More stories

  • in

    Effects of a bacteria-produced algicide on non-target marine invertebrate species

    Algicide preparation
    Four batches of algicide were used for experiments, labeled Batch 3, Batch 4–5–6, Batch 7, and Batch 8, following methods used by Grasso27. For each batch, a single colony of Shewanella sp. IRI-160 was transferred from a modified LM medium plate to liquid LM medium for overnight growth, then inoculated into f/2 with 0.05% casamino acids and incubated for 10 days at room temperature with bubbling. Bacteria and other compounds greater than 60 kDa in size were filtered out using a HemoFlow HF80S 60 kDa dialysis cartridge (Fresenius Medical Care, Waltham, MA), creating a batch of sterile filtered exudate referred to as IRI-160AA. Samples of the algicide were diluted with ultrapure water, then total nitrogen (TN) was measured with a TOC-V total organic carbon analyzer equipped with a Total Nitrogen Measuring Unit (Shimadzu Corp., Kyoto, Japan). The algicide has approximately 5.02 mg/L TN. The 24-h EC50 for K. veneficum differed among batches but was always close to 1% (actual EC50s ranged from 0.93% in Batch 4–5–6 to 1.5% in Batch 3), thus a 1% concentration of the algicide was included in all invertebrate assays27. Animals were also exposed to a media control to ensure mortality was due to the algicide.
    Statistical analyses
    For all statistics, data were analyzed using Shapiro–Wilk normality tests and Brown-Forsythe equal variance tests. If they failed either, data were transformed and reanalyzed. If transformed data passed both tests, then analysis proceeded. If neither log or square-root transformed data passed both normality and equal variance tests, then a non-parametric test was run if possible. Specific details on statistical analyses are provided in each section below.
    Copepod mortality
    Mortality experiments followed established methods for determining acute toxicity in aquatic animals30,31,33,49. For A. tonsa adults, we collected animals in Fall of 2018 after sunset near the mouth of the Broadkill River (Delaware, USA) using a plankton net. Cod ends were diluted and maintained in field collected seawater with ambient food at room temperature (~ 20 °C) until use in experiments. Adults were filtered out of the bulk collection with a 500-μm mesh, then sorted for adult females. We transferred one adult female (n = 24 for 40%, 48 for 30%, and 72 for all other concentrations) into each well of a 12-well plate containing 5 mL of test solution; test solutions included a seawater control (0%); algicide mixtures prepared from Batch 3 of the IRI-160AA in 20 psu, 0.2 μm-filtered sea water collected from Indian River Inlet, DE, USA (FSW) (1%, 5%, 10%, 13.5%, 18%, 24%, 30%, and 40% v/v); and a 24% media solution as a media control. The plates were incubated at 25 °C in low-light (~ 2.37 × 1013 photons cm-2 s−1) on a 14:10 h day:night cycle for 48 h. Every 6 h for the first 24 h, and again at 48 h, we counted the number alive and dead.
    For A. tonsa nauplii, adult females and males were placed in two 1 L beakers at room temperature with a 150-μm mesh placed several centimeters off the bottom (to prevent egg cannibalism), a slow bubbler (~ 2 small bubbles s−1), and ambient seawater diluted with 20 psu FSW until the water was mostly clear. Adults were allowed to mate in the beaker for approximately 24 h, after which we removed the mesh, thus removing the adults and leaving behind any nauplii and eggs. After another 24 h, the contents of the beakers were poured through a 20-μm mesh, and we extracted the nauplii and placed them into experimental treatments (0% seawater control, algicide at 1%, 5%, 10%, 13.5%, 18%, 24%, and 30% v/v concentrations, plus a 24% media control; n = 48 animals for all concentrations) following the procedure outlined above for the adult female copepods. This experiment was conducted three times; the first two mortality experiments used Batch 3 of the IRI-160AA, and the third mortality experiment used Batch 8.
    From the data collected, we generated a Probit model50 and obtained a 24-h LC50. Another approach looks at mortality over several time points in order to generate a time series of survival (e.g., Robineau et al.51, Keller et al.52). This also allows the generation of an LC50 at several time points (e.g., 6, 12, 18, and 24 h), which can better inform how a certain animal may survive over time. We used SigmaPlot to generate graphs of survival over time, and R statistical software53 and the R package ecotoxicology54 for generating and graphing the Probit model and running a χ2 test to evaluate the model.
    Crab mortality
    We conducted mortality experiments for the blue crab (Callinectes sapidus) in larval (Z1-stage zoeae) and postlarval (megalopae) stages in a similar manner to mortality experiments with Acartia tonsa. We collected ovigerous female blue crabs during the Summer of 2018 by dip net and drop net at sunset from the Delaware Bay (similar to methods used by Kernehan55) in Cape Henlopen State Park and maintained them in a recirculating water tray containing filtered ambient seawater (~ 30 psu) at room temperature. We staged egg masses every few days55, and females predicted to hatch within ~ 3 days were moved to 7-gallon buckets in a 25 °C incubator containing ~ 30 psu sea water and a bubbler. Zoea larvae (Z1-stage) hatched from these females were kept in large finger bowls with 30 psu sea water at room temperature and were fed lab-reared rotifers (Brachionus rotundiformis, Reed Mariculture). These animals became subjects for mortality and sub-lethal experiments within approximately a day of hatching. Four experiments were conducted; three mortality experiments used Batch 4–5–6 of the IRI-160AA, while the fourth experiment (24 individuals for each concentration) used Batch 7.
    Megalopae were collected by plankton net set on rising tides at night during the Summer and Fall of 2018. They were maintained in large finger bowls at room temperature and fed with Artemia nauplii and went into experiments within a few days of collection. Only megalopae in intermolt based on morphology56 were used in experiments. Megalopae experiments used Batch 3 of the IRI-160AA.
    Both zoeae and megalopae were exposed to 1%, 5%, 10%, 13.5%, 18%, and 24% algicide concentrations, plus a 0% seawater control and a 24% media control (n = 84 animals for the 0% concentration and 60 for all other concentrations for zoeae, and n = 24 animals for megalopae for all concentrations). Animals were incubated at 25 °C under low-light (~ 2.37 × 1013 photons cm-2 s-1) on a 14:10 light:dark cycle for the duration of experiments. We checked on zoeae and megalopae every 6 h for 24 h; megalopae were checked at an additional 48-h time point.
    Oyster mortality
    Oyster larvae (eyed pediveligers of Crassostrea virginica) were provided by University of Maryland’s Horn Point Laboratory. Animals were maintained on a damp coffee filter in a sealed plastic container on ice during transport, then released into room-temperature fingerbowls containing 20 psu water and fed a locally-isolated alga (Storeatula major) at room temperature. Experiments occurred in similar fashion to those conducted on Acartia tonsa and Callinectes sapidus. Larvae were assayed in 12-well plates (n = 36 animals for all concentrations). Animals were exposed to 1%, 5%, 10%, 13.5%, 18%, and 24% algicide concentrations, plus a 0% seawater control, and 24% media control. Animals were incubated at 25 °C under a 14:10 light:dark cycle for the duration of experiments. Survival was evaluated every 6 h for 24 h and again at 48 h. Larvae were additionally examined at the start of the experiment and at the 24- and 48-h time points for an activity assay. These experiments used Batch 3 of the IRI-160AA.
    Wild-type adult C. virginica were collected from the Delaware Bay near the University of Delaware Lewes Campus, while Haskins-disease-resistant strain individuals were collected from aquaculture cages maintained by the Delaware Center for the Inland Bays. On the first day, individuals were cleaned with a wire brush, and divided into two buckets containing approximately 10 L of 20 psu seawater and were fed Isochrysis galbana (~ 100,000 cells L−1). On the second day the water was changed and they were again fed. On the third day, water was changed and animals were not fed. On the fourth day, individuals were removed from the buckets, dried with a paper towel, labeled with permanent marker, and placed in pairs into forty-one 1 L plastic containers containing 1 L of various algicide solutions: 0%, 1%, 5%, 10%, 13.5%, 18%, and 24% (n = 28 for 0%, 22 for 1% and 18%, and 20 for all other concentrations). Individuals were checked every 6 h for 24 h and assessed if they were alive or dead. Closed individuals were assumed to be alive. If open individuals were observed, we gently tapped on the container to see if the individual shut its shell; animals that responded to this stimulus were marked as alive. Only animals that did not respond to repeated stimuli were scored as dead. Proportion surviving was compared across algicide concentration and strain. These experiments all used Batch 8 of the IRI-160AA.
    Copepod sub-lethality
    Respiration
    We conducted respiration experiments on A. tonsa adult females and young nauplii in a 24-well microplate respirometer (Loligo Systems). First, we sorted animals into fingerbowls containing 100 mL of their respective algicide concentrations. After 24 h of algicide exposure, we removed animals via pipette and put one animal into each well of the respirometer plate (200 μL wells for adult females and 80 μL for nauplii) filled with 0.2 μm filtered FSW, then sealed the plate with Parafilm and a weight. Each experiment also had 4 to 6 wells with only FSW to calculate background oxygen consumption. The experiment occurred in darkness within a 25 °C incubator at night and lasted several hours (n = 26–39 animals for adult females, 11–18 for nauplii). Oxygen concentrations in each well were recorded every minute. At the end of the experiment, respiration rates were calculated in R statistical software using the respR package57 over a period of time when the animals were still in independent respiration, and a one-way ANOVA on ranks in SigmaPlot (Systat Software, San Jose, CA) compared treatments. Experiments with adult females used Batch 3 of IRI-160AA, while nauplii experiments used Batch 8.
    Activity
    Experiments determining effects on swimming activity utilized Locomotor Activity Monitors (LAMs; TriKinetics). Three beams of infrared light cross a 3 mL test tube containing an animal and register when the animal crosses the beams. We sorted batches of adult female A. tonsa into fingerbowls containing different algicide treatments. Animals were incubated at 25 °C in low-light conditions (~ 2.37 × 1013 photons cm−2 s−1) for 24 h on a 11:13-h light:dark cycle. Animals were pipetted into plastic test tubes (one animal per tube) containing ~ 3 mL of FSW, which then went into the LAMs (n = 21–36 animals). The experiment lasted 24 h with beam breaks summed at one-minute intervals, allowing the data to be analyzed wholly for the 24-h period as well as across different light phases to account for light:dark mediated activity rhythms. Experiments started in the afternoon and ran overnight, creating an initial light phase (L1), a dark phase (D), and a second light phase (L2). Comparing treatments across the entire time period was done using a one-way ANOVA on ranks, while analyzing the data based on the different light phases was performed via a one-way repeated-measures ANOVA. Additionally, at the end of the LAM activity experiments we collected the individuals and noted mortality. This data was analyzed via a one-way ANOVA on ranks. Copepod activity experiments used Batch 3 of the IRI-160AA. Nauplii were too small to generate a reliable signal in the LAMs and were not used in these experiments.
    Crab sub-lethality
    Respiration
    Respiration experiments followed methods described for A. tonsa above and involved zoeae and megalopae. A one-way ANOVA on ranks was calculated using the data for each life stage. The first four zoeae experiments used Batch 4–5-6 of IRI-160AA, while the last two experiments used Batch 7. Megalopae experiments all used Batch 3.
    Activity
    Activity level experiments followed methods described for A. tonsa above and involved zoeae and megalopae. The 24-h data were analyzed using a one-way ANOVA on square root transformed data for zoeae, and a one-way ANOVA on ranks for megalopae. The data broken down by light phase were analyzed via one-way repeated measures ANOVA on log-transformed data for both zoeae and megalopae. These experiments all used Batch 3 of IRI-160AA.
    At the end of experiments we collected the individuals and noted mortality. This data was analyzed via a one-way ANOVA for zoeae and a one-way ANOVA on ranks for the megalopae.
    Metamorphosis
    We sorted megalopae into finger bowls containing 100 mL of filtered estuary water with different concentrations of the IRI-160AA algicide (0%, 1%, and 17% v/v). After 24-h of exposure, we sorted animals into 12-well plates containing FSW (n = 60 individuals for each treatment). Water was changed daily, and animals were fed freshly hatched Artemia daily. Every 12 h, we counted how many megalopae had molted into first crabs until most had metamorphosed (5.5 days) and used a Kaplan–Meier Survival Analysis with a Gehan-Breslow test to determine if there was a difference in time to metamorphosis (TTM) across treatments. These experiments used Batch 3 of the IRI-160AA.
    Abdomen Pumping and Grooming
    Crabs with egg masses were collected from the Delaware Bay near Lewes, DE and separated into numbered baskets and maintained in a flow-through sea water table. They were fed thawed squid (Loligo opalescens) every day, and eggs were photographed every two to three days under a dissecting scope until they reached ~ 6 days until hatching (i.e., late-stage sensu Tankersley et al.)36. Homogenized egg water (seawater plus homogenized eggs, designated SW + HE, ~ 20 eggs mL−1) was utilized to induce pumping and grooming behavior and made according to Tankersley et al.36.
    Ovigerous females were exposed to several sub-lethal concentrations of algicide combined with the homogenized egg solution and monitored for pumping and grooming behavior. Test solutions were diluted to 1.5 L with filtered 30 psu seawater, and 3.75 mL aliquot of a pre-prepared homogenized egg solution was added to achieve a final concentration of ~ 20 eggs/mL. These experiments used Batch 4–5–6, Batch 7, and Batch 3 of the IRI-160AA.
    Between three and six crabs were tested at a time, and all crabs were staged the day of the experiment to verify that their eggs were no more than six days from hatching. All experiments were performed under dim red light to reduce disturbance. Each crab was tested in every treatment. A crab was placed into a translucent container (20.1 × 16.5 × 11.4 cm) with a given treatment condition and acclimated for 2.5 min. Then, for the following 2.5 min, the number of times the crab pumped its abdomen was recorded. Immediately following the end of the first crab’s measurement period, another crab was placed into the same treatment to begin its acclimation period. Each crab was returned to a flowing water table between treatments and remained there for at least twenty minutes before beginning the acclimation period of its next treatment. The treatment series began and ended with 30 psu seawater (SW), and proceeded through an increasing gradient of 0, 7, 11, and 17% IRI-160AA in SW + HE.
    Each measurement period of the pumping experiments was filmed. The videos were reviewed later, and the time the crabs spent grooming their egg masses was recorded.
    A χ2 test was performed for the 24 crabs tested to assess if the proportion of crabs performing the behaviors differed among treatments. A one-way repeated-measures ANOVA (Friedman Repeated Measures Analysis of Variance on Ranks) was used to assess trends in the number of pumps and the time spent grooming. Only crabs that performed the behavior were included in each analysis.
    Oyster sub-lethality
    Respiration
    Respiration on oyster pediveligers following methods described for A. tonsa nauplii above. Two individuals were placed in each 80 µl well, with rates calculated per individual. Data were analyzed via a one-way ANOVA on Ranks. These experiments all used Batch 3 of IRI-160AA.
    Activity
    Activity experiments on pediveliger larvae were conducted in LAMs and followed similar methods to Acartia tonsa and Callinectes sapidus. The 24-h data was tested via a one-way ANOVA on ranks, while the data broken down by light phase was analyzed via a one-way repeated measures ANOVA. These experiments used Batch 3 of IRI-160AA.
    An additional analysis of pediveliger activity occurred during the mortality experiment by ranking how active each animal appeared to be on a scale of 1 (High Activity, HA, animal was actively swimming), 2 (Medium Activity, MA, animal had its velum extended and cilia active, sometimes scooting across the bottom), 3 (Low Activity, LA, animal was enclosed in its shell but viscera moved when the shell was touched), and 4 (Dead/No Activity, D, animal was completely unresponsive even to repeated stimulation). Ranking occurred at the start of the experiment (where all animals scored as HA), at the 24-h mark, and at the 48-h mark. This assessment was analyzed via a χ2 test for both the 24-h and 48-h data sets. At the end of the LAM experiments, animals were analyzed in the same manner.
    Activity experiments on the wild-type adult C. virginica occurred during the mortality experiments. At each 6-h time point, animals in the containers (0%, 1%, 5%, 10%, 13.5%, 18%, and 24% v/v IRI-160AA treatments) were scored as either Open (O) or Closed (C), and analyzed via a two-way repeated measures ANOVA on the proportion of animals that opened at each time point in each concentration.
    Feeding
    Feeding experiments occurred only on adult C. virginica. Animals and containers from the mortality experiments were rinsed to remove algicide residue, then filled with 1 L of 20 psu seawater and Isochrysis galbana at ~ 100,000 cells L−1, and one animal from each container was returned to it. Five milliliters from each container were removed immediately and in vivo chlorophyll a florescence was measured using a fluorometer (Turner Systems). Air stones were added to the containers to keep the algae in suspension, and lids were added to prevent liquid from bubbling out. After 6 h, another fluorescence reading was taken. Animals were given another 6 h to feed, and a final fluorescence reading was taken at the 12-h time point. Clearance rates (CR) were calculated according to Thessen et al.58 from time zero to six hours (initial rate, 0–6), and from six to twelve hours (end rate, 6–12), and compared across time ranges and treatments and strains using a three-way ANOVA. More

