More stories

  • in

    Quantifying the impacts of land cover change on gross primary productivity globally

    GPP dataAs our primary productivity product we used the GOSIF GPP dataset21 which utilizes the linear relationship between GPP and remotely-sensed SIF34. GOSIF GPP is available globally at 0.05° spatial resolution for the period 2000–2021, with the period 2001–2015 selected here (for a short summary of all datasets used in this study see Supplementary Table 3). GOSIF GPP is based on a gridded SIF product (GOSIF)34 which uses MODIS enhanced vegetation index and meteorological data for spatial scaling and is trained with millions of SIF observations from the coarser-resolution Orbiting Carbon Observatory-235. The global coverage of GOSIF and the close relationship between SIF and GPP allow for an independent assessment of how land cover changes affect GPP in different regions around the world. For instance, SIF has been shown to capture the high GPP in the US Corn Belt derived from flux towers, while ecosystem models underestimated it36. While GPP can thus be empirically estimated from satellite SIF observations relatively reliably (even though some assumptions like the linear GPP–SIF relationship and its universality across biomes are still debated20,37,38,39), the calculation of NPP needs additional assumptions of autotrophic respiration. Therefore, we focused our study on GPP, but we included an NPP product in our uncertainty analysis. In addition to that, to account for the challenges and uncertainties in global GPP estimates we included four alternative GPP products in our sensitivity analysis (see below).Land cover mappingGridded land cover was derived from ESA-CCI22, a global land cover product designed for climate science. ESA-CCI is available at 300 m spatial resolution for the 1992–2020 period (https://cds.climate.copernicus.eu/). We first classified ESA-CCI land covers to forests, grasslands, and croplands according to IPCC classification: classes 50–100, 160, 170 forests (2,022,283 grid cells); classes 110 and 130 grasslands (509,297 grid cells); classes 10–40 croplands (950,025 grid cells). We focus on these three major land cover types to facilitate our analysis. We then converted the resulting map to 0.05° resolution by determining the prevalent (i.e., mode) land cover for each grid cell using the aggregate function from the raster package40 and only included grid cells in our training data in which the prevalent land cover was constant over the period 2001–2015. Other classes (e.g., cropland/natural vegetation mosaics) and grid cells where the land cover changed over the 2001–2015 period were not used for the RF training.Random forestsRF is a popular and efficient supervised machine learning technique which can be applied for classification and regression problems41. While complex, it is still easier to interpret compared to other machine learning methods such as Artificial Neural Networks. It has recently been applied to a wide range of ecological research questions, including the prediction of food42 and bioenergy43 crop yields, potential natural vegetation31, forest aboveground biomass44, soil respiration45, and soil carbon emissions from land-use change5 and is thus well suited for our approach. The “Forests” refer to a number of individual decision trees. For each tree, a random sample of the training data is selected and split multiple times based on a random subset of variables from which the one minimizing the weighted variance is selected by the algorithm. Model performance is computed directly on out-of-bag (OOB) data which is randomly omitted from the training data (36.8% of all grid cells). When RF is applied to new data, a weighted prediction of each individual decision tree contributes to the overall prediction. Variance in the individual trees, e.g., by selecting random subsets of the observations and random variables at each node improves the overall RF predictive skill. Model training and prediction were done using the R ranger package46. After initial testing (see Supplementary Fig. S11) we decided to set the number of individual decision trees to 800 and the number of variables to possibly split at in each node to 10. As the good evaluation measures of RF algorithms can be related to spatial autocorrelation24 we also tested a coordinate-only model and performed a leave-one-out cross validation including spatial buffers (Supplementary Discussion 2, Supplementary Fig. S3). Due to the large computational effort we reduced the number of decision trees to 100 for the buffered leave-one-out cross validation.Predictor variablesWe predicted forest, grassland, and cropland potential GPP using the following 20 predictor variables in our RF algorithm: mean annual surface temperature (Tmean), mean diurnal temperature range (Tdiurnal), temperature seasonality (Tseason; standard deviation), minimum temperature of the coldest month (Tmin), annual temperature range (Tannual), mean temperature of the warmest quarter (Twarmest), mean annual precipitation (Pmean), precipitation seasonality (Pseason; coefficient of variation), precipitation of the wettest quarter (Pwettest), precipitation of the driest quarter (Pdriest), precipitation of the warmest quarter (Pwarmest), mean annual solar radiation (SR), growing degree days (GDD), relative humidity (RH), soil clay content (Clay), elevation (EL), nitrogen deposition (Ndep), nitrogen fertilization (NF), pesticide application (Pest), and gross domestic product (GDP; a proxy for agricultural management input other than NF and Pest). Overall Tmean, Tannual, and Pmean were the most important predictor variables (see Supplementary Discussion 3 and Fig. S12). We also tested other predictors (including additional bioclimatic variables, soil pH, irrigation, or phosphate fertilization) but found only negligible improvements in RF evaluation metrics and hence decided to restrict our analysis to the 20 predictors mentioned above.Climate variables were taken from the CHELSA dataset47,48, remapped to 0.05° spatial resolution using the aggregate function from the raster package40. To only include years overlapping with our GPP data we used the CHELSA time-series data for the 2001–2013 period if available and 1979–2013 climatologies elsewise. Clay was derived from the Regridded Harmonized World Soil Database v1.249. Ndep was taken from ISIMIP2b50, bilinear remapped from 0.5° to 0.05° spatial resolution using Climate Data Operators33. Elevation was obtained from WorldClim51. NF and Pest were derived from country-specific FAO data (e.g., https://ourworldindata.org/grapher/pesticide-use-per-hectare-of-cropland), i.e., we used the same value for all grid cells in a country. GDP was obtained from ref.52.Suitable areaFor the comparison of potential forest, grassland, and cropland GPP in Fig. 1g–i we only included grid cells suitable for all three land cover types. For forests, we assumed forest cover possible if the grid cell is currently forested (e.g., all grid cells of our forest training data) or if the potential natural forest cover exceeds 36.3%. This threshold represents the 5th percentile of all currently forested grid cells. Potential natural forest cover was derived from a potential natural vegetation map, available for 20 biomes at 0.00833° spatial resolution31. To convert these biomes into potential natural forest cover we assumed 100% forest cover for the ten forest biomes and 30% forest cover for tropical savannah. Other biomes were not considered. We then aggregated the map to 0.05° spatial resolution by computing the mean of 36 grid cells using the aggregate function form the raster package40 (see Supplementary Fig. S5 for the resulting map). For grasslands and croplands, we computed the 5th percentile of Tmean and Pmean in the training data (− 9.9 °C and 165 mm for grasslands and 2.7 °C and 295 mm for croplands, respectively) and removed all grid cells below those thresholds, assuming these areas to be too cold or too dry for the respective land cover type. Finally, we calculated the land cover with the highest potential GPP for all overlapping grid cells.Sensitivity analysisTo explore the sensitivity and uncertainty of our RF approach we repeated our prediction using different input datasets, potential forest cover, and machine-learning approaches. The importance of the underlying potential forest map was estimated by replacing our potential forest map (Supplementary Fig. S5) by the LUH2 potential forest map (Supplementary Fig. S13)23. To explore the dependency on the land cover product we repeated our RF prediction using the spatially aggregated MODIS land cover map (MCD12C1; IGBP scheme), available at 0.05° spatial resolution53. We classified grid cells of classes 1, 2, 3, 4, 5, (all forests), 8 (woody savannahs) and 9 (savannahs) as forest. Classes 8 and 9 were included in forest because otherwise forest cover would be underestimated in the temperate and boreal zone. Class 10 was classified as grassland and class 12 as cropland. A comparison of ESA-CCI with MODIS reveals a substantially larger cropland area in ECA-CCI but a smaller grassland area (Supplementary Fig. S14).The sensitivity to the climate product was tested by repeating our analysis using predictor variables from the WorldClim climatologies (1970–2000)51, aggregated from 30 s to 0.05° spatial resolution using the aggregate function from the raster package40. In contrast to CHELSA, growing degree days and relative humidity were not available from WorldClim but we included water vapour pressure as additional predictor.We also tested four alternative global GPP products. The vegetation photosynthesis model (VPM) product, available for the period of interest at 0.05° spatial resolution, is based on improved light use efficiency theory and is driven by remotely sensed datasets and reanalysis climate data and land cover classification which also distinguishes C3 vs. C4 photosynthesis pathways54. The second product is derived from remote sensing considering radiation and canopy conductance limitations on GPP and is available at 0.05° resolution for the 2001–2012 period55. Land cover is not an input variable. The third product, FLUXCOM, uses machine learning to scale FLUXNET site GPP to the globe56,57. FLUXCOM is available at 0.0833° resolution and was conservative remapped to 0.05° using Climate Data Operators33 meaning that the GPP of different land cover types might be mixed in regions with heterogeneous land cover patterns. The forth product is the MODIS MOD17A3 GPP product58, available for the 2001–2013 period and aggregated to 0.05° resolution using the raster package40. It is derived from meteorological data, fraction of absorbed photosynthetic active radiation/leaf area index, and land cover. As there is also a MOD17A3 NPP product available we additionally conducted a prediction for potential NPP. The MOD17A3 NPP product is calculated as GPP minus maintenance and growth respiration estimated from allometric relationships linking daily biomass and annual growth of plant tissues to leaf area index58. This leads to additional uncertainty compared to the MOD17A3 GPP product.To test the effect of an alternative RF algorithm we repeated our prediction with the RF algorithm from the Python scikit-learn library59 using the same number of decision trees (800). Additionally, we tested another machine-learning technique, a deep neural network (DNN), using the PyTorch library60. We selected 10 linear layers with 5 times alternating 128 and 256 nodes and a sigmoid output function. All layers were connected using the rectified linear unit activation function. We used the adamW optimizer with 0.0003 learning rate and 2000 epochs of training. To prevent overfitting, we included a 10% dropout after the 7th layer. Lastly, we included a very simple estimate of the most productive land cover based on the nearest neighbour using scikit-learn’s BallTree implementation together with the Haversine formula. For each grid cell we searched for the nearest forest, grassland, and cropland grid cell and assigned the respective GPP also to this grid cell. We thus assumed that environmental conditions are more or less identical in these grid cells, which might be a reasonable assumption for many locations but less reliable in complex terrain or in large homogeneous regions like the central Amazon rainforest where the nearest cropland/grassland grid cell might be located far away.Land-use change scenariosTo estimate the effects of historical and potential future land cover changes on global GPP we applied LUH2 scenarios23 which also serve as input data for ESMs participating in CMIP6. Land-use changes over the historical period are based on the HYDE reconstruction3, while future projections were developed by different Integrated Assessment Models combining various assumptions of socio-economic behaviour (SSPs) with climate mitigation targets (RCPs). Annual fractions for the two land cover classes cropland (sum of 5 crop types) and managed grassland (sum of pasture and rangeland) were available for each scenario at 0.25° resolution (https://luh.umd.edu/). We converted to 0.05° resolution assuming the same land cover fractions for all 25 grid cells around the LUH2 grid cells. We considered the following land cover transitions: forest to managed grassland, forest to cropland, and natural grassland to cropland (and reverse transitions for future scenarios). Transitions in areas suitable for only two land cover types were also included. Conversions of natural grasslands to managed grasslands were assumed not to affect productivity. We assumed the original land cover of a grid cell to be either forest (i.e., potential forest cover  > 36.3%) or natural grassland and accordingly multiplied the converted areas by the differences in potential GPP derived from our RF approach. Our broad forest definition including open tree cover (see above) and the fact that we assumed a change from 100 to 0% forest area in deforested grid cells results in a total historical deforestation area substantially larger than estimated in a recent study (2.4 Mkm2 vs. 1.6 Mkm2)61. These assumptions, however, do not impair our GPP estimate as our approach implicitly accounts for gradients in forest productivity (open forests tend to have lower GPP than closed forests). To test the sensitivity of the resulting GPP reduction we also applied the potential GPP maps from our uncertainty analysis to historical land-use changes (Supplementary Fig. S6). For future land cover changes we investigated changes over the 2015–2100 period for all available LUH2 scenarios: SSP1-1.9, SSP2-2.6, SSP4-3.4, SSP5-3.4, SSP2-4.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. Land-use activities in these scenarios range from large-scale deforestation (e.g., SSP3-7.0) to reforestation (e.g., SSP1-1.9) (Supplementary Fig. S7).Earth System ModelsWe compared the potential GPP estimated by our RF algorithm to simulations of eight ESMs participating in CMIP6 (CESM2-CLM562, CNRM-ESM2.1-Surfex 8.0c63, EC-Earth3-Veg-LPJ-GUESSv464, GFDL-ESM4-GFDL-LM4.165, IPSL-CM6A-LR-ORCHIDEEv2.066, MIROC-ES2L-MATSIRO6.0 + VISIT-e ver.1.067, MPI-ESM1-2-LR-JSBACH3.2068, UKESM1-0-LL-JULES-ES-1.069) with an explicit representation of natural vegetation and at least one agricultural land cover class (cropland or managed grassland) in their vegetation sub-model. We selected these ESMs so that all vegetation models implemented in more than one ESM were represented only once (e.g., the JSBACH vegetation model is a component of both MPI-ESM1-2-LR and AWI-ESM). For each ESM, the variable gppLut was downloaded from the CMIP6 archive (https://esgf-data.dkrz.de/search/cmip6-dkrz/) for the historical simulations. These files contain simulated GPP for natural vegetation, pasture, and cropland for which we calculated the 2001–2014 mean (2014 is the last year of the historical period). ESMs use fractional land covers for each grid cell, meaning that climatic drivers are inherently the same for all land cover types within a grid cell and simulated productivities can therefore be directly compared. As ESMs differ in their spatial resolution we bilinear remapped all output to 0.05° resolution using Climate Data Operators33 to allow for a fair comparison across models. To assess the sensitivity of our results to the interpolation method we also tested conservative remapping which results in slightly different maps (Supplementary Fig. S15) and usually larger model biases (Supplementary Table 2). In addition, ESMs differ in where they simulate forests in natural vegetation areas, and therefore we removed all grid cells from the comparison where at least one ESM simulated no tree productivity/cover/biomass in order to avoid comparing the GPP of natural grasslands to managed grasslands. We provide maps based on the original output for each ESM in Supplementary Fig. S10.FLUXNET dataWe compared our predictions of potential GPP to FLUXNET Tier 1 eddy covariance measurements (Supplementary Fig. S16)70. We included all forest, woody savannah (classified as forest), grassland and cropland sites21 which were located in suitable areas for the respective land cover. Mean GPP was calculated as the mean of the GPP estimates based on the night-time (GPP_NT_VUT_REF) and day-time (GPP_DT_VUT_REF) partitioning method. As some sites only had a few years of data, all available years were considered (i.e., site mean GPP was calculated for a different time period than 2001–2015). Comparisons were made with the potential GPP in the respective grid cell in which the site was located (i.e., not calibrated to site conditions). More

