More stories

  • in

    Abiotic and biotic factors controlling the dynamics of soil respiration in a coastal dune ecosystem in western Japan

    Site descriptionThe study site (about 1 ha) is within a coastal dune ecosystem (35° 32′ 26.0″ N, 134° 12′ 27.5″ E) located at the Arid Land Research Center of Tottori University, Tottori, Japan. The mean annual temperature is 15.2 °C, and the mean total precipitation is 1931 mm, based on records collected from 1991 to 2020 at the Tottori observation station of the Japan Meteorological Agency. Dominant plant species around the measurement plot were Vitex rotundifolia and Artemisia capillaris. Carex kobomugi and Ischaemum anthephoroides were also scattered around the coastal side of the study site, and planted Pinus thunbergii trees cover the inland side.Experimental designIn May 2020, we established four measurement plots at the study site (Fig. 9). Plot 1 was a gap area surrounded by V. rotundifolia seedlings. Plot 2 consisted of clusters of V. rotundifolia seedlings and was adjacent to plot 1. Within plots 1 and 2, C. kobomugi and I. anthephoroides were also scattered. Plot 3 was in a mixed area of V. rotundifolia and A. capillaris; this plot was in the center of the study site. Plot 4 was located in front of P. thunbergii trees and was in the most inland area of the study site. On 10 June 2020, we set an environmental measurement system at the center of the study site adjacent to plot 3, and we then obtained continuous data for soil temperature and soil moisture. In each plot (main plot), we set 10 plastic (polypropylene) collars (n = 10) before the start of the Rs measurement. We measured Rs every 2 weeks from 15 June to 2 December 2020 in the main plots. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June and early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July (Fig. 9), and flux calculations for plot 1 were conducted after removing data from the invaded area measured on June 15.Figure 9Diagram and photos of measurement plots in the focal coastal dune ecosystem. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June to early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July.Full size imageEnvironmental measurement systemThe environmental measurement system was composed of a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA), battery (SC dry battery, Kind Techno Structure Co. Ltd, Saitama, Japan), solar panel (RNG-50D-SS, RENOGY International Inc., Ontario, CA, USA), charge controller (Solar Amp mini, CSA-MN05-8, DENRYO, Tokyo, Japan), thermocouples (E type), and soil moisture sensors (CS616, Campbell Scientific Inc.). The data logger, battery, and charge controller were kept in a plastic box to avoid exposure to rainfall and sand. Each end of the thermocouple was inserted into a copper tube (4-mm inner diameter, 5-cm length) and affixed with glue. To measure the reference soil temperature at different depths, copper tubes enclosing E-type thermocouples were buried horizontally in the sand at depths of 5, 10, 30, and 50 cm (n = 1 for each depth) at the center of plot 3 as reference soil temperature (the data was recorded every 30 min). In addition, we set stand-alone soil temperature sensors (Thermochron SL type, KN Laboratories, Inc. Osaka, Japan) at the center of plots 1 and 4 at depths of 5, 10, and 30 cm (n = 1 for each plot, each depth), and they recorded soil temperature data every 30 min. Reference soil temperature at the depth of 5, 10, and 30 cm was used for gap-filling for soil temperature measured by stand-alone sensors at each depth and plot. Soil moisture sensors were buried horizontally in the sand at a depth of 30 cm in the center of plots 1, 3, and 4 (n = 1 for each plot) and recorded data every 30 min. Raw values of soil moisture sensors were converted to volumetric soil moisture (%) using a calibration line from 0 to 15% measured in the laboratory using dune sand and three sensors (CS616) referring to the procedure of Bongiovanni et al.53. Data for precipitation at the local meteorological observatory in Tottori was downloaded from the home page of the Japan Meteorological Agency (https://www.data.jma.go.jp/gmd/risk/obsdl/index.php).
    R
    s measurement in the main plotsPolypropylene collars (30-cm inner diameter, 5-cm depth, n = 10) were set in each measurement plot in late May 2020. The first Rs measurement was conducted on 15 June 2020. However, V. rotundifolia and C. kobomugi then invaded about half of the gap area of plot 1, so on 1 July we set 5 new polypropylene collars for plot 1 to replace the 5 invaded measurement points (Fig. 9). The second Rs measurement was conducted on 2 July, and all polypropylene collars then remained in the same position until the end of the measurement period.Rs was measured using an automated closed dynamic chamber system54 composed of two cylindrical aluminum chambers (30 cm diameter, 30 cm height) equipped with thermistor temperature sensors (44006, Omega Engineering, Stanford, CA, USA) for measuring air temperature inside the chamber during Rs measurement. Those chambers were connected to a control box equipped with a pump, data logger (CR1000, Campbell Scientific Inc.), CO2 analyzer (Gascard NG infrared gas sensor, Edinburgh Sensors, Lancashire, UK), and thermometer (MHP, Omega Engineering). The composition of the control box is basically the same as used in previous studies54,55. The measurement period for each point was 3 min, and the CO2 concentration and air temperature inside the chamber were recorded every 5 s. During the measurement, another chamber was set on the next polypropylene collar with the lid opened, and the next measurement was started at that moment of finishing the previous measurement by automatically closing the chamber lid on the next polypropylene collar in the same plot. Soil temperature at a depth of 0–5 cm was recorded simultaneously by inserting the rod of the thermometer vertically into the soil surface near the polypropylene collar (about 1–2 m from the collar).Rs was calculated by using the following equation:$$R_{{text{s}}} = frac{{PV}}{{RS(T_{{{text{air}}}} + 273.15)}}frac{{partial C}}{{partial t}},$$
    (1)
    where P is the air pressure (Pa), V is the effective chamber volume (m3), R is the ideal gas constant (8.314 Pa m3 K−1 mol−1), S is the soil surface area (m2), Tair is the air temperature inside the chamber (°C). ∂C/∂t is the rate of change of the CO2 mole fraction (μmol mol−1 s−1), which was calculated using least-squares regression of the CO2 changes inside the chamber12. For the flux calculation, we removed data for the first 35 s (dead band) of each measurement as an outlier.Trench treatment and soil CO2 efflux (F
    c) measurement in subplotsIn November 2020, we conducted root-cut treatment (trench treatment) in subplots using polyvinyl chloride (PVC) tubes to estimate the contribution of Ra to Rs in the soil layer above 50 cm in each plot (Ra_50/Rs). Small PVC collars (10.7 cm inner diameter, 5 cm depth, n = 10 for each plot), with the upper ends about 1–2 cm above the soil surface, were set in subplots adjacent to the main plots on 23 October 2020. Rs was measured in subplots using two cylindrical mini PVC chambers (11.8 cm inner diameter at the bottom, 30 cm height, equipped with the same thermistors as cylindrical aluminum chambers for air temperature measurement) connected to the same control box as used for Rs measurement in the main plots. The measurement period was 3 min, and the measurement procedure and the flux calculation were the same as the main plot. Rs was first measured in subplots on 3 November to examine the spatial variation of Rs before trench treatment. Using the data, we selected subplots to conduct trench treatment and control plots for comparison, while aiming to achieve a minimal difference in the average Rs between control and pre-trenched plots. On 4 November, we inserted PVC tubes (10.7 cm inner diameter, 50 cm length) into about half (n = 3–5) of the subplots (the same position as PVC collars were set on 23 October) by using a hammer and aluminum lid until the upper end of each PVC tube was 1–2 cm above the soil surface to exclude roots to a depth of about 50 cm. On 19 November, after 15 days of trench treatment, respiration was measured in the same subplots.The Ra_50/Rs was calculated as follows:$$R_{{{text{a}}_{5}0}} /R_{{text{s}}} = (F_{{{text{c}}_{text{control}}}} -F_{{{text{c}}_{text{trenched}}}}) /F_{{{text{c}}_{text{control}}}} ,$$
    (2)
    where Fc_trenched and Fc_control (= Rs) are the Fc values in trenched and control plots on 19 November, respectively.In late December 2020, all the belowground plant biomass (BPB) in subplots (control and trenched plots) to a depth of 50 cm was collected for biomass analysis, about 2 months after trench treatment. In the laboratory, all the collected plant materials were washed and oven-dried for 72 h at 70 °C, and then the dry weight of the BPB samples was measured.Biomass measurementWe conducted BPB analysis from 18 May to 8 June 2021 in each plot (n = 1). At that time, 100 cm × 100 cm sampling plots near the CO2 measurement plots (100 cm × 100 cm for plots 2–4 and 50 cm × 50 cm in plot 1 because of the narrow gap area) were dug to a depth of 100–220 cm, according to the root distribution in each plot, and all plant materials were collected by passing the soil through 5- to 7-mm sieves. Once we reached a depth where no roots were visible, no more digging was conducted. In plots 2 and 3, stolons of V. rotundifolia were difficult to distinguish from roots if underground. Therefore, we defined plant material as BPB if it was underground. In the laboratory, all of the collected plant materials were washed and air-dried at room temperature for 0–6 days depending on the biomass. After that, samples were oven-dried for 15–25 h at 70–80 °C, and the dry weight of those samples was then measured.Soil organic carbon and nitrogenOn 21 October 2020, soil pits were dug to a depth of 50 cm near each plot (n = 3), and soil core samples were collected. Cylindrical stainless core samplers (5 cm diameter, 5 cm height, 100 cc) were horizontally inserted into the soil pit at depths of 0–5, 5–10, 10–20, and 20–30 cm. In the laboratory, soil core samples were weighed and oven-dried at 105 °C for 48 h, and the dry weight was measured. Oven-dried soil samples were sieved with a 2-mm-pore stainless wire mesh screen, and visible fungal mycelia in soil samples from plot 4 were removed as well as possible. Sieved samples were ground with an agate mortar. Samples (fine powder) were oven-dried for 24 h at 105 °C and weighed before SOC and nitrogen analysis. About 1.5 g of powdered samples were used for the analysis. Organic carbon content (combustion at 400 °C) and total nitrogen in samples were analyzed using a Soli TOC cube (Elementar Analysensysteme GmbH, Langenselbold, Germany) by the combustion method.Microbial abundanceOn 21 October 2020, soil samples for microbial analysis were collected at the same time as soil core sampling for SOC and nitrogen analysis. Soil samples were collected at depths of 0–10, 10–20, and 20–30 cm using a stainless spatula and placed individually in a polyethylene bag. The bags were kept in a cooler box with ice in the field and then placed in a freezer (− 30 °C) in the laboratory soon after sampling.DNA was extracted from 0.5 g of the fresh soils using NucleoSpin Soil (Takara Bio, Inc., Shiga, Japan) according to the manufacturer’s instructions (SL1 buffer), and the extracts were stored at − 20 °C until further analysis. Bacterial and archaeal 16S rRNA and fungal internal transcribed spacer (ITS) gene were targeted to investigate the microbial abundance. Bacterial and archaeal 16S rRNA (V4 region) and fungal ITS were determined using the universal primer sets 515F/806R and ITS1F_KYO2/ITS2_KYO2, respectively56,57.For qPCR, samples were prepared with 10 μL of the KAPA SYBR Fast qPCR kit (Kapa Biosystems, Wilmington, MA, USA), 0.8 μL of forward primer, 0.8 μL of reverse primer, and 3 μL of 1–50 × diluted soil DNA. Nuclease-free water was added to make up to a final volume of 20 μL. Cycling conditions of 16S rRNA were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min. Cycling conditions of ITS were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 55 °C for 1 min, and 72 °C for 1 min. A melting curve analysis was performed in a final cycle of 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 15 s. High amplification efficiencies of 99% for bacterial and archaeal 16S rRNA genes and 101% for the fungal ITS were obtained based on the standard curves.Data analysisTo examine the environmental response (soil temperature and soil moisture) of Rs, nonlinear and quadratic regression models were applied. We conducted F-tests by comparing the regression model to a constant model whose value is the mean of the observations (significance set at p  More

  • in

    Global systematic review with meta-analysis reveals yield advantage of legume-based rotations and its drivers

    Beillouin, D., Ben-Ari, T., Malezieux, E., Seufert, V. & Makowski, D. Positive but variable effects of crop diversification on biodiversity and ecosystem services. Glob. Change Biol. 27, 4697–4710 (2021).CAS 
    Article 

    Google Scholar 
    Ditzler, L. et al. Current research on the ecosystem service potential of legume inclusive cropping systems in Europe. A review. Agron. Sustain. Dev. 41, 26 (2021).Article 

