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    Long-term High Resolution Image Dataset of Antarctic Coastal Benthic Fauna

    Rogers, A. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the southern ocean. Annual Review of Marine Science 12, 87–120, https://doi.org/10.1146/annurev-marine-010419-011028 (2020).Article 
    PubMed 

    Google Scholar 
    Tin, T. et al. Impacts of local human activities on the antarctic environment. Antarctic Science 21, 3–33, https://doi.org/10.1017/S0954102009001722 (2009).Article 

    Google Scholar 
    Pineda-Metz, S. E. A., Gerdes, D. & Richter, C. Benthic fauna declined on a whitening antarctic continental shelf. Nature Communications 11, 2226, https://doi.org/10.1038/s41467-020-16093-z (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Convey, P. Antarctic terrestrial biodiversity in a changing world. Polar Biology 34, 1629, https://doi.org/10.1007/s00300-011-1068-0 (2011).Article 

    Google Scholar 
    Kang, Y. H. et al. Composition and structure of the marine benthic community in terra nova bay, antarctica: Responses of the benthic assemblage to disturbances. PLOS ONE 14, 1–16, https://doi.org/10.1371/journal.pone.0225551 (2019).Article 

    Google Scholar 
    Piazza, P. et al. Underwater photogrammetry in antarctica: long-term observations in benthic ecosystems and legacy data rescue. Polar Biology 42, 1061–1079, https://doi.org/10.1007/s00300-019-02480-w (2019).Article 

    Google Scholar 
    SOOS. Southern Ocean Observing System – Report on the 2017 Ross Sea Working Group Meeting. http://www.soos.aq. [Online; accessed 2022/11/15] (2017).SCAR. Scientific Committee on Antarctic Research. https://www.scar.org. [Online; accessed 2022/11/15] (2021).ANTOS. Antarctic near-shore and terrestrial observing system. https://www.scar.org/science/antos/home. [Online; accessed 2022/11/15] (2015).Dayton, P. K. et al. Benthic responses to an antarctic regime shift: food particle size and recruitment biology. Ecological Applications 29, e01823, https://doi.org/10.1002/eap.1823 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Watters, G. M., Hinke, J. T. & Reiss, C. S. Long-term observations from antarctica demonstrate that mismatched scales of fisheries management and predator-prey interaction lead to erroneous conclusions about precaution. Scientific Reports 10, 2314, https://doi.org/10.1038/s41598-020-59223-9 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolinesi, F. et al. Spatial-related community structure and dynamics in phytoplankton of the ross sea, antarctica. Frontiers in Marine Science 7, https://doi.org/10.3389/fmars.2020.574963 (2020).Stenni, B. et al. Three-year monitoring of stable isotopes of precipitation at concordia station, east antarctica. The Cryosphere 10, 2415–2428, https://doi.org/10.5194/tc-10-2415-2016 (2016).Article 

    Google Scholar 
    Ramesh, K. & Soni, V. Perspectives of antarctic weather monitoring and research efforts. Polar Science 18, 183–188, https://doi.org/10.1016/j.polar.2018.04.005 (2018). Recent Advances in Climate Science of Polar Region (to commemorate the contributions of Late Dr. S.Z. Qasim, a pioneering doyen of the Indian Polar programme).Article 

    Google Scholar 
    Shepherd, A. et al. Mass balance of the antarctic ice sheet from 1992 to 2017. Nature 558, 219–222, https://doi.org/10.1038/s41586-018-0179-y (2018).Article 

    Google Scholar 
    Budge, J. S. & Long, D. G. A comprehensive database for antarctic iceberg tracking using scatterometer data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 434–442, https://doi.org/10.1109/JSTARS.2017.2784186 (2018).Article 

    Google Scholar 
    Rignot, E. et al. Four decades of antarctic ice sheet mass balance from 1979–2017. Proceedings of the National Academy of Sciences of the United States of America 116, 1095–1103, https://doi.org/10.1073/pnas.1812883116 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barbat, M. M., Rackow, T., Wesche, C., Hellmer, H. H. & Mata, M. M. Automated iceberg tracking with a machine learning approach applied to sar imagery: A weddell sea case study. ISPRS Journal of Photogrammetry and Remote Sensing 172, 189–206, https://doi.org/10.1016/j.isprsjprs.2020.12.006 (2021).Article 

    Google Scholar 
    Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environmental Science & Technology 53, 6616–6631, https://doi.org/10.1021/acs.est.9b00409 (2019).Article 

    Google Scholar 
    Piazza, P., Gattone, S., Guzzi, A. & Schiaparelli, S. Towards a robust baseline for long-term monitoring of antarctic coastal benthos. Hydrobiologia 847, 1753–1771, https://doi.org/10.1007/s10750-020-04177-2 (2020).Article 

    Google Scholar 
    Rountree, R. et al. Towards an optimal design for ecosystem-level ocean observatories. Oceanography and Marine Biology 58, 79–105, https://doi.org/10.1201/9780429351495-2 (2020).Article 

    Google Scholar 
    Katsanevakis, S. et al. Monitoring marine populations and communities: Methods dealing with imperfect detectability. Aquatic Biology 16, 31–52, https://doi.org/10.3354/ab00426 (2012).Article 

    Google Scholar 
    Zampoukas, N. et al. Technical guidance on monitoring for the marine strategy framework directive. Tech. Rep., European Commission, Report EUR 26499 (2014).Bicknell, A. W., Godley, B. J., Sheehan, E. V., Votier, S. C. & Witt, M. J. Camera technology for monitoring marine biodiversity and human impact. Frontiers in Ecology and the Environment 14, 424–432, https://doi.org/10.1002/fee.1322 (2016).Article 