  • in

    Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia

    Plant materials and genome sequencingFresh leaves of a wild P. koreana plant in the Changbai Mountains of Jilin province in China were collected, and the total genomic DNA was extracted using the CTAB method. For the Illumina short-read sequencing, paired-end libraries with insert sizes of 350 bp were constructed and sequenced using an Illumina HiSeq X Ten platform. For the long-read sequencing, the genomic libraries with 20-kbp insertions were constructed and sequenced using the PromethION platform of Oxford Nanopore Technologies (ONT). For the Hi-C experiment, approximately 3 g of fresh young leaves of the same P. koreana accession was ground to powder in liquid nitrogen. A sequencing library was then constructed by chromatin extraction and digestion, DNA ligation, purification, and fragmentation53 and was subsequently sequenced on an Illumina HiSeq X Ten platform.Genome assembly and scaffoldingThe quality-controlled reads were first corrected via a self-align method using the NextCorrect module in the software NextDenovo v2.0-beta.1 (https://github.com/Nextomics/NextDenovo) with parameters “reads_cutoff=1k (filter reads with length 20, percent of unqualified bases More

  • in

    Tiger sharks support the characterization of the world’s largest seagrass ecosystem

    Ground-truth surveys of seagrass habitatTo obtain georeferenced field data on benthic cover levels from habitats of the Bahama Banks, we employed two similar, in-water survey and image approaches: (1) swimmer-based photo-transects; and (2) tow board photo transects (Supplementary Fig. 6), resulting in a total of 2542 surveys.For (1), free-divers swam over the bottom of the seafloor at a fixed height with a digital camera (Canon 5D mIV, GoPro Hero) set to capture images manually. Photographs were captured using automatic settings in a 1.0 m × 1.0 m footprint, 1.5 m above the seafloor following [39]. A center console vessel was used to run the transects at distances of 5–7 km, whereby the free-diver would capture successive photos at a horizontal distance of between 400–800 m, and the location was logged using either a handheld GPS (Garmin GPS 73) or a boat-mounted GPS with a depth sounder (Garmin EchoMap DV). Transect locations were chosen based on a priori local expert knowledge of varying benthic cover in the region. Surveyed areas included: southern New Providence (24.948862°, −77.387834°), southeast of New Providence (24.980265°, −77.229168°), south of Rose Island (25.066268°, −77.160063°), the middle Great Bahama Bank (24.735355°, −77.212998°), and the northern Exumas (24.729973°, −76.889488°). For (2), snorkeling observers were pulled from a research vessel on tow boards affixed with underwater action cameras (GoPro Hero 3+) traveling at ~1 m/s. The start and end of a tow were delineated with either a handheld GPS (Garmin eTrex 30) or a boat mounted GPS with depth-finder (Garmin EchoMap DV), and tows proceeded in a straight line recorded by the GPS. Cameras recorded images at 0.5 Hz throughout the tow, starting in conjunction with creating a waypoint. Samples (i.e., paired image and geolocated point) were sub-selected from the tow once movement began, at the midpoint of a tow, and immediately before movement stopped. Images were manually quality controlled such that if a selected image contained obstructions or was out of focus, the nearest clear image was selected to replace it. If no images within 10 s were clear (i.e., 10 m maximum spatial error), the sample was discarded. If the GPS track contained gaps or segments larger than 10 m, only images/point pairs at the start and end waypoints were sampled.Surveys focused on historical fishing grounds for queen conch (Lobatus gigas) between 2015 and 2018 following the sampling design and methods of ref. 32. A stratified random design was used to allocate 6000 m2 of observation effort into each cell of a 1’ by 1’ grid placed over each fishing ground. This effort was split into multiple tows between 200 and 1000 m in length, thus images were separated by at least 100 m.Fishing grounds extended from the edge of a deepwater sound to between 7 and 10 km up the bank and were limited to the depths used by freediving fishers. Surveyed fishing grounds included: the Exumas (24.382207°, −76.631058°), the southwestern Berry Islands (25.455529°, −78.014214°), south of Bimini (25.375592°, −79.187609°), the Grassy Cays (23.666864°, −77.383547°), the Joulter Cays (25.321297°, −78.109251°) and the southeast tip of the Tongue of the Ocean (23.376417°, −76.621943°). For details on image processing, see section on remote sensing below.Sediment coringTo gather the sediment cores analyzed for organic carbon content on the Bahama Banks, we collected samples from various benthic habitats that included varying densities of seagrass habitat (Thalassia testidinum and Syringodium filiforme). We percussed, via SCUBA, an acrylic cylinder tube perpendicular to the seafloor into marine sediment until rejection at various penetration depths up to 30 cm. The sample was then extracted vertically from the marine sediment and capped at the bottom to avoid loss of material. This sample was then transported vertically through the water column to a research vessel where it was removed from the coring device and immediately capped on top with an air-tight cap. Compression rates were negligible (~5 cm) across the first 5 cores, and as such were not subsequently measured. The samples were then labeled, photographed, geotagged, and the first 30 centimeters of each core was extruded. To complete the extrusion process, we placed each sample on top of a capped piston device in the same orientation as collection (deepest portion of collected sediment still on the bottom). The bottom cap was removed to thread the acrylic cylinder tube onto the piston device and then was lowered to various measured lengths to collect corresponding depth sections of the sediment core. These sections were sliced (every 1–5 centimeters), labeled, and placed into whirl pack bags to collect the wet weight of each sample. All samples were then frozen and stored for future laboratory analyses. All samples were dried in a laboratory oven at 55 °C for 48 h until constant dry weights were reached. The samples were then weighed to collect their corresponding dry weights. The dry bulk density (DBD) was calculated by diving the sample dry weight (g) by the sample volume (cm3). The samples were then further ground with a mortar and pestle until a homogeneous fine grain size was achieved. Sediment samples collected from the Exuma Cays (142 samples from 16 cores) were analyzed for Corg content. Sediment samples were weighed accurately into silver capsules and acidified with 4% HCl until no effervescence was detected in two consecutive cycles. The samples were then dried in a 60 °C oven overnight, encapsulated into tin capsules and analyzed using an Organic Elemental Analyzer Flash 2000 (Thermo Fisher Scientific, Massachusetts, USA). We then conducted a standard loss on ignition (LOI) methodology at our laboratory facility (Braintree, Massachusetts, USA) for all the samples. Each sample was subsequently sub sampled with 5–15 grams of representative material and placed into a ceramic crucible to collect its mass. The crucibles were then loaded into a separate muffle laboratory oven and heated at 550 °C for 6 h. Upon completion of this muffle, the crucibles were then immediately weighed to collect the LOI of organic material from each sample, defined as the weight lost in the muffle (g) divided by the subsample dry weight (g). A fitted regression between the Corg and LOI from the Exuma Cays cores was generated (Supplementary Fig. 7), and then used to predict the sediment Corg contents from LOI measurements in the Grand Bahama cores. Sediment Corg stocks were quantified by multiplying Corg and DBD data by soil depth increment (1–5 cm) of the sampled soil cores. The cores from the Exuma Cays (15 cm) and Grand Bahama (30 cm) were collected with different depths, we therefore fitted a regression between Corg stock in 15 cm-depth and Corg stock in 30 cm-depth for the