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Fig. 8 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 8 | Total microbial diversity across vertebrate hindguts and within multi- plebodysites of fish. a Hindgutmicrobiotasamplesfrom 569 speciesof vertebrates were rarified to 5000 reads and unique or shared ASVs determined for each class. b The percentage of unique ASVs only found in a given class (not shared in other classes) as compared to the total ASVs within that class. c Rarefaction of cumulative gamma diversity as a function of unique vertebrate species. Included is a single fish species, S. japonicus, sampled over 3 years "black dots" and the unrarefied FMP samples which had detectable bacteria in all four body sites (gill, skin, midgut, and hindgut).d Gammadiversity of 68 fishspeciesacrossfour bodysites.e Percentageof unique ASVs associatedwitha given bodysiteacrossthe 68 fish species.f Rarefaction curve of increasing gamma diversity (inclusive of four body sites) as a function of increasing fish species. ASV amplified sequence variant, 5k 5000.

Fig. 7 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 7 | Microbial source tracking analysis. a Microbial sources of 60 sea water (blue circle) samples taken from 30 unique sampling stations from two timepoints are distributed on a 10 km transect from Torrey Pines beach to Mission Bay. Microbial sources of 108 marine sediment samples (red stars) from San Diego coastalenvironmentincludes 60 paired samples (samelocationsas seawater) from the same 10 km transect along with 58 samples from the various reef habitats near La Jolla. Geographic data presented using ArcGIS. b Sourcetracker2 analysis of likely sources for the four body sites of the fish comparing contributions of beach sand, marine sediment, sea water, and "unknown". Unknown refers to microbes from an unknown source which could include diet and other animals or locations not sampled. c Specific microbial contributions of sea water to the four mucosal body sites and d specific microbial contributions of marine sediment to the four mucosalbodysites b–d: distributionisin medianand interquartilerange.Statistical differences determined using non-parametrictesting Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. e Proportion of microbes (distribution is in median and interquartile range) likely originating from the sea water vs. sediment for each unique body site (sea water vs. sediment pairwise comparison for each body site using Mann–Whitney test p <0.05).f Spearmanrho valuesfromcomparisons ofthe ratio of sea water "SW" and marine sediment "SED" against various continuous fish life history metadata variables for each unique body site (Spearman correlation p <0.05). g Comparison of the SW:SED ratios across the habitats from which the fish live. Comparisons performed on each unique body site (Kruskal–Wallis test, p <0.05). *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001, ASV amplified sequence variant ~unique sub-Operational Taxonomic Unit, SD standard deviation, MG midgut, HG hindgut, KW Kruskal–Wallis test statistic "H", IQR inter quartile range, SW sea water.

Fig. 5 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 5 | Biological and life history drivers of mucosal microbiota in diverse sampling of marine fish from Southern California. a Multivariate analysis of biological and life history parameters evaluated using unweighted and weighted normalized UniFrac distances. Statistical significance (PERMANOVA p = 0.001) indicated by yellow blocks (left) and effect size (right). All samples compared together (all) along with individual sample types (gill, skin, midgut, hindgut). b Impact of trophic level on similarity between midgut and hindgut (within a species) (linear model:p p value, mslope,dottedlinesare 95% confidence interval). F-Stat test statistic used in PERMANOVA analysis, all row names in a are metadata column names used in the analysis, MG midgut, HG hindgut, Gen. Weighted UniFrac generalized weighted UniFrac.

Fig. 6 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 6 | Evidence forphylosymbiosis across fishbody sites. Effectof evolutionary distance (low divergence time indicates a short branch length or similar fish species) of all fish compared to a skin unweighted UniFrac distance, b gill generalized weighted UniFrac distance, c hindgut generalized weighted UniFrac distance. Comparisons performed using Mantel test with multiple testing by FDR. Divergence time between fish species calculated using timetree.org. Gen. Weighted UniFrac generalized weighted UniFrac.

