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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.

opencc-by-4.0May 2023View details →

Dataset 1 for "Host-specificity and repeatability of haemosporidian infection parameters and potential consequences when testing host species-level hypotheses"

Dataset with 9 host species (min. 5 study sites and min 45 sampled individuals sampled per site) for the first part of the analysis in "Host-specificity and repeatability of haemosporidian infection parameters and potential consequences when testing host species-level hypotheses". One file contains the data table. One file contains a table with descriptions of the columns in the data table.

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Host species differences in the thermal mismatch of host–parasitoid interactions

Extreme high temperatures associated with climate change can affect species directly, and indirectly through temperature-mediated species interactions. In most host–parasitoid systems, parasitization inevitably kills the host, but differences in heat tolerance between host and parasitoid, and between different hosts, may alter their interactions. Here, we explored the effects of extreme high temperatures on the ecological outcomes – including, in some rare cases, escape from the developmental disruption of parasitism – of the parasitoid wasp, Cotesia congregata, and two co-occurring congeneric larval hosts, Manduca sexta and M. quinquemaculata. Both host species had higher thermal tolerance than C. congregata, resulting in a thermal mismatch characterized by parasitoid (but not host) mortality under extreme high temperatures. Despite parasitoid death at high temperatures, hosts typically remain developmentally disrupted from parasitism. However, high temperatures resulted in a partial developmental recovery from parasitism (reaching the wandering stage at the end of host larval development) in some host individuals, with a significantly higher frequency of this partial developmental recovery in M. quinquemaculata than in M. sexta. Hosts species also differed in their growth and development in the absence of parasitoids, with M. quinquemaculata developing faster and larger at high temperatures relative to M. sexta. Our results demonstrate that co-occurring congeneric species, despite shared environments and phylogenetic histories, can vary in their responses to temperature, parasitism and their interaction, resulting in altered ecological outcomes.

opencc-zeroJun 2023View details →

opencc-by-4.0Mar 2021View details →

Figs 13-16 – 13 in The egg endoparasitoids of Macrolenes dentipes (Olivier) (Coleoptera: Chrysomelidae), with description of a new species of Aprostocetus Westwood and notes on its host (Hymenoptera: Eulophidae)

Figs 13-16 – 13, adults of Macrolenes dentipes infesting lentisk; 14, M. dentipes in copulation; 15, egg cluster; 16, egg with attached strand.

opencc-by-4.0Mar 2021View details →

Figs 5-8 in The egg endoparasitoids of Macrolenes dentipes (Olivier) (Coleoptera: Chrysomelidae), with description of a new species of Aprostocetus Westwood and notes on its host (Hymenoptera: Eulophidae)

Figs 5-8 – Aprostocetus macrolenei Viggiani, sp. nov. 5, male antenna; 6, genitalia; 7, particular of genitalia; 8, last instar larva of A. macrolenei.

opencc-by-4.0Mar 2021View details →

Figs 1-4 in The egg endoparasitoids of Macrolenes dentipes (Olivier) (Coleoptera: Chrysomelidae), with description of a new species of Aprostocetus Westwood and notes on its host (Hymenoptera: Eulophidae)

Figs 1-4 – Aprostocetus macrolenei Viggiani, sp. nov. 1, female; 2, antenna; 3, metanotum, and propodeum; 4, fore wing.

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Host-enemy interactions provide limited biotic resistance for a range-expanding species via reduced apparent competition