    Google Scholar 
    Snapp, S. S., Blackie, M. J., Gilbert, R. A., Bezner-Kerr, R. & Kanyama-Phiri, G. Y. Biodiversity can support a greener revolution in Africa. Proc. Natl Acad. Sci. USA 107, 20840–20845 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Rodriguez, C., Mårtensson, L.-M. D., Jensen, E. S. & Carlsson, G. Combining crop diversification practices can benefit cereal production in temperate climates. Agron. Sustain. Dev. 41, 48 (2021).Article 

    Google Scholar 
    Zeng, Z. H. et al. in Crop Rotations: Farming Practices, Monitoring and Environmental Benefits (ed. Ma, B. L.) Ch. 1, 51–70 (Nova Science Publishers, 2016).Cusworth, G., Garnett, T. & Lorimer, J. Legume dreams: the contested futures of sustainable plant-based food systems in Europe. Glob. Environ. Change 69, 102321 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Reckling, M. et al. Grain legume yields are as stable as other spring crops in long-term experiments across northern Europe. Agron. Sustain. Dev. 38, 63 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Snapp, S. S., Cox, C. M. & Peter, B. G. Multipurpose legumes for smallholders in sub-Saharan Africa: identification of promising ‘scale out’ options. Glob. Food Secur-Agr. 23, 22–32 (2019).Article 

    Google Scholar 
    Hegewald, H., Wensch-Dorendorf, M., Sieling, K. & Christen, O. Impacts of break crops and crop rotations on oilseed rape productivity: a review. Eur. J. Agron. 101, 63–77 (2018).Article 

    Google Scholar 
    Angus, J. F. et al. Break crops and rotations for wheat. Crop . Sci. 66, 523–552 (2015).
    Google Scholar 
    Franke, A. C., van den Brand, G. J., Vanlauwe, B. & Giller, K. E. Sustainable intensification through rotations with grain legumes in Sub-Saharan Africa: a review. Agric. Ecosyst. Environ. 261, 172–185 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Preissel, S., Reckling, M., Schlaefke, N. & Zander, P. Magnitude and farm-economic value of grain legume pre-crop benefits in Europe: a review. Field Crops Res. 175, 64–79 (2015).Article 

    Google Scholar 
    Zhao, J. et al. Does crop rotation yield more in China? A meta-analysis. Field Crops Res. 245, 107659 (2020).Article 

    Google Scholar 
    Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Cernay, C., Makowski, D. & Pelzer, E. Preceding cultivation of grain legumes increases cereal yields under low nitrogen input conditions. Environ. Chem. Lett. 16, 631–636 (2018).CAS 
    Article 

    Google Scholar 
    Peoples, M. B. et al. The contributions of nitrogen-fixing crop legumes to the productivity of agricultural systems. Symbiosis 48, 1–17 (2009).CAS 
    Article 

    Google Scholar 
    Watson, C. A. et al. Grain legume production and use in European agricultural systems. Adv. Agron. 144, 235–303 (2017).Article 

    Google Scholar 
    Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production:The challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71 (2012).PubMed 
    Article 

    Google Scholar 
    Drinkwater, L. E., Wagoner, P. & Sarrantonio, M. Legume-based cropping systems have reduced carbon and nitrogen losses. Nature 396, 262–265 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    Smith, C. J. & Chalk, P. M. Grain legumes in crop rotations under low and variable rainfall: are observed short-term N benefits sustainable? Plant Soil 453, 271–279 (2020).CAS 
    Article 

    Google Scholar 
    Pullens, J. W. M., Sorensen, P., Melander, B. & Olesen, J. E. Legacy effects of soil fertility management on cereal dry matter and nitrogen grain yield of organic arable cropping systems. Eur. J. Agron. 122, 126169 (2021).CAS 
    Article 

    Google Scholar 
    Tognetti, P. M. et al. Negative effects of nitrogen override positive effects of phosphorus on grassland legumes worldwide. Proc. Natl Acad. Sci. USA 118, 28 (2021).Article 

    Google Scholar 
    Kirkegaard, J., Christen, O., Krupinsky, J. & Layzell, D. Break crop benefits in temperate wheat production. Field Crops Res. 107, 185–195 (2008).Article 

    Google Scholar 
    Brisson, N. et al. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Res. 119, 201–212 (2010).Article 

    Google Scholar 
    Anderson, R. L. Synergism: a rotation effect of improved growth efficiency. Adv. Agron. 112, 205–226 (2011).Article 

    Google Scholar 
    Bonilla-Cedrez, C., Chamberlin, J. & Hijmans, R. Fertilizer and grain prices constrain food production in sub-Saharan Africa. Nat. Food 2, 766–772 (2021).Article 

    Google Scholar 
    Seufert, V., Ramankutty, N. & Foley, J. A. Comparing the yields of organic and conventional agriculture. Nature 485, 229–232 (2012).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Barbieri, P., Pellerin, S., Seufert, V. & Nesme, T. Changes in crop rotations would impact food production in an organically farmed world. Nat. Sustain. 2, 378–385 (2019).Article 

    Google Scholar 
    Barbieri, P. et al. Global option space for organic agriculture is delimited by nitrogen availability. Nat. Food 2, 363–372 (2021).Article 

    Google Scholar 
    Muller, A. et al. Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun. 8, 1290 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nowak, B., Nesme, T., David, C. & Pellerin, S. Disentangling the drivers of fertilising material inflows in organic farming. Nutr. Cycl. Agroecosyst. 96, 79–91 (2013).Article 

    Google Scholar 
    Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 
    Article 

    Google Scholar 
    Mariotte, P. et al. Plant-soil feedback: Bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).PubMed 
    Article 

    Google Scholar 
    Everwand, G., Cass, S., Dauber, J., Williams, M. & Stout, J. Legume crops and biodiversity. Legumes in Cropping Systems, 4, 55–69 (2017).Peoples, M. B., Giller, K. E., Jensen, E. S. & Herridge, D. F. Quantifying country-to-global scale nitrogen fixation for grain legumes: I. Reliance on nitrogen fixation of soybean, groundnut and pulses. Plant Soil 469, 1–14 (2021).CAS 
    Article 

    Google Scholar 
    Abalos, D., van Groenigen, J. W., Philippot, L., Lubbers, I. M. & De Deyn, G. B. Plant trait-based approaches to improve nitrogen cycling in agroecosystems. J. Appl. Ecol. 56, 2454–2466 (2019).Article 

    Google Scholar 
    Garland, G. et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. Nat. Food 2, 28–37 (2021).Article 

    Google Scholar 
    Pandey, A., Li, F., Askegaard, M., Rasmussen, I. A. & Olesen, J. E. Nitrogen balances in organic and conventional arable crop rotations and their relations to nitrogen yield and nitrate leaching losses. Agric. Ecosyst. Environ. 265, 350–362 (2018).CAS 
    Article 

    Google Scholar 
    Cook, R. J. Toward cropping systems that enhance productivity and sustainability. Proc. Natl Acad. Sci. USA 103, 18389–18394 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gan, Y. T. et al. Improving farming practices reduces the carbon footprint of spring wheat production. Nat. Commun. 5, 13 (2014).
    Google Scholar 
    Hufnagel, J., Reckling, M. & Ewert, F. Diverse approaches to crop diversification in agricultural research. A review. Agron. Sustain. Dev. 40, 14 (2020).Article 

    Google Scholar 
    Ma, B. L. & Wu, W. in Crop Rotations: Farming Practices, Monitoring and Environmental Benefits (ed Ma B. L.) Ch. 1, 1–35 (Nova Science Publishers, 2016).Seymour, M., Kirkegaard, J. A., Peoples, M. B., White, P. F. & French, R. J. Break-crop benefits to wheat in Western Australia – insights from over three decades of research. Crop. Sci. 63, 1–16 (2012).
    Google Scholar 
    Sileshi, G., Akinnifesi, F. K., Ajayi, O. C. & Place, F. Meta-analysis of maize yield response to woody and herbaceous legumes in sub-Saharan Africa. Plant Soil 307, 1–19 (2008).CAS 
    Article 

    Google Scholar 
    Bullock, D. G. Crop rotation. Crit. Rev. Plant Sci. 11, 309–326 (1992).Article 

    Google Scholar 
    Danga, B. O., Ouma, J. P., Wakindiki, I. I. C. & Bar-Tal, A. Legume-wheat ration effects on residual soil moisture, nitrogen and wheat yield in tropical regions. Adv. Agron. 101, 315–349 (2009).Article 

    Google Scholar 
    Ghosh, P. K. et al. Legume effect for enhancing productivity and nutrient use-efficiency in major cropping systems – An Indian perspective: a review. J. Sustain. Agric. 30, 59–86 (2007).Article 

    Google Scholar 
    Karlen, D. L., Varvel, G. E., Bullock, D. G. & Cruse, R. M. Crop rotation for the 21st century. Adv. Agron. 53, 1–45 (1994).Article 

    Google Scholar 
    Martin, G. et al. Role of ley pastures in tomorrow’s cropping systems. A review. Agron. Sustain. Dev. 40, 17 (2020).Article 

    Google Scholar 
    Ruisi, P. et al. Agro-ecological benefits of faba bean for rainfed Mediterranean cropping systems. Ital. J. Agron. 12, 233–245 (2017).
    Google Scholar 
    Ryan, J., Singh, M. & Pala, M. Long-term cereal-based rotation trials in the Mediterranean region: Implications for cropping sustainability. Adv. Agron. 97, 273–319 (2008).CAS 
    Article 

    Google Scholar 
    Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Grp, P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J. Clin. Epidemiol. 62, 1006–1012 (2009).PubMed 
    Article 

    Google Scholar 
    Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded Harmonized World Soil Database v1.2. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/1247 (2014).Soil Survey Staff. Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. 2nd edition. Natural Resources Conservation Service. U.S. Department of Agriculture Handbook 436. (1999).FAO. World Programme of the Census of Agriculture 2020. Vol. 1 (2015).Tiemann, L. K., Grandy, A. S., Atkinson, E. E., Marin-Spiotta, E. & McDaniel, M. D. Crop rotational diversity enhances belowground communities and functions in an agroecosystem. Ecol. Lett. 18, 761–771 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).CAS 
    Article 

    Google Scholar 
    Yates, F. The analysis of experiments containing different crop rotations. Biometrics 10, 324–346 (1954).Article 

    Google Scholar 
    Zhao, J. et al. Dataset for evaluating global yield advantage and its drivers of legume-based rotations. Figshare, https://doi.org/10.6084/m9.figshare.20290923 (2022).Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

    Google Scholar 
    Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).Article 

    Google Scholar 
    Van Lissa, C. MetaForest: Exploring Heterogeneity in Meta-analysis Using Random Forests. (2017).Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–CO603 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
    Article 

    Google Scholar 
    Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Rosenberg, M. S. The file-drawer problem revisited: a general weighted method for calculating fail-safe numbers in meta-analysis. Evolution 59, 464–468 (2005).PubMed 
    Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing v.4.0.3 (R Foundation for Statistical Computing, Vienna, Austria, 2021). More

  • in

    Fine-scale movement of northern Gulf of Mexico red snapper and gray triggerfish estimated with three-dimensional acoustic telemetry

    Fodrie, F. J. et al. Measuring individuality in habitat use across complex landscapes: Approaches, constraints, and implications for assessing resource specialization. Oecologia 178, 75–87 (2015).ADS 
    PubMed 
    Article 

    Google Scholar 
    Bacheler, N. M., Michelot, T., Cheshire, R. T. & Shertzer, K. W. Fine-scale movement patterns and behavioral states of gray triggerfish Balistes capriscus determined from acoustic telemetry and hidden Markov models. Fish. Res. 215, 76–89 (2019).Article 

    Google Scholar 
    Furey, N. B., Dance, M. A. & Rooker, J. R. Fine-scale movements and habitat use of juvenile southern flounder Paralichthys lethostigma in an estuarine seascape. J. Fish Biol. 82, 1469–1483 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Froehlich, C. Y. M., Garcia, A. & Kline, R. J. Daily movement patterns of red snapper (Lutjanus campechanus) on a large artificial reef. Fish. Res. 209, 49–57 (2019).Article 