Fig. 3 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 3 | Alpha diversity and biomass comparisons across ecological and biolo- gical gradients in marine fish. Comparison of microbial diversity a "Chao1", b "Faith's Phylogenetic Diversity", c "Shannon", or d microbial biomassacrossbody site (gill, skin, midgut, and hindgut). Distributions in "red" are median with inter- quartile range. Statistical differences determined using non-parametric testing Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. Further testing computed for each unique body site for a variety of biological and ecological metadata categories. Metadata whichis e categorical istestedusing Kruskal–Wallis f whereas numeric metadata tested using Spearmancorrelation. Onlysignificant associations are represented in e (Kruskal–Wallis p <0.05) and f (Spearman p <0.05). KW or KW stat "H" test statistic from Kruskal–Wallis test, MG midgut, HG hindgut, Faith PD Faith's Phylogenetic Diversity metric, GI:TL gastrointestinal length to fish total length "ratio", TL total length of fish.

Fig. 4 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 4 | Associations between fishmass and collection location as measuredby and d distance from shore with gill microbial biomass. e Comparison of fish mass distancefrom shorewith fishgill microbialbiomassand alphadiversity. Subset and f distancefromshorewithalpha diversitymetrics (Chao1).g Comparison of fish of fish from EPO and Atlantic (n = 54) collected from ocean (excludes bay and mass and h distance from shore with alpha diversity metric: Faith's PD. estuary samples) and from the neritic zone (<200 m depth). a Correlation matrix c–h (Confidenceintervalsof 95% aredisplayedasdotted lines). habita- between sample metadata where values are rho and significance indicated by t_act_collection refers to the metadata column name from where this habitat clas- *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001 (Spearmancorrelation). sification can be found…, SZsurf zone, RIT rocky intertidal, RST rocky subtidal, IS b Comparison of gill microbial biomass (log cells per gram) across habitat types inner shelf, KBRF kelp bed rocky reef, MDRF mid depth rocky reef, CP coastal from which the fish were collected. Distribution is in median and interquartile pelagic, P pelagic. Mass_g_log = log 10 (mass of the fish in grams), dis- range. Statistical differences determined using non-parametric testing tance_from_shore_m_log = log 10 (distance from nearest point on shore in meters Kruskal–Wallistest with 0.05 FDRBenjamini–Hochberg. c Comparison of fish mass from where the fish was caught).

Fig. 2 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 2 | Limit of detection, sample exclusion, and microbial biomassestimation for FMP101 dataset. a Application of KatharoSeq formula to calculate limit of detection of microbiota platesusing the Bacillus/Paracoccus mock community (1150 reads at 90%). b Limit of detection based on cell counts of Bacillus/ Paracoccus mock community (~16 cells into extraction at 90%). c Model fit of the log(sequencing read counts) of positive extraction controls vs. the log cell counts of those positive extraction controls (empirically determined using plate counts. The linear regression of the line is indicative of the quality of method to estimate microbial biomass from sequencing read counts. Con- fidenceintervalsof 95% aredisplayedas dottedlines. Thismethodissimilar to a qPCR curvewherethe log (Ct) would beequivalent to the log(read counts).This equation is then used to estimate the number of "microbial density" of the existing samples which is then further normalized by the volume of the DNA extraction,biomass of materialgoinginto theextraction and finallynormalized to at estimated microbial cells per gram of tissue. d Community analysis comparison and validation of compositionality of controls of twosets of mock community controls (section 1 = Bacillus/Paracoccus mock community; section 2 = zymo mock community). Putative contaminant g__Geobacillus identified (presentin 93% of negatives and higherrelative abundance ascompared to positives and samples). e Number of samples successful across the four body sites collected from the broad fish microbiota dataset. QC quality control, g__ refers to a genus of bacteria, HM mock homemade mock or human made mixture of bacteria to use as a control whereas zymo mock = mock microbial community created by a company "Zymo".

Fig. 1 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fig. 1 | Samplingdesignof 116 speciesof marine fish. a Using ArcGIS todepictthe general area from which fish were sampled: black dots indicate the locations of the 101 unique species of marine fish sampled from the California Current Ecosystem in the Eastern Pacific Ocean primarily in the waters of San Diego CA. Red circles depict the locations of an additional 17 species of fish (15 unique species with 2 species duplicates) collected from the Western Atlantic primarily in the waters of New York. When multiple species of fish were caught in the same location, a single circle is used to indicate the location. b Fish were sampled across a gradient of depth and distances from shore. c Biometric measurements taken for nearly all fish include total length, fork length, mass, gape, and GI length. Various ratios from these lengths were also calculated. Microbiota samples from the gill were primarily whole tissue specimens from the entire left second gill arch or a section of the top middle and bottom of the entire filament. Skin mucus samples were taken by scraping using a razor blade. Midgut digesta material was collected from directly posterior of the stomach or if stomach was absent, the beginning of the GI tract. Hindgut digesta samples were taken from near the anus. Image from phylopic. MG midgut, HG hindgut, GI gastrointestinal tract, m meters.