Aim: As species' ranges shift poleward in response to anthropogenic change, they may lose antagonistic interactions if they move into less diverse communities, fail to interact with novel populations or species effectively, or if ancestral interacting populations or species fail to shift synchronously. We leveraged a poleward range expansion in a tractable insect host-enemy community to uncover mechanisms by which altered antagonistic interactions between native and recipient communities contributed to "high niche opportunities" (limited biotic resistance) for a range-expanding insect. Location: North America, Pacific Northwest Methods: We created quantitative insect host-enemy interaction networks by sampling oak gall wasps on 400 trees of a dominant oak species in the native and expanded range of a range-expanding gall wasp species. We compared host-enemy network structure between regions. We measured traits (phenology, morphology) of galls and interacting parasitoids, predicting greater trait divergence in the expanded range. We measured function relating to host control and explored if altered interactions and traits contributed to reduced function or biotic resistance. Results: Interaction networks had fewer species in the expanded range and lower complementarity of parasitoid assemblages among host species. While networks were more generalized, interactions with the range-expanding species were more specialized in the expanded range. Specialist enemies effectively tracked the range-expanding host, and there was reduced apparent competition with co-occurring hosts by shared generalist enemies. Phenological divergence of enemy assemblages interacting with the range-expanding and co-occurring hosts was greater in the expanded range, potentially contributing to weak apparent competition. Biotic resistance was lower in the expanded range, where fewer parasitoids emerged from galls of the range-expanding host. Main conclusions: Changes in interactions with generalist enemies created high niche opportunities, and limited biotic resistance, suggesting weak apparent competition may be a mechanism of enemy release for range-expanding insects embedded within generalist enemy networks.

opencc-zeroSep 2023View details →

Fig. 12 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 12. Habitat of Orinda (Scapulorinda) scapularis (Jacobi, 1928), Lake Eacham car park, 6 May 2022.

Fig. 10 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 10. Habitat and host plant of Orinda (Montorinda) montana sp. nov., Mount Walsh National Park, 14 Dec. 2019. A. Mount Walsh as seen from the car park. B. Landscape on the top of Mount Walsh with shrubs growing between the rocks. C. Host plant, Grevillea whiteana Mc Gill. (Proteaceae). D–E. Host plant, G. whiteana, detail.

Fig. 9 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 9. Orinda (Montorinda) montana sp. nov., Ô, holotype (QM), terminalia. A–D. Pygofer, anal tube and gonostyli. A. Left lateral view. B. Left posterolateral view. C. Posterior view. D. Dorsal view. E–L. Aedeagus. E. Left lateral view. F. Posterior view. G. Left laterodorsal view. H. Left lateroventral view. I. Dorsal view. J. Anterodorsal view. K. Aedeagus posteroventral view. L. Ventral view. Abbreviations: see Material and methods.

Fig. 8 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 8. Orinda (Montorinda) montana sp. nov., ♀, paratype (QM). A. Habitus, dorsal view. B. Habitus, ventral view. C. Habitus, lateral view. D. Posterior wing. E. Habitus, anterolateral view. F. Habitus, perpendicular view of frons. G. Left posterior leg, apical half of tibia and tarsus, ventral view.

Fig. 1 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 1. Orinda (Montorinda) eungellana sp. nov., Ô, holotype (QM). A. Habitus, dorsal view. B. Habitus, ventral view. C. Habitus, lateral view. D. Posterior wing. E. Habitus, anterolateral view. F. Habitus, perpendicular view of frons. G. Left posterior leg, apical half of tibia and tarsus, ventral view.

Fig. 4 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 4. Orinda (Montorinda) spp., adeagus of holotypes. A–B. O. (Montorinda) eungellana sp. nov. A. Left lateral view. B. Posterior view. C–D. O. (Montorinda) montana sp. nov. C. Left lateral view. D. Posterior view.

Fig. 3 in The Australian issid planthopper genus Orinda Kirkaldy, 1907: New subgenera, new species, host plant and identification key (Hemiptera: Fulgoromorpha: Issidae)

Fig. 3. Orinda (Montorinda) eungellana sp. nov., Ô, holotype (QM), terminalia. A–D. Pygofer, anal tube and gonostyli. A. Left lateral view. B. Left posterolateral view. C. Posterior view. D. Dorsal view. E–L. Aedeagus. E. Left lateral view. F. Posterior view. G. Left laterodorsal view. H. Left lateroventral view. I. Dorsal view. J. Anterodorsal view. K. Posteroventral view. L. Ventral view. Abbreviations: see Material and methods.