  • in

    Mapping coral calcification strategies from in situ boron isotope and trace element measurements of the tropical coral Siderastrea siderea

    1.
    Cesar, H. J. S., Burke, L. & Pet-Soede, L. The Economics of Worldwide Coral Reef Degradation. 23 (Cesar Environmental Economics Consulting: The Netherlands). https://www.icran.org/pdf/cesardegradationreport.pdf (2003).
    2.
    Tambutté, E. et al. Observations of the tissue-skeleton interface in the scleractinian coral Stylophora pistillata. Coral Reefs 26, 517–529 (2007).
    ADS  Article  Google Scholar 

    3.
    Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl. Acad. Sci. 115, 1754–1759 (2018).
    ADS  CAS  Article  Google Scholar 

    4.
    Mass, T. et al. Cloning and characterization of four novel coral acid-rich proteins that precipitate carbonates in vitro. Curr. Biol. 23, 1126–1131. https://doi.org/10.1016/j.cub.2013.05.007 (2013).
    CAS  Article  PubMed  Google Scholar 

    5.
    Guo, W. Seawater temperature and buffering capacity modulate coral calcifying pH. Sci. Rep. 9, 1–13 (2019).
    Article  Google Scholar 

    6.
    McCulloch, M. et al. Resilience of cold-water scleractinian corals to ocean acidification: boron isotopic systematics of pH and saturation state up-regulation. Geochim. Cosmochim. Acta 87, 21–34 (2012).
    ADS  CAS  Article  Google Scholar 

    7.
    Guo, W. et al. Ocean acidification has impacted coral growth on the great barrier reef. Geophys. Res. Lett. https://doi.org/10.1029/2019gl086761 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    8.
    Sevilgen, D. S. et al. Full in vivo characterization of carbonate chemistry at the site of calcification in corals. Sci. Adv. https://doi.org/10.1126/sciadv.aau7447 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    9.
    Venn, A., Tambutté, E., Holcomb, M., Allemand, D. & Tambutté, S. Live tissue imaging shows reef corals elevate pH under their calcifying tissue relative to seawater. PLoS ONE 6, e20013 (2011).
    ADS  CAS  Article  Google Scholar 

    10.
    Cai, W.-J. et al. Microelectrode characterization of coral daytime interior pH and carbonate chemistry. Nat. Commun. 7, 1–8 (2016).
    Google Scholar 

    11.
    Holcomb, M. et al. Coral calcifying fluid pH dictates response to ocean acidification. Sci. Rep. 4, 5207. https://doi.org/10.1038/srep05207 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    12.
    DeCarlo, T. M., Holcomb, M. & McCulloch, M. T. Reviews and syntheses: revisiting the boron systematics of aragonite and their application to coral calcification. Biogeosciences 15, 2819–2834. https://doi.org/10.5194/bg-15-2819-2018 (2018).
    ADS  CAS  Article  Google Scholar 

    13.
    McCulloch, M. T., D’Olivo, J. P., Falter, J., Holcomb, M. & Trotter, J. A. Coral calcification in a changing world and the interactive dynamics of pH and DIC upregulation. Nat. Commun. 8, 15686. https://doi.org/10.1038/ncomms15686 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    14.
    Horvath, K. M. et al. Next-century ocean acidification and warming both reduce calcification rate, but only acidification alters skeletal morphology of reef-building coral Siderastrea siderea. Sci. Rep. 6, 29613. https://doi.org/10.1038/srep29613 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Tambutté, E. et al. Morphological plasticity of the coral skeleton under CO2-driven seawater acidification. Nat. Commun. 6, 7368. https://doi.org/10.1038/ncomms8368 (2015).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    16.
    Stewart, J. A., Anagnostou, E. & Foster, G. L. An improved boron isotope pH proxy calibration for the deep-sea coral Desmophyllum dianthus through sub-sampling of fibrous aragonite. Chem. Geol. 447, 148–160. https://doi.org/10.1016/j.chemgeo.2016.10.029 (2016).
    ADS  CAS  Article  Google Scholar 

    17.
    Allison, N., Finch, A. A. & EIMF. δ11B, Sr, Mg and B in a modern Porites coral: the relationship between calcification site pH and skeletal chemistry. Geochim. Cosmochim. Acta 74, 1790–1800 (2010).
    ADS  CAS  Article  Google Scholar 

    18.
    Rollion-Bard, C. & Blamart, D. SIMS method and examples of applications in coral biomineralization. In Biomineralization Sourcebook: Characterization of Biominerals and Biomimetic Materials (eds DiMasi, E. & Gower, L. B.) 249–261 (CRC Press, Boca Raton, 2014).
    Google Scholar 

    19.
    Trotter, J. et al. Quantifying the pH ‘vital effect’ in the temperate zooxanthellate coral Cladocora caespitosa: validation of the boron seawater pH proxy. Earth Planet. Sci. Lett. 303, 163–173. https://doi.org/10.1016/j.epsl.2011.01.030 (2011).
    ADS  CAS  Article  Google Scholar 

    20.
    Krief, S. et al. Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim. Cosmochim. Acta 74, 4988–5001 (2010).
    ADS  CAS  Article  Google Scholar 

    21.
    Hönisch, B. et al. Assessing scleractinian corals as recorders for paleo-pH: empirical calibration and vital effects. Geochim. Cosmochim. Acta 68, 3675–3685. https://doi.org/10.1016/j.gca.2004.03.002 (2004).
    ADS  CAS  Article  Google Scholar 

    22.
    Tanaka, K. et al. Response of Acropora digitifera to ocean acidification: constraints from δ11B, Sr, Mg, and Ba compositions of aragonitic skeletons cultured under variable seawater pH. Coral Reefs 34, 1139–1149 (2015).
    ADS  Article  Google Scholar 

    23.
    Reynaud, S., Hemming, N. G., Juillet-Leclerc, A. & Gattuso, J.-P. Effect of pCO2 and temperature on the boron isotopic composition of the zooxanthellate coral Acropora sp. Coral Reefs 23, 539–546 (2004).
    Google Scholar 

    24.
    Anagnostou, E., Huang, K.-F., You, C.-F., Sikes, E. & Sherrell, R. Evaluation of boron isotope ratio as a pH proxy in the deep sea coral Desmophyllum dianthus: evidence of physiological pH adjustment. Earth Planet. Sci. Lett. 349, 251–260 (2012).
    ADS  Article  Google Scholar 

    25.
    Jurikova, H. et al. Boron isotope composition of the cold-water coral Lophelia pertusa along the Norwegian margin: zooming into a potential pH-proxy by combining bulk and high-resolution approaches. Chem. Geol. 513, 143–152. https://doi.org/10.1016/j.chemgeo.2019.01.005 (2019).
    ADS  CAS  Article  Google Scholar 

    26.
    Kasemann, S. A., Schmidt, D. N., Bijma, J. & Foster, G. L. In situ boron isotope analysis in marine carbonates and its application for foraminifera and palaeo-pH. Chem. Geol. https://doi.org/10.1016/j.chemgeo.2008.12.015 (2009).
    Article  Google Scholar 

    27.
    Rollion-Bard, C., Chaussidon, M. & France-Lanord, C. pH control on oxygen isotopic composition of symbiotic corals. Earth Planet. Sci. Lett. 215, 275–288. https://doi.org/10.1016/S0012-821X(03)00391-1 (2003).
    ADS  CAS  Article  Google Scholar 

    28.
    Standish, C. D. et al. The effect of matrix interferences on in situ boron isotope analysis by laser ablation multi-collector inductively coupled plasma mass spectrometry. Rapid Commun. Mass Spectrom. 33, 959–968 (2019).
    ADS  CAS  Article  Google Scholar 

    29.
    Sadekov, A. et al. Accurate and precise microscale measurements of boron isotope ratios in calcium carbonates using laser ablation multicollector-ICPMS. J. Anal. At. Spectrom. 34, 550–560 (2019).
    CAS  Article  Google Scholar 

    30.
    Fietzke, J. et al. Boron isotope ratio determination in carbonates via LA-MC-ICP-MS using soda-lime glass standards as reference material. J. Anal. At. Spectrom. 25, 1953–1957 (2010).
    CAS  Article  Google Scholar 

    31.
    Oppelt, A., López, M. & Rocha, C. Biogeochemical analysis of the calcification patterns of cold-water corals Madrepora oculata and Lophelia pertusa along contact surfaces with calcified tubes of the symbiotic polychaete Eunice norvegica: evaluation of a ‘mucus’ calcification hypothesis. Deep Sea Res. I Oceanogr. Res. Pap. 127, 90–104. https://doi.org/10.1016/j.dsr.2017.08.006 (2017).
    ADS  CAS  Article  Google Scholar 

    32.
    Fowell, S. et al. Historical trends in pH and carbonate biogeochemistry on the Belize Mesoamerican Barrier Reef System. Geophys. Res. Lett. 45, 3228–3237 (2018).
    ADS  CAS  Article  Google Scholar 