FIGURE 3 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 3 Variation in HeV AUC from flying foxes. (A) The forest plot displays annual estimates and 95% confidence intervals ordered by latitude and year; points are scaled by the inverse sampling variance. The horizontal axis uses a modulus transformation to accommodate wide upper bounds of some confidence intervals. (B) Fitted values and 95% confidence intervals for the top GAM, with raw data (scaled by inverse sampling variance) and modelled means coloured by roost type. Transparency denotes AUC derived from truncated time series (≤20 weeks)

FIGURE 2 Fitted HeV urine pool prevalence and 95 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 2 Fitted HeV urine pool prevalence and 95% confidence intervals from the most parsimonious GAMM with week, seasonal interactions with roost type and previous food shortages, and an adjustment for relative abundance of Pteropus alecto. Weekly data are overlaid, coloured by roost type, and sized by corresponding P. alecto relative abundance. Thin lines show the fitted curves from the random factor smooth including each roost per year

FIGURE 1 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 1 Spatiotemporal variation in HeV shedding for the nine Australian flying fox roosts sampled from 2012 through 2014. Curve height indicates the weekly proportion of HeV-positive urine pools, with roosts shown in order of latitude and coloured by roost type. Ticks show sampling time points. Dark grey shading indicates regional acute food shortage events, and dashed lines with light grey shading indicate the Austral winter (i.e. June through August)

FIGURE 4 in Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts

FIGURE 4 Spatiotemporal variation in regional HeV spillover events during the flying fox surveillance period (2012–2014) and its relationship with HeV AUC. (a) Maps display the annual distributions of spillovers (coloured by year) in relation to the nine analysed roosts. (b) Modelled relationships between AUC and spillover counts are shown with fitted values and 95% confidence intervals from GAMs for 50, 100, 200, 300, 400 and 500 km buffers of each roost. Raw data are overlaid and scaled by the inverse of the sampling variance for AUC

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Data from: A well-studied parasitoid fly of field crickets uses multiple alternative hosts in its introduced range

Organisms and their natural enemies can have dynamic coevolutionary trajectories, but anthropogenic effects like species introductions interrupt existing coevolutionary relationships. For parasites in particular, if they are introduced to a location without their hosts, they can only persist in the new environment if alternative hosts are 1) present, 2) detectable to parasites, and 3) capable of sustaining parasites. The circumstances surrounding the addition of alternative hosts to a parasite's repertoire are rarely observed. The parasitoid fly Ormia ochracea locates its field cricket hosts by orienting acoustically to their conspicuous mating songs. In Hawaii, O. ochracea is only known to parasitize one species, Teleogryllus oceanicus, but rapid evolution of T. oceanicus mating song over the past 20 years has led to several prevalent morphs of the cricket that produce no song or novel songs that the flies cannot detect. Yet flies persist in populations that lack ancestral singing T. oceanicus, prompting us to investigate the possibility of alternative hosts in Hawaii. We demonstrate first that three potential alternative hosts (Gryllodes sigillatus, Gryllus bimaculatus, and Modicogryllus pacificus) are present. Second, O. ochracea exhibits a positive phonotactic response to all three species' songs in the field and in the lab. And third, O. ochracea can successfully develop to pupae and emerge as adults in all three species. Our discovery of alternative hosts for O. ochracea in Hawaii infuses the system with intriguing complexity and offers extensive opportunities for future work.

Figures 1–2 in First host record, nesting behavior, and taxonomic position of the spider wasp genus Hesperopompilus Evans and some other Evans genera (Hymenoptera: Pompilidae)

Figures 1–2. Hesperopompilus sp. (undescribed). 1) Female dragging a Eustala anastera (Araneidae), adult or subadult female, backwards across an exposed tree rootlet, grasping the immobilized orb-weaver spider by its left second coxa with her mandibles. The spider was held slightly above the substrate in a cephalothorax upright position. 2) Female in front of her nest entrance excavating a burrow in sandy soil using her forelegs alternately. She held her apical antenna segments against the floor of the opening and wings flat on her dorsum. Photographs © Yukioz.