Grand Bahama cores (Supplementary Fig. 8) and used this regression to extrapolate Corg stock of the Exuma Cays cores into 30 cm-depth. Moreover, to allow direct comparison among other studies27, the Corg stock per unit area was standardized to 1 m-thick deposits by multiplying 100/30.Tiger shark taggingThe research and protocols conducted in this study complies with relevant ethical regulations as approved by the Carleton University Animal Care Committee. The shark data used in this paper were collected as part of a multi-year, long-term research program evaluating the interannual behavior and physiology of large sharks throughout the coastal waters of The Commonwealth of The Bahamas23. All sharks were captured using standardized circle-hook drumlines33 on the Great and Little Bahama Banks throughout the country, focusing efforts in three primary locations: off New Providence Island, the Exuma Cays, and off West End, Grand Bahama, from 2011–2019. All sharks were secured alongside center console research vessels and local dive boats, where their sex, morphometric measurements, and blood samples were taken. A mark-recapture identification tag was applied to the shark at the base of the dorsal fin. Some of the sharks sampled in the present study were also tagged with a coded acoustic transmitter which was surgically implanted ventrally into the peritoneal cavity and then sutured, as part of a concurrent study on shark habitat use and residency within the region23.Pop-off archival satellite tags were affixed to eight tiger sharks (seven female, one male; 298 ± 28 cm total length; mean ± SD) in The Bahamas from 2011–2019, permitting measurements of swimming depth and water temperature recorded at either 4-min (Sea-Tag MODS, Desert Star Systems LCC, USA) or 10-s intervals (miniPAT tags, Wildlife Computers, USA). Pop-off satellite tags were inserted into the dorsal musculature of the sharks using stainless steel anchors and tethers. All pop-off satellite tags were either recovered manually, permitting access to the full time-series, or popped-off and transmitted their data to an Earth-orbiting Argos satellite, resulting in a subset of the full time-series (transmission frequencies: 2.5 min [miniPAT], 10 min [PSATGEO], daily average [Sea-Tag MOD]). Tiger shark positions were estimated from the satellite data using tag-specific proprietary state space algorithms from Wildlife Computers (GPE3; based on ref. 34) and Desert Star Systems35. With miniPAT tags, positions were further filtered to remove the least reliable positions ( More

  • in

    Symbiont genotype influences holobiont response to increased temperature

    Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).Article 
    ADS 
    PubMed 

    Google Scholar 
    Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).Article 
    ADS 
    PubMed 

    Google Scholar 
    terHorst, C. P., Miller, T. E. & Levitan, D. R. Evolution of prey in ecological time reduces the effect size of predators in experimental microcosms. Ecology 91, 629–636 (2010).Article 
    PubMed 

    Google Scholar 
    Duffy, M. A. & Sivars-Becker, L. Rapid evolution and ecological host-parasite dynamics. Ecol. Lett. 10, 44–53 (2007).Article 
    PubMed 

    Google Scholar 
    Diamond, S. E., Chick, L. D., Perez, A., Strickler, S. A. & Martin, R. A. Evolution of thermal tolerance and its fitness consequences: parallel and non-parallel responses to urban heat islands across three cities. Proc. Biol. Sci. 285, 20180036 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Franks, S. J., Sim, S. & Weis, A. E. Rapid evolution of flowering time by an annual plant in response to a climate fluctuation. Proc. Natl. Acad. Sci. USA 104, 1278–1282 (2007).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    terHorst, C. P., Lennon, J. T. & Lau, J. A. The relative importance of rapid evolution for plant-microbe interactions depends on ecological context. Proc. R. Soc. B Biol. Sci. 281, 20140028 (2014).Article 

    Google Scholar 
    Bradshaw, W. E. & Holzapfel, C. M. Evolutionary response to rapid climate change. Science https://doi.org/10.1126/science.1127000 (2006).Article 
    PubMed 

    Google Scholar 
    Gonzalez, A., Ronce, O., Ferriere, R. & Hochberg, M. E. Evolutionary rescue: an emerging focus at the intersection between ecology and evolution. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120404 (2013).Article 

    Google Scholar 
    Carlson, S. M., Cunningham, C. J. & Westley, P. A. H. Evolutionary rescue in a changing world. Trends Ecol. Evol. 29, 521–530 (2014).Article 
    PubMed 

    Google Scholar 
    Lau, J. A. & terHorst, C. P. Evolutionary responses to global change in species-rich communities. Ann. N. Y. Acad. Sci. 1476, 43–58 (2020).Article 
    ADS 
    PubMed 

    Google Scholar 
    Lau, J. A., Shaw, R. G., Reich, P. B. & Tiffin, P. Indirect effects drive evolutionary responses to global change. New Phytol. 201, 335–343 (2014).Article 
    PubMed 

    Google Scholar 
    Tseng, M. & O’Connor, M. I. Predators modify the evolutionary response of prey to temperature change. Biol. Lett. 11, 20150798 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    terHorst, C. P. et al. Evolution in a community context: Trait responses to multiple species interactions. Am. Nat. 191, 368–380 (2018).Article 

    Google Scholar 
    Hussa, E. A. & Goodrich-Blair, H. It takes a village: Ecological and fitness impacts of multipartite mutualism. Annu. Rev. Microbiol. 67, 161–178 (2013).Article 
    PubMed 

    Google Scholar 
    Hoegh-Guldberg, O. Climate change, coral bleaching and the future of the world’s coral reefs. Mar. Freshw. Res. 50, 839–866 (1999).
    Google Scholar 
    Death, G., Fabricius, K. E., Sweatman, H. & Puotinen, M. The 27–year decline of coral cover on the Great Barrier Reef and its causes. PNAS 109, 17995–17999 (2012).Article 
    ADS 

    Google Scholar 
    Heron, S. F., Maynard, J. A., van Hooidonk, R. & Eakin, C. M. Warming trends and bleaching stress of the world’s coral reefs 1985–2012. Sci. Rep. 6, 38402 (2016).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    van Hooidonk, R. et al. Local-scale projections of coral reef futures and implications of the Paris Agreement. Sci Rep 6, 39666 (2016).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oliver, J. K., Berkelmans, R. & Eakin, C. M. Coral bleaching in space and time. In Coral Bleaching: Patterns, Processes, Causes and Consequences (eds. van Oppen, M. J. H. & Lough, J. M.) 27–49 (Springer, 2018). https://doi.org/10.1007/978-3-540-69775-6_3.Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
    ADS 
    PubMed 

    Google Scholar 
    Impacts of 1.5°C global warming on natural and human systems. In Global Warming of 1.5°C: IPCC Special Report on Impacts of Global Warming of 1.5°C above Pre-industrial Levels in Context of Strengthening Response to Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (ed. IPCC) 175–312 (Cambridge University Press, 2022). https://doi.org/10.1017/9781009157940.005.Glynn, P. W. & D’Croz, L. Experimental evidence for high temperature stress as the cause of El Niño-coincident coral mortality. Coral Reefs 8, 181–191 (1990).Article 
    ADS 

    Google Scholar 
    Eakin, C. M. et al. Caribbean corals in crisis: Record thermal stress, bleaching, and mortality in 2005. PLoS ONE 5, e13969 (2010).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coral bleaching event: insights and impacts. Coral Reefs 38, 539–545 (2019).Article 
    ADS 

    Google Scholar 
    Baker, A. C., Starger, C. J., McClanahan, T. R. & Glynn, P. W. Corals’ adaptive response to climate change. Nature 430, 741–741 (2004).Article 
    ADS 
    PubMed 

    Google Scholar 
    Mieog, J. C., Van Oppen, M. J. H., Berkelmans, R., Stam, W. T. & Olsen, J. L. Quantification of algal endosymbionts (Symbiodinium) in coral tissue using real-time PCR. Mol. Ecol. Resour. 9, 74–82 (2009).Article 
    PubMed 

    Google Scholar 
    Silverstein, R. N., Correa, A. M. S. & Baker, A. C. Specificity is rarely absolute in coral–algal symbiosis: Implications for coral response to climate change. Proc. R. Soc. B Biol. Sci. 279, 2609–2618 (2012).Article 