    33.
    Runcorn, S. K. Corals as paleontological clocks. Sci. Am. 215, 26–33 (1966).
    Article  Google Scholar 

    34.
    DeCarlo, T. M. & Cohen, A. L. Dissepiments, density bands and signatures of thermal stress in Porites skeletons. Coral Reefs 36, 749–761. https://doi.org/10.1007/s00338-017-1566-9 (2017).
    ADS  Article  Google Scholar 

    35.
    Barnes, D. & Lough, J. On the nature and causes of density banding in massive coral skeletons. J. Exp. Mar. Biol. Ecol. 167, 91–108 (1993).
    Article  Google Scholar 

    36.
    DeCarlo, T. M. et al. Coral Sr-U thermometry. Paleoceanography 31, 626–638 (2016).
    ADS  Article  Google Scholar 

    37.
    Gagnon, A. C., Adkins, J. F., Fernandez, D. P. & Robinson, L. F. Sr/Ca and Mg/Ca vital effects correlated with skeletal architecture in a scleractinian deep-sea coral and the role of Rayleigh fractionation. Earth Planet. Sci. Lett. 261, 280–295. https://doi.org/10.1016/j.epsl.2007.07.013 (2007).
    ADS  CAS  Article  Google Scholar 

    38.
    Blamart, D. et al. Correlation of boron isotopic composition with ultrastructure in the deep-sea coral Lophelia pertusa: implications for biomineralization and paleo-pH. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2007GC001686 (2007).
    Article  Google Scholar 

    39.
    Jokiel, P. L. Coral reef calcification: carbonate, bicarbonate and proton flux under conditions of increasing ocean acidification. Proc. Biol. Sci. 280, 20130031–20130031. https://doi.org/10.1098/rspb.2013.0031 (2013).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    40.
    Galli, G. & Solidoro, C. ATP supply may contribute to light-enhanced calcification in corals more than abiotic mechanisms. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00068 (2018).
    Article  Google Scholar 

    41.
    Barott, K. L., Venn, A. A., Perez, S. O., Tambutté, S. & Tresguerres, M. Coral host cells acidify symbiotic algal microenvironment to promote photosynthesis. Proc. Natl. Acad. Sci. 112, 607–612. https://doi.org/10.1073/pnas.1413483112 (2015).
    ADS  CAS  Article  PubMed  Google Scholar 

    42.
    Bernardet, C., Tambutté, E., Techer, N., Tambutté, S. & Venn, A. Ion transporter gene expression is linked to the thermal sensitivity of calcification in the reef coral Stylophora pistillata. Sci. Rep. 9, 1–13 (2019).
    Article  Google Scholar 

    43.
    Le Goff, C. et al. In vivo pH measurement at the site of calcification in an octocoral. Sci. Rep. 7, 11210. https://doi.org/10.1038/s41598-017-10348-4 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    44.
    Zoccola, D. et al. Bicarbonate transporters in corals point towards a key step in the evolution of cnidarian calcification. Sci. Rep. 5, 9983. https://doi.org/10.1038/srep09983 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    45.
    Furla, P., Galgani, I., Durand, I. & Allemand, D. Sources and mechanisms of inorganic carbon transport for coral calcification and photosynthesis. J. Exp. Biol. 203, 3445–3457 (2000).
    CAS  PubMed  Google Scholar 

    46.
    DeLong, K. L., Maupin, C. R., Flannery, J. A., Quinn, T. M. & Shen, C.-C. Refining temperature reconstructions with the Atlantic coral Siderastrea siderea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 462, 1–15. https://doi.org/10.1016/j.palaeo.2016.08.028 (2016).
    Article  Google Scholar 

    47.
    Castillo, K. D., Ries, J. B. & Weiss, J. M. Declining coral skeletal extension for forereef colonies of Siderastrea siderea on the Mesoamerican Barrier Reef System Southern Belize. PLoS ONE 6, e14615 (2011).
    ADS  CAS  Article  Google Scholar 

    48.
    Castillo, K. D., Ries, J. B., Weiss, J. M. & Lima, F. P. Decline of forereef corals in response to recent warming linked to history of thermal exposure. Nat. Clim. Change 2, 756–760. https://doi.org/10.1038/nclimate1577 (2012).
    Article  Google Scholar 

    49.
    Foster, G. L. Seawater pH, pCO2 and CO32− variations in the Caribbean Sea over the last 130 kyr: a boron isotope and B/Ca study of planktic forminifera. Earth Planet. Sci. Lett. 271, 254–266. https://doi.org/10.1016/j.epsl.2008.04.015 (2008).
    ADS  CAS  Article  Google Scholar 

    50.
    Foster, G. L. et al. Interlaboratory comparison of boron isotope analyses of boric acid, seawater and marine CaCO3 by MC-ICPMS and NTIMS. Chem. Geol. 358, 1–14. https://doi.org/10.1016/j.chemgeo.2013.08.027 (2013).
    ADS  CAS  Article  Google Scholar 

    51.
    le Roux P. J. et al. In situ, multiplemultiplier, laser ablation ICP‐MS measurement of boron isotopic composition (δ11B) at the nanogram level. Chem. Geol. 203(1–2), 123–138. https://doi.org/10.1016/j.chemgeo.2003.09.006 (2004).
    ADS  CAS  Article  Google Scholar 

    52.
    Inoue, M., Nohara, M., Okai, T., Suzuki, A. & Kawahata, H. Concentrations of trace elements in carbonate reference materials coral JCp-1 and Giant Clam JCt-1 by inductively coupled plasma-mass spectrometry. Geostand. Geoanal. Res. 28, 411–416. https://doi.org/10.1111/j.1751-908X.2004.tb00759.x (2004).
    CAS  Article  Google Scholar 

    53.
    Thil, F. et al. Development of laser ablation multi-collector inductively coupled plasma mass spectrometry for boron isotopic measurement in marine biocarbonates: new improvements and application to a modern Porites coral. Rapid Commun. Mass Spectrom. 30, 359–371 (2016).
    CAS  Article  Google Scholar 

    54.
    Hathorne, E. C. et al. Interlaboratory study for coral Sr/Ca and other element/Ca ratio measurements. Geochem. Geophys. Geosyst. 14, 3730–3750. https://doi.org/10.1002/ggge.20230 (2013).
    ADS  CAS  Article  Google Scholar 

    55.
    Hijmans, R. & Van Etten, J. Geographic analysis and modeling with raster data. R Package Version 2, 1–25 (2012).
    Google Scholar 

    56.
    R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2010).

    57.
    Foster, G. L., von Strandmann, P. & Rae, J. W. B. Boron and magnesium isotopic composition of seawater. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2010gc003201 (2010).
    Article  Google Scholar 

    58.
    Klochko, K., Kaufman, A. J., Yao, W. S., Byrne, R. H. & Tossell, J. A. Experimental measurement of boron isotope fractionation in seawater. Earth Planet. Sci. Lett. https://doi.org/10.1016/j.epsl.2006.05.034 (2006).
    Article  Google Scholar 

    59.
    Holcomb, M., DeCarlo, T., Gaetani, G. & McCulloch, M. Factors affecting B/Ca ratios in synthetic aragonite. Chem. Geol. 437, 67–76 (2016).
    ADS  CAS  Article  Google Scholar 

    60.
    Dickson, A. G. Thermodynamics of the dissociation of boric acid in synthetic seawater from 273.15 to 318.15 K. Deep Sea Res. Oceanogr. Res. Pap. 37, 755–766. https://doi.org/10.1016/0198-0149(90)90004-F (1990).
    ADS  CAS  Article  Google Scholar 

    61.
    Lee, K. et al. The universal ratio of boron to chlorinity for the North Pacific and North Atlantic oceans. Geochim. Cosmochim. Acta 74, 1801–1811 (2010).
    ADS  CAS  Article  Google Scholar 

    62.
    Zeebe, R. E. & Wolf-Gladrow, D. A. CO2in Seawater: Equilibrium, Kinetics, Isotopes in Seawater: Equilibrium, Kinetics, Isotopes Vol. 65 (Elsevier, Amsterdam, 2001).
    Google Scholar 

    63.
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar  More

  • in

    Stoichiometric niche, nutrient partitioning and resource allocation in a solitary bee are sex-specific and phosphorous is allocated mainly to the cocoon

    1.
    Stearns, S. C. The Evolution of Life Histories (Oxford University Press, Oxford, 1996).
    Google Scholar 
    2.
    Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, Princeton, 2002).
    Google Scholar 

    3.
    Kaspari, M. & Powers, J. S. Biogeochemistry and geographical ecology: Embracing all twenty-five elements required to build organisms. Am. Nat. 188, S62–S73 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Kozlowski, J. Why life histories are diverse. Polish J. Ecol. 54, 585–605 (2006).
    Google Scholar 

    5.
    Ejsmond, M. J., Varpe, Ø., Czarnoleski, M. & Kozłowski, J. Seasonality in offspring value and trade-offs with growth explain capital breeding. Am. Nat. 186, E111–E125 (2015).
    Article  Google Scholar 

    6.
    Filipiak, M. A better understanding of bee nutritional ecology is needed to optimize conservation strategies for wild bees-the application of ecological stoichiometry. Insects 9, 85 (2018).
    PubMed Central  Article  Google Scholar 

    7.
    Filipiak, Z. M. & Filipiak, M. The scarcity of specific nutrients in wild bee larval food negatively influences certain life history traits. Biology (Basel). 9, 462 (2020).

    8.
    Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton University Press, Princeton, 2012).
    Google Scholar 

    9.
    Bärlocher, F. & Rennenberg, H. Food chains and nutrient cycles. In Ecological biochemistry (eds Krauss, G. J. & Nies, D. H.) 92–122 (Wiley, New York, 2014).
    Google Scholar 

    10.
    DeAngelis, D. L. Dynamics of Nutrient Cycling and Food Webs (Springer Netherlands, Amsterdam, 1992).
    Google Scholar 

    11.
    Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry (Academic Press, London, 2020).
    Google Scholar 

    12.
    Jeyasingh, P. D., Cothran, R. D. & Tobler, M. Testing the ecological consequences of evolutionary change using elements. Ecol. Evol. 4, 528–538 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Jeyasingh, P. D., Goos, J. M., Thompson, S. K., Godwin, C. M. & Cotner, J. B. Ecological stoichiometry beyond redfield: An ionomic perspective on elemental homeostasis. Front. Microbiol. 8, 722 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    González, A. L. et al. Ecological mechanisms and phylogeny shape invertebrate stoichiometry: A test using detritus-based communities across Central and South America. Funct. Ecol. 32, 2448–2463 (2018).
    Article  Google Scholar 

    15.
    Peñuelas, J. et al. The bioelements, the elementome, and the biogeochemical niche. Ecology 100, e02652 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Fagan, W. F. & Denno, R. F. Stoichiometry of actual vs. potential predator-prey interactions: Insights into nitrogen limitation for arthropod predators. Ecol. Lett. 7, 876–883 (2004).
    Article  Google Scholar 

    17.
    Kay, A. D. et al. Toward a stoichiometric framework for evolutionary biology. Oikos 109, 6–17 (2005).
    Article  Google Scholar 

    18.
    Cherif, M. et al. An operational framework for the advancement of a molecule-to-biosphere stoichiometry theory. Front. Mar. Sci. 4, 1–16 (2017).
    ADS  Article  Google Scholar 

    19.
    Welti, N. et al. Bridging food webs, ecosystem metabolism, and biogeochemistry using ecological stoichiometry theory. Front. Microbiol. 8, 1298 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    Hessen, D. O., Elser, J. J., Sterner, R. W. & Urabe, J. Ecological stoichiometry: An elementary approach using basic principles. Limnol. Oceanogr. 58, 2219–2236 (2013).
    ADS  CAS  Article  Google Scholar 

    21.
    Lemoine, N. P., Giery, S. T. & Burkepile, D. E. Differing nutritional constraints of consumers across ecosystems. Oecologia 174, 1367–1376 (2014).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Morehouse, N. I., Nakazawa, T., Booher, C. M., Jeyasingh, P. D. & Hall, M. D. Sex in a material world: Why the study of sexual reproduction and sex-specific traits should become more nutritionally-explicit. Oikos 119, 766–778 (2010).
    Article  Google Scholar 

    23.
    Filipiak, M. Key pollen host plants provide balanced diets for wild bee larvae: A lesson for planting flower strips and hedgerows. J. Appl. Ecol. 56, 1410–1418 (2019).
    CAS  Article  Google Scholar 

    24.
    Goos, J. M., Cothran, R. D. & Jeyasingh, P. D. Within-population variation in the chemistry of life: The stoichiometry of sexual dimorphism in multiple dimensions. Evol. Ecol. 31, 635–651 (2017).
    Article  Google Scholar 

    25.
    Halvorson, H. M., Scott, J. T., Sanders, A. J. & Evans-White, M. A. A stream insect detritivore violates common assumptions of threshold elemental ratio bioenergetics models. Freshw. Sci. 34, 508–518 (2015).
    Article  Google Scholar 