    Google Scholar 
    Williams-Grove, L. J. & Szedlmayer, S. T. Acoustic positioning and movement patterns of red snapper, Lutjanus campechanus, around artificial reefs in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 553, 233–251 (2016).ADS 
    Article 

    Google Scholar 
    Secor, D. H., Zhang, F., O’Brien, M. H. P. & Li, M. Ocean destratification and fish evacuation caused by a Mid-Atlantic tropical storm. ICES J. Mar. Sci. 76, 573–584 (2019).Article 

    Google Scholar 
    Bacheler, N. M., Shertzer, K. W., Cheshire, R. T. & MacMahan, J. H. Tropical storms influence the movement behavior of a demersal oceanic fish species. Sci. Rep. 9, 1–13 (2019).CAS 
    Article 

    Google Scholar 
    Lowerre-Barbieri, S. K., Walters, S., Bickford, J., Cooper, W. & Muller, R. Site fidelity and reproductive timing at a spotted seatrout spawning aggregation site: Individual versus population scale behavior. Mar. Ecol. Prog. Ser. 481, 181–197 (2013).ADS 
    Article 

    Google Scholar 
    Espinoza, M., Farrugia, T. J., Webber, D. M., Smith, F. & Lowe, C. G. Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals. Fish. Res. 108, 364–371 (2011).Article 

    Google Scholar 
    Roy, R. et al. Testing the VEMCO positioning system: Spatial distribution of the probability of location and the positioning error in a reservoir. Anim. Biotelemetry 2, 1 (2014).CAS 
    Article 

    Google Scholar 
    Guzzo, M. M. et al. Field testing a novel high residence positioning system for monitoring the fine-scale movements of aquatic organisms. Methods Ecol. Evol. 9, 1478–1488 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smedbol, S., Smith, F., Webber, D., Vallée, R. & King, T. Using underwater coded acoustic telemetry for fine scale positioning of aquatic animals. In 20th Symposium of the International Society on Biotelemetry Proceedings, 9–11 (2014).Dean, M. J., Hoffman, W. S., Zemeckis, D. R. & Armstrong, M. P. Fine-scale diel and gender-based patterns in behaviour of Atlantic cod (Gadus morhua) on a spawning ground in the western Gulf of Maine. ICES J. Mar. Sci. 71, 1474–1489 (2014).Article 

    Google Scholar 
    Tarnecki, J. H. & Patterson, W. F. A mini ROV-based method for recovering marine instruments at depth. PLoS One 15, 1–9 (2020).
    Google Scholar 
    Ellis, R. D. et al. Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Fish. Res. 209, 186–195 (2019).Article 

    Google Scholar 
    Friess, C. et al. Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network. Mar. Ecol. Prog. Ser. 663, 157–177 (2021).ADS 
    Article 

    Google Scholar 
    Walters, C. J. & Juanes, F. Recruitment limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Can. J. Fish. Aquat. Sci. 50, 2058–2070 (1993).Article 

    Google Scholar 
    Ahrens, R. N. M., Walters, C. J. & Christensen, V. Foraging arena theory. Fish Fish. 13, 41–59 (2012).Article 

    Google Scholar 
    Schwartzkopf, B. D., Langland, T. A. & Cowan, J. H. Habitat selection important for red snapper feeding ecology in the northwestern Gulf of Mexico. Mar. Coast. Fish. 9, 373–387 (2017).Article 

    Google Scholar 
    Wells, R. J. D., Cowan, J. H. Jr. & Fry, B. Feeding ecology of red snapper Lutjanus campechanus in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 361, 213–225 (2008).ADS 
    Article 

    Google Scholar 
    Goldman, S. F., Glasgow, D. M. & Falk, M. M. Feeding habits of 2 reef-associated fishes, red porgy (Pagrus pagrus) and gray triggerfish (Balistes capriscus), off the Southeastern United States. Fish. Bull. 114, 317–329 (2016).Article 

    Google Scholar 
    Villegas-Ríos, D., Réale, D., Freitas, C., Moland, E. & Olsen, E. M. Personalities influence spatial responses to environmental fluctuations in wild fish. J. Anim. Ecol. 87, 1309–1319 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rooker, J. R. et al. Seascape connectivity and the influence of predation risk on the movement of fishes inhabiting a back-reef ecosystem. Ecosphere 9, e02200 (2018).Article 

    Google Scholar 
    Forman, R. T. T. & Godron, M. Patches and structural components for a landscape ecology. Bioscience 31, 733–740 (1981).Article 

    Google Scholar 
    Dahl, K. A. & Patterson, W. F. Movement, home range, and depredation of invasive lionfish revealed by fine-scale acoustic telemetry in the northern Gulf of Mexico. Mar. Biol. 167, 1–22 (2020).Article 
    CAS 

    Google Scholar 
    Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Moulton, D. L. et al. Habitat partitioning and seasonal movement of red drum and spotted seatrout. Estuaries Coasts 40, 905–916 (2017).Article 

    Google Scholar 
    Hammerschlag, N., Luo, J., Irschick, D. J. & Ault, J. S. A Comparison of spatial and movement patterns between sympatric predators: bull sharks (Carcharhinus leucas) and Atlantic tarpon (Megalops atlanticus). PLoS ONE 7, e45958 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Novak, A. J. et al. Scale of biotelemetry data influences ecological interpretations of space and habitat use in yellowtail snapper. Mar. Coast. Fish. 12, 364–377 (2020).Article 

    Google Scholar 
    Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

    Google Scholar 
    Werner, E. E. & Gilliam, J. F. The ontogenetic niche and species interactions in size-structured populations. Annu. Rev. Ecol. Syst. 15, 393–425 (1984).Article 

    Google Scholar 
    Reale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

    Google Scholar 
    Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
    Article 

    Google Scholar 
    Huntingford, F. A. The relationship between anti-predator behavior and aggression among conspecifics in the three-spined stickleback, Gasterosteus aculeatus. Anim. Behav. 24, 245–260 (1976).Article 

    Google Scholar 
    Wilson, D. S., Clark, A. B., Coleman, K. & Dearstyne, T. Shyness and boldness in humans and other animals. Trends Ecol. Evol. 9, 442–446 (1994).Article 

    Google Scholar 
    Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

    Google Scholar 
    Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hussey, N. E. et al. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642–1255642 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Lowerre-Barbieri, S. K., Kays, R., Thorson, J. T. & Wikelski, M. The ocean’s movescape: Fisheries management in the bio-logging decade (2018–2028). ICES J. Mar. Sci. 76, 477–488 (2019).Article 

    Google Scholar 
    National Marine Fisheries Service. Fisheries Economics of the United State 2016. NOAA Tech. Memo. NMFS-F/SPO-187a. https://www.fisheries.noaa.gov/resource/document/fisheries-economics-united-states-report-2016 (2018). Accessed 08 January 2018.Patterson, W. F. III, Tarnecki, J., Addis, D. T. & Barbieri, L. R. Reef fish community structure at natural versus artificial reefs in the northern Gulf of Mexico. In Proc. 66th Gulf Caribb. Fish. Inst. 4–8 (2014).Streich, M. K. et al. Effects of a new artificial reef complex on red snapper and the associated fish community: An evaluation using a before–after control–impact approach. Mar. Coast. Fish. 9, 404–418 (2017).Article 

    Google Scholar 
    Dance, M. A., Patterson, W. F. III. & Addis, D. T. Fish community and trophic structure at artificial reef sites in the northeastern Gulf of Mexico. Bull. Mar. Sci. 87, 301–324 (2011).Article 

    Google Scholar 
    Cowan, J. H. Red snapper in the Gulf of Mexico and the U.S. South Atlantic: data, doubt, and debate. Fisheries 36, 319–331 (2011).Article 

    Google Scholar 
    Addis, D. T., Patterson, W. F. III. & Dance, M. A. The potential for unreported artificial reefs to serve as refuges from fishing mortality for reef fishes. N. Am. J. Fish. Manag. 36, 131–139 (2016).Article 

    Google Scholar 
    McCawley, J. R., Cowan, J. H. Jr. & Shipp, R. L. Feeding periodicity and prey habitat preference of red snapper, Lutjanus campechanus (Poey, 1860), on Alabama artificial reefs. Gulf Mex. Sci. 24, 14–27 (2006).
    Google Scholar 
    Glenn, H. D., Cowan, J. H. Jr. & Powers, J. E. A comparison of red snapper reproductive potential in the northwestern Gulf of Mexico: Natural versus artificial habitats. Mar. Coast. Fish. 9, 139–148 (2017).Article 

    Google Scholar 
    Kulaw, D. H., Cowan, J. H. Jr. & Jackson, M. W. Temporal and spatial comparisons of the reproductive biology of northern Gulf of Mexico (USA) red snapper (Lutjanus campechanus) collected a decade apart. PLoS One 12, e0172360 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Vose, F. E. & Nelson, W. G. Gray triggerfish (Balistes capriscus Gmelin) feeding from artificial and natural substrate in shallow Atlantic waters of Florida. Bull. Mar. Sci. 55, 1316–1323 (1994).
    Google Scholar 
    Herbig, J. L. & Szedlmayer, S. T. Movement patterns of gray triggerfish, Balistes capriscus, around artificial reefs in the northern Gulf of Mexico. Fish. Manag. Ecol. 23, 418–427 (2016).Article 

    Google Scholar 
    Szedlmayer, S. T. & Schroepfer, R. L. Long-term residence of red snapper on artificial reefs in the northeastern Gulf of Mexico. Trans. Am. Fish. Soc. 134, 315–325 (2005).Article 

    Google Scholar 
    Watterson, J. C. III., Patterson, W. F. I. I. I., Shipp, R. L. & Cowan, J. H. Jr. Movement of red snapper, Lutjanus campechanus, in the north central Gulf of Mexico: Potential effects of hurricanes. Gulf Mex. Sci. 16, 92–104 (1998).
    Google Scholar 
    Ingram, G. W. Jr. & Patterson, W. F. I. I. I. Movement patterns of red snapper (Lutjanus campechanus), greater amberjack (Seriola dumerili), and gray triggerfish (Balistes capriscus) in the Gulf of Mexico and the utility of marine reserves as management tools. Proc. Gulf Caribb. Fish. Inst. 52, 686–699 (2001).
    Google Scholar 
    Strelcheck, A. J., Cowan, J. H. Jr. & Patterson, W. F. III. Site fidelity, movement, and growth of red snapper Lutjanus campechanus: implications for artificial reef management. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 147–162 (2007).Addis, D. T., Patterson, W. F. I. I. I., Dance, M. A. & Ingram, G. W. Jr. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

    Google Scholar 
    Topping, D. T. & Szedlmayer, S. T. Site fidelity, residence time and movements of red snapper Lutjanus campechanus estimated with long-term acoustic monitoring. Mar. Ecol. Prog. Ser. 437, 183–200 (2011).ADS 
    Article 

    Google Scholar 
    Everett, A. G., Szedlmayer, S. T. & Gallaway, B. J. Movement patterns of red snapper Lutjanus campechanus based on acoustic telemetry around oil and gas platforms in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 649, 155–173 (2020).Article 

    Google Scholar 
    Tarnecki, J. H. & Patterson, W. F. I. I. I. Changes in red snapper diet and trophic ecology following the Deepwater Horizon Oil Spill. Mar. Coast. Fish. 7, 135–147 (2015).Article 

    Google Scholar 
    McCawley, J. R. & Cowan, J. H. Jr. Seasonal and size specific diet and prey demand of Red Snapper on Alabama artificial reefs. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III., Cowan, J. H. Jr., Fitzhugh, G. R. & Nieland, D. L.), 77–104 (2007).Piraino, M. N. & Szedlmayer, S. T. Fine-scale movements and home ranges of red snapper around artificial reefs in the northern Gulf of Mexico. Trans. Am. Fish. Soc. 143, 988–998 (2014).Article 

    Google Scholar 
    Williams-Grove, L. J. & Szedlmayer, S. T. Depth preferences and three-dimensional movements of red snapper, Lutjanus campechanus, on an artificial reef in the northern Gulf of Mexico. Fish. Res. 190, 61–70 (2017).Article 

    Google Scholar 
    Topping, D. T. & Szedlmayer, S. T. Home range and movement patterns of red snapper (Lutjanus campechanus) on artificial reefs. Fish. Res. 112, 77–84 (2011).Article 