    Google Scholar 
    Hoadley, K. D. et al. Host–symbiont combinations dictate the photo-physiological response of reef-building corals to thermal stress. Sci. Rep. 9, 9985 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parkinson, J. E. & Baums, I. B. The extended phenotypes of marine symbioses: ecological and evolutionary consequences of intraspecific genetic diversity in coral-algal associations. Front. Microbiol. 5, 445 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Karim, W., Nakaema, S. & Hidaka, M. Temperature effects on the growth rates and photosynthetic activities of symbiodinium cells. J. Mar. Sci. Eng. 3, 368–381 (2015).Article 

    Google Scholar 
    Grégoire, V., Schmacka, F., Coffroth, M. A. & Karsten, U. Photophysiological and thermal tolerance of various genotypes of the coral endosymbiont Symbiodinium sp. (Dinophyceae). J. Appl. Phycol. 29, 1893 (2017).Article 

    Google Scholar 
    Díaz-Almeyda, E. M. et al. Intraspecific and interspecific variation in thermotolerance and photoacclimation in Symbiodinium dinoflagellates. Proc. R. Soc. B Biol. Sci. 284, 20171767 (2017).Article 

    Google Scholar 
    Bayliss, S. L. J., Scott, Z. R., Coffroth, M. A. & terHorst, C. P. Genetic variation in Breviolum antillogorgium, a coral reef symbiont, in response to temperature and nutrients. Ecol. Evol. 9, 2803–2813 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pelosi, J., Eaton, K. M., Mychajliw, S., terHorst, C. P. & Coffroth, M. A. Thermally tolerant symbionts may explain Caribbean octocoral resilience to heat stress. Coral Reefs 40, 1113–1125 (2021).Article 

    Google Scholar 
    Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: The hologenome theory of evolution. Fems Microbiol. Rev. 32, 723–735 (2008).Article 
    PubMed 

    Google Scholar 
    Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Chang. 2, 116–120 (2012).Article 
    ADS 

    Google Scholar 
    Chakravarti, L. J., Beltran, V. H. & van Oppen, M. J. H. Rapid thermal adaptation in photosymbionts of reef-building corals. Glob. Chang. Biol. 23, 4675–4688 (2017).Article 
    ADS 
    PubMed 

    Google Scholar 
    Chakravarti, L. J. & van Oppen, M. J. H. Experimental evolution in coral photosymbionts as a tool to increase thermal tolerance. Front. Mar. Sci. 5, 227 (2018).Article 

    Google Scholar 
    Buerger, P. et al. Heat-evolved microalgal symbionts increase coral bleaching tolerance. Sci. Adv. https://doi.org/10.1126/sciadv.aba2498 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hofmann, D. K. & Kremer, B. P. Carbon metabolism and strobilation in Cassiopea andromedea (Cnidaria: Scyphozoa): Significance of endosymbiotic dinoflagellates. Mar. Boil. 65, 25 (1981).Article 

    Google Scholar 
    Welsh, D., Dunn, R. & Meziane, T. Oxygen and nutrient dynamics of the upside down jellyfish (Cassiopea sp.) and its influence on benthic nutrient exchanges and primary production. Hydrobiologia 635, 351 (2009).Article 

    Google Scholar 
    Freeman, C. J., Stoner, E. W., Easson, C. G., Matterson, K. O. & Baker, D. M. Symbiont carbon and nitrogen assimilation in the Cassiopea-Symbiodinium mutualism. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps11605 (2016).Article 

    Google Scholar 
    Bigelow, R. P. The Anatomy and Development of Cassiopea xamachana. 1–72 (Pub. by the Boston Society of Natural History, 1900). https://doi.org/10.5962/bhl.title.31420.Colley, N. J. & Trench, R. K. Selectivity in phagocytosis and persistence of symbiotic algae in the scyphistoma stage of the jellyfish Cassiopeia xamachana. Proc. R. Soc. Lond. B Biol. Sci. 219, 61–82 (1983).Article 
    ADS 
    PubMed 

    Google Scholar 
    Hofmann, D. K., Fitt, W. K. & Fleck, J. Checkpoints in the life-cycle of Cassiopea spp.: Control of metagenesis and metamorphosis in a tropical jellyfish. Int. J. Dev. Biol. 40, 331–338 (1996).PubMed 

    Google Scholar 
    Stat, M. & Gates, R. D. Clade D symbiodinium in scleractinian corals: A “Nugget” of hope, a selfish opportunist, an ominous sign, or all of the above?. J. Mar. Biol. 2011, e730715 (2010).
    Google Scholar 
    Correa, A. M. S. & Baker, A. C. Disaster taxa in microbially mediated metazoans: how endosymbionts and environmental catastrophes influence the adaptive capacity of reef corals. Glob. Change Biol. 17, 68–75 (2011).Article 
    ADS 

    Google Scholar 
    Silverstein, R. N., Cunning, R. & Baker, A. C. Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob. Chang. Biol. 21, 236–249 (2015).Article 
    ADS 
    PubMed 

    Google Scholar 
    Leal, M. C. et al. Symbiont type influences trophic plasticity of a model cnidarian-dinoflagellate symbiosis. J. Exp. Biol. 218, 858–863 (2015).Article 
    PubMed 

    Google Scholar 
    Klein, S. G. et al. Symbiodinium mitigate the combined effects of hypoxia and acidification on a noncalcifying cnidarian. Glob. Chang. Biol. 23, 3690–3703 (2017).Article 
    ADS 
    PubMed 

    Google Scholar 
    Cziesielski, M. J. et al. Multi-omics analysis of thermal stress response in a zooxanthellate cnidarian reveals the importance of associating with thermotolerant symbionts. Proc. R. Soc. B. 285, 20172654 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cunning, R. & Baker, A. C. Thermotolerant coral symbionts modulate heat stress-responsive genes in their hosts. Mol. Ecol. 29, 2940–2950 (2020).Article 
    PubMed 

    Google Scholar 
    Newkirk, C. R., Frazer, T. K., Martindale, M. Q. & Schnitzler, C. E. Adaptation to bleaching: Are thermotolerant symbiodiniaceae strains more successful than other strains under elevated temperatures in a model symbiotic cnidarian?. Front. Microbiol. 11, 822 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trench, R. K. MICROALGAL-INVERTEBRATESYMBIOSES: A REVIEW. Cell Res. 41 (1993).Yellowlees, D., Rees, T. A. V. & Leggat, W. Metabolic interactions between algal symbionts and invertebrate hosts. Plant Cell Environ. 31, 679–694 (2008).Article 
    PubMed 

    Google Scholar 
    Swain, T. D., Chandler, J., Backman, V. & Marcelino, L. Consensus thermotolerance ranking for 110 Symbiodinium phylotypes: an exemplar utilization of a novel iterative partial-rank aggregation tool with broad application potential. Funct. Ecol. 31, 172–183 (2017).Article 

    Google Scholar 
    Klueter, A., Trapani, J., Archer, F. I., McIlroy, S. E. & Coffroth, M. A. Comparative growth rates of cultured marine dinoflagellates in the genus Symbiodinium and the effects of temperature and light. PLoS ONE 12, e0187707 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen, B. et al. Dispersal, genetic variation, and symbiont interaction network of heat-tolerant endosymbiont Durusdinium trenchii: Insights into the adaptive potential of coral to climate change. Sci. Total Environ. 723, 138026 (2020).Article 
    ADS 
    PubMed 

    Google Scholar 
    van Oppen, M. J. H., Souter, P., Howells, E. J., Heyward, A. & Berkelmans, R. Novel genetic diversity through somatic mutations: Fuel for adaptation of reef corals?. Diversity 3, 405–423 (2011).Article 

    Google Scholar 
    van Oppen, M. J. H., Oliver, J. K., Putnam, H. M. & Gates, R. D. Building coral reef resilience through assisted evolution. Proc. Natl. Acad. Sci. USA 112, 2307–2313 (2015).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ohdera, A. H. et al. Upside-down but headed in the right direction: Review of the highly versatile Cassiopea xamachana system. Front. Ecol. Evol. 6, 35 (2018).Article 

    Google Scholar 
    Fitt, W. K. & Costley, K. The role of temperature in survival of the polyp stage of the tropical rhizostome jellyfish Cassiopea xamachana. J. Exp. Mar. Biol. Ecol. 222, 79–91 (1998).Article 

    Google Scholar 
    Aljbour, S. M., Zimmer, M. & Kunzmann, A. Cellular respiration, oxygen consumption, and trade-offs of the jellyfish Cassiopea sp. in response to temperature change. Journal of Sea Research 128, 92–97 (2017).Rahat, M. & Adar, O. Effect of symbiotic zooxanthellae and temperature on budding and strobiliation in Cassiopeia andromeda (Eschscholz). Biol. Bull. 159, 394–401 (1980).Article 

    Google Scholar 
    Cole, L. C. The population consequences of life history phenomena. Q. Rev. Biol. 29, 103–137 (1954).Article 
    PubMed 

    Google Scholar 
    Brommer, J. E., Merilä, J. & Kokko, H. Reproductive timing and individual fitness. Ecol. Lett. 5, 802–810 (2002).Article 

    Google Scholar 
    Hofmann, D. K., Neumann, R. & Henne, K. Strobilation, budding and initiation of scyphistoma morphogenesis in the rhizostome Cassiopea andromeda (Cnidaria: Scyphozoa). Mar. Biol. 47, 161–176 (1978).Article 

    Google Scholar 
    Thornhill, D. J., LaJeunesse, T. C., Kemp, D. W., Fitt, W. K. & Schmidt, G. W. Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion. Mar. Biol. 148, 711–722 (2006).Article 

    Google Scholar 
    Mellas, R. E., McIlroy, S. E., Fitt, W. K. & Coffroth, M. A. Variation in symbiont uptake in the early ontogeny of the upside-down jellyfish, Cassiopea spp.. J. Exp. Mar. Biol. Ecol. 459, 38–44 (2014).Article 

    Google Scholar 
    Fransolet, D., Roberty, S. & Plumier, J.-C. Establishment of endosymbiosis: The case of cnidarians and Symbiodinium. J. Exp. Mar. Biol. Ecol. 420–421, 1–7 (2012).Article 