    26.
    Meunier, C. L. et al. From elements to function: Toward unifying ecological stoichiometry and trait-based ecology. Front. Environ. Sci. 5, 1–10 (2017).
    Article  Google Scholar 

    27.
    Sperfeld, E., Wagner, N. D., Halvorson, H. M., Malishev, M. & Raubenheimer, D. Bridging ecological stoichiometry and nutritional geometry with homeostasis concepts and integrative models of organism nutrition. Funct. Ecol. 31, 286–296 (2017).
    Article  Google Scholar 

    28.
    Filipiak, M. & Weiner, J. Plant–insect interactions: The role of ecological stoichiometry. Acta Agrobot. 70, 1–16 (2017).
    Article  Google Scholar 

    29.
    Elser, J. J., Dobberfuhl, D. R., MacKay, N. A. & Schampel, J. H. Organism size, life history, and N: P stoichiometry: Toward a unified view of cellular and ecosystem processes. Bioscience 46, 674–684 (1996).
    Article  Google Scholar 

    30.
    Polidori, C. et al. Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta). Org. Divers. Evol. https://doi.org/10.1007/s13127-020-00448-x (2020).
    Article  Google Scholar 

    31.
    Bosch, J., Sgolastra, F. & Kemp, W. P. Life cycle ecophysiology of Osmia mason bees used as crop pollinators. In Bee Pollination in Agricultural Eco-systems (eds James, R. & Pitts-Singer, T. L.) 83–105 (Oxford Scholarship Online, Oxford, 2008).
    Google Scholar 

    32.
    Giejdasz, K. & Wilkaniec, Z. Individual development of the red mason bee (Osmia rufa L., Megachilidae) under natural and laboratory conditions. J. Apic. Sci. 46, 51–57 (2002).
    Google Scholar 

    33.
    Gruber, B., Eckel, K., Everaars, J. & Dormann, C. F. On managing the red mason bee (Osmia bicornis) in apple orchards. Apidologie 42, 564–576 (2011).
    Article  Google Scholar 

    34.
    Kaspari, M. The seventh macronutrient: How sodium shortfall ramifies through populations, food webs and ecosystems. Ecol. Lett. 23, 1153–1168 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    35.
    Rizzuto, M. et al. Patterns and potential drivers of intraspecific variability in the body C, N, and P composition of a terrestrial consumer, the snowshoe hare (Lepus americanus). Ecol. Evol. 9, 14453–14464 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    36.
    Sitters, J. & Olde Venterink, H. The need for a novel integrative theory on feedbacks between herbivores, plants and soil nutrient cycling. Plant Soil 396, 421–426 (2015).
    CAS  Article  Google Scholar 

    37.
    Sitters, J. et al. Nutrient availability controls the impact of mammalian herbivores on soil carbon and nitrogen pools in grasslands. Glob. Change Biol. 26, 2060–2071 (2020).
    ADS  Article  Google Scholar 

    38.
    Sitters, J. et al. The stoichiometry of nutrient release by terrestrial herbivores and its ecosystem consequences. Front. Earth Sci. 5, 1–8 (2017).
    Article  Google Scholar 

    39.
    González, A. L., Fariña, J. M., Kay, A. D., Pinto, R. & Marquet, P. A. Exploring patterns and mechanisms of interspecific and intraspecific variation in body elemental composition of desert consumers. Oikos 120, 1247–1255 (2011).
    Article  Google Scholar 

    40.
    Seidelmann, K. Optimal progeny body size in a solitary bee, Osmia bicornis (Apoidea: Megachilidae). Ecol. Entomol. 39, 656–663 (2014).
    Article  Google Scholar 

    41.
    Kim, J. Y. Female size and fitness in the leaf-cutter bee Megachile apicalis. Ecol. Entomol. 22, 275–282 (1997).
    Article  Google Scholar 

    42.
    Markow, T. et al. Elemental stoichiometry of Drosophila and their hosts. Funct. Ecol. 13, 78–84 (1999).
    Article  Google Scholar 

    43.
    Bergwitz, C. & Jüppner, H. Phosphate sensing. Adv. Chronic Kidney Dis. 18, 132–144 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Werner, A. & Kinne, R. K. H. Evolution of the Na-Pi cotransport systems. Am. J. Physiol. Regul. Integr. Comp. Physiol. 280, R301–R312 (2001).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    Morgan, A. J., Kille, P. & Stürzenbaum, S. R. Microevolution and ecotoxicology of metals in invertebrates. Environ. Sci. Technol. 41, 1085–1096 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Bednarska, A. J., Świątek, Z. M. & Labecka, A. M. Effects of cadmium bioavailability in food on its distribution in different tissues in the ground beetle Pterostichus oblongopunctatus. Bull. Environ. Contam. Toxicol. 103, 421–427 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    Świątek, Z. M. & Bednarska, A. J. Energy reserves and respiration rate in the earthworm Eisenia andrei after exposure to zinc in nanoparticle or ionic form. Environ. Sci. Pollut. Res. Int. 26, 24933–24945 (2019).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    48.
    Cohen, A. C. Insect Diets: Science and Technology (CRC Press, Boca Raton, 2005).
    Google Scholar 

    49.
    Seidelmann, K. Optimal resource allocation, maternal investment, and body size in a solitary bee, Osmia bicornis. Entomol. Exp. Appl. 166, 790–799 (2018).
    Article  Google Scholar 

    50.
    Bosch, J. & Vicens, N. Relationship between body size, provisioning rate, longevity and reproductive success in females of the solitary bee Osmia cornuta. Behav. Ecol. Sociobiol. 60, 26–33 (2006).
    Article  Google Scholar 

    51.
    Seidelmann, K., Ulbrich, K. & Mielenz, N. Conditional sex allocation in the Red Mason bee, Osmia rufa. Behav. Ecol. Sociobiol. 64, 337–347 (2010).
    Article  Google Scholar 

    52.
    González, A. L., Dézerald, O., Marquet, P. A., Romero, G. Q. & Srivastava, D. S. The multidimensional stoichiometric niche. Front. Ecol. Evol. 5, 110 (2017).
    Article  Google Scholar 

    53.
    Lemmen, K. D., Butler, O. M., Koffel, T., Rudman, S. M. & Symons, C. C. Stoichiometric traits vary widely within species: A meta-analysis of common garden experiments. Front. Ecol. Evol. 7, 1–15 (2019).
    Article  Google Scholar 

    54.
    Prater, C., Wagner, N. D. & Frost, P. C. Interactive effects of genotype and food quality on consumer growth rate and elemental content. Ecology 98, 1399–1408 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    55.
    Sherman, R. E., Chowdhury, P. R., Baker, K. D., Weider, L. J. & Jeyasingh, P. D. Genotype-specific relationships among phosphorus use, growth and abundance in Daphnia pulicaria. R. Soc. Open Sci. 4, 170770 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    56.
    Zajitschek, F. & Connallon, T. Partitioning of resources: The evolutionary genetics of sexual conflict over resource acquisition and allocation. J. Evol. Biol. 30, 826–838 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Moe, S. J. et al. Recent advances in ecological stoichiometry: Insights for population and community ecology. Oikos 109, 29–39 (2005).
    Article  Google Scholar 

    58.
    Peñuelas, J., Sardans, J., Ogaya, R. & Estiarte, M. Nutrient stoichiometric relations and biogeochemical niche in coexisting plant species: Effect of simulated climate change. Polish J. Ecol. 56, 613–622 (2008).
    Google Scholar 

    59.
    Urbina, I. et al. Plant community composition affects the species biogeochemical niche. Ecosphere 8, e01801 (2017).
    Article  Google Scholar 

    60.
    Jeyasingh, P. D., Goos, J. M., Lind, P. R., Roy Chowdhury, P. & Sherman, R. E. Phosphorus supply shifts the quotas of multiple elements in algae and Daphnia: Ionomic basis of stoichiometric constraints. Ecol. Lett. 23, 1064–1072 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    61.
    Ruedenauer, F. A. et al. Best be (e) on low fat: Linking nutrient perception, regulation and fitness. Ecol. Lett. 23, 545–554 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    62.
    Trinkl, M. et al. Floral species richness correlates with changes in the nutritional quality of larval diets in a stingless bee. Insects 11, E125 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    63.
    Roswell, M., Dushoff, J. & Winfree, R. Male and female bees show large differences in floral preference. PLoS ONE 14, e0214909 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    Vaudo, A. D. et al. Pollen protein: Lipid macronutrient ratios may guide broad patterns of bee species floral preferences. Insects 11, 132 (2020).
    PubMed Central  Article  Google Scholar 

    65.
    Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 

    66.
    Smilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data using CANOCO 5 (Cambridge University Press, Cambridge, 2014).
    Google Scholar  More

  • in

    Quantification of dissolved O2 in bulk aqueous solutions and porous media using NMR relaxometry

    1.
    Seevers, D. O. A nuclear magnetic method for determining the permeability of sandstones. Presented at the SPWLA 7th Annual Logging Symposium, Tulsa, OK, 9–11 May 1966.
    2.
    Timur, A. Effective porosity and permeability of sandstones investigated through nuclear magnetic principles. Log Anal. 10(1), 3 (1969).
    Google Scholar 

    3.
    Coates, G. R., Xiao, L. & Prammer, M. G. NMR Logging Principles and Applications (Halliburton Energy Services, Houston, 1999).
    Google Scholar 

    4.
    Korringa, J., Seevers, D. O. & Torrey, H. C. Theory of spin pumping and relaxation in systems with a low concentration of electron spin resonance centers. Phys. Rev. 127(4), 1143–1150 (1962).
    ADS  CAS  Article  Google Scholar 

    5.
    Kleinberg, R. L., Kenyon, W. E. & Mitra, P. P. Mechanism of NMR relaxation of fluids in rock. J. Magn. Reson. Ser. A 108(2), 206–214 (1994).
    ADS  CAS  Article  Google Scholar 

    6.
    Watson, A. T. & Chang, C. T. P. Characterizing porous media with NMR methods. Prog. Nucl. Magn. Reson. Spectrosc. 31(4), 343–386 (1997).
    CAS  Article  Google Scholar 

    7.
    Godefroy, S., Fleury, M., Deflandre, F. & Korb, J. P. Temperature effect on NMR surface relaxation in rocks for well logging applications. J. Phys. Chem. B 106(43), 11183–11190 (2002).
    CAS  Article  Google Scholar 

    8.
    Glasel, J. A. & Lee, K. H. On the interpretation of water nuclear magnetic resonance relaxation times in heterogeneous systems. J. Am. Chem. Soc. 96(4), 970–978 (1974).
    CAS  Article  Google Scholar 

    9.
    Foley, I., Farooqui, S. A. & Kleinberg, R. L. Effect of paramagnetic ions on NMR relaxation of fluids at solid surfaces. J. Magn. Reson. Ser. A 123(1), 95–104 (1996).
    ADS  CAS  Article  Google Scholar 

    10.
    Mitchell, J., Stark, S. C. & Strange, J. H. Probing surface interactions by combining NMR cryoporometry and NMR relaxometry. J. Phys. D Appl. Phys. 38(12), 1950–1958 (2005).
    ADS  CAS  Article  Google Scholar 

    11.
    Keating, K. & Knight, R. A laboratory study to determine the effect of iron oxides on proton NMR measurements. Geophysics 72(1), E27–E32 (2007).
    ADS  Article  Google Scholar 

    12.
    Saidian, M. & Prasad, M. Effect of mineralogy on porosity, pore size distribution and surface relaxivity on nuclear magnetic resonance characterizations: A case study of Middle Bakken and Three Forks Formations. J. Fuel 161, 197–206 (2015).
    CAS  Article  Google Scholar 

    13.
    Benedekt, G. B. & Purcell, E. M. Nuclear magnetic resonance in liquids under high pressure. J. Chem. Phys. 22(12), 2003–2012 (1954).
    ADS  Article  Google Scholar 

    14.
    Nestle, N., Baumann, T. & Niessner, R. Oxygen determination in oxygen-supersaturated drinking waters by NMR relaxometry. Water Res. 37(14), 3361–3366 (2003).
    CAS  PubMed  Article  Google Scholar 

    15.
    Shikhov, I. & Arns, C. H. Temperature-dependent oxygen effect on NMR D-T2 relaxation-diffusion correlation of n-alkanes. Appl. Magn. Reson. 47(12), 1391–1408 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Horvath, I. T. & Millar, J. M. NMR under high gas pressure. Chem. Rev. 91(7), 13339–21351 (1991).
    Article  Google Scholar 

    17.
    Kamatari, Y. O., Kitahara, R., Yamada, H., Yokoyama, S. & Akasaka, K. High-pressure NMR spectroscopy for characterizing folding intermediates and denatured states of proteins. Methods 34(1), 133–143 (2004).
    CAS  PubMed  Article  Google Scholar 

    18.
    Bezonova, I., Forman-Kay, J. & Prosser, R. S. Molecular oxygen as a paramagnetic NMR probe of protein solvent exposure and topology. Concepts Magn. Reson. Part A 32(4), 239–253 (2008).
    Article  CAS  Google Scholar 