    Google Scholar 
    Baker, M. S. J. & Wilson, C. A. Use of bomb radiocarbon to validate otolith section ages of red snapper Lutjanus campechanus from the northern Gulf of Mexico. Limnol. Oceanogr. 46, 1819–1824 (2001).ADS 
    Article 

    Google Scholar 
    Allman, R. J., Fioramonti, C. L., Patterson, W. F. III. & Pacicco, A. E. Validation of annual growth-zone formation in gray triggerfish Balistes capriscus dorsal spines, fin rays, and vertebrae. Gulf Mex. Sci. 33, 68–76 (2016).
    Google Scholar 
    Frazer, T. K., Lindberg, W. J. & Stanton, G. R. Predation on sand dollars by gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Bull. Mar. Sci. 48, 159–164 (1991).
    Google Scholar 
    Delorenzo, D. M., Bethea, D. M. & Carlson, J. K. An assessment of the diet and trophic level of Atlantic sharpnose shark Rhizoprionodon terraenovae. J. Fish Biol. 86, 385–391 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Aines, A. C., Carlson, J. K., Boustany, A., Mathers, A. & Kohler, N. E. Feeding habits of the tiger shark, Galeocerdo cuvier, in the northwest Atlantic Ocean and Gulf of Mexico. Environ. Biol. Fish. 101, 403–415 (2018).Article 

    Google Scholar 
    Castro, J. I. The Sharks of North America (Oxford University Press, 2011).
    Google Scholar 
    Springer, S. A collection of fishes from the stomachs of sharks taken off Salerno, Florida. Copeia 3, 174–175 (1946).Article 

    Google Scholar 
    Bohaboy, E. C., Guttridge, T. L., Hammerschlag, N., Van Zinnicq Bergmann, M. P. M. & Patterson, W. F. III. Application of three-dimensional acoustic telemetry to assess the effects of rapid recompression on reef fish discard mortality. ICES J. Mar. Sci. 77, 83–96 (2020).Article 

    Google Scholar 
    Drymon, J. M., Powers, S. P., Dindo, J., Dzwonkowski, B. & Henwood, T. Distributions of sharks across a continental shelf in the northern Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 2, 440–450 (2010).Article 

    Google Scholar 
    Ajemian, M. J. et al. Movement patterns and habitat use of tiger sharks (Galeocerdo cuvier) across ontogeny in the Gulf of Mexico. PLoS One 15, 1–24 (2020).
    Google Scholar 
    Ouzts, A. C. & Szedlmayer, S. T. Diel feeding patterns of Red Snapper on artificial reefs in the north-central Gulf of Mexico. Trans. Am. Fish. Soc. 132, 1186–1193 (2003).Article 

    Google Scholar 
    White, D. B. & Palmer, S. M. Age, growth, and reproduction of the red snapper, Lutjanus campechanus, from the Atlantic waters of the Southeastern US. Bull. Mar. Sci. 75, 335–360 (2004).
    Google Scholar 
    Fitzhugh, G. R., Lyon, H. M. & Barnett, B. K. Reproductive parameters of gray triggerfish (Balistes capriscus) from the Gulf of Mexico: Sex ratio, maturity and spawning fraction. SEDAR43-WP-03. (2015). http://sedarweb.org/sedar-82-rd14-sedar43-wp-03reproductive-parameters-gray-triggerfish-balistes-capriscus-gulf-mexico. Accessed 12 April 2021.Kelly-Stormer, A. et al. Gray Triggerfish reproductive biology, age, and growth off the Atlantic coast of the Southeastern USA. Trans. Am. Fish. Soc. 146, 523–538 (2017).Article 

    Google Scholar 
    Porch, C. E., Fitzhugh, G. R., Lang, E. T., Lyon, H. M. & Linton, B. C. Estimating the dependence of spawning frequency on size and age in Gulf of Mexico red snapper. Mar. Coast. Fish. 7, 233–245 (2015).Article 

    Google Scholar 
    Lang, E. T. & Fitzhugh, G. R. Oogenesis and fecundity type of gray triggerfish in the Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 7, 338–348 (2015).Article 

    Google Scholar 
    Woods, M. K. et al. Size and age at maturity of female red snapper Lutjanus campechanus in the Northern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 54, 526–537 (2003).
    Google Scholar 
    Simmons, C. M. & Szedlmayer, S. T. Territoriality, reproductive behavior, and parental care in gray triggerfish, Balistes capriscus, from the Northern Gulf of Mexico. Bull. Mar. Sci. 88, 197–209 (2012).Article 

    Google Scholar 
    Mackichan, C. A. & Szedlmayer, S. T. Reproductive behavior of the gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 59, 213–218 (2007).
    Google Scholar 
    Diamond, S. L. et al. Movers and stayers: Individual variability in site fidelity and movements of red snapper off Texas. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 163–187 (2007).Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 
    PubMed 
    Article 

    Google Scholar 
    Smith, F. Understanding HPE in the VEMCO Positioning System (VPS). (2013).US Department of Defense. Global Positioning System Standard Positioning Service Performance Standard. http://www.gps.gov/technical/ps/2008-SPS-performance-standard.pdf (2008). Accessed 08 July 2020.Heupel, M. R., Reiss, K. L., Yeiser, B. G. & Simpfendorfer, C. A. Effects of biofouling on performance of moored data logging acoustic receivers. Limnol. Oceanogr. Methods 6, 327–335 (2008).Article 

    Google Scholar 
    National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42012—Orange Beach. http://www.ndbc.noaa.gov/station_page.php?station=42012 (2017). Accessed 07 November 2017.National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42040- Luke Offshore Test Platform. https://www.ndbc.noaa.gov/station_page.php?station=42040 (2019). Accessed 07 January 2019.Lazaridis, E. R Package ‘lunar’: lunar phase & distance, seasons and other environmental factors. https://cran.r-project.org/web/packages/lunar/lunar.pdf (2015). Accessed 12 August 2019.Thieurmel, B. & Elmarhraoui, A. R Package ‘suncalc’: compute sun position, sunlight phases, moon position and lunar phase. https://cran.r-project.org/web/packages/suncalc/suncalc.pdf (2019). Accessed 22 June 2019.National Geophysical Data Center. U.S. Coastal Relief Model—Central Gulf of Mexico. https://doi.org/10.7289/V54Q7RW0 (2001).Cox, D. R. & Oakes, D. Analysis of Survival Data (Chapman and Hall, 1984).Benhamou, S. Dynamic approach to space and habitat use based on biased random bridges. PLoS One 6, e14592 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).PubMed 
    Article 

    Google Scholar 
    Tracey, J. A. et al. Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation. PLoS One 9, e101205 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Tracey, J. A. et al. R Package ‘mkde’: 2D and 3D movement-based kernel density estimates (MKDEs). https://CRAN.R-project.org/package=mkde (2014). Accessed 17 June 2019.Worton, B. J. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

    Google Scholar 
    Wood, S. N. Package ‘mgcv’: Mixed GAM computation vehicle with automatic smoothness estimation. https://doi.org/10.1201/9781315370279 (2019). More

  • in

    The gut microbiota affects the social network of honeybees

    Wilson, E. O. Sociobiology: The New Synthesis (Harvard Univ. Press, 1975).Diamond, J. M. & Ordunio, D. Guns, Germs, and Steel (Books on Tape, 1999).Couzin, I. D. et al. Self-organization and collective behavior in vertebrates. Adv. Study Behav. 32, 1–75 (2003).
    Google Scholar 
    Keller, L. Adaptation and the genetics of social behaviour. Philos. Trans. R. Soc. Lond. B 364, 3209–3216 (2009).
    Google Scholar 
    Kay, T., Keller, L. & Lehmann, L. The evolution of altruism and the serial rediscovery of the role of relatedness. Proc. Natl Acad. Sci. USA 117, 28894–28898 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).CAS 
    PubMed 

    Google Scholar 
    Johnson, K. V. A. & Foster, K. R. Why does the microbiome affect behaviour? Nat. Rev. Microbiol. 16, 647–655 (2018).CAS 
    PubMed 

    Google Scholar 
    Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).CAS 
    PubMed 

    Google Scholar 
    Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).CAS 
    PubMed 

    Google Scholar 
    Sharon, G. et al. Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell 177, 1600–1618 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zhang, M. et al. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. Sci. Adv. 6, eaba3760 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu, W.-L. et al. Microbiota regulate social behaviour via stress response neurons in the brain. Nature 595, 409–414 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Douglas, A. E. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17, 764–775 (2019).CAS 
    PubMed 

    Google Scholar 
    Schretter, C. E. Links between the gut microbiota, metabolism, and host behavior. Gut Microbes 11, 245–248 (2020).PubMed 

    Google Scholar 
    Liberti, J. & Engel, P. The gut microbiota–brain axis of insects. Curr. Opin. Insect Sci. 39, 6–13 (2020).PubMed 

    Google Scholar 
    O’Donnell, M. P., Fox, B. W., Chao, P.-H., Schroeder, F. C. & Sengupta, P. A neurotransmitter produced by gut bacteria modulates host sensory behaviour. Nature 583, 415–420 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Wilson, E. O. The Insect Societies (Harvard Univ. Press, 1971).Hölldobler, B. & Wilson, E. O. The Ants (Harvard Univ. Press, 1990).Teseo, S. et al. The scent of symbiosis: gut bacteria may affect social interactions in leaf-cutting ants. Anim. Behav. 150, 239–254 (2019).
    Google Scholar 
    Vernier, C. L. et al. The gut microbiome defines social group membership in honey bee colonies. Sci. Adv. 6, eabd3431 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, L. et al. Gut microbiome drives individual memory variation in bumblebees. Nat. Commun. 12, 6588 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Choi, S. H. et al. Individual variations lead to universal and cross-species patterns of social behavior. Proc. Natl Acad. Sci. USA 117, 31754–31759 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Geffre, A. C. et al. Honey bee virus causes context-dependent changes in host social behavior. Proc. Natl Acad. Sci. USA 117, 10406–10413 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).CAS 
    PubMed 

    Google Scholar 
    Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc. Natl Acad. Sci. USA 114, 4775–4780 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, e2003467 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814 (2020).PubMed 

    Google Scholar 
    Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).CAS 
    PubMed 

    Google Scholar 
    Stroeymeyt, N. et al. Social network plasticity decreases disease transmission in a eusocial insect. Science 362, 941–945 (2018).CAS 
    PubMed 

    Google Scholar 
    Kao, A. B. & Couzin, I. D. Modular structure within groups causes information loss but can improve decision accuracy. Philos. Trans. R. Soc. Lond. B 374, 20180378 (2019).
    Google Scholar 
    de Groot, A. P. Protein and amino acid requirements of the honeybee (Apis mellifica L.). Physiol. Comp. Oecol. 3, 197–285 (1953).
    Google Scholar 
    Billard, J.-M. d-Amino acids in brain neurotransmission and synaptic plasticity. Amino Acids 43, 1851–1860 (2012).CAS 
    PubMed 

    Google Scholar 
    Marcaggi, P. & Attwell, D. Role of glial amino acid transporters in synaptic transmission and brain energetics. Glia 47, 217–225 (2004).PubMed 

    Google Scholar 
    Gage, S. L., Calle, S., Jacobson, N., Carroll, M. & DeGrandi-Hoffman, G. Pollen alters amino acid levels in the honey bee brain and this relationship changes with age and parasitic stress. Front. Neurosci. 14, 231 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Kawase, T. et al. Gut microbiota of mice putatively modifies amino acid metabolism in the host brain. Br. J. Nutr. 117, 775–783 (2017).CAS 
    PubMed 

    Google Scholar 
    Socha, E., Koba, M. & Koslinski, P. Amino acid profiling as a method of discovering biomarkers for diagnosis of neurodegenerative diseases. Amino Acids 51, 367–371 (2019).CAS 
    PubMed 

    Google Scholar 
    Tarlungeanu, D. C. et al. Impaired amino acid transport at the blood brain barrier is a cause of autism spectrum disorder. Cell 167, 1481–1494 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Maynard, T. M. & Manzini, M. C. Balancing act: maintaining amino acid levels in the autistic brain. Neuron 93, 476–479 (2017).CAS 
    PubMed 