    Google Scholar 
    Jones, A. M., Berkelmans, R., van Oppen, M. J. H., Mieog, J. C. & Sinclair, W. A community change in the algal endosymbionts of a scleractinian coral following a natural bleaching event: field evidence of acclimatization. Proc. R. Soc. B Biol. Sci. 275, 1359–1365 (2008).Article 

    Google Scholar 
    Baskett, M. L., Gaines, S. D. & Nisbet, R. M. Symbiont diversity may help coral reefs survive moderate climate change. Ecol. Appl. 19, 3–17 (2009).Article 
    PubMed 

    Google Scholar 
    Baker, A. C. Reef corals bleach to survive change. Nature 411, 765–766 (2001).Article 
    ADS 
    PubMed 

    Google Scholar 
    Berkelmans, R. & van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a ‘nugget of hope’ for coral reefs in an era of climate change. Proc. R. Soc. B Biol. Sci. 273, 2305–2312 (2006).Article 

    Google Scholar 
    Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–261 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wolfowicz, I. et al. Aiptasia sp. larvae as a model to reveal mechanisms of symbiont selection in cnidarians. Sci. Rep. 6, 32366 (2016).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Little, A. F., van Oppen, M. J. H. & Willis, B. L. Flexibility in algal endosymbioses shapes growth in reef corals. Science https://doi.org/10.1126/science.1095733 (2004).Article 
    PubMed 

    Google Scholar 
    Jones, A. & Berkelmans, R. Potential costs of acclimatization to a warmer climate: Growth of a reef coral with heat tolerant vs sensitive symbiont types. PLOS ONE 5, e10437 (2010).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ortiz, J. C., González-Rivero, M. & Mumby, P. J. Can a thermally tolerant symbiont improve the future of Caribbean coral reefs?. Glob. Change Biol. 19, 273–281 (2013).Article 
    ADS 

    Google Scholar 
    Sprouffske, K. & Wagner, A. Growthcurver: An R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinform. 17, 172 (2016).Article 

    Google Scholar  More

  • in

    Genomic insights into phage-host interaction in the deep-sea chemolithoautotrophic Campylobacterota, Nitratiruptor

    Jeanthon C. Molecular ecology of hydrothermal vent microbial communities. Antonie Van Leeuwenhoek. 2000;77:117–33.Article 
    CAS 
    PubMed 

    Google Scholar 
    Nercessian O, Reysenbach A-L, Prieur D, Jeanthon C. Archaeal diversity associated with in situ samplers deployed on hydrothermal vents on the East Pacific Rise (13oN). Environ Microbiol. 2003;5:492–502.Article 
    PubMed 

    Google Scholar 
    Nakagawa S, Takai K, Inagaki F, Hirayama H, Nunoura T, Horikoshi K, et al. Distribution, phylogenetic diversity and physiological characteristics of epsilon-Proteobacteria in a deep-sea hydrothermal field. Environ Microbiol. 2005;7:1619–32.Article 
    CAS 
    PubMed 

    Google Scholar 
    Brazelton WJ, Schrenk MO, Kelley DS, Baross JA. Methane- and sulfur-metabolizing microbial communities dominate the Lost City hydrothermal field ecosystem. Appl Environ Microbiol. 2006;72:6257–70.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Takai K, Nakagawa S, Reysenbach A-L, Hoek J. Microbial ecology of mid-ocean ridges and back-arc basins. In: Christie DM, Fisher CR, Lee S-M, Givens S, editors. Geophysical Monograph Series. 2006. Washington, D. C.: American Geophysical Union; 2006. pp. 185–213.Nakagawa S, Takai K. Deep-sea vent chemoautotrophs: diversity, biochemistry and ecological significance. FEMS Microbiol Ecol. 2008;65:1–14.Article 
    CAS 
    PubMed 

    Google Scholar 
    Jørgensen BB, Boetius A. Feast and famine—microbial life in the deep-sea bed. Nat Rev Microbiol. 2007;5:770–81.Article 
    PubMed 

    Google Scholar 
    Campbell BJ, Engel AS, Porter ML, Takai K. The versatile ε-proteobacteria: key players in sulphidic habitats. Nat Rev Microbiol. 2006;4:458–68.Article 
    CAS 
    PubMed 

    Google Scholar 
    Oren A, Garrity GM. Valid publication of the names of forty-two phyla of prokaryotes. Int J Syst Evol Microbiol. 2021;71:005056.Article 

    Google Scholar 
    Nakagawa S, Takaki Y. Nonpathogenic Epsilonproteobacteria. Encyclopedia of Life Sciences (eLS). Chichester, UK: John Wiley & Sons, Ltd; 2009.Nakagawa S, Takaki Y, Shimamura S, Reysenbach A-L, Takai K, Horikoshi K. Deep-sea vent ε-proteobacterial genomes provide insights into emergence of pathogens. Proc Natl Acad Sci USA. 2007;104:12146–50.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Porcelli I, Reuter M, Pearson BM, Wilhelm T, van Vliet AH. Parallel evolution of genome structure and transcriptional landscape in the Epsilonproteobacteria. BMC Genom. 2013;14:616.Article 
    CAS 

    Google Scholar 
    Zhang Y, Sievert SM. Pan-genome analyses identify lineage- and niche-specific markers of evolution and adaptation in Epsilonproteobacteria. Front Microbiol. 2014;5:110.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vorwerk H, Huber C, Mohr J, Bunk B, Bhuju S, Wensel O, et al. A transferable plasticity region in Campylobacter coli allows isolates of an otherwise non-glycolytic food-borne pathogen to catabolize glucose. Mol Microbiol. 2015;98:809–30.Article 
    CAS 
    PubMed 

    Google Scholar 
    Jiang SC, Kellogg CA, Paul JH. Characterization of marine temperate phage-host systems isolated from Mamala Bay, Oahu, Hawaii. Appl Environ Microbiol. 1998;64:535–42.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Paul JH. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. 2008;2:579–89.Article 
    CAS 
    PubMed 

    Google Scholar 
    Harrison E, Brockhurst MA. Ecological and evolutionary benefits of temperate phage: what does or doesn’t kill you makes you stronger. BioEssays. 2017;39:1700112.Article 

    Google Scholar 
    Fouts DE, Mongodin EF, Mandrell RE, Miller WG, Rasko DA, Ravel J, et al. Major structural differences and novel potential virulence mechanisms from the genomes of multiple Campylobacter species. PLoS Biol. 2005;3:e15.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang M, He L, Li Q, Sun H, Gu Y, You Y, et al. Genomic characterization of the Guillain-Barre syndrome-associated Campylobacter jejuni ICDCCJ07001 isolate. PLoS ONE. 2010;5:e15060.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Miller WG, Yee E, Chapman MH, Smith TPL, Bono JL, Huynh S, et al. Comparative genomics of the Campylobacter lari group. Genome Biol Evol. 2014;6:3252–66.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parker CT, Quiñones B, Miller WG, Horn ST, Mandrell RE. Comparative genomic analysis of Campylobacter jejuni strains reveals diversity due to genomic elements similar to those present in C. jejuni strain RM1221. J Clin Microbiol. 2006;44:4125–35.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark CG, Ng L-K. Sequence variability of Campylobacter temperate bacteriophages. BMC Microbiol. 2008;8:49.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quiñones B, Guilhabert MR, Miller WG, Mandrell RE, Lastovica AJ, Parker CT. Comparative genomic analysis of clinical strains of Campylobacter jejuni from South Africa. PLoS ONE. 2008;3:e2015.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark CG, Chen C, Berry C, Walker M, McCorrister SJ, Chong PM, et al. Comparison of genomes and proteomes of four whole genome-sequenced Campylobacter jejuni from different phylogenetic backgrounds. PLoS ONE. 2018;13:e0190836.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark CG, Grant CC, Pollari F, Marshall B, Moses J, Tracz DM, et al. Effects of the Campylobacter jejuni CJIE1 prophage homologs on adherence and invasion in culture, patient symptoms, and source of infection. BMC Microbiol. 2012;12:269.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaasbeek EJ, Wagenaar JA, Guilhabert MR, Wösten MMSM, van Putten JPM, van der Graaf-van Bloois L, et al. A DNase encoded by integrated element CJIE1 inhibits natural transformation of Campylobacter jejuni. J Bacteriol. 2009;191:2296–306.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaasbeek EJ, Wagenaar JA, Guilhabert MR, van Putten JPM, Parker CT, van der Wal FJ. Nucleases encoded by the integrated elements CJIE2 and CJIE4 inhibit natural transformation of Campylobacter jejuni. J Bacteriol. 2010;192:936–41.Article 
    CAS 
    PubMed 

    Google Scholar 
    Yoshida-Takashima Y, Takaki Y, Shimamura S, Nunoura T, Takai K. Genome sequence of a novel deep-sea vent epsilonproteobacterial phage provides new insight into the co-evolution of Epsilonproteobacteria and their phages. Extremophiles. 2013;17:405–19.Article 
    PubMed 

    Google Scholar 
    Glasby GP, Notsu K. Submarine hydrothermal mineralization in the Okinawa Trough, SW of Japan: an overview. Ore Geol Rev. 2003;23:299–339.Article 

    Google Scholar 
    Yoshida-Takashima Y, Nunoura T, Kazama H, Noguchi T, Inoue K, Akashi H, et al. Spatial distribution of viruses associated with planktonic and attached microbial communities in hydrothermal environments. Appl Environ Microbiol. 2012;78:1311–20.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Takai K, Inagaki F, Nakagawa S, Hirayama H, Nunoura T, Sako Y, et al. Isolation and phylogenetic diversity of members of previously uncultivated ε-Proteobacteria in deep-sea hydrothermal fields. FEMS Microbiol Lett. 2003;217:167–74.
    Google Scholar 
    Sako Y, Takai K, Ishida Y, Uchida A, Katayama Y. Rhodothemus obamensis sp. nov., a modern lineage of extremely thermophilic marine bacteria. Int J Syst Bacteriol. 1996;46:1099–104.Article 
    CAS 
    PubMed 