    19.
    Prosser, R. S. & Evanics, F. Paramagnetic effects of dioxygen in solution NMR—studies of membrane immersion depth, protein topology, and protein interactions. In Modern Magnetic Resonance (ed. Webb, G. A.) 475–483 (Springer, Dordrecht, 2008).
    Google Scholar 

    20.
    Erriah, B. & Elliot, S. J. Experimental evidence for the role of paramagnetic oxygen concentration on the decay of long-lived nuclear spin order. R. Soc. Chem. Adv. 9, 23418–23424 (2019).
    CAS  Google Scholar 

    21.
    Debye, P. Polar Molecules (New York, 1945).

    22.
    Chiarotti, G., Cristiani, G. & Giulotto, L. Proton relaxation in pure liquids and in liquids containing paramagnetic gases in solution. Il Nuovo Cimento 1(5), 863–873 (1955).
    Article  Google Scholar 

    23.
    Mirhej, M. E. Proton spin relaxation by paramagnetic molecular oxygen. Can. J. Chem. 43(5), 1130–1138 (1964).
    Article  Google Scholar 

    24.
    Parker, D. S. & Harmon, J. F. Dipolar spin-lattice relaxation in water containing oxygen. Chem. Phys. Lett. 25(4), 505–506 (1974).
    ADS  CAS  Article  Google Scholar 

    25.
    Morriss, C. E. et al. Hydrocarbon saturation and viscosity estimation from NMR logging in the Belridge Diatomite. Log Analyst 38(2), 44–72 (1997).
    MathSciNet  Google Scholar 

    26.
    Lo, S. W., Hirasaki, G. J., House, W. V. & Kobayashi, R. Mixing rules and correlations of NMR relaxation time with viscosity, diffusivity, and gas/oil ratios of methane/hydrocarbon mixtures. SPE J. 7(1), 24–34 (2002).
    CAS  Article  Google Scholar 

    27.
    Mutina, A. R. & Hurlimann, M. D. Effect of oxygen on the NMR relaxation properties of crude oils. Appl. Magn. Reson. 29, 503–516 (2005).
    CAS  Article  Google Scholar 

    28.
    Lawson, C. L. & Hanson, R. J. Solving Least Square Problems (Prentice-Hall, Englewood Cliffs, 1974).
    Google Scholar 

    29.
    Hirasaki, G. J., Lo, S. & Zhang, Y. NMR properties of petroleum reservoir fluids. Magn. Reson. Imaging 21(3–4), 269–277 (2003).
    CAS  PubMed  Article  Google Scholar 

    30.
    Ferrell, F. T. & Himmelblau, D. M. Diffusion coefficients of nitrogen and oxygen in water. J. Chem. Eng. Data 12(1), 111–115 (1967).
    CAS  Article  Google Scholar 

    31.
    Niesar, U., Corongiu, G., Clementi, E. & Bhattacharya, D. K. Molecular dynamics simulations of liquid water using the NCC ab initio potential. J. Phys. Chem. 94(20), 7949–7956 (1991).
    Article  Google Scholar 

    32.
    Martin, D., McKenna, H. & Livina, V. The human physiological impact of global deoxygenation. J. Physiol Sci. 67(1), 97–106 (2017).
    CAS  PubMed  Article  Google Scholar 

    33.
    Majid, A., Saidian, M., Prasad, M. & Koh, C. A. Measurement of water droplets in water-in-oil emulsions using low field nuclear magnetic resonance for gas hydrate slurry application. Can. J. Chem. 93(9), 1007–1013 (2015).
    CAS  Article  Google Scholar 

    34.
    Scardina, P. & Edwards, M. Prediction and measurement of bubble formation in water treatment. J. Environ. Eng. 17(11), 968–973 (2001).
    Article  Google Scholar 

    35.
    Carr, H. & Purcell, E. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Phys. Rev. 94(3), 630–638 (1954).
    ADS  CAS  Article  Google Scholar 

    36.
    Meiboom, S. & Gill, D. Modified spin echo method for measuring nuclear relaxation times. Rev. Sci. Instrum. 29(8), 668–691 (1958).
    ADS  Article  Google Scholar 

    37.
    Buttler, J. P., Reeds, J. A. & Dawson, S. V. Estimating solution of first kind integral equations with non-negative constraints and optimal smoothing. Siam J. Numer. Anal. 18(3), 381–397 (1981).
    ADS  MathSciNet  Article  Google Scholar 

    38.
    Benson, B. B. & Krause, D. The concentration and isotopic fractionation of oxygen dissolved in freshwater and seawater in equilibrium with the atmosphere. Am. Soc. Limnol. Oceanogr. 29(3), 620–632 (1984).
    ADS  CAS  Article  Google Scholar 

    39.
    Geng, M. & Duan, Z. Prediction of oxygen solubility in pure water and brines up to high temperatures and pressures. Geochim. Cosmochim. Acta 74(2010), 5631–5640 (2010).
    ADS  CAS  Article  Google Scholar  More

  • in

    Oilbirds disperse large seeds at longer distance than extinct megafauna

    1.
    Terborgh, J. et al. Tree recruitment in an empty forest. J. Ecol. 89, 1757–1768 (2008).
    Article  Google Scholar 
    2.
    Stevenson, P. The abundance of large ateline monkeys is positively associated with the diversity of plants regenerating in Neotropical forests. Biotropica 43, 512–519 (2011).
    Article  Google Scholar 

    3.
    Peres, C., Emilio, T., Schietti, J., Desmoulière, S. & Levi, T. Dispersal limitation induces long-term biomass collapse in overhunted Amazonian forests. Proc. Natl. Acad. Sci. 113, 892–897 (2016).
    CAS  PubMed  Article  ADS  Google Scholar 

    4.
    Bello, C. et al. Defaunation affects carbon storage in tropical forests. Sci. Adv. 1, e1501105 (2015).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    5.
    Chanthorn, W., Hartig, F., Brockelman, W. Y., Srisang, W., Nathalang, A. & Santon, J. Defaunation of large-bodied frugivores reduces carbon storage in a tropical forest of Southeast Asia. Sci. Rep. 9 (2019).

    6.
    Davis, M. & Shaw, R. Range shifts and adaptive responses to quaternary climate change. Science 292, 673–679 (2001).
    CAS  PubMed  Article  ADS  Google Scholar 

    7.
    Corlett, R. T. Seed dispersal distances and plant migration potential in tropical East Asia. Biotropica 41, 592–598 (2009).
    Article  Google Scholar 

    8.
    Duque, A., Stevenson, P. & Feeley, K. Thermophilization of adult and juvenile tree communities in the northern tropical Andes. Proc. Natl. Acad. Sci. 112, 10744–10749 (2015).
    CAS  PubMed  Article  ADS  Google Scholar 

    9.
    Howe, H. & Smallwood, J. Ecology of seed dispersal. Annu. Rev. Ecol. Syst. 13, 201–228 (1982).
    Article  Google Scholar 

    10.
    Wright, S. J. Plant diversity in tropical forests: A review of mechanisms of species coexistence. Oecologia 130, 1–14 (2002).
    PubMed  Article  ADS  Google Scholar 

    11.
    Sugiyama, A., Comita, L., Masaki, T., Condit, R. & Hubbell, S. Resolving the paradox of clumped seed dispersal: Positive density and distance dependence in a bat-dispersed species. Ecology 99, 2583–2591 (2018).
    PubMed  Article  Google Scholar 

    12.
    Bagchi, R. et al. Spatial patterns reveal negative density dependence and habitat associations in tropical trees. Ecology 92, 1723–1729 (2011).
    PubMed  Article  Google Scholar 

    13.
    Clark, J.S. Why trees migrate so fast: Confronting theory with dispersal biology and the paleorecord. Am. Nat. 152, 204-224 (1998)

    14.
    Nathan, R. Long-distance dispersal of plants. Science 313, 786–788 (2006).
    CAS  PubMed  Article  ADS  Google Scholar 

    15.
    Nathan, R. et al. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23, 638–647 (2008).
    PubMed  Article  Google Scholar 

    16.
    Abedi-Lartey, M., Dechmann, D. K. N., Wikelski, M., Scharf, A. K. & Fahr, J. Long-distance seed dispersal by straw-coloured fruit bats varies by season and landscape. Glob. Ecol. Conserv. 7, 12–24 (2016).
    Article  Google Scholar 

    17.
    Baraloto, C., Forget, P. M. & Goldberg, D. E. Seed mass, seedling size and Neotropical tree seedling establishment. J. Ecol. 96, 1156–1166 (2005).
    Article  CAS  Google Scholar 

    18.
    Mack, A. L. An advantage of large seed size: tolerating rather than succumbing to seed predators. Biotropica 30, 604–608 (1998).
    Article  Google Scholar 

    19.
    Peres, C. A., Roosmalen, M. V., Levey, D. J., Silva, W. & Galetti, M. Primate frugivory in two species-rich Neotropical forests: implications for the demography of large-seeded plants in overhunted areas. In Seed dispersal and frugivory: ecology, evolution and conservation (eds. Levey Silva, D. J. W. & Galetti, M.) 407–421 (Wallingford: CAB International, 2002).

    20.
    Galetti, M. & Dirzo, R. Ecological and evolutionary consequences of living in a defaunated world. Biol. Conserv. 163, 1–6 (2013).
    Article  Google Scholar 

    21.
    Doughty, C., Wolf, A. & Malhi, Y. The legacy of the Pleistocene megafauna extinctions on nutrient availability in Amazonia. Nat. Geosci. 6, 761–764 (2013).
    CAS  Article  ADS  Google Scholar 

    22.
    Galetti, M. et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. Camb. Philos. Soc. 93, 845–862 (2018).
    PubMed  Article  Google Scholar 

    23.
    Pires, M., Guimarães, P., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long-distance seed-dispersal services. Ecography 41, 153–163 (2017).
    Article  Google Scholar 

    24.
    Bosque, C. & Parra, O. Digestive efficiency and rate of food passage in oilbird nestlings. The Condor 94, 557–571 (1992).
    Article  Google Scholar 

    25.
    Rojas-Lizarazo, G. Diet and reproduction in a high mountain oilbird (Steatornis caripensis) colony in Colombia. Ornitol. Colomb. 53–69 (2016).

    26.
    Stevenson, P., Cardona, L., Acosta Rojas, D., Henao Díaz, F. & Cardenas, S. Diet of oilbirds (Steatornis caripensis) in Cueva de los Guácharos National Park (Colombia): Temporal variation in fruit consumption, dispersal and seed morphology. Ornitol. Neotrop. 28, 295–307 (2017).
    Google Scholar 

    27.
    McAtee, W. L. Notes on the food of the Guacharo (Steatornis caripensis). Auk 39, 108–109 (1922).
    Article  Google Scholar 

    28.
    Holland, R. A., Wikelski, M., Kümmeth, F. & Bosque, C. The secret life of oilbirds: New insights into the movement ecology of a unique avian frugivore. PLoS ONE 4, e8264 (2009).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    29.
    Karubian, J. et al. Seed dispersal by Neotropical birds: Emerging patterns and underlying processes. Ornitol. Neotrop. 23, 9–24 (2012).
    Google Scholar 

    30.
    McKey, D. In Coevolution of animals and plants (eds. Gilben, L. E. & Raven, P. H.) 159–191 (University Texas Press, 1975).