    Google Scholar 
    European Marine Board. Working Group on Big Data in Marine Science. https://www.marineboard.eu/publications/big-data-marine-science. [Online; accessed 2022/11/15] (2020).Zurowietz, M. & Nattkemper, T. W. Current trends and future directions of large scale image and video annotation: Observations from four years of biigle 2.0. Frontiers in Marine Science 8, https://doi.org/10.3389/fmars.2021.760036 (2021).Kim, S. L., Thurber, A., Hammerstrom, K. & Conlan, K. Seastar response to organic enrichment in an oligotrophic polar habitat. Journal of Experimental Marine Biology and Ecology 346, 66–75, https://doi.org/10.1016/j.jembe.2007.03.004 (2007).Article 

    Google Scholar 
    Peirano, A., Bordone, A., Marini, S., Piazza, P. & Schiaparelli, S. A simple time-lapse apparatus for monitoring macrozoobenthos activity in antarctica. Antarctic Science 28, 473–474, https://doi.org/10.1017/S0954102016000377 (2016).Article 

    Google Scholar 
    Peirano, A., Marini, S., Bordone, A. & Schiaparelli, S. ICE-LAPSE: Analysis of antarctic benthos dynamics by using non-destructive monitoring devices and permanent stations, pnra 2013/az1.16, funded by the italian national antarctic program (2015-2016).Marini, S. et al. Long-term automated visual monitoring of antarctic benthic fauna. Methods in Ecology and Evolution 13, 1746–1764, https://doi.org/10.1111/2041-210X.13898 (2022).Article 

    Google Scholar 
    Marini, S. et al. EP2863257 (A1) – Underwater images acquisition and processing system. https://data.epo.org/gpi/EP2863257B1. [Online; accessed 2022/11/15] (2013).Corgnati, L. et al. Looking inside the ocean: Toward an autonomous imaging system for monitoring gelatinous zooplankton. Sensors 16, 2124, https://doi.org/10.3390/s16122124 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marini, S. et al. Automated estimate of fish abundance through the autonomous imaging device guard1. Measurement 126, 72–75, https://doi.org/10.1016/j.measurement.2018.05.035 (2018).Article 

    Google Scholar 
    Pensieri, S. et al. Environmental acoustic noise observations in tethys bay (terra nova bay, ross sea, antarctica). In 2014 Oceans – St. John’s, 1–6, https://doi.org/10.1109/OCEANS.2014.7003196 (2014).Jung, J. et al. Multibeam bathymetry and distribution of clay minerals on surface sediments of a small bay in terra nova bay, antarctica. Minerals 11, https://doi.org/10.3390/min11010072 (2021).Balog, I. et al. Estimation of direct normal irradiance at antarctica for concentrated solar technology. Applied System Innovation 2, https://doi.org/10.3390/asi2030021 (2019).Caputi, S. S. et al. Seasonal food web dynamics in the antarctic benthos of tethys bay (ross sea): Implications for biodiversity persistence under different seasonal sea-ice coverage. Frontiers in Marine Science 7, 1046, https://doi.org/10.3389/fmars.2020.594454 (2020).Article 

    Google Scholar 
    van Leeuwe, M. A. et al. Annual patterns in phytoplankton phenology in antarctic coastal waters explained by environmental drivers. Limnology and Oceanography 65, 1651–1668, https://doi.org/10.1002/lno.11477 (2020).Article 

    Google Scholar 
    OEngineering. OEngineering s.r.l. – GUARD-1, Underwater Autonomous Smart Camera. https://www.oengineering.eu//GUARD-1/. [Online; accessed 2022/11/15] (2021).Magic Lantern. https://magiclantern.fm. [Online; accessed 2022/11/15] (2021).Marini, S. et al. Guard1: An autonomous system for gelatinous zooplankton image-based recognition. In OCEANS 2015 – Genova, 1–7, https://doi.org/10.1109/OCEANS-Genova.2015.7271704 (2015).CR2. The Canon RAW (CRW) File Format. https://exiftool.org/canon_raw.html. [Online; accessed 2022/11/15] (2022).Marini, S. et al. ICE-LAPSE image dataset. Zenodo https://doi.org/10.5281/zenodo.6418163 (2022).LabelImg. A graphical image annotation tool. https://github.com/tzutalin/labelImg. [Online; accessed 2022/11/15] (2021).Schoening, T. et al. Making marine image data fair. Scientific Data 9, 414, https://doi.org/10.1038/s41597-022-01491-3 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cattaneo-Vietti, R., Chiantore, M., Schiaparelli, S. & Albertelli, G. Shallow- and deep-water mollusc distribution at terra nova bay (ross sea, antarctica). Polar Biology 23, 173–182, https://doi.org/10.1007/s003000050024 (2000).Article 

    Google Scholar 
    Cattaneo-Vietti, R. et al. Spatial and Vertical Distribution of Benthic Littoral Communities in Terra Nova Bay, 503–514 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2000).Cummings, V. J. et al. Linking ross sea coastal benthic communities to environmental conditions: Documenting baselines in a spatially variable and changing world. Frontiers in Marine Science 5, 232, https://doi.org/10.3389/fmars.2018.00232 (2018).Article 

    Google Scholar 
    Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788, https://doi.org/10.1109/CVPR.2016.91 (2016).YOLO V5. https://github.com/ultralytics/yolov5. [Online; accessed 2022/11/15] (2022). More

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    Analysis of influencing factors of phenanthrene adsorption