Figures 3–4. Pompilid spp. and their hosts. 3 in First host record, nesting behavior, and taxonomic position of the spider wasp genus Hesperopompilus Evans and some other Evans genera (Hymenoptera: Pompilidae)

Figures 3–4. Pompilid spp. and their hosts. 3) Ammosphex anomalus, identified initially as Perissopompilus phoenix, female dragging a Xysticus sp. (Thomisidae), adult female, backwards across stony ground, grasping the femur of its right hind leg with her mandibles. Photograph © Alice Abela. 4) Xerochares expulsus female dragging an Olios giganteus (Sparassidae), immature male, backwards across the ground, grasping the trochanter of its left foreleg with her mandibles. Photograph © Kelly Harrington.

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Pollinator and host sharing lead to hybridization and introgression in Panamanian free-standing figs, but not in their pollinator wasps

Obligate pollination mutualisms, in which plant and pollinator lineages depend on each other for reproduction, often exhibit high levels of species-specificity. However, cases in which two or more pollinator species share a single host species (host sharing), or two or more host species share a single pollinator species (pollinator sharing), are known to occur in the current ecological time. Further, evidence for host switching in evolutionary time is increasingly being recognized in these systems. The degree to which departures from strict specificity differentially affect the potential for hybridization and introgression in the associated host or pollinator is unclear. We addressed this question using genome-wide sequence data from five sympatric Panamanian free-standing fig species (Ficus subgenus Pharmacosycea, section Pharmacosycea) and their six associated fig pollinator wasp species (Tetrapus). Two of the five fig species, F. glabrata and F. maxima, were found to regularly share pollinators. In these species, ongoing hybridization was demonstrated by the detection of several first-generation (F1) hybrid individuals, and historical introgression was indicated by phylogenetic network analysis. In contrast, although two of the pollinator species regularly share hosts, all six species were genetically distinct and deeply divergent, with no evidence for either hybridization or introgression. This pattern is consistent with results from other obligate pollination mutualisms, suggesting that, in contrast to their host plants, pollinators appear to be reproductively isolated, even when different species of pollinators mate in shared hosts.

Figure 2 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms

Figure 2. (A) Silicon leaf, total protein and phenol contents, (B) Activity of POD, CAT, and PPO of S. melongena- treated plants. Means followed by the same letter are not significantly different using Tukey's HSD Test at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3= OSAB 4 mL L−1, T4= Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.

Figure 1 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms

Figure 1. Mean number ± SE of the different stages of T. urticae on S. melongena leaves 10, 30 and 50 days after spraying (DAS). Means followed by the same letter are not significantly different using Tukey's HSD at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3 = OSAB 4 mL L−1, T4 = Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.

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Data for: Off-host survival of blacklegged ticks in eastern North America: A multi-stage, multi-year, and multi-site study

Climatic conditions are widely thought to govern the distribution and abundance of ectoparasites, such as the blacklegged tick (Ixodes scapularis), vector of the agents of Lyme disease and other emerging human pathogens. However, translating physiological tolerances to distributional limits or mortality is challenging. Ticks may be able to avoid or tolerate unsuitable conditions, and what is lethal to one life history stage may not extend to others. Thus, even after decades of research there are clear gaps in our knowledge about how climatic conditions determine tick distributions or patterns of abundance. We present the results of a comprehensive, three-year study of the influence of local temperatures and vapor pressure deficits on the survival of each free-living, off-host stage of I. scapularis in semi-natural enclosures across three locations that span their current distribution in eastern North America. We found that only larvae are clearly sensitive to direct mortality from climatic conditions, specifically desiccating conditions, whereas mortality of nymphs and adults appears to stem from exhausted energy reserves. We also found strong evidence that key developmental transitions in the tick's life cycle—fed larvae molting into to nymphs, fed nymphs molting into adults, and fed females producing larvae (via egg masses)—were all strongly temperature-dependent, though temperatures were not limiting in any of our sites. Collectively, our results suggest that climate is likely to impact I. scapularis largely through its impact on the larval stage.