    31.
    Cárdenas, S., Cardona, L. M., Echeverry-Galvis, M. & Stevenson, P. R. Movement patterns and habitat preference of oilbirds (Steatornis caripensis) in the southern Andes of Colombia. Avian Cons. Ecol. 15, 5 (2020).
    Google Scholar 

    32.
    Cárdenas, S., Echeverry-Galvis, M. & Stevenson, P. R. Seed dispersal effectiveness by oilbirds (Steatornis caripensis) in the Southern Andes of Colombia. Biotropica. https://doi.org/10.1111/btp.12908 (2020).
    Article  Google Scholar 

    33.
    Anderson, J. T., Nuttle, T., Saldaña Rojas, J. S., Pendergast, T. H. & Flecker, A. S. Extremely long-distance seed dispersal by an overfished Amazonian frugivore. Proc. R. Soc. Lond., Ser. B: Biol. Sci. 278, 3329–3335 (2011).
    Google Scholar 

    34.
    Wood, C. A. The Polynesian fruit pigeon, Globicera pacifica, its food and digestive apparatus. Auk 41, 433–438 (1924).
    Article  Google Scholar 

    35.
    Stocker, G. C. & Irvine, A. K. Seed dispersal by cassowaries (Casuarius casuarius) in North Queensland’s Rainforests. Biotropica 15, 170–176 (1983).
    Article  Google Scholar 

    36.
    Gautier-Hion, A. et al. Fruit characters as a basis of fruit choice and seed dispersal in a tropical forest vertebrate community. Oecologia 65, 324–337 (1985).
    CAS  PubMed  Article  ADS  Google Scholar 

    37.
    Lieberman, D., Lieberman, M. & Martin, C. Notes on seeds in elephant dung from Bia National Park Ghana. Biotropica 19, 365 (1987).
    Article  Google Scholar 

    38.
    Guillotin, M., Dubost, G. & Sabatier, D. Food choice and food competition among the three major primate species of French Guiana. J. Zool. 233, 551–579 (1994).
    Article  Google Scholar 

    39.
    Fragoso, J. M. V. & Huffman, J. M. Seed-dispersal and seedling recruitment patterns by the last Neotropical megafaunal element in Amazonia, the tapir. J. Trop. Ecol. 16, 369–385 (2000).
    Article  Google Scholar 

    40.
    Naranjo, E. Ecology and conservation of Baird’s Tapir in Mexico. Trop. Conserv. Sci. 2, 140–158 (2009).
    Article  Google Scholar 

    41.
    Kitamura, S., Madsri, S. & Poonswad, P. Characteristics of hornbill-dispersed fruits in lowland Dipterocarp forests of southern Thailand. Raffles Bul. Zool. 24, 137–147 (2011).
    Google Scholar 

    42.
    Stevenson, P., Link, A., Onshuus, A., Quiroz, A. & Velasco, M. Estimation of seed shadows generated by Andean woolly monkeys (Lagothrix lagothricha lugens). Int. J. Primatol. 35, 1021–1036 (2014).
    Article  Google Scholar 

    43.
    Chen, S. C. & Moles, A. T. A mammoth mouthful? A test of the idea that larger animals ingest larger seeds. Global Ecol. Biogeogr. 24, 1269–1280 (2015).
    Article  Google Scholar 

    44.
    Norconk, M., Grafton, B. & Conklin-Brittain, N. Seed dispersal by Neotropical seed predators. Am. J. Primatol. 45, 103–126 (1998).
    CAS  PubMed  Article  Google Scholar 

    45.
    Lord, J. M. Frugivore gape size and the evolution of fruit size and shape in southern hemisphere floras. Austral Ecol. 29, 430–436 (2004).
    Article  Google Scholar 

    46.
    Vellend, M., Myers, J., Gardescu, S. & Marks, P. Dispersal of Trillium seeds by deer: Implications for long-distance migration of forest herbs. Ecology 84, 1067–1072 (2003).
    Article  Google Scholar 

    47.
    Baños-Villalba, A. et al. Seed dispersal by macaws shapes the landscape of an Amazonian ecosystem. Sci. Rep. 7 (2017).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    48.
    Jansen, P. et al. Thieving rodent as substitute dispersers of megafaunal seeds. Proc. Natl. Acad. Sci. 109, 12610–12615 (2012).
    CAS  PubMed  Article  ADS  Google Scholar 

    49.
    Blanco, G., Tella, J. L., Hiraldo, F. & Díaz-Luque, J. A. Multiple external seed dispersers challenge the megafaunal syndrome anachronism and the surrogate ecological function of livestock. Front. Ecol. Evol. 7, 328 (2019).
    Article  Google Scholar 

    50.
    Prada, C. & Stevenson, P. Plant composition associated with environmental gradients in tropical montane forests (Cueva de Los Guácharos National Park, Huila, Colombia). Biotropica 48, 568–576 (2016).
    Article  Google Scholar 

    51.
    Bosque, C. & Parra, O. Digestive efficiency and rate of food passage in oilbird nestlings. The Condor 94, 557–571 (1992).
    Article  Google Scholar 

    52.
    Calenge, C. The package “adehabitat” for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).
    Article  Google Scholar 

    53.
    R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, Austria 2014).

    54.
    Chen, S. C. & Moles, A. T. A mammoth mouthful? A test of the idea that larger animals ingest larger seeds. Glob. Ecol. Biogeogr. 24, 1269–1280 (2015).
    Article  Google Scholar 

    55.
    Fox, J. & Weisberg, S. An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA https://socialsciences.mcmaster.ca/jfox/Books/Companion/ (2019). More

  • in

    Important contributions of non-fossil fuel nitrogen oxides emissions

    Global δ15Nw-NO3− observations
    Publications of δ15Nw-NO3− studies were obtained through the databases of the Web of Science (http://isiknowledge.com), Google Scholar (http://scholar.google.com.hk), and Baidu Scholar (http://xueshu.baidu.com) by searching keywords of “nitrogen isotope”, “nitrate”, “rainfall”, and “precipitation”. By the end of December 2018, a total of 128 publications were available (Supplementary Text 1), spanning the sampling time of 1956–2017 (Supplementary Fig. 11). We extracted δ15Nw-NO3− values of individual precipitation samples by using the software of Web Plot Digitizer37.
    There are totally 3483 individual δ15Nw-NO3− data and 222 sampling sites when multiple observations in different sampling years at the same site were counted once only (Fig. 1). There are 56 urban sites, 158 non-urban sites, and eight arctic sites (Fig. 1), in which non-urban sites are mainly situated in rural, mountain, forest, and lake areas. Due to the sparsity of available data before 2000 (Supplementary Fig. 11), we analyzed δ15Nw-NO3− data at major urban and non-urban sites in East Asia, Europe, and North America during 2000–2017 to ensure a better site representation and to reduce the uncertainty caused by inconsistency in sampling time (Fig. 1). To describe spatial differences in δ15Nw-NO3− values between urban and non-urban sites among three regions (totally 214 sites), only site-based mean values during the period of 2000–2017 (totally 169 sites) were used (detailed in Fig. 2). To describe temporal variations of δ15Nw-NO3− values in urban and non-urban areas of each region, respectively (Fig. 3), we counted observation sites by different sampling years, given that δ15Nw-NO3− observations at few sites have been conducted in different sampling years. In this way, there were a total of 206 sites during 2000–2017 (detailed in Fig. 3). In addition, 35%, 29%, and 36% of the δ15Nw-NO3− observations were conducted in warmer, cooler, and the whole year, respectively. The seasonal effects of NOx emissions may not substantially influence the patterns of regional δ15Nw-NO3− variations.
    Differences between δ15Nw-NO3− and δ15Ni-NOx values
    NO is normally insoluble in water, and w-NO3− is scavenged only from the ambient NO2 and the oxidized NOx (i.e., HNO3 and p-NO3−) (Supplementary Fig. 1)32,38,39. Moreover, isotopic effects during the NOx cycles lead to differences between δ15NNOx and δ15NNO2. Therefore, substantial differences exist between the δ15Nw-NO3− and δ15Ni-NOx values in the atmosphere (hereafter denoted as 15∆i-NOx→w-NO3−). In this study, we calculated 15∆i-NOx→w-NO3− values by using the following equation (Eq. (2)):

    $${,}^{15}{Delta}_{{mathrm{i}} – {mathrm{NO}x} to {mathrm{w}} – {mathrm{NO3}} – } = delta ^{15}{mathrm{N}}_{{mathrm{w}} – {mathrm{NO3}} – } – delta ^{15}{mathrm{N}}_{{mathrm{i}} – {mathrm{NO}x}}.$$
    (2)

    Combined Eq. (1) with Eq. (2), we get Eq. (3) to calculate the 15∆i-NOx→w-NO3− values.

    $$ {,}^{15}{Delta}_{{mathrm{i}} – {mathrm{NO}x} to {mathrm{w}} – {mathrm{NO3}}} = delta ^{15}{mathrm{N}}_{{mathrm{w}} – {mathrm{NO3}} – }\ quad- left({delta}^{15}{mathrm{N}}_{{mathrm{NO}x}} times {mathrm{C}}_{{mathrm{NO2}}}/f_{{mathrm{NO2}}} + delta ^{15}{mathrm{N}}_{{mathrm{HNO3}}} times {mathrm{C}}_{{mathrm{HNO3}}} + delta ^{15}{mathrm{N}}_{{mathrm{p}} – {mathrm{NO3}} – } times {mathrm{C}}_{{mathrm{p}} – {mathrm{NO3}}}right)/\ quad left({mathrm{C}}_{{mathrm{NO2}}}/f_{{mathrm{NO2}}} + {mathrm{C}}_{{mathrm{HNO3}}} + {mathrm{C}}_{{mathrm{p}} – {mathrm{NO3}} – }right).$$
    (3)

    To obtain more accurate 15∆i-NOx→w-NO3− values, we estimated the 15∆i-NOx→w-NO3− values in two independent scenarios. In Scenario 1, mean values of global δ15NNOx and fNO2 values, simultaneously observed values of ambient CNO2, CHNO3, Cp-NO3−, δ15NHNO3, δ15Np-NO3−, and δ15Nw-NO3− were used for the calculation in Eq. (3). In Scenario 2, non-synchronously observed values of ambient fNO2, CNO2, CHNO3, Cp-NO3−, δ15NNOx, δ15NHNO3, δ15Np-NO3−, and δ15Nw-NO3− were used for the calculation in Eq. (3). The values and data sources of parameters used for estimating ambient 15∆i-NOx→w-NO3− values are included in Supplementary Table 1. Because data of fNO2 and δ15NNOx are very sparse globally, we used global mean values and considered their SD values into the uncertainty analysis by the Monte Carlo method. Furthermore, because of no significant difference between 15∆i-NOx→w-NO3− values obtained in Scenario 1 (2.1 ± 1.7‰) and Scenario 2 (5.7 ± 3.2‰) (Supplementary Fig. 2), we used a mean value of them (3.9 ± 1.8‰; Supplementary Fig. 2) in the calculations of source contributions (Eqs. (4) and (5)).
    Contributions of dominant fossil fuel and non-fossil fuel NOx sources
    Based on δ15Nw-NO3−, 15∆i-NOx→w-NO3−, and δ15N values of NOx sources, we estimated relative contributions of dominant fossil fuel and non-fossil fuel NOx sources to total NOx emissions by using the isotope mass-balance method. We considered coal combustion (denoted as S1) and vehicle exhausts (S2) as dominant fossil fuel NOx sources, and biomass burning (S3), and microbial N cycles (S4) as dominant non-fossil fuel NOx sources. The major reasons include: (1) these four sources have been considered as dominant sources of total NOx emissions in studies of both emission inventory and deposition modeling2,9,11,13,14,15,19,20,21; (2) they are also the dominant sources influencing δ15N variations of NOx and NO3− in the atmosphere;26,27 (3) their mean δ15N values of NOx emission sources differ significantly (P  More

  • in

    Long rDNA amplicon sequencing of insect-infecting nephridiophagids reveals their affiliation to the Chytridiomycota and a potential to switch between hosts

    1.
    Stork, N. E. How many species of insects and other terrestrial arthropods are there on Earth?. Annu. Rev. Entomol. 63, 31–45 (2018).
    CAS  PubMed  Article  Google Scholar 
    2.
    Stork, N. E., McBroom, J., Gely, C. & Hamilton, A. J. New approaches narrow global species estimates for beetles, insects, and terrestrial arthropods. Proc. Natl. Acad. Sci. U. S. A. 112, 7519–7523 (2015).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    3.
    Lange, C. E. & Lord, J. C. Protistan entomopathogens. In Insect Pathology (eds. Vega, F. E. & Kaya, H. K.) 367–394 (Academic Press, 2012). https://doi.org/10.1016/B978-0-12-384984-7.00010-5.