    Google Scholar 
    Yoshida M, Yoshida-Takashima Y, Nunoura T, Takai K. Genomic characterization of a temperate phage of the psychrotolerant deep-sea bacterium Aurantimonas sp. Extremophiles. 2015;19:49–58.Article 
    CAS 
    PubMed 

    Google Scholar 
    Yoshida T, Takashima Y, Tomaru Y, Shirai Y, Takao Y, Hiroishi S, et al. Isolation and characterization of a cyanophage infecting the toxic cyanobacterium Microcystis aeruginosa. Appl Environ Microbiol. 2006;72:1239–47.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leggett RM, Clavijo BJ, Clissold L, Clark MD, Caccamo M. NextClip: an analysis and read preparation tool for Nextera long mate pair libraries. Bioinformatics. 2014;30:566–8.Article 
    CAS 
    PubMed 

    Google Scholar 
    Boetzer M, Henkel CV, Jansen HJ, Butler D, Pirovano W. Scaffolding pre-assembled contigs using SSPACE. Bioinformatics. 2011;27:578–9.Article 
    CAS 
    PubMed 

    Google Scholar 
    Corkill JE, Graham R, Hart CA, Stubbs S. Pulsed-field gel electrophoresis of degradation-sensitive DNAs from Clostridium difficile PCR ribotype 1 strains. J Clin Microbiol. 2000;38:2791–2.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Besemer J. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 2001;29:2607–18.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Delcher A. Improved microbial gene identification with GLIMMER. Nucleic Acids Res. 1999;27:4636–41.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, et al. The Pfam protein families database. Nucleic Acids Res. 2012;40:D290–301.Article 
    CAS 
    PubMed 

    Google Scholar 
    Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, et al. InterProScan: protein domains identifier. Nucleic Acids Res. 2005;33:W116–20.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marchler-Bauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 2011;39:D225–9.Article 
    CAS 
    PubMed 

    Google Scholar 
    Krogh A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J Mol Biol. 2001;305:567–80.Article 
    CAS 
    PubMed 

    Google Scholar 
    Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol. 2019;37:420–3.Article 
    CAS 
    PubMed 

    Google Scholar 
    Huang S, Wang K, Jiao N, Chen F. Genome sequences of siphoviruses infecting marine Synechococcus unveil a diverse cyanophage group and extensive phage-host genetic exchanges. Environ Microbiol. 2012;14:540–58.Article 
    CAS 
    PubMed 

    Google Scholar 
    Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. The RAST Server: Rapid Annotations using Subsystems Technology. BMC Genomics. 2008;9:75.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 2016;32:929–31.Article 
    CAS 
    PubMed 

    Google Scholar 
    Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucleic Acids Res. 2016;44:W16–21.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Roberts RJ, Vincze T, Posfai J, Macelis D. REBASE—a database for DNA restriction and modification: enzymes, genes and genomes. Nucleic Acids Res. 2015;43:D298–9.Article 
    CAS 
    PubMed 

    Google Scholar 
    Couvin D, Bernheim A, Toffano-Nioche C, Touchon M, Michalik J, Néron B, et al. CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins. Nucleic Acids Res. 2018;46:W246–51.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tamura K, Stecher G, Kumar S. MEGA11: molecular evolutionary genetics analysis version 11. Mol Biol Evol. 2021;38:3022–7.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Meier-Kolthoff JP, Göker M. VICTOR: genome-based phylogeny and classification of prokaryotic viruses. Bioinformatics. 2017;33:3396–404.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nakagawa S, Takai K, Inagaki F, Horikoshi K, Sako Y. Nitratiruptor tergarcus gen. nov., sp. nov. and Nitratifractor salsuginis gen. nov., sp. nov., nitrate-reducing chemolithoautotrophs of the ε-Proteobacteria isolated from a deep-sea hydrothermal system in the Mid-Okinawa Trough. Int J Syst Evol Microbiol. 2005;55:925–33.Article 
    CAS 
    PubMed 

    Google Scholar 
    Richter M, Rosselló-Móra R. Shifting the genomic gold standard for the prokaryotic species definition. Proc Natl Acad Sci USA. 2009;106:19126–31.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pedulla ML, Ford ME, Houtz JM, Karthikeyan T, Wadsworth C, Lewis JA, et al. Origins of highly mosaic mycobacteriophage genomes. Cell. 2003;113:171–82.Article 
    CAS 
    PubMed 

    Google Scholar 
    Mercier C, Lossouarn J, Nesbø CL, Haverkamp THA, Baudoux AC, Jebbar M, et al. Two viruses, MCV1 and MCV2, which infect Marinitoga bacteria isolated from deep-sea hydrothermal vents: functional and genomic analysis. Environ Microbiol. 2018;20:577–87.Article 
    CAS 
    PubMed 

    Google Scholar 
    Samson JE, Magadán AH, Sabri M, Moineau S. Revenge of the phages: defeating bacterial defences. Nat Rev Microbiol. 2013;11:675–87.Article 
    CAS 
    PubMed 

    Google Scholar 
    Meyer JL, Huber JA. Strain-level genomic variation in natural populations of Lebetimonas from an erupting deep-sea volcano. ISME J. 2014;8:867–80.Article 
    CAS 
    PubMed 

    Google Scholar 
    Frost LS, Leplae R, Summers AO, Toussaint A. Mobile genetic elements: the agents of open source evolution. Nat Rev Microbiol. 2005;3:722–32.Article 
    CAS 
    PubMed 

    Google Scholar 
    Ramisetty BCM, Sudhakari PA. Bacterial ‘grounded’ prophages: hotspots for genetic renovation and innovation. Front Genet. 2019;10:65.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Labrie SJ, Samson JE, Moineau S. Bacteriophage resistance mechanisms. Nat Rev Microbiol. 2010;8:317–27.Article 
    CAS 
    PubMed 

    Google Scholar 
    Piel D, Bruto M, Labreuche Y, Blanquart F, Goudenège D, Barcia-Cruz R, et al. Phage–host coevolution in natural populations. Nat Microbiol. 2022;7:1075–86.Article 
    CAS 
    PubMed 

    Google Scholar 
    Lynch KH, Stothard P, Dennis JJ. Genomic analysis and relatedness of P2-like phages of the Burkholderia cepacia complex. BMC Genomics. 2010;11:599.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Godde JS, Bickerton A. The repetitive DNA elements called CRISPRs and their associated genes: evidence of horizontal transfer among prokaryotes. J Mol Evol. 2006;62:718–29.Article 
    CAS 
    PubMed 

    Google Scholar 
    Nobrega FL, Walinga H, Dutilh BE, Brouns SJJ. Prophages are associated with extensive CRISPR–Cas auto-immunity. Nucleic Acids Res. 2020;48:12074–84.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yi H, Huang L, Yang B, Gomez J, Zhang H, Yin Y. AcrFinder: genome mining anti-CRISPR operons in prokaryotes and their viruses. Nucleic Acids Res. 2020;48:W358–65.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maier L-K, Lange SJ, Stoll B, Haas KA, Fischer SM, Fischer E, et al. Essential requirements for the detection and degradation of invaders by the Haloferax volcanii CRISPR/Cas system I-B. RNA Biol. 2013;10:865–74.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boudry P, Semenova E, Monot M, Datsenko KA, Lopatina A, Sekulovic O, et al. Function of the CRISPR-Cas system of the human pathogen Clostridium difficile. mBio. 2015;6:e01112–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barrangou R, Fremaux C, Deveau H, Richards M, Boyaval P, Moineau S, et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science. 2007;315:1709–12.Article 
    CAS 
    PubMed 

    Google Scholar 
    Deveau H, Barrangou R, Garneau JE, Labonté J, Fremaux C, Boyaval P, et al. Phage response to CRISPR-encoded resistance in Streptococcus thermophilus. J Bacteriol. 2008;190:1390–400.Article 
    CAS 
    PubMed 

    Google Scholar 
    Nozawa T, Furukawa N, Aikawa C, Watanabe T, Haobam B, Kurokawa K, et al. CRISPR inhibition of prophage acquisition in Streptococcus pyogenes. PLoS ONE. 2011;6:e19543.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Makarova KS, Wolf YI, Iranzo J, Shmakov SA, Alkhnbashi OS, Brouns SJJ, et al. Evolutionary classification of CRISPR–Cas systems: a burst of class 2 and derived variants. Nat Rev Microbiol. 2020;18:67–83.Article 
    CAS 
    PubMed 

    Google Scholar  More

  • in

    Impact of host age on viral and bacterial communities in a waterbird population

    Woolhouse MEJ, Gowtage-Sequeria S. Host range and emerging and reemerging pathogens. Emerg Infect Dis. 2005;11:1842–7.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Allen T, Murray KA, Zambrana-Torrelio C, Morse SS, Rondinini C, Di Marco M, et al. Global hotspots and correlates of emerging zoonotic diseases. Nat Commun. 2017;8:1–10.Article 
    CAS 

    Google Scholar 
    Van Kerkhove MD, Ly S, Holl D, Guitian J, Mangtani P, Ghani AC, et al. Frequency and patterns of contact with domestic poultry and potential risk of H5N1 transmission to humans living in rural Cambodia. Influenza Other Respir Viruses. 2008;2:155–63.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaythorpe KAM, Hamlet A, Cibrelus L, Garske T, Ferguson NM. The effect of climate change on yellow fever disease burden in Africa. eLife. 2020;9:1–27.Article 

    Google Scholar 
    Faust CL, McCallum HI, Bloomfield LSP, Gottdenker NL, Gillespie TR, Torney CJ, et al. Pathogen spillover during land conversion. Ecol Lett. 2018;21:471–83.Article 
    PubMed 

    Google Scholar 
    Gog J, Woodroffe R, Swinton J. Disease in endangered metapopulations: The importance of alternative hosts. Proc R Soc B Biol Sci. 2002;269:671–6.Article 

    Google Scholar 
    Jones BA, Grace D, Kock R, Alonso S, Rushton J, Said MY, et al. Zoonosis emergence linked to agricultural intensification and environmental change. Proc Natl Acad Sci USA. 2013;110:8399–404.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    White LA, Forester JD, Craft ME. Disease outbreak thresholds emerge from interactions between movement behavior, landscape structure, and epidemiology. Proc Natl Acad Sci USA. 2018;115:7374–9.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Altizer S, Bartel R, Han BA. Animal migration and infectious disease risk. Science. 2011;331:296–302.Article 
    CAS 
    PubMed 