    Google Scholar 
    Kurochkin, I. et al. Metabolome signature of autism in the human prefrontal cortex. Commun. Biol. 2, 234 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    van der Velpen, V. et al. Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res. Ther. 11, 93 (2019).
    Google Scholar 
    Aldana, B. I. et al. Glutamate–glutamine homeostasis is perturbed in neurons and astrocytes derived from patient iPSC models of frontotemporal dementia. Mol. Brain 13, 125 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Galizia, C. G., Eisenhardt, D. & Giurfa M. (eds) Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel (Springer Science & Business Media, 2011).Menzel, R. The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768 (2012).CAS 
    PubMed 

    Google Scholar 
    Ellegaard, K. M. & Engel, P. Genomic diversity landscape of the honey bee gut microbiota. Nat. Commun. 10, 446 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bruno, F., Angilica, A., Cosco, F., Luchi, M. L. & Muzzupappa, M. Mixed prototyping environment with different video tracking techniques. In IMProVe 2011 International Conference on Innovative Methods in Product Design (eds Concheri, G. et al.) 105–113 (Libreria Internazionale Cortina Padova, 2011).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Anderson, K. E., Rodrigues, P. A. P., Mott, B. M., Maes, P. & Corby-Harris, V. Ecological succession in the honey bee gut: shift in Lactobacillus strain dominance during early adult development. Microb. Ecol. 71, 1008–1019 (2016).CAS 
    PubMed 

    Google Scholar 
    Almasri, H., Liberti, J., Brunet, J. L., Engel, P. & Belzunces, L. P. Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota. Sci. Rep. 12, 4281 (2022).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).Gallup, J. M. in PCR Troubleshooting and Optimization: The Essential Guide (eds Kennedy, S. & Oswald, N.) 23–65 (Caister Academic Press, 2011).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
    Google Scholar 
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).
    Google Scholar 
    Patassini, S. et al. Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochem. Biophys. Res. Commun. 468, 161–166 (2015).CAS 
    PubMed 

    Google Scholar 
    Gonzalez-Riano, C., Garcia, A. & Barbas, C. Metabolomics studies in brain tissue: a review. J. Pharm. Biomed. Anal. 130, 141–168 (2016).CAS 
    PubMed 

    Google Scholar 
    Belle, J. E. L., Harris, N. G., Williams, S. R. & Bhakoo, K. K. A comparison of cell and tissue extraction techniques using high-resolution 1H-NMR spectroscopy. NMR Biomed. 15, 37–44 (2002).PubMed 

    Google Scholar 
    Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N. & Khoomrung, S. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Comput. Struct. Biotechnol. J. 17, 611–618 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
    PubMed 

    Google Scholar 
    Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics 20, 275 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).CAS 
    PubMed 

    Google Scholar 
    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).CAS 
    PubMed 

    Google Scholar 
    Reijnders, M. J. & Waterhouse, R. M. Summary visualisations of gene ontology terms with GO-Figure! Front. Bioinform. 1, 638255 (2021).
    Google Scholar  More

  • in

    Seasonal dynamics in picocyanobacterial abundance and clade composition at coastal and offshore stations in the Baltic Sea

    Flombaum, P. et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. PNAS 110, 9824–9829 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Honda, D. & Yokota, A. Detection of seven major evolutionary lineages in cyanobacteria based on the 165 rRNA gene sequence analysis with new sequences of five marine Synechococcus strains. J Mol Evol 48, 723–739 (1999).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Robertson, B. R., Tezuka, N. & Watanabe, M. M. Phylogenetic analyses of Synechococcus strains (cyanobacteria) using sequences of 16S rDNA and part of the phycocyanin operon reveal multiple evolutionary lines and reflect phycobilin content. Int. J. Syst. Evol. Microbiol. 51, 861–871 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stomp, M. et al. Adaptive divergence in pigment composition promotes phytoplankton biodiversity. Nature 432, 104–107 (2004).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Albrecht, M., Pröschold, T. & Schumann, R. Identification of Cyanobacteria in a eutrophic coastal lagoon on the Southern Baltic Coast. Front. Microbiol. 8, 1–16 (2017).Article 

    Google Scholar 
    Bertos-Fortis, M. et al. Unscrambling cyanobacteria community dynamics related to environmental factors. Front. Microbiol. 7, 625 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hunter-Cevera, K. R. et al. Seasons of syn. Limnol. Oceanogr. 65, 1–18 (2019).
    Google Scholar 
    Kuosa, H. Picoplanktonic algae in the northern Baltic Sea: Seasonal dynamics and flagellate grazing. Mar. Ecol. Prog. Ser. 73, 269–276 (1991).ADS 
    Article 

    Google Scholar 
    Sathicq, M. B., Unrein, F. & Gómez, N. Recurrent pattern of picophytoplankton dynamics in estuaries around the world: The case of Río de la Plata. Mar. Environ. Res. 161, 105136 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rajaneesh, K. M. & Mitbavkar, S. Factors controlling the temporal and spatial variations in Synechococcus abundance in a monsoonal estuary. Mar. Environ. Res. 92, 133–143 (2013).Article 
    CAS 

    Google Scholar 
    Crosbie, N. D., Pöckl, M. & Weisse, T. Dispersal and phylogenetic diversity of nonmarine picocyanobacteria, inferred from 16S rRNA gene and cpcBA-intergenic spacer sequence analyses. Appl. Environ. Microbiol. 69, 5716–5721 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ernst, A., Becker, S., Wollenzien, U. I. A. & Postius, C. Ecosystem-dependent adaptive radiations ofpicocyanobacteria inferred from 16S rRNA and ITS-1 sequence analysis. Microbiology 149, 217–228 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sánchez-Baracaldo, P., Handley, B. A. & Hayest, P. K. Picocyanobacterial community structure of freshwater lakes and the Baltic Sea revealed by phylogenetic analyses and clade-specific quantitative PCR. Microbiology 154, 3347–3357 (2008).PubMed 
    Article 
    CAS 

    Google Scholar 
    Hu, Y. O. O., Karlson, B., Charvet, S. & Andersson, A. F. Diversity of pico- to mesoplankton along the 2000 km salinity gradient of the Baltic Sea. Front. Microbiol. 7, 679 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Larsson, J. et al. Picocyanobacteria containing a novel pigment gene cluster dominate the brackish water Baltic Sea. ISME J. 8, 1892–1903 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Celepli, N. et al. Meta-omic analyses of Baltic Sea cyanobacteria: Diversity, community structure and salt acclimation. Environ. Microbiol. 19, 673–686 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
    Article 

    Google Scholar 
    Cabré, A., Marinov, I. & Leung, S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Clim. Dyn. 45, 1253–1280 (2015).Article 

    Google Scholar 
    Wang, T., Chen, X., Qin, S. & Li, J. Phylogenetic and phenogenetic diversity of Synechococcus along a yellow sea section reveal its environmental dependent distribution and co-occurrence microbial pattern. J. Mar. Sci. Eng. 9, 1018 (2021).Article 

    Google Scholar 
    Tai, V. & Palenik, B. Temporal variation of Synechococcus clades at a coastal Pacific Ocean monitoring site. ISME J. 3, 903–915 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ahlgren, N. A. & Rocap, G. Diversity and distribution of marine Synechococcus: Multiple gene phylogenies for consensus classification and development of qPCR assays for sensitive measurement of clades in the ocean. Front. Microbiol. 3, 1–24 (2012).Article 
    CAS 

    Google Scholar 
    Rajaneesh, K. M., Mitbavkar, S., Anil, A. C. & Sawant, S. S. Synechococcus as an indicator of trophic status in the Cochin backwaters, west coast of India. Ecol. Indic. 55, 118–130 (2015).Article 

    Google Scholar 
    Campbell, L. & Carpenter, E. J. Characterization of phycoerythrin-containing Synechococcus spp. populations by immunofluorescence. J. Plankton Res. 9, 1167–1181 (1987).Article 

    Google Scholar 
    Stomp, M. et al. Colourful coexistence of red and green picocyanobacteria in lakes and seas. Ecol. Lett. 10, 290–298 (2007).PubMed 
    Article 

    Google Scholar 
    Callieri, C. & Stockner, J. G. Freshwater autotrophic picoplankton: A review. J. Limnol. 61, 1–14 (2002).Article 

    Google Scholar 
    Liu, H., Jing, H., Wong, T. H. C. & Chen, B. Co-occurrence of phycocyanin- and phycoerythrin-rich Synechococcus in subtropical estuarine and coastal waters of Hong Kong. Environ. Microbiol. Rep. 6, 90–99 (2014).PubMed 
    Article 
    CAS 

    Google Scholar 
    Haverkamp, T. et al. Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons. Environ. Microbiol. 10, 174–188 (2008).CAS 
    PubMed 

    Google Scholar 
    Otero-Ferrer, J. L. et al. Factors controlling the community structure of picoplankton in contrasting marine environments. Biogeosciences 15, 6199–6220 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Ploug, H. et al. Carbon, nitrogen and O2 fluxes associated with the cyanobacterium Nodularia spumigena in the Baltic Sea. ISME J. 5, 1549–1558 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ohlendieck, U., Stuhr, A. & Siegmund, H. Nitrogen fixation by diazotrophic cyanobacteria in the Baltic Sea and transfer of the newly fixed nitrogen to picoplankton organisms. J. Mar. Syst. 25, 213–219 (2000).Article 

    Google Scholar 
    Klawonn, I. et al. Untangling hidden nutrient dynamics: Rapid ammonium cycling and single-cell ammonium assimilation in marine plankton communities. ISME J. 13, 1960–1974 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lin, Y., Wang, L., Xu, K., Huang, H. & Ren, H. Algae biofilm reduces microbe-derived dissolved organic nitrogen discharges: Performance and mechanisms. Environ. Sci. Technol. 55, 6227–6238 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Berthelot, H., Bonnet, S., Camps, M., Grosso, O. & Moutin, T. Assessment of the dinitrogen released as ammonium and dissolved organic nitrogen by unicellular and filamentous marine diazotrophic cyanobacteria grown in culture. Front. Mar. Sci. https://doi.org/10.3389/fmars.2015.00080 (2015).Article 

    Google Scholar 
    Loick-Wilde, N. et al. De novo amino acid synthesis and turnover during N2 fixation. Limnol. Ocean. 63, 1076–1092 (2018).CAS 
    Article 

    Google Scholar 
    Glibert, P. M. & Bronk, D. A. Release of dissolved organic nitrogen by marine diazotrophic cyanobacteria Trichodesmium spp.. Appl. Environ. Microbiol. 60, 3996–4000 (1994).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kuo, J. et al. Picoplankton dynamics and picoeukaryote diversity in a hyper-eutrophic subtropical lagoon. J. Environ. Sci. Heal. 4, 521–523 (2014).
    Google Scholar 
    Grébert, T. et al. Light color acclimation is a key process in the global ocean distribution of Synechococcus cyanobacteria. PNAS 115, E2010–E2019 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Urbach, E., Scanlan, D., Distel, D., Waterbury, J. & Chisholm, S. Rapid diversification of marine picophytoplankton with dissimilar light-harvesting structures inferred from sequences of Prochlorococcus and Synechococcus (cyanobacteria). J. Mol. Biol. 46, 188–201 (1998).ADS 
    CAS 

    Google Scholar 
    Farrant, G. K. et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. PNAS 113, E3365–E3374 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rocap, G., Distel, D. L., Waterbury, J. B. & Chisholm, S. W. Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S–23S ribosomal DNA internal transcribed spacer sequences. Appl. Environ. Microbiol. 68, 1180–1191 (2002).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mazard, S., Ostrowski, M., Partensky, F. & Scanlan, D. J. Multi-locus sequence analysis, taxonomic resolution and biogeography of marine Synechococcus. Environ. Microbiol. 14, 372–386 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Huang, S. et al. Novel lineages of Prochlorococcus and Synechococcus in the global oceans. ISME J. 6, 285–297 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Choi, D. H. & Noh, J. H. Phylogenetic diversity of Synechococcus strains isolated from the East China Sea and the East Sea. FEMS Microbiol. Ecol. 69, 439–448 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lee, M. D. et al. Marine Synechococcus isolates representing globally abundant genomic lineages demonstrate a unique evolutionary path of genome reduction without a decrease in GC content. Environ. Microbiol. 21, 1677–1686 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Paerl, R., Foster, R., Jenkins, B., Montoya, J. & Zehr, J. Phylogenetic diversity of cyanobacterial narB genes from various marine habitats. Environ. Microbiol. 10, 3377–3387 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fuller, N. et al. Clade-specific 16S ribosomal DNA oligonucleotides reveal the predominance of a single marine Synechococcus clade throughout a stratified water column in the Red sea. Appl. Environ. Microbiol. 69, 2430–2443 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Scanlan, D. J. et al. Ecological genomics of marine Picocyanobacteria. Microbiol. Mol. Biol. Rev. 73, 249–299 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mazard, S., Wilson, W. H. & Scanlan, D. J. Dissecting the physiological response to phosphorus stress in marine Synechococcus isolates (cyanophyceae). J. Phycol. 48, 94–105 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, J. et al. Synechococcus bloom in the Pearl River Estuary and adjacent coastal area –With special focus on flooding during wet seasons. Sci. Total Environ. 692, 769–783 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Zwirglmaier, K. et al. Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environ. Microbiol. 10, 147–161 (2008).PubMed 