    4.
    Fabel, P., Radek, R. & Storch, V. A new spore-forming protist, Nephridiophaga blaberi sp. nov., in the Death’s head cockroach Blaberus craniifer. Eur. J. Protistol. 36, 387–395 (2000).
    Article  Google Scholar 

    5.
    Ivanić, M. Die Entwicklungsgeschichte und die parasitäre Zerstörungsarbeit einer in den Zellen der Malpighischen Gefäße der Honigbiene (Apis mellifera) schmarotzenden Haplosporidie Nephridiophaga apis n. g. n. sp.. Cellule 45, 291–324 (1937).
    Google Scholar 

    6.
    Ormières, R. & Manier, J.-F. Observations sur Nephridiophaga forficulae (Léger, 1909). Ann. Parasitol. Hum. Comparée 48, 1–10 (1973).
    Article  Google Scholar 

    7.
    Radek, R., Wellmanns, D. & Wolf, A. Two new species of Nephridiophaga (Zygomycota) in the Malpighian tubules of cockroaches. Parasitol. Res. 109, 473–482 (2011).
    PubMed  Article  Google Scholar 

    8.
    Radek, R. & Herth, W. Ultrastructural investigation of the spore-forming protist Nephridiophaga blattellae in the Malpighian tubules of the German cockroach Blattella germanica. Parasitol. Res. 85, 216–231 (1999).
    CAS  PubMed  Article  Google Scholar 

    9.
    Woolever, P. Life history and electron microscopy of a haplosporidian, Nephridiophaga blattellae (Crawley) n. comb, in the Malphigian tubules of the German Cockroach, Blattella germanica (L.). J. Protozool. 13, 622–642 (1966).
    Article  Google Scholar 

    10.
    Radek, R., Klein, G. & Storch, V. The spore of the unicellular organism Nephridiophaga blattellae: ultrastructure and substances of the spore wall. Acta Protozool. 41, 169–181 (2002).
    Google Scholar 

    11.
    Purrini, K. & Weiser, J. Light and electron microscope studies on a protozoan, Oryctospora alata n. gen., n. sp. (Protista, Coelosporidiidae), parasitizing a natural population of the rhinoceros beetle, Oryctes monoceros Oliv. (Coleoptera, Scarabaeidae). Zool. Beitraege 332, 209–220 (1990).
    Google Scholar 

    12.
    Purrini, K. & Rohde, M. Light and electron microscope studies on two new protists, Coelosporidium schalleri n. sp. and Coelosporidium meloidorum n. sp. (Protista) infecting natural populations of the flea beetle, Podagrica fuscicornis, and flower beetle, Mylabris maculiventris. Zool. Anz. 220, 323–333 (1988).
    Google Scholar 

    13.
    Lange, C. E. Unclassified protists of arthropods: the ultrastructure of Nephridiophaga periplanetae (Lutz & Splendore, 1903) n. comb., and the affinities of the Nephridiophagidae to other protists. J. Eukaryot. Microbiol. 40, 689–700 (1993).
    Article  Google Scholar 

    14.
    Perrin, W. S. Observations on the structure and life-history of Pleistophora periplanetæ, Lutz and Splendore. J. Cell Sci. 49, 615–633 (1906).
    Google Scholar 

    15.
    Sprague, V. Recent problems of taxonomy and morphology of Haplosporidia. J. Parasitol. 56, 327–328 (1970).
    Google Scholar 

    16.
    Wylezich, C., Radek, R. & Schlegel, M. Phylogenetische Analyse der 18S rRNA identifiziert den parasitischen Protisten Nephridiophaga blattellae (Nephridiophagidae) als Vertreter der Zygomycota (Fungi). Denisia 13, 435–442 (2004).
    Google Scholar 

    17.
    Radek, R. et al. Morphologic and molecular data help adopting the insect-pathogenic nephridiophagids (Nephridiophagidae) among the early diverging fungal lineages, close to the Chytridiomycota. MycoKeys 25, 31–50 (2017).
    Article  Google Scholar 

    18.
    Evangelista, D. A. et al. An integrative phylogenomic approach illuminates the evolutionary history of cockroaches and termites (Blattodea). Proc. R. Soc. B Biol. Sci. 286, 20182076 (2019).
    Article  Google Scholar 

    19.
    Baumann, P., Moran, N. A. & Baumann, L. The evolution and genetics of aphid endosymbionts. Bioscience 47, 12–20 (1997).
    Article  Google Scholar 

    20.
    Peek, A. S., Feldman, R. A., Lutz, R. A. & Vrijenhoek, R. C. Cospeciation of chemoautotrophic bacteria and deep sea clams. Proc. Natl. Acad. Sci. U. S. A. 95, 9962–9966 (1998).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    21.
    Hosokawa, T., Kikuchi, Y., Nikoh, N., Shimada, M. & Fukatsu, T. Strict host-symbiont cospeciation and reductive genome evolution in insect gut bacteria. PLOS Biol. 4, e337 (2006).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    22.
    Hughes, J., Kennedy, M., Johnson, K. P., Palma, R. L. & Page, R. D. M. Multiple cophylogenetic analyses reveal frequent cospeciation between pelecaniform birds and Pectinopygus lice. Syst. Biol. 56, 232–251 (2007).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    23.
    Desai, M. S. et al. Strict cospeciation of devescovinid flagellates and Bacteroidales ectosymbionts in the gut of dry-wood termites (Kalotermitidae). Environ. Microbiol. 12, 2120–2132 (2010).
    CAS  PubMed  PubMed Central  Google Scholar 

    24.
    Wijayawardene, N. et al. Outline of fungi and fungus-like taxa. Mycosphere 11, 1060–1456 (2020).
    Article  Google Scholar 

    25.
    Tedersoo, L., Anslan, S., Bahram, M., Kõljalg, U. & Abarenkov, K. Identifying the ‘unidentified’ fungi: a global-scale long-read third-generation sequencing approach. Fungal Divers. 103, 273–293 (2020).
    Article  Google Scholar 

    26.
    Crawley, H. Interrelationships of the Sporozoa. Am. Nat. 39, 607–624 (1905).
    Article  Google Scholar 

    27.
    White, M. M. et al. Phylogeny of the Zygomycota based on nuclear ribosomal sequence data. Mycologia 98, 872–884 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    28.
    Letcher, P. M., Powell, M. J., Churchill, P. F. & Chambers, J. G. Ultrastructural and molecular phylogenetic delineation of a new order, the Rhizophydiales (Chytridiomycota). Mycol. Res. 110, 898–915 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Van den Wyngaert, S., Rojas-Jimenez, K., Seto, K., Kagami, M. & Grossart, H.-P. Diversity and hidden host specificity of chytrids infecting colonial volvocacean algae. J. Eukaryot. Microbiol. 65, 870–881 (2018).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    30.
    James, T. Y. et al. A molecular phylogeny of the flagellated fungi (Chytridiomycota) and description of a new phylum (Blastocladiomycota). Mycologia 98, 860–871 (2006).
    PubMed  Article  Google Scholar 

    31.
    Powell, M. J., Letcher, P. M., Chambers, J. G. & Roychoudhury, S. A new genus and family for the misclassified chytrid, Rhizophlyctis harderi. Mycologia 107, 419–431 (2015).
    PubMed  Article  Google Scholar 

    32.
    Letcher, P. M., Powell, M. J., Lopez, S., Lee, P. A. & McBride, R. C. A new isolate of Amoeboaphelidium protococcarum, and Amoeboaphelidium occidentale, a new species in phylum Aphelida (Opisthosporidia). Mycologia 107, 522–531 (2015).
    PubMed  Article  Google Scholar 

    33.
    Strassert, J. F. H. et al. Single cell genomics of uncultured marine alveolates shows paraphyly of basal dinoflagellates. ISME J. 12, 304–308 (2018).
    CAS  PubMed  Article  Google Scholar 

    34.
    Jamy, M. et al. Long-read metabarcoding of the eukaryotic rDNA operon to phylogenetically and taxonomically resolve environmental diversity. Mol. Ecol. Resour. 20, 429–443 (2020).
    CAS  PubMed  Article  Google Scholar 

    35.
    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).
    CAS  PubMed  Article  Google Scholar 

    36.
    Lartillot, N., Rodrigue, N., Stubbs, D. & Richer, J. Phylobayes mpi: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst. Biol. 62, 611–615 (2013).
    CAS  PubMed  Article  Google Scholar 

    37.
    Hoang, D. T., Chernomor, O., Von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).
    CAS  PubMed  Article  Google Scholar 

    38.
    Lloyd, D. & Harris, J. C. Giardia: highly evolved parasite or early branching eukaryote?. Trends Microbiol. 10, 122–127 (2002).
    CAS  PubMed  Article  Google Scholar 

    39.
    Burki, F. et al. Phylogenomics of the intracellular parasite Mikrocytos mackini reveals evidence for a mitosome in Rhizaria. Curr. Biol. 23, 1541–1547 (2013).
    CAS  PubMed  Article  Google Scholar 

    40.
    Abbott, C. L. Evolution: hidden at the end of a very long branch. Curr. Biol. 27, R271–R273 (2014).
    Article  CAS  Google Scholar 

    41.
    Keeling, P. J. & Fast, N. M. Microsporidia: biology and evolution of highly reduced intracellular parasites. Annu. Rev. Microbiol. 56, 93–116 (2002).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    42.
    Mozley-Standridge, S. E., Letcher, P. M., Longcore, J. E., Porter, D. & Simmons, D. R. Cladochytriales—a new order in Chytridiomycota. Mycol. Res. 113, 498–507 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    43.
    Jerônimo, G. H., Jesus, A. L., Simmons, D. R., James, T. Y. & Pires-Zottarelli, C. L. A. Novel taxa in Cladochytriales (Chytridiomycota): Karlingiella (gen. nov.) and Nowakowskiella crenulata (sp. nov.). Mycologia 111, 506–516 (2019).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    44.
    Gutiérrez, M. H., Jara, A. M. & Pantoja, S. Fungal parasites infect marine diatoms in the upwelling ecosystem of the Humboldt current system off central Chile. Environ. Microbiol. 18, 1646–1653 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    45.
    Lepelletier, F. et al. Dinomyces arenysensis gen. et sp. nov. (Rhizophydiales, Dinomycetaceae fam. Nov.), a chytrid infecting marine dinoflagellates. Protist 165, 230–244 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    46.
    Hassett, B. T. & Gradinger, R. Chytrids dominate arctic marine fungal communities. Environ. Microbiol. 18, 2001–2009 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Comeau, A. M., Vincent, W. F., Bernier, L. & Lovejoy, C. Novel chytrid lineages dominate fungal sequences in diverse marine and freshwater habitats. Sci. Rep. 6, 30120 (2016).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    48.
    Lefèvre, E., Roussel, B., Amblard, C. & Sime-Ngando, T. The molecular diversity of freshwater picoeukaryotes reveals high occurrence of putative parasitoids in the plankton. PLoS ONE 3, e2324 (2008).
    PubMed  PubMed Central  Article  ADS  CAS  Google Scholar 

    49.
    Fisher, M. C., Garner, T. W. J. & Walker, S. F. Global emergence of Batrachochytrium dendrobatidis and amphibian chytridiomycosis in space, time, and host. Annu. Rev. Microbiol. 63, 291–310 (2009).
    CAS  PubMed  Article  Google Scholar 

    50.
    Powell, M. J. & Letcher, P. M. Chytridiomycota, Monoblepharidomycota, and Neocallimastigomycota. In Systematics and Evolution: The Mycota VII Part A (eds. McLaughlin, D. J. & Spatafora, J. W.) 141–175 (Springer, 2014). https://doi.org/10.1007/978-3-642-55318-9.

    51.
    Cali, A., Becnel, J. J. & Takvorian, P. M. Microsporidia. In Handbook of the Protists: Second Edition (eds. Archibald, J. M. et al.) 1559–1618 (Springer, 2017). https://doi.org/10.1007/978-3-319-28149-0_27.

    52.
    Powell, M. J. Chytridiomycota. In Handbook of the Protists: Second Edition (eds. Archibald, J. M. et al.) 1523–1558 (Springer, 2017). https://doi.org/10.1007/978-3-319-28149-0_18.

    53.
    Schulte, R. D., Makus, C., Hasert, B., Michiels, N. K. & Schulenburg, H. Multiple reciprocal adaptations and rapid genetic change upon experimental coevolution of an animal host and its microbial parasite. Proc. Natl. Acad. Sci. U. S. A. 107, 7359–7364 (2010).
    CAS  PubMed  PubMed Central  Article  ADS  Google Scholar 

    54.
    Ebert, D. Host-parasite coevolution: insights from the Daphnia-parasite model system. Curr. Opin. Microbiol. 11, 290–301 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    55.
    Spurr, A. R. A low-viscosity epoxy resin embedding medium for electron microscopy. J. Ultrasructure Res. 26, 31–43 (1969).
    CAS  Article  Google Scholar 

    56.
    Reynolds, E. S. The use of lead citrate at high pH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208–212 (1963).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    57.
    Wurzbacher, C. et al. Introducing ribosomal tandem repeat barcoding for fungi. Mol. Ecol. Resour. 19, 118–127 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Roehr, J. T., Dieterich, C. & Reinert, K. Flexbar 3.0—SIMD and multicore parallelization. Bioinformatics 33, 2941–2942 (2017).
    CAS  PubMed  Article  Google Scholar 

    61.
    Nakamura, T., Yamada, K. D., Tomii, K. & Katoh, K. Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics 34, 2490–2492 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    62.
    Medlin, L., Elwood, H. J., Stickel, S. & Sogin, M. L. The characterization of enzymatically amplified eukaryotic 16S-like rRNA-coding regions. Gene 71, 491–499 (1988).
    CAS  PubMed  Article  Google Scholar 

    63.
    Liu, H. & Beckenbach, A. T. Evolution of the mitochondrial cytochrome oxidase II gene among 10 orders of insects. Mol. Phylogenet. Evol. 1, 41–52 (1992).
    CAS  PubMed  Article  Google Scholar 

    64.
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Capella-Gutierrez, S., Silla-Martinez, J. M. & Gabaldon, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11, e0163962 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    67.
    Nguyen, L. T., Schmidt, H. A., Von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).
    CAS  Article  Google Scholar 

    68.
    Kalyaanamoorthy, S., Minh, B. Q., Wong, T. K. F., von Haeseler, A. & Jermiin, L. S. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Shimodaira, H. An approximately unbiased test of phylogenetic tree selection. Syst. Biol. 51, 492–508 (2002).
    PubMed  Article  PubMed Central  Google Scholar 

    70.
    Madeira, F. et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 47, W636–W641 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    71.
    Le, V. S., Dang, C. C. & Le, Q. S. Improved mitochondrial amino acid substitution models for metazoan evolutionary studies. BMC Evol. Biol. 17, 136 (2017).
    PubMed  PubMed Central  Article  Google Scholar  More

  • in

    Neon-green fluorescence in the desert gecko Pachydactylus rangei caused by iridophores

    1.
    Sparks, J. S. et al. The covert world of fish biofluorescence: a phylogenetically widespread and phenotypically variable phenomenon. PLoS ONE 9, e83259 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 
    2.
    Wucherer, M. F. & Michiels, N. K. A fluorescent chromatophore changes the level of fluorescence in a reef fish. PLoS ONE 7, e37913 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    3.
    Gruber, D. F. et al. Biofluorescence in catsharks (Scyliorhinidae): fundamental description and relevance for elasmobranch visual ecology. Sci. Rep. 6, 24751 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Gruber, D. F. & Sparks, J. S. First observation of fluorescence in marine turtles. Am. Mus. Novit. 3845, 1–8 (2015).
    Article  Google Scholar 

    5.
    Kohler, A. M., Olson, E. R., Martin, J. G. & Anich, P. S. Ultraviolet fluorescence discovered in New World flying squirrels (Glaucomys). J. Mammal. 100, 21–30 (2019).
    Article  Google Scholar 

    6.
    Jeng, M.-L. Biofluorescence in terrestrial animals, with emphasis on fireflies: a review and field observation in Bioluminescence—Analytical Applications and Basic Biology 1–16 (Hirobumi Suzuki, IntechOpen, 2019).