    Google Scholar 
    Ludwig SC, Roos S, Bubb D, Baines D. Long-term trends in abundance and breeding success of red grouse and hen harriers in relation to changing management of a Scottish grouse moor. Wildl Biol. 2017;2017:wlb.00246.Article 

    Google Scholar 
    Newton I. Weather-related mass-mortality events in migrants. Ibis. 2007;149:453–67.Article 

    Google Scholar 
    Ropert-Coudert Y, Kato A, Meyer X, Pellé M, MacIntosh AJJ, Angelier F, et al. A complete breeding failure in an Adélie penguin colony correlates with unusual and extreme environmental events. Ecography. 2015;38:111–3.Article 

    Google Scholar 
    Newmark WD, Stanley TR. Habitat fragmentation reduces nest survival in an Afrotropical bird community in a biodiversity hotspot. Proc Natl Acad Sci USA. 2011;108:11488–93.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tuyttens FaM, Macdonald DW, Rogers LM, Cheeseman CL, Roddam AW. Comparative study on the consequences of culling badgers (Meles meles) on biometrics, population dynamics and movement. J Anim Ecol. 2000;69:567–80.Article 

    Google Scholar 
    Frafjord K. Influence of reproductive status: Home range size in water voles (Arvicola amphibius). PLoS ONE. 2016;11:1–13.Article 

    Google Scholar 
    Begg CM, Begg KS, Du Toit JT, Mills MGL. Spatial organization of the honey badger Mellivora capensis in the southern Kalahari: Home-range size and movement patterns. J Zool. 2005;265:23–35.Article 

    Google Scholar 
    Bronikowski AM, Cords M, Alberts SC, Altmann J, Brockman DK, Fedigan LM, et al. Female and male life tables for seven wild primate species. Sci Data. 2016;3:1–8.Article 

    Google Scholar 
    Mitchell GW, Woodworth BK, Taylor PD, Norris DR. Automated telemetry reveals age specific differences in flight duration and speed are driven by wind conditions in a migratory songbird. Mov Ecol. 2015;3:1–13.Article 

    Google Scholar 
    Frankish CK, Manica A, Phillips RA. Effects of age on foraging behavior in two closely related albatross species. Mov Ecol. 2020;8:1–17.Article 

    Google Scholar 
    Tirpak JM, Giuliano WM, Allen TJ, Bittner S, Edwards JW, Friedhof S, et al. Ruffed grouse-habitat preference in the central and southern Appalachians. Ecol Manag. 2010;260:1525–38.Article 

    Google Scholar 
    Zhu WW, Garber PA, Bezanson M, Qi XG, Li BG. Age- and sex-based patterns of positional behavior and substrate utilization in the golden snub-nosed monkey (Rhinopithecus roxellana). Am J Primatol. 2015;77:98–108.Article 
    PubMed 

    Google Scholar 
    Tian H, Yu P, Bjørnstad ON, Cazelles B, Yang J, Tan H, et al. Anthropogenically driven environmental changes shift the ecological dynamics of hemorrhagic fever with renal syndrome. PLOS Pathog. 2017;13:e1006198.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    George DB, Webb CT, Farnsworth ML, O’Shea TJ, Bowen RA, Smith DL, et al. Host and viral ecology determine bat rabies seasonality and maintenance. Proc Natl Acad Sci USA. 2011;108:10208–13.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    van Dijk JG, Verhagen JH, Wille M, Waldenström J. Host and virus ecology as determinants of influenza A virus transmission in wild birds. Curr Opin Virol. 2018;28:26–36.Article 
    PubMed 

    Google Scholar 
    Chong R, Shi M, Grueber CE, Holmes EC, Hogg CJ, Belov K, et al. Fecal Viral Diversity of Captive and Wild Tasmanian Devils Characterized Using Virion-Enriched Metagenomics and Metatranscriptomics. J Virol. 2019;93:e00205–19.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    François S, Pybus OG. Towards an understanding of the avian virome. J Gen Virol. 2020;101:785–90.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Springer A, Fichtel C, Al-Ghalith GA, Koch F, Amato KR, Clayton JB, et al. Patterns of seasonality and group membership characterize the gut microbiota in a longitudinal study of wild Verreaux’s sifakas (Propithecus verreauxi). Ecol Evol. 2017;7:5732–45.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aivelo T, Laakkonen J, Jernvall J. Population-and individual-level dynamics of the intestinal microbiota of a small primate. Appl Environ Microbiol. 2016;82:3537–45.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    van Dongen WFD, White J, Brandl HB, Moodley Y, Merkling T, Leclaire S, et al. Age-related differences in the cloacal microbiota of a wild bird species. BMC Ecol. 2013;13:11.Cleaveland S, Laurenson MK, Taylor LH. Diseases of humans and their domestic mammals: Pathogen characteristics, host range and the risk of emergence. Philos Trans R Soc B Biol Sci. 2001;356:991–9.Article 
    CAS 

    Google Scholar 
    Wille M, Shi M, Hurt AC, Klaassen M, Holmes EC. RNA virome abundance and diversity is associated with host age in a bird species. Virology. 2021;561:98–106.Article 
    CAS 
    PubMed 

    Google Scholar 
    Negrey JD, Thompson ME, Langergraber KE, Machanda ZP, Mitani JC, Muller MN, et al. Demography, life-history trade-offs, and the gastrointestinal virome of wild chimpanzees. Philos Trans R Soc B Biol Sci. 2020;375:20190613.Article 

    Google Scholar 
    Hill SC, Manvell RJ, Schulenburg B, Shell W, Wikramaratna PS, Perrins C, et al. Antibody responses to avian influenza viruses in wild birds broaden with age. Proc R Soc B Biol Sci. 2016;283:20162159.Article 

    Google Scholar 
    Perrins CM, Ogilvie MA. A study of the Abbotsbury mute swans (Cygnus olor). Wildfowl. 1981;32:35–47.
    Google Scholar 
    Perrins CM, McCleery RH, Ogilvie MA. A study of the breeding Mute Swans Cygnus olor at Abbotsbury. Wildfowl. 1994;45:1–14.
    Google Scholar 
    Perrins C. Survival rates of young mute swans Cygnus olor. Wildfowl Suppl. 1991;45:95–103.
    Google Scholar 
    McCleery RH, Perrins C, Wheeler D, Groves S. Population structure, survival rates and productivity of mute swans breeding in a colony at Abbotsbury, Dorset, England. Waterbirds Waterbird Soc. 2002;25:201.
    Google Scholar 
    Matrozis R A 30-year (1988–2017) study of Mute Swans Cygnus olor in Riga, Latvia. Wildfowl. 2019;14:164–77.Charmantier A, Perrins C, McCleery RH, Sheldon BC. Quantitative genetics of age at reproduction in wild swans: Support for antagonistic pleiotropy models of senescence. Proc Natl Acad Sci USA. 2006;103:6587–92.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hill SC, Hansen R, Watson S, Coward V, Russell C, Cooper J, et al. Comparative micro-epidemiology of pathogenic avian influenza virus outbreaks in a wild bird population. Philos Trans R Soc B Biol Sci. 2019;374:20180259.Cotten M, Oude Munnink B, Canuti M, Deijs M, Watson SJ, Kellam P, et al. Full genome virus detection in fecal samples using sensitive nucleic acid preparation, deep sequencing, and a novel iterative sequence classification algorithm. PLoS ONE. 2014;9:e93269.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Boom R, Sol CJA, Salimans MMM, Janses CL, Wertheim Van Dillen PME, Van Der Noordaa J. Rapid and simple method for purification of nucleic acids R. J Clin Microbiol. 1990;28:495–503.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Endoh D, Mizutani T, Kirisawa R, Maki Y, Saito H, Kon Y, et al. Species-independent detection of RNA virus by representational difference analysis using non-ribosomal hexanucleotides for reverse transcription. Nucleic Acids Res. 2005;33:1–11.Article 

    Google Scholar 
    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMB. 2011;17:10–12.
    Google Scholar 
    Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008;18:821–9.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.Article 
    PubMed 

    Google Scholar 
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.Article 
    CAS 
    PubMed 

    Google Scholar 
    Langmead B Aligning short sequencing reads with Bowtie. Curr Protoc Bioinforma. 2010; Chapter 11: Unit 11.7.Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinforma Oxf Engl. 2012;28:1647–9.Article 

    Google Scholar 
    Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muhire BM, Varsani A, Martin DP SDT: A virus classification tool based on pairwise sequence alignment and identity calculation. PLoS ONE. 2014;9:e108277.Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinforma Oxf Engl. 2011;27:1164–5.Article 
    CAS 

    Google Scholar 
    Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kapoor A, Simmonds P, Lipkin WI, Zaidi S, Delwart E. Use of nucleotide composition analysis to infer hosts for three novel picorna-like viruses. J Virol. 2010;84:10322–8.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wood DE, Salzberg SL Kraken: Ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 2014;15:R46.Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;3:e104.Article 

    Google Scholar 
    Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:633–42.Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. 2019. R Foundation for Statistical Computing, Vienna, Austria.RStudio Team. RStudio: Integrated Development for R. 2015. Boston, MA, USA.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

    Google Scholar 
    Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
    Google Scholar 
    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.
    Google Scholar 
    Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001;29:1165–88.Article 

    Google Scholar 
    Oakley BB, Lillehoj HS, Kogut MH, Kim WK, Maurer JJ, Pedroso A, et al. The chicken gastrointestinal microbiome. FEMS Microbiol Lett. 2014;360:100–12.Article 
    CAS 
    PubMed 

    Google Scholar 
    Waite DW, Taylor MW. Characterizing the avian gut microbiota: Membership, driving influences, and potential function. Front Microbiol. 2014;5:1–12.Article 

    Google Scholar 
    Waite DW, Taylor MW. Exploring the avian gut microbiota: Current trends and future directions. Front Microbiol. 2015;6:1–12.Article 