    Google Scholar 
    Sohm, J. A. et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 10, 333–345 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bunse, C. et al. High frequency multi-year variability in Baltic Sea microbial plankton stocks and activities. Front. Microbiol. 10, 1–18 (2019).Article 

    Google Scholar 
    Alegria Zufia, J., Farnelid, H. & Legrand, C. Seasonality of coastal picophytoplankton growth, nutrient limitation and biomass contribution. Front. Microbiol. 12, 1–13 (2021).Article 

    Google Scholar 
    Granéli, E., Wallström, K., Larsson, U., Granéli, W. & Elmgren, R. Nutrient limitation of primary production in the Baltic Sea Area. Ambio 19, 142–151 (1990).
    Google Scholar 
    Mazur-Marzec, H. et al. Occurrence of cyanobacteria and cyanotoxin in the Southern Baltic Proper. Filamentous cyanobacteria versus single-celled picocyanobacteria. Hydrobiologia 701, 235–252 (2013).CAS 
    Article 

    Google Scholar 
    Stal, L. et al. BASIC: Baltic Sea cyanobacteria. An investigation of the structure and dynamics of water blooms of cyanobacteria in the Baltic Sea—Responses to a changing environment. Cont. Shelf Res. 23, 1695–1714 (2003).ADS 
    Article 

    Google Scholar 
    Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hugerth, L. W. et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 16, 1–18 (2015).Article 
    CAS 

    Google Scholar 
    Walve, J. & Larsson, U. Seasonal changes in Baltic Sea seston stoichiometry: The influence of diazotrophic cyanobacteria. Mar. Ecol. Prog. Ser. 407, 13–25 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    Huber, P. et al. Primer design for an accurate view of picocyanobacterial community structure by using high-throughput sequencing. Appl. Environ. Microbiol. 85, 1–17 (2019).Article 

    Google Scholar 
    Jiang, T. et al. Temporal and spatial variations of abundance of phycocyanin- and phycoerythrin-rich Synechococcus in Pearl River Estuary and adjacent coastal area. J. Ocean Univ. China 15, 897–904 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Li, S. et al. Unexpected predominance of photosynthetic picoeukaryotes in shallow eutrophic lakes. J. Plankton Res. 38, 830–842 (2016).CAS 
    Article 

    Google Scholar 
    Collos, Y. et al. Oligotrophication and emergence of picocyanobacteria and a toxic dinoflagellate in Thau lagoon, southern France. J. Sea Res. 61, 68–75 (2009).ADS 
    Article 

    Google Scholar 
    Bec, B., Husseini-Ratrema, J., Collos, Y., Souchu, P. & Vaquer, A. Phytoplankton seasonal dynamics in a Mediterranean coastal lagoon: Emphasis on the picoeukaryote community. J. Plankton Res. 27, 881–894 (2005).CAS 
    Article 

    Google Scholar 
    Hunter-Cevera, K. R. et al. Physiological and ecological drivers of early spring blooms of coastal phytoplankter. Science 354, 326–329 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Albertano, P., Di Somma, D. & Capucci, E. Cyanobacterial picoplankton from the central Baltic Sea: Cell size classification by image analyzed fluorescence microscopy. J. Plankton Res. 19, 1405–1416 (1997).Article 

    Google Scholar 
    Paulsen, M. L. et al. Synechococcus in the Atlantic gateway to the Arctic Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00191 (2016).Article 

    Google Scholar 
    Felföldi, T. et al. Diversity and seasonal dynamics of the photoautotrophic picoplankton in Lake Balaton (Hungary). Aquat. Microb. Ecol. 63, 273–287 (2011).Article 

    Google Scholar 
    Grinienė, E., Šulčius, S. & Kuosa, H. Size-selective microzooplankton grazing on the phytoplankton in the Curonian Lagoon (SE Baltic Sea). Oceanologia 58, 292–301 (2016).Article 

    Google Scholar 
    Tsai, A. Y., Gong, G. C., Huang, Y. W. & Chao, C. F. Estimates of bacterioplankton and Synechococcus spp. mortality from nanoflagellate grazing and viral lysis in the subtropical Danshui River estuary. Estuar. Coast. Shelf Sci. 153, 54–61 (2015).ADS 
    Article 

    Google Scholar 
    Camacho, A., Miracle, M. R. & Vicente, E. Which factors determine the abundance and distribution of picocyanobacteria in inland waters? A comparison among different types of lakes and ponds. Arch. Hydrobiol. 157(321), 338 (2003).
    Google Scholar 
    Berry, D. L. et al. Shifts in cyanobacterial strain dominance during the onset of harmful algal blooms in Florida Bay, USA. Microb. Ecol. 70, 361–371 (2015).PubMed 
    Article 

    Google Scholar 
    Zborowsky, S. & Lindell, D. Resistance in marine cyanobacteria differs against specialist and generalist cyanophages. PNAS 116, 16899–16908 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wall, C. C., Rodgers, B. S., Gobler, C. J. & Peterson, B. J. Responses of loggerhead sponges Spechiospongia vesparium during harmful cyanobacterial blooms in a sub-tropical lagoon. Mar. Ecol. Prog. Ser. 451, 31–43 (2012).ADS 
    Article 

    Google Scholar 
    Glibert, P. M. et al. Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions. Limnol. Oceanogr. 61, 165–197 (2016).ADS 
    Article 

    Google Scholar 
    Herbert, R. A. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol. Rev. 23, 563–590 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cai, J., Hodoki, Y. & Nakano, S. I. Phylogenetic diversity of the picocyanobacterial community from a novel winter bloom in Lake Biwa. Limnology 22, 161–167 (2021).Article 

    Google Scholar 
    Guyet, U. et al. Synergic effects of temperature and irradiance on the physiology of the marine Synechococcus strain WH7803. Front. Microbiol. 11, 1707 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Meier, H. E. M. et al. Ensemble modeling of the Baltic Sea ecosystem to provide scenarios for management. Ambio 43, 37–48 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Neumann, T. et al. Extremes of temperature, oxygen and blooms in the baltic sea in a changing climate. Ambio 41, 574–585 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Andersson, A. et al. Projected future climate change and Baltic Sea ecosystem management. Ambio 44, 345–356 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schmidt, K. et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob. Change Biol. 26, 5574–5587 (2020).ADS 
    Article 

    Google Scholar 
    Legrand, C. et al. Interannual variability of phyto-bacterioplankton biomass and production in coastal and offshore waters of the Baltic Sea. Ambio 44, 427–438 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Capuzzo, E. et al. A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Glob. Change Biol. 24, e352–e364 (2017).Article 

    Google Scholar 
    Valderrama, J. C. Methods of nutrient analysis. In Manual on Harmful Marine Microalgae (eds Hallagraeff, G. M. et al.) 251–268 (IOC Manuals and Guides, 1995).
    Google Scholar 
    Jespersen, A. M. & Christoffersen, K. Measurements of chlorophyll-a from phytoplankton using ethanol as extraction solvent. Arch. Hydrobiol. 109, 445–454 (1987).CAS 

    Google Scholar 
    Edler, L. Recommendations on methods for marine biological studies in the Baltic Sea. Phytoplankton and chlorophyll (Baltic Marine Biologists BMB (Sweden), 1979).HELCOM Phytoplankton Expert Group. Phytoplankton biovolume and carbon content. https://www.ices.dk/data/Documents/ENV/PEG_BVOL.zip (2013).Mostböck, S. FCSalyzer (2021).Gregory Caporaso, J. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods https://doi.org/10.1038/nmeth.f.303 (2010).Article 
    PubMed 

    Google Scholar 
    Callahan, B. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Crosbie, N. D., Pöckl, M. & Weisse, T. Rapid establishment of clonal isolates of freshwater autotrophic picoplankton by single-cell and single-colony sorting. J. Microbiol. Methods 55, 361–370 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Silva, C. S. P., Genuário, D. B., Vaz, M. G. M. V. & Fiore, M. F. Phylogeny of culturable cyanobacteria from Brazilian mangroves. Syst. Appl. Microbiol. 37, 100–112 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Marsan, D., Wommack, K. E. & Ravel, J. Draft genome sequence of Synechococcus sp. strain CB0101, isolated from the Chesapeake Bay estuary. Genome Announc. 2, e01111 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. R version 3.5.1. https://www.r-project.org/ (2019).Oksanen, J. et al. Package ‘vegan’ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2016) (ISBN 978-3-319-24277-4).MATH 
    Book 

    Google Scholar 
    Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gill, A. E. Atmosphere-Ocean Dynamics (Academic Press, USA, 1982).
    Google Scholar 
    Li, X., Wang, Y., Li, J. & Lei, B. Physical and socioeconomic driving forces of land-use and land-cover changes: A Case Study of Wuhan City, China. Discret Dyn. Nat. Soc. 2016 (2016).Paliy, O. & Shankar, V. Application of multivariate statistical techniques in microbial ecology. Mol. Ecol. 25, 1032–1057 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    New data from the first discovered paleoparadoxiid (Desmostylia) specimen shed light into the morphological variation of the genus Neoparadoxia