    7.
    Evtukh, G. Fluorescence among Fraterculinae subfamily. Pyccкий opнитoлoгичecкий жypнaл 28, 2134–2142 (2019).
    Google Scholar 

    8.
    Wilkinson, B. P., Johns, M. E. & Warzybok, P. Fluorescent ornamentation in the Rhinoceros Auklet Cerorhinca monocerata. Ibis 161, 694–698 (2019).
    Article  Google Scholar 

    9.
    Arnold, K., Owens, I. P. & Marshall, N. J. Fluorescent signalling in parrots. Science 295, 92 (2002).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Barreira, A., Lagorio, M. G., Lijtmaer, D., Lougheed, S. & Tubaro, P. Fluorescent and ultraviolet sexual dichromatism in the blue-winged parrotlet. J. Zool. 288, 135–142 (2012).
    Article  Google Scholar 

    11.
    Goutte, S. et al. Intense bone fluorescence reveals hidden patterns in pumpkin toadlets. Sci. Rep. 9, 1–8 (2019).
    CAS  Article  Google Scholar 

    12.
    Taboada, C., Brunetti, A. E., Alexandre, C., Lagorio, M. G. & Faivovich, J. Fluorescent frogs: a herpetological perspective. S. Am. J. Herpetol. 12, 1–13 (2017).
    Article  Google Scholar 

    13.
    Taboada, C. et al. Naturally occurring fluorescence in frogs. Proc. Nat. Acad. Sci. USA 114, 3672–3677 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Deschepper, P., Jonckheere, B. & Matthys, J. A light in the dark: the discovery of another fluorescent frog in the Costa Rican rainforests. Wilderness Environ. Med. 29, 4212134–2142422 (2018).
    Google Scholar 

    15.
    Lamb, J. Y. & Davis, M. P. Salamanders and other amphibians are aglow with biofluorescence. Sci. Rep. 10, 1–7 (2020).
    Article  CAS  Google Scholar 

    16.
    Thompson, M. E., Saporito, R., Ruiz-Valderrama, D. H., Medina-Rangel, G. F. & Donnelly, M. A. A field-based survey of fluorescence in tropical tree frogs using an LED UV-B flashlight. Herpetol. Notes 12, 987–990 (2019).
    Google Scholar 

    17.
    Gray, R. J. Biofluorescent lateral patterning on the Mossy Bushfrog (Philautus macroscelis): the first report of biofluorescence in a rhacophorid frog. Herpetol. Notes 12, 363–364 (2019).
    Google Scholar 

    18.
    Munoz, D. Plethodon cinereus (Eastern Red-backed Salamander) Fluorescence. Herpetol. Rev. 49, 512–513 (2018).
    Google Scholar 

    19.
    Tah, M.M.T.-M., Puan, C. L., Chuang, M.-F., Othman, S. N. & Borzée, A. First record of ultraviolet fluorescence in Bent-toed Gecko Cyrtodactylus quadrivirgatus (Gekkonidae: Sauria). Herpetol. Notes 13, 211–212 (2020).
    Google Scholar 

    20.
    Sloggett, J. J. Field observations of putative bone-based fluorescence in a gecko. Curr. Zool. 64, 319–320 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Prötzel, D. et al. Widespread bone-based fluorescence in chameleons. Sci. Rep. 8, 698 (2018).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    22.
    Maitland, D. & Hart, A. A fluorescent vertebrate: the Iberian Worm-lizard Blanus cinereus (Amphisbaenidae). Herpetol. Rev. 39, 50 (2008).
    Google Scholar 

    23.
    Andrews, K., Reed, S. M. & Masta, S. E. Spiders fluoresce variably across many taxa. Biol. Lett. 3, 265–267 (2007).
    PubMed  PubMed Central  Article  Google Scholar 

    24.
    Macel, M.-L. et al. Sea as a color palette: the ecology and evolution of fluorescence. Zool. Lett. 6, 1–11 (2020).
    Article  Google Scholar 

    25.
    Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    26.
    Kloock, C. T., Kubli, A. & Reynolds, R. Ultraviolet light detection: a function of scorpion fluorescence. J. Arachnol. 38, 441–445 (2010).
    Article  Google Scholar 

    27.
    Haddock, S. H. & Dunn, C. W. Fluorescent proteins function as a prey attractant: experimental evidence from the hydromedusa Olindias formosus and other marine organisms. Biol. Open 4, 1094–1104 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    28.
    Gandía-Herrero, F., García-Carmona, F. & Escribano, J. Botany: floral fluorescence effect. Nature 437, 334 (2005).
    ADS  PubMed  Article  CAS  PubMed Central  Google Scholar 

    29.
    Mazel, C., Cronin, T., Caldwell, R. & Marshall, N. Fluorescent enhancement of signaling in a mantis shrimp. Science 303, 51 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    30.
    Lim, M. L., Land, M. F. & Li, D. Sex-specific UV and fluorescence signals in jumping spiders. Science 315, 481 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Kloock, C. T. A comparison of fluorescence in two sympatric scorpion species. J. Photochem. Photobiol. B 91, 132–136 (2008).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    32.
    Michiels, N. K. et al. Red fluorescence in reef fish: a novel signalling mechanism?. BMC Ecol. 8, 1–16 (2008).
    Article  Google Scholar 

    33.
    Gerlach, T., Sprenger, D. & Michiels, N. K. Fairy wrasses perceive and respond to their deep red fluorescent coloration. Proc. R. Soc. B 281, 20140787 (2014).
    PubMed  Article  PubMed Central  Google Scholar 

    34.
    Lagorio, M. G., Cordon, G. B. & Iriel, A. Reviewing the relevance of fluorescence in biological systems. Photochem. Photobiol. Sci. 14, 1538–1559 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    35.
    Bachman, C. H. & Ellis, E. H. Fluorescence of bone. Nature 206, 1328–1331 (1965).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    36.
    Rebouças, R. et al. Is the conspicuous dorsal coloration of the Atlantic forest pumpkin toadlets aposematic?. Salamandra 55, 39–47 (2019).
    Google Scholar 

    37.
    Werner, Y. L. Ecological comments on some gekkonid lizards of the Namib Desert, South West Africa. Modoqua 1977, 157–169 (1977).
    Google Scholar 

    38.
    Russell, A. & Bauer, A. Substrate excavation in the Namibian web-footed gecko, Palmatogecko rangei Andersson 1908, and its ecological significance. Trop. Zool. 3, 197–207 (1990).
    Article  Google Scholar 

    39.
    Vitt, L. J. & Caldwell, J. P. Herpetology: An Introductory Biology of Amphibians and Reptiles 776 (Academic Press, London, 2013).
    Google Scholar 

    40.
    Schmidt, W. J. Die Chromatophoren der Reptilienhaut. Arch. Mikrosk. Anat. 90, 98–259 (1918).
    Article  Google Scholar 

    41.
    Szydłowski, P., Madej, J. P. & Mazurkiewicz-Kania, M. Histology and ultrastructure of the integumental chromatophores in tokay gecko (Gekko gecko) (Linnaeus, 1758) skin. Zoomorphology 136, 233–240 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    42.
    Saenko, S. V., Teyssier, J., Van Der Marel, D. & Milinkovitch, M. C. Precise colocalization of interacting structural and pigmentary elements generates extensive color pattern variation in Phelsuma lizards. BMC Biol. 11, 105 (2013).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    43.
    Teyssier, J., Saenko, S. V., Van Der Marel, D. & Milinkovitch, M. C. Photonic crystals cause active colour change in chameleons. Nat. Commun. 6, 6368 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Avallone, B., Tizzano, M., Cerciello, R., Buglione, M. & Fulgione, D. Gross anatomy and ultrastructure of Moorish Gecko, Tarentola mauritanica skin. Tissue Cell 51, 62–67 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    45.
    Morrison, R. L., Sherbrooke, W. C. & Frost-Mason, S. K. Temperature-sensitive, physiologically active iridophores in the lizard Urosaurus ornatus: an ultrastructural analysis of color change. Copeia 1996, 804–812 (1996).
    Article  Google Scholar 

    46.
    Polewski, K., Zinger, D., Trunk, J., Monteleone, D. C. & Sutherland, J. C. Fluorescence of matrix isolated guanine and 7-methylguanine. J. Photochem. Photobiol. B 24, 169–177 (1994).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    47.
    Turrisi, R. et al. Stokes shift/emission efficiency trade-off in donor–acceptor perylenemonoimides for luminescent solar concentrators. J. Mater. Chem. A 3, 8045–8054 (2015).
    CAS  Article  Google Scholar 

    48.
    Suzuki, K. et al. Reevaluation of absolute luminescence quantum yields of standard solutions using a spectrometer with an integrating sphere and a back-thinned CCD detector. Phys. Chem. Chem. Phys. 11, 9850–9860 (2009).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    49.
    Szydłowski, P., Madej, J. P. & Mazurkiewicz-Kania, M. Ultrastructure and distribution of chromatophores in the skin of the leopard gecko (Eublepharis macularius). Acta Zool. 97, 370–375 (2016).
    Article  Google Scholar 

    50.
    Hibbitts, T. J., Pianka, E. R., Huey, R. B. & Whiting, M. J. Ecology of the common barking gecko (Ptenopus garrulus) in southern Africa. J. Herpetol. 39, 509–515 (2005).
    Article  Google Scholar 

    51.
    Olivier, J. Spatial distribution of fog in the Namib. J. Arid Environ. 29, 129–138 (1995).
    ADS  Article  Google Scholar 

    52.
    Gottlieb, T. R., Eckardt, F. D., Venter, Z. S. & Cramer, M. D. The contribution of fog to water and nutrient supply to Arthraerua leubnitziae in the central Namib Desert, Namibia. J. Arid Environ. 161, 35–46. https://doi.org/10.1016/j.jaridenv.2018.11.002 (2019).
    ADS  Article  Google Scholar 

    53.
    Prötzel, D. D. Palmatogecko—ein sozialer Gecko?. Reptilia 107, 4–5 (2014).
    Google Scholar 

    54.
    Nørgaard, T., Henschel, J. R. & Wehner, R. The night-time temporal window of locomotor activity in the Namib Desert long-distance wandering spider, Leucorchestris arenicola. J. Comp. Physiol. A 192, 365–372 (2006).
    Article  Google Scholar 

    55.
    Roth, L. S. & Kelber, A. Nocturnal colour vision in geckos. Proc. R. Soc. B 271, 485–487 (2004).
    Article  Google Scholar 

    56.
    Pinto, B. J., Nielsen, S. V. & Gamble, T. Transcriptomic data support a nocturnal bottleneck in the ancestor of gecko lizards. Mol. Phylogenet. Evol. 141, 106639 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    57.
    Iriel, A. & Lagorio, M. G. Implications of reflectance and fluorescence of Rhododendron indicum flowers in biosignaling. Photochem. Photobiol. Sci. 9, 342–348 (2010).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Spurr, A. R. A low-viscosity epoxy resin embedding medium for electron microscopy. J. Ultrastruct. Res. 26, 31–43 (1969).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    59.
    Richardson, K., Jarett, L. & Finke, E. Embedding in epoxy resins for ultrathin sectioning in electron microscopy. Stain Technol. 35, 313–323 (1960).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Reynolds, E. S. The use of lead citrate at high pH as an electron-opaque stain in electron microscopy. J. Cell Biol. 17, 208 (1963).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    61.
    Rueden, C. T. et al. Image J2: ImageJ for the next generation of scientific image data. BMC Bioinform. 18, 529 (2017).
    Article  Google Scholar 

    62.
    R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2020).

    63.
    Wickham, H. ggplot2: Elegant Graphics for Data Analysis 2nd edn. (Springer, Berlin, 2016).
    Google Scholar  More