    Google Scholar 
    Zell R, Delwart E, Gorbalenya AE, Hovi T, King AMQ, Knowles NJ, et al. ICTV virus taxonomy profile: Picornaviridae. J Gen Virol. 2017;98:2421–2.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cotmore SF, Agbandje-McKenna M, Canuti M, Chiorini JA, Eis-Hubinger AM, Hughes J, et al. ICTV virus taxonomy profile: Parvoviridae. J Gen Virol. 2019;100:367–8.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bosch A, Guix S, Krishna N, Méndez E, Monroe SS, Pantin-Jackwood M, et al. Astroviridae. In: King A, Adams M, Carstens E, Lefkowitz E (eds). Virus taxonomy. Classification and nomenclature of viruses: ninth report of the International Committee on the Taxonomy of Viruses. 2011. Elsevier, London, pp 953-9.Risely A. Applying the core microbiome to understand host–microbe systems. J Anim Ecol. 2020;89:1549–58.Article 
    PubMed 

    Google Scholar 
    Piepenbring AK, Enderlein D, Herzog S, Kaleta EF, Heffels-Redmann U, Ressmeyer S, et al. Pathogenesis of avian bornavirus in experimentally infected Cockatiels. Emerg Infect Dis. 2012;18:234–41.Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anzil AP, Blinzinger K, Mayr A. Persistent Borna virus infection in adult hamsters. Arch Für Gesamt Virusforsch. 1973;40:52–57.Article 
    CAS 

    Google Scholar 
    Heffels-Redmann U, Enderlein D, Herzog S, Piepenbring A, Bürkle M, Neumann D, et al. Follow-Up Investigations on Different Courses of Natural Avian Bornavirus Infections in Psittacines. Avian Dis. 2012;56:153–9.Article 
    PubMed 

    Google Scholar 
    Rubbenstroth D, Brosinski K, Rinder M, Olbert M, Kaspers B, Korbel R, et al. No contact transmission of avian bornavirus in experimentally infected cockatiels (Nymphicus hollandicus) and domestic canaries (Serinus canaria forma domestica). Vet Microbiol. 2014;172:146–56.Article 
    PubMed 

    Google Scholar 
    Olsen I The Family Fusobacteriaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Firmicutes and Tenericutes, 4th ed. 2014. pp 109-32.Imhoff JF The Family Chlorobiaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 501-14.Cho JC The Family Lentisphaeraceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 705-10.Karami A, Sarshar M, Ranjbar R, Zanjani RS The Phylum Spirochaetaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 915-29.McBride MJ The Family Flavobacteriaceae. In: Rosenberg E, Delong EF, Lory S, Stackebrandt E, Thompson F (eds). The Prokaryotes: Other Major Lineages of Bacteria and The Archaea, 4th ed. 2014. pp 643-76.Van Dijk JGB, Hoye BJ, Verhagen JH, Nolet BA, Fouchier RAM, Klaassen M. Juveniles and migrants as drivers for seasonal epizootics of avian influenza virus. J Anim Ecol. 2014;83:266–75.Article 
    PubMed 

    Google Scholar 
    Chevalier V, Marsot M, Molia S, Rasamoelina H, Rakotondravao R, Pedrono M, et al. Serological evidence of West Nile and Usutu viruses circulation in domestic and wild birds in wetlands of Mali and Madagascar in 2008. Int J Environ Res Public Health. 2020;17:1998.Guy, JS Turkey Viral Hepatitis. Diseases of Poultry, 12th Edition. 2008. Wiley Blackwell, pp 426-30.Davies ZG, Fuller RA, Dallimer M, Loram A, Gaston KJ. Household factors influencing participation in bird feeding activity: a national scale analysis. PLOS ONE. 2012;7:e39692.Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Shutt JD, Trivedi UH, Nicholls JA. Faecal metabarcoding reveals pervasive long-distance impacts of garden bird feeding. Proc R Soc B Biol Sci. 2021;288:20210480.Article 

    Google Scholar 
    Minich JJ, Sanders JG, Amir A, Humphrey G, Gilbert JA, Knight R. Quantifying and understanding well-to-well contamination in microbiome research. mSystems. 2019;4:e00186–19.Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    COVID variants to watch, and more — this week’s best science graphics

    COVID variant family expandsSince the Omicron variant of SARS-CoV-2 emerged in late 2021, it has spawned a series of subvariants that have sparked global waves of infection. In the past few months, scientists have identified more than a dozen extra subvariants to watch. There are so many that they’re being called a swarm, or ‘variant soup’. BQ.1.1 (a descendant of BQ.1) and XBB seem to be rising to the top, possibly because they have many mutations in a key region of the viral spike protein called the receptor binding domain, which is required to infect cells.

    Source: NextStrain

    The variants near youIn Europe and North America, SARS-CoV-2 variants in the BQ.1 family are rising quickly and are likely to drive infection waves as these regions enter winter. They are also a common ingredient of the variant soup in South Africa, Nigeria and elsewhere in Africa. XBB, by contrast, looks likely to dominate infections in Asia; it recently drove a wave of infections in Singapore.

    Source: Moritz Gerstung, Cov-Spectrum.org and GISAID

    Money worries for science studentsEighty-five per cent of graduate students who responded to a Nature survey are worried about the increasing cost of living, and 25% are concerned about their growing student debt. Forty-five per cent said that rising inflation could cause them to reconsider whether to continue their science studies. The survey involved more than 3,200 self-selected PhD and master’s students from around the world.

    How species suffer in heatwavesEven a small temperature rise has a severe effect on animal mortality, and understanding this relationship is important for predicting the effects of heatwaves caused by climate change. A paper in Nature used published data to examine how changes in temperature affect the rate of biological processes, such as movement or metabolism, at permissive temperatures — those at which species function normally. They also looked at how higher, stressful temperatures affect the rate of heat failure (irreversible heat injuries that result in death). This graph shows that rising temperatures drive a very rapid increase in heat-failure rates in frogs and molluscs. These high sensitivities suggest that when there is no way to escape hot conditions, species can quickly succumb. More

  • in

    Grassland coverage change and its humanity effect factors quantitative assessment in Zhejiang province, China, 1980–2018

    Vegetation is the main component of terrestrial ecosystems and as an indicator of ecosystem changes. In the world’s land area, forest land accounts for about 30%, grassland accounts for 26%, and cultivated land accounts for about 12%1. China has the second largest grassland area in the world. The total grassland is 392 million hectares in China, which is about 12% of the world’s grassland area and about 41% of China’s national territorial area, which is about twice of China’s arable land2. In China, the type of grassland ranks first in the world, mainly including northern grasslands, southern grassy hills and slopes, coastal beaches, wetlands, and natural grasslands in agricultural areas. It includes 18 major categories, 38 subcategories, and more than 1,000 types. The grassland resources also contain extremely rich biodiversity, with more than 7,000 pastures and thousands of animals, making it as the largest biological gene pool in Asia and also the world.
    Grassland plays an important role in ecological environment protection and animal husbandry development. Like the grasslands in Europe, China’s land use forms, management objectives, and use systems are becoming increasingly diversified3. Grassland has not only made great contributions to preventing soil erosion, purifying chemical fertilizers and pesticides, regulating groundwater, and promoting biodiversity, but also as a basic nutrient for herbivores and ruminants, providing environmental benefits for ensuring the health of grassland animal products. In addition, grassland has aesthetic and entertainment functions, and it can provide functions that other agricultural land use types do not have. In addition, grassland also has an important ecological function of regulating climate4,5,6, for example, grasslands can significantly contribute to climate mitigation while providing substantial additional ecosystem services7. Grassland is the only land use type that can accomplish so many tasks and meet so many requirements.Grasslands are highly vulnerable to climate change or human activities8, the research on the relationship between grassland coverage change and its human influencing factors can reflect the scope and degree of influence of natural conditions and human activities on grassland coverage change and has a reference significance for balancing economic development and environmental protection. Grassland is not only an important material basis and means of production for the development of animal husbandry but also an important natural barrier to economic development in southeast china. Zhejiang province is located in the Yangtze River Delta, the transportation is quite convenient, the economic foundation is very well and the economy develops very rapid9. Meanwhile, with the rapid development of industrialization and urbanization, the change in land use form has been breathtaking, and human activities have improved the degree of land exploitation and utilization. The natural grassland area in Zhejiang Province is 3 million hm2, about 30% of the total land area of the province, of which the available grassland area is 600,000 hm2, for about 20% of the total area of natural grassland. Accordingly, there is enormous potential for developing the grassland industry in Zhejiang province10.There are three ways to calculate the grassland coverage, (1) field measurement method, (2) remote sensing estimation method, and (3) integrated measurement method of field measurement and remote sensing estimation11. The field measurement method is not suitable for large-scale measurement and measuring alone in various applications, because the measurement range of this method is limited, it is only suitable for the selected field plot. For remote sensing estimation method does not depend on field measurement data, and can reduce the workload and save time, so it is suitable for large-scale grass coverage estimation. At the same time, the field measurement method is an indispensable auxiliary and verification method for modern measurement methods such as remote sensing. Therefore, the comprehensive measurement method of field measurement and remote sensing can obtain more reliable data.With the rapid development of aerospace science and technology, more and more remote sensing data can be used to monitor land use form12. Currently, the most commonly used remote sensing images include Landsat MSS/TM/ETM+, NOAA/AVHRR, and EOS-MODIS. In recent years, satellite SAR, SPOT, CBERS, and other images have also been widely used in research. For global or state-scale land research, NOAA/AVHRR and MODIS data are mainly used. For regional scale, as long as Landsat TM/ETM+ and other high-resolution data are applied.The change of grassland coverage in Zhejiang Province and its effect factors are of great significance to the development of animal husbandry, the rational development and utilization of land, and the balanced development of the economy and environment. However, there are few studies have been done about this. Therefore, we present the following questions: (1) How did the grassland coverage change in Zhejiang Province from 1980 to 2018? (2) What are the main factors that affect the change in grassland coverage? This study aims to make clear grassland coverage Change and quantitative assessment of its effect factors. Meanwhile, the result of this study will provide a more comprehensive knowledge of the grassland of Zhejiang Province as well as useful suggestions for grassland resource management and sustainable development. More