    Discovery and historiography of USNM PAL V 11367With basic image enhancement tools (e.g., Adobe Photoshop), we were able to better resolve the original but faded specimen label in the collections associated with USNM PAL V 11367 (Fig. 1 and Related file 1). Specifically, we were able to make the now-faded handwritten notes legible (Fig. 1A,B), revealing critical information about the specimen. The widespread availability of image enhancement for faded fieldnotes and labels provides a new source of information for uncovering legacy issues in museum collections (e.g.21,22,23), especially in cases where locality data or collecting information cannot be well resolved.Accession files with this specimen (Related file 1) show that it was gifted from Arthur M. Ames to the United States National Museum (now the National Museum of Natural History, Smithsonian Institution) on 15 October 1925, and approved by George P. Merrill, head curator of geology from 1917 to 1929. Prior to its accession to the museum, an anonymous individual identified the tooth as belonging to Desmostylus hesperus. Forty years later, on 17 November 1965, Charles A. Repenning reidentified this specimen as Paleoparadoxia sp. (Fig. 1A,B), an assertion that was incorporated into its catalog information. According to the label, USNM PAL V 11367 was collected in the city of Corona, Riverside County, California, yet no precise information of its geological provenance was recorded. On the backside of the label, there are notes (Fig. 1B) referring to the US Geologic Survey Corona South 7.5′ quadrangle map for Riverside and Orange counties, California24. However, no geographic location, exact horizon, nor lithology was stated, and the specimen’s collector, A. M. Ames, lived in Santa Barbara, California but died on 25 August 193921,22,23.In nearly a century after its discovery, the only mention of USNM PAL V 11367 was by Panofsky25, who listed it in a catalog of desmostylian tooth specimens used as a comparative basis for a mandible restoration of the “Stanford specimen” N. repenningi. Panofsky25 identified USNM PAL V 11367 as a left m2 with six main cusps, with no additional cusps (Table 1 in25), while also stating that this specimen has “an open lake in the center of each of the seven cusps” (25: p. 103). The inconsistency of this description differs from our own, which we attribute to differences in morphological criteria or a typographic error.Geological horizon and age of USNM PAL V 11367In this paper, we refer to the “Topanga” Formation following recent studies20,26,27 of this geologic unit. This formation was originally based on a sequence of marine sandstones exposed in an anticline just west of Old Topanga Canyon in the central Santa Monica Mountains of Los Angeles County, California28. After its initial description, the name of the formation was applied to a much thicker and heterogeneous sequence of sedimentary and volcanic rocks29. Campbell et al.30 compiled the history and chronology of changes in usage of “Topanga” in the Miocene stratigraphic nomenclature in Southern California, showing that the criteria of continuous deposition and shared provenance were not demonstrated in every instance. Campbell et al.30 argued that strata assigned to the Topanga Formation in the Los Angeles Basin and eastern Ventura Basin areas are different from other units that have also been referred to the Topanga Formation in Orange County or in the Santa Monica Mountains of Los Angeles and Ventura counties. To distinguish these units, here we follow recent studies20,26,27 and use the name of “Topanga” Formation for the early to middle Miocene rocks bearing fossil marine mammals20,26,31,32,33 in Southern California.According to the collections records (Fig. 1), USNM PAL V 11367 was collected in the city of Corona, Riverside County, California, USA. This city is in the western part of Riverside County, comprising an approximate area of 100 km234. Previously, Panofsky25 suggested that USNM PAL V 11367 would have derived from the Temblor Formation (14.8 to 15.8 Ma35), likely as a guess based on the prevalence of desmostylian teeth recovered from this unit in central California, yet today there are no Temblor Formation outcrops mapped near Corona24,36; the closest Temblor outcrops are located in Fresno and Kern counties37, approximately 200 km away.The geologic maps of Riverside County24,36,38 indicate that the city limits of Corona encompass a wide variety of sedimentary rocks from the Jurassic to the Holocene in age, but only a few marine deposits, such as the Jurassic Bedford Canyon Formation and the middle Miocene “Topanga” Formation are exposed24,39. Specifically, the marine sandstones of the “Topanga” Formation occur within the fault zone at the southeast and northwest of Corona.Outside of Riverside County, the “Topanga” Formation has yielded a diverse assemblage of fossil marine vertebrates in Southern California20,26,31, including desmostylians referred to Desmostylus hesperus and Paleoparadoxia sp. in Orange County (Supplementary 1). USNM PAL V 11367 represents the second reported fossil marine mammal from Riverside County. Previously, an isolated record of “Cetacea indet.” was mentioned from the Zanclean stage Imperial Formation40 and Supplementary Data 2), which is exposed far east of Corona’s city limits.In assessing the age of the “Topanga” Formation in Southern California, Boessenecker and Churchill26,31 argued that the land mammals (late Hemingfordian North American Land Mammal Age, represented by Aepycamelus, Copemys and Merychippus; 17.5–15.9 Ma35,41), benthic foraminifera, fossil mollusks, and K/Ar dating all placed the age range between 17.5 and 15 Ma for this geological unit41 in Orange County. More recently, Velez-Juarbe20 revised the age of “Topanga” Formation in this county to 16.5–14.5 Ma based on new foraminiferal zones presented in Ogg et al.42.We propose that USNM PAL V 11367 derives from exposures of the “Topanga” Formation in Riverside County. If this mapped unit in Riverside can be correlated with “Topanga” Formation units in Orange County, it would imply a middle Miocene age, likely 16.5–14.5 Ma20, and given the morphological similarities of this isolated tooth with more complete paleoparadoxiid material in Orange County with stronger age constraints, we think a middle Miocene age for USNM PAL V 11367 is warranted. Given the reduced distribution of outcrops of the “Topanga” Formation24,36 in Corona, we identify two potential localities for USNM PAL V 11367 (Fig. 3). These two localities are situated in urbanized areas, less than 21 km apart, in the northwest and the southeast corners of Corona’s city limits (see Fig. 3B). Both are notably less than 40 km apart from the type locality of N. cecilialina in Orange County, but we urge skepticism for a direct correlation as the marine units of Riverside County requires detailed stratigraphic revision to determine their age constraints; they likely belong to a different depositional basin than “Topanga” Formation exposures in westward Southern California counties.Morphological variation and potential diversity of PaleoparadoxiidaeOur comparisons reveal considerable morphological variation in the arrangement and number of dental cusps across Paleoparadoxiidae (Fig. 4). The cusps arrangement for the m2-3 of Archaeoparadoxia and Paleoparadoxia were previously reported by Inuzuka et al.43 (Fig. 4B), but the addition of another specimen (USNM PAL V 11367) reveals larger morphological variability than previously known for the genus Neoparadoxia (Fig. 4C). Specifically, the holotype of N. cecilialina displays slightly different configurations between its right and left m2, driven mainly by the position of the hypoconulid in occlusal view (Fig. 4C). USNM PAL V 11367, the second known Neoparadoxia m2 (or the first m3), is comparable in size and shape with the same teeth in the type specimen of N. cecilialina, especially the right m2. Both the Smithsonian and LACM specimens display a horizontal alignment of the extra cusp, the hypoconulid, and the entoconid; nevertheless, USNM PAL V 11367 shows a tighter configuration, lacking a wide internal spacing between cusps characteristic of the type specimen of N. cecilialina (Fig. 4C). Given the known ontogenetic changes that affect the dental nomenclature in desmostylians32,44, the addition of more comparative material should help discriminate between competing statements of homology45. The identification of USNM PAL V 11367 from the “Topanga” Formation of Corona represents a second diagnostic record of Neoparadoxia from three separate Middle Miocene units in Southern California, reaffirming its presence as a Middle Miocene taxon: USNM PAL V 11367 from the “Topanga” Formation of Riverside County; Neoparapdoxia (LACM 6920) from the Altamira Shale46; Neoparadoxia from the Topanga Formation of Orange County46,47; and the holotype of N. cecilialina from the lower part of Monterey Formation in the Capistrano syncline, Orange County46. It is possible that other records of Palaeoparadoxiidae from Orange County (e.g.47) and elsewhere in California may represent Neoparadoxia. For example, Awalt et al.32 noted that a palaeoparadoxiid from Orange County identified by Panofsky as Paleoparadoxia sp. (LACM 131889)25 is better referred to Paleoparadoxidae sp., pending a more detailed evaluation of this material, which differs in clear ways from N. ceciliana. One of the benefits of continued descriptive work on desmostylian material from well-constrained stratigraphic contexts in Southern California will be the biostratigraphic opportunities for cross-basin comparisons, especially for exposures of the “Topanga” Formation.Parham et al.46 emphasized that Neoparadoxia occurs widely in middle Miocene units across California: besides the aforementioned ones, Parham et al.46 noted records of this genus from the Sharktooth Hill Bonebed (LACM 120023), the Altamira Shale (LACM 6920), and the Ladera Sandstone15 (UCMP 81302). To date, Neoparadoxia is only known from California, yet it is likely that other paleoparadoxiid material tentatively assigned to other genera may expand the geographic range of this taxon. Interestingly, on the west side of the Pacific (Russia–Japan) and some parts of the east side of the Pacific (Oregon–Washington), Desmostylus spp. and paleoparadoxiids rarely co-occurred from the same formation48,49, yet there are many geological units in South California where desmostylids and paleoparadoxiids co-occurred (e.g., Santa Margarita Formation50,51, Rosarito Beach Formation52, Tortugas Formation51, and Temblor Formation3,4). The abundance of new material from the “Topanga” Formation from Orange and Riverside counties should contribute to the discussion of desmostylian environmental preferences48,53.Lastly, like other marine mammal lineages, desmostylian body sizes reached their maximum body size late in their evolutionary history54. By the middle to late Miocene, desmostylians were the largest herbivorous marine mammals along the North Pacific coastlines54, although they likely competed ecologically with co-occurring sirenians, which later eclipsed desmostylians in body size and survived until historical times in the North Pacific Ocean55. Specifically, in the “Topanga” Formation of Orange County, desmostylians co-occurred with sirenians such as Metaxytherium arctodites56, an ecological association that likely was repeated elsewhere in the mid-Miocene of California (e.g., coeval deposits of the Round Mountain Silt). Given the improving stratigraphic picture of Southern California marine mammal-bearing localities, future work on desmostylian paleoecology could test hypotheses of competition with taxonomic co-occurrence data grounded in strong comparative taphonomic and sedimentological frameworks. More

  • in

    Spatial and temporal stability in the genetic structure of a marine crab despite a biogeographic break

    Thorson, G. Reproductive and larval ecology of marine bottom invertebrates. Biol. Rev. 25, 1–45 (1950).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weersing, K. & Toonen, R. J. Population genetics, larval dispersal, and connectivity in marine systems. Mar. Ecol. Progr. Ser. 393, 1–12 (2009).ADS 
    Article 

    Google Scholar 
    Hedgecock, D. Is gene flow from pelagic larval dispersal important in the adaptation and evolution of marine invertebrates?. Bull. Mar. Sci. 39, 550–564 (1986).
    Google Scholar 
    Jenkins, S. R. & Hawkins, S. J. Barnacle larval supply to sheltered rocky shores: a limiting factor?. Hydrobiologia 503, 143–151 (2003).Article 

    Google Scholar 
    Pineda, J., Hare, J. A. & Sponaugle, S. Consequences for population connectivity. Oceanography 20, 22–39 (2007).Article 

    Google Scholar 
    Shanks, A. L. Mechanisms of cross-shelf dispersal of larval invertebrates and fish. In Ecology of Marine Invertebrate Larvae (ed. McEdward, L. R.) 324–367 (CRC, Boca Raton, 1995).
    Google Scholar 
    Shanks, A. L. Pelagic larval duration and dispersal distance revisited. Biol. Bull. 216, 373–385 (2009).PubMed 
    Article 

    Google Scholar 
    Bradford, R. W., Griffin, D. & Bruce, B. D. Estimating the duration of the pelagic phyllosoma phase of the southern rock lobster, Jasus edwardsii (Hutton). Mar. Freshw. Res. 66, 213–219 (2015).Article 

    Google Scholar 
    Mileikovsky, S. A. Speed of active movement of pelagic larvae of marine bottom invertebrates and their ability to regulate their vertical position. Mar. Biol. 23, 11–17 (1973).Article 

    Google Scholar 
    Garrison, L. P. Vertical migration behavior and larval transport in brachyuran crabs. Mar. Ecol. Progr. Ser. 176, 103–113 (1999).ADS 
    Article 

    Google Scholar 
    Morgan, S. G. & Fisher, J. L. Larval behavior regulates nearshore retention and offshore migration in an upwelling shadow and along the open coast. Mar. Ecol. Progr. Ser. 404, 109–126 (2010).ADS 
    Article 

    Google Scholar 
    Cowen, R. K. & Castro, L. R. Relation of coral reef fish larval distributions to island scale circulation around Barbados, west indies. Bull. Mar. Sci. 54, 228–224 (1994).
    Google Scholar 
    Rudorff, C. A. G., Lorenzzetti, J. A., Gherardia, D. F. M. & Lins-Oliveira, J. E. Modeling spiny lobster larval dispersion in the Tropical Atlantic. Fish. Res. 96, 206–215 (2009).Article 

    Google Scholar 
    Allee, W. C. Studies in marine ecology. IV. The effect of temperature in limiting the geographic range of invertebrates of the Woods Hole littoral. Ecology 4, 341–354 (1923).Article 

    Google Scholar 
    Burton, R. S. Intraspecific phylogeography across the Point Conception biogeographic boundary. Evolution 52, 734–745 (1998).PubMed 
    Article 

    Google Scholar 
    Lancellotti, D. A. & Vasquez, J. A. Biogeographical patterns of benthic macroinvertebrates in the southeastern Pacific littoral. J. Biogeogr. 26, 1001–1006 (1999).Article 

    Google Scholar 
    Hormazabal, S., Shaffer, G. & Leth, O. Coastal transition zone off Chile. J. Geophys. Res. 109, C01021 (2004).ADS 

    Google Scholar 
    Mcdonald, A. M. The global ocean circulation: a hydrographic estimate and regional analysis. Prog. Oceanogr. 41, 281–382 (1998).ADS 
    Article 

    Google Scholar 
    Montecino, V. & Lange, C. B. The Humboldt Current System: Ecosystem components and processes, fisheries, and sediment studies. Progr. Oceanogr. 83, 65–79 (2009).ADS 
    Article 

    Google Scholar 
    Haye, P. A. et al. Phylogeographic structure in benthic marine invertebrates of the southeast Pacific Coast of Chile with differing dispersal potential. PLoS ONE 9, e88613 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kelly, R. P. & Palumbi, S. R. Genetic structure among 50 species of the northeastern Pacific rocky intertidal community. PLoS ONE 5, e8594 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gaylord, B. & Gaines, S. D. Temperature or transport? Range limits in marine species mediated solely by flow. Am. Nat. 155, 769–789 (2000).PubMed 
    Article 

