosystem, soils of the agriculture-influenced midwest, showed metabolic similarities. Here, we compared our mussel microbial assemblage to soil samples from Midwestern locales (Waseca farm soil (4441091.3), soybean field (4442657.3), prairie remnant (4442656.3), 2^nd yr prairie (4442658.3), 20^th year prairie (4442659.3), 33^rd year prairie (4441281.3)). For all comparisons, we used a non-redundant protein database with an e-value cut-off of 10⁻⁵. We recognize that the ‘discovery rate’ for proteins may depend upon the efficacy of DNA extraction and the length of sequences that result, features that may vary among studies. Although we normalized the number of proteins identified with different metabolic functions by the number of proteins that were found per 100 fragments, we had no means of controlling for the different contiguous sequence lengths that occurred among different studies.
The mussel associated sequences are publicly available in the MG-RAST system under the following project identifiers (IDs 4441185.3 (tidepool), 4441191.3 (emergent,bench). The data in this manuscript and the analyses and comparisons to other public data sets are available via MG-RAST. MIGS/MIMS [30] compliant metadata describing the locations, sampling, data extraction and data is available in GCDML [31] (link) format from within the MG-RAST system as well.

The sclerotia and mycelia of W. cocos were immediately stored in liquid nitrogen until further processing. Total RNA was extracted from1 g sclerotia and mycelia using TRIzol reagent (Life Technologies Inc., Carlsbad CA, USA) and was treated with RNase-free DNase I for 30 min at 37°C (Qiagen Inc., Duesseldorf, Germany) to remove residual DNA. The RNA sample was sent to BGI (Beijing Genomic Institute) for RNA sequencing(BGI Inc., Shenzhen,China). Sequencing was carried out as follows. Beads with oligo(dT) were used to isolate poly(A) mRNA from total RNA. Fragmentation buffer was added to fragment mRNA into short fragments of 200–700 bp. Taking these short fragments as templates, random hexamer primer was used to synthesize the first-strand cDNA. The second-strand cDNA was synthesized using buffer, dNTPs, RNaseH and DNA polymerase I. Short fragments were purified with QiaQuick PCR extraction kit and resolved with EB buffer for end reparation and adding poly(A) (Qiagen Inc., Duesseldorf, Germany). The short fragments were connected with sequencing adapters, and after agarose gel electrophoresis, suitable fragments were selected for PCR amplification as templates. Finally, the library was sequenced using Illumina HiSeq 2000(Illumina Inc., San Diego CA, USA).

The present experiment was carried out during 2021-22 at National phytotron facility, ICAR-Indian Agricultural Research Institute, New Delhi. The materials for present investigation comprised of 10 germplasms of cucumber collected from various parts of India. On the basis of their performance in two rounds of screening and performance under open field conditions, these genotypes are grouped into thermotolerant and thermosensitive. Five thermotolerant ‘TT’ and five thermosensitive ‘TS’ cucumber lines were grown in the pots with standard NPH potting mixture of soil, sand and coco peat in ratio 2:1:1 (v/v) (Table 1; Figure 1). Three seeds were sown in each pot and 5 replications were maintained for each genotype in both control and treatment conditions. A completely randomized design (CRD) with three replications per genotype per treatment was used. Data were taken from 3 randomly selected replications from each genotype. Seedlings were irrigated by sprayer cans with water and Hoagland nutrient solution every day morning and kept in such a condition that there was no water deficit. More frequent watering of plants was done under treatment to avoid any moisture stress. To avoid infections with fungal diseases, seedlings were occasionally sprayed with captan 2g/liter.

In this experiment, we implement a single-stage training on AIRES dataset to check the performance drop. Pre-trained weights on COCO dataset were used.