    Google Scholar 
    Wares, J. P., Gaines, S. D. & Cunningham, C. W. A comparative study of asymmetric migration events across a marine biogeographic boundary. Evolution 55, 295–306 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rumrill, S. S. Natural mortality of marine invertebrate larvae. Ophelia 32, 163–198 (1990).Article 

    Google Scholar 
    Jenkins, S. R., Marshall, D. & Fraschetti, S. Settlement and recruitment. In Marine Hard Bottom Communities, Ecological Studies Vol. 206 (ed. Wahl, M.) 177–190 (Springer, Berlin, 2009).Chapter 

    Google Scholar 
    Marino, I. A. M. et al. Genetic heterogeneity in populations of the Mediterranean shore crab, Carcinus aestuarii (Decapoda, Portunidae), from the Venice Lagoon. Estuar. Coast. Shelf. Sci. 87, 135–144 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    Sernapesca. Estadística de pesca de Chile. http://www.sernapesca.cl/informes/estadisticas (2022).Nation JD (1975) The Genus Cancer: Crustacea: Brachyura): Systematics, biogeography and fossil record. Nat. Hist. Mus. Los Angeles County Sci, Bull. 23 (1975).Pardo, L. M., Fuentes, J. P., Olguin, A. & Orensanz, J. M. L. Reproductive maturity in the edible Chilean crab Cancer edwardsii: methodological and management considerations. J. Mar. Biol. Assoc. U. K. 89, 1627–1634 (2009).Article 

    Google Scholar 
    Rojas-Hernández, N., Veliz, D. & Pardo, L. M. Use of novel microsatellite markers for population and paternity analysis in the commercially important crab Metacarcinus edwardsii. Mar. Biol. Res. 10, 839–844 (2014).Article 

    Google Scholar 
    Pardo, L. M., Riveros, M. P., Fuentes, J. P., Rojas-Hernández, N. & Veliz, D. An effective sperm competition avoidance strategy in crabs drives genetic monogamy despite evidence of polyandry. Behav. Ecol. Sociobiol. 70, 73–81 (2016).Article 

    Google Scholar 
    Pardo, L. M. et al. High fishing intensity reduces females’ sperm reserve and brood fecundity in a eubrachyuran crab subject to sex- and size biased harvest. ICES J. Mar. Sci. 74, 2459–2469 (2017).Article 

    Google Scholar 
    Pardo, L. M., Mora-Vásquez, P. & Garcés-Vargas, J. Asentamiento diario de megalopas de jaibas del género Cancer en un estuario micromareal. Lat. Am. J. Aquat. Res. 40, 142–152 (2012).Article 

    Google Scholar 
    Pardo, L. M., Rubilar, P. R. & Fuentes, J. P. North Patagonian estuaries appear to function as nursery habitats for marble crab (Metacarcinus edwardsii). Reg. Stud. Mar. Sci. 36, 101315 (2020).
    Google Scholar 
    Quintana, R. Larval development of the Edible crab, Cancer edwardsi Bell, 1835 under laboratory conditions (Decapoda, Brachyura). Rep. USA Mar. Biol. Inst. 5, 1–19 (1983).
    Google Scholar 
    Rojas-Hernández, N., Veliz, D., Riveros, M. P., Fuentes, J. P. & Pardo, L. M. Highly connected populations and temporal stability in allelic frequencies of a harvested crab from southern Pacific. PLoS ONE 11, e0166029 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Strub, P. T., James, C., Montecino, V., Rutllant, J. A. & Blanco, J. L. Ocean circulation along the southern Chile transition region (38°–46°S): Mean, seasonal and interannual variability, with a focus on 2014–2016. Progr. Oceanogr. 172, 159–198 (2019).ADS 
    Article 

    Google Scholar 
    Beerli, P., Mashayekhi, S., Sadeghi, M., Khodaei, M. & Shaw, K. Population genetic inference with MIGRATE. Curr. Protoc. Bioinform. 68, e87 (2019).Article 

    Google Scholar 
    Kilian, A. et al. Diversity arrays technology: A generic genome profiling technology on open platforms. Methods Mol. Biol. 888, 67–89 (2012).PubMed 
    Article 

    Google Scholar 
    Chang, C. C. et al. Second-generation PLINK: Rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Weiss, M. et al. Influence of temperature on the larval development of the edible crab, Cancer pagurus. J. Mar Biol. Assoc. UK 89, 753–759 (2009).CAS 
    Article 

    Google Scholar 
    Pampoulie, C. et al. A pilot genetic study reveals the absence of spatial genetic structure in Norway lobster (Nephrops norvegicus) on fishing grounds in Icelandic waters. ICES J. Mar. Sci. 68, 20–25 (2011).Article 

    Google Scholar 
    Costlow, J. D. J. & Bookhout, C. G. The larval development of Callinectes sapidus Rathbun reared in the laboratory. Biol. Bull. 116, 373–396 (1959).Article 

    Google Scholar 
    Ungfors, A., McKeown, N. J., Shaw, P. W. & Andre, C. Lack of spatial genetic variation in the edible crab (Cancer pagurus) in the Kattegat – Skagerrak area. ICES J. Mar. Sci. 66, 462–469 (2009).Article 

    Google Scholar 
    Lacerda, A. L. F. et al. High connectivity among blue crab (Callinectes sapidus) populations in the Western South Atlantic. PLoS ONE 11, e0153124 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Taylor, M. S. & Hellberg, M. E. Comparative phylogeography in a genus of coral reef fishes: biogeographic and genetic concordance in the Caribbean. Mol. Ecol. 15, 695–707 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Arranz, V., Fewster, R. M. & Lavery, S. D. Geographic concordance of genetic barriers in New Zealand coastal marine species. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 3607–3625 (2021).Article 

    Google Scholar 
    Ayre, D. J., Minchinton, T. E. & Perrin, C. Does life history predict past and current connectivity for rocky intertidal invertebrates across a marine biogeographic barrier?. Mol. Ecol. 18, 1887–1903 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barber, P. H., Erdmann, M. V. & Palumbi, S. R. Comparative phylogeography of three codistributed stomatopods: origins and timing of regional lineage diversification in the coral triangle. Evolution 60, 1825–1839 (2006).PubMed 
    Article 

    Google Scholar 
    Macaya, E. C. & Zuccarello, G. C. Genetic structure of the giant kelp Macrocystis pyrifera along the southeastern Pacific. Mar. Ecol. Progr. Ser. 420, 103–112 (2010).ADS 
    Article 

    Google Scholar 
    Ruiz, M., Tarifeño, E., Llanos-Rivera, A., Padget, C. & Campos, B. Efecto de la temperatura en el desarrollo embrionario y larval del mejillón, Mytilus galloprovincialis (Lamarck 1819). Rev. Biol. Mar. Oceanogr. 431, 51–61 (2008).
    Google Scholar 
    Toro, J. E., Castro, G. C., Ojeda, J. A. & Vergara, A. M. Allozymic variation and differentiation in the Chilean blue mussel, Mytilus chilensis, along its natural distribution. Genet. Mol. Biol. 29, 174–179 (2006).CAS 
    Article 

    Google Scholar 
    Araneda, C., Larraín, M. A., Hecht, B. & Narum, S. Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments. Ecol. Evol. 6, 3632–3644 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Disalvo, L. H. Observations on the larval and post-metamorphic life of Concholepas concholepas (Bruguière, 1789) in laboratory culture. Veliger 30, 358–368 (1988).
    Google Scholar 
    Cardenas, L., Castilla, J. C. & Viard, F. Hierarchical analysis of the population genetic structure in Concholepas concholepas, a marine mollusk with a long-lived dispersive larva. Mar. Ecol. 37, 359–369 (2016).ADS 
    Article 

    Google Scholar 
    Domingues, C. P., Creer, S., Taylor, M. I., Queiroga, H. & Carvalho, G. R. Genetic structure of Carcinus maenas within its native range: larval dispersal and oceanographic variability. Mar. Ecol. Progr. Ser. 410, 111–123 (2010).ADS 
    Article 

    Google Scholar 
    Domingues, C. P., Creer, S., Taylor, M. I., Queiroga, H. & Carvalho, G. R. Temporal genetic homogeneity among shore crab (Carcinus maenas) larval events supplied to an estuarine system on the Portuguese northwest coast. Heredity 106, 832–840 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Vadopalas, B., Pietsch, T. & Friedman, C. The proper name for the geoduck: resurrection of Panopea generosa Gould, 1850, from the synonymy of Panopea abrupta (Conrad, 1849) (Bivalvia: Myoida: Hiatellidae). Malacologia 52, 169–173 (2010).Article 

    Google Scholar 
    Cassista, M. C. & Hart, M. W. Spatial and temporal genetic homogeneity in the Arctic surfclam (Mactromeris polynyma). Mar. Biol. 152, 569–579 (2007).Article 

    Google Scholar 
    Li, G. & Hedgecock, D. Genetic heterogeneity, detected by PCR-SSCP, among samples of larval Pacific oysters (Crassostrea gigas) supports the hypothesis of large variance in reproductive success. Can. J. Fish. Aquat. Sci. 55, 1025–1033 (1998).CAS 
    Article 

    Google Scholar 
    Schmidt, P. S., Phifer-Rixey, M., Taylor, G. M. & Christner, J. Genetic heterogeneity among intertidal habitats in the flat periwinkle, Littorina obtusata. Mol. Ecol. 16, 2393–2404 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dambach, J., Raupach, M. J., Leese, F., Schwarzer, J. & Engler, J. O. Ocean currents determine functional connectivity in an Antarctic deep-sea shrimp. Mar. Ecol. 37, 1336–1344 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Reid, K. et al. Secondary contact and asymmetrical gene flow in a cosmopolitan marine fish across the Benguela upwelling zone. Heredity 117, 307–315 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hu, Z.-M., Zhang, J., Lopez-Bautista, J. & Duan, D.-L. Asymmetric genetic exchange in the brown seaweed Sargassum fusiforme (Phaeophyceae) driven by oceanic currents. Mar. Biol. 160, 1407–1414 (2013).Article 

    Google Scholar 
    Xuereb, A. et al. Asymmetric oceanographic processes mediate connectivity and population genetic structure, as revealed by RADseq, in a highly dispersive marine invertebrate (Parastichopus californicus). Mol. Ecol. 27, 2347–2364 (2018).PubMed 
    Article 

    Google Scholar 
    Becker, R. A. & Wilks, A. R. R version by Ray Brownrigg. mapdata: Extra Map Databases. R package version 2.3.0. (2018b).Becker, R.A. & Wilks, A. R. R version by Ray Brownrigg. Enhancements by TP Minka and A Deckmyn.maps: Draw Geographical Maps. R package version 3.3.0. https://CRAN.R-project.org/package=maps (2018a).
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2022).
    Google Scholar 
    Grube, B., Unmack, P. J., Berry, O. F. & Georges, A. dartr: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18, 691–699 (2018).Article 

    Google Scholar 
    Foll, M. & Gaggiotti, O. E. A genome scan method to identify selected loci appropriate for both dominant and codominant markers: A Bayesian perspective. Genetics 180, 977–993 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Flanagan, S. P. & Jones, A. G. Constraints on the Fst-heterozygosity outlier approach. J. Hered 108, 561–573 (2017).PubMed 
    Article 

    Google Scholar 
    Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodohl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol 4, 782–788 (2013).Article 

    Google Scholar 
    Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. & Bonhomme, F. GENETIX 4.05, Logiciel sous Windows pour la Genetique des Populations. Laboratoire Genome, Populations, Interactions, CNRS UMR 5000 (Université de Montpellier II, Montpellier, France, 2000).Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pritchard, J. K., Wen, X. & Falush, D. Documentation for Structure Software: Version 2.3. University of Oxford http://pritch.bsd.uchicago.edu/structure.html (2010).Beerli, P. & Felsenstein, J. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. U.S.A. 98, 4563–4568 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 
    MATH 
    Article 

    Google Scholar 
    Petkova, D., Novembre, J. & Stephens, M. Visualizing spatial population structure with estimated effective migration surfaces. Nat. Genet. 48, 94–100 (2016).CAS 
    PubMed 
    Article 

    Google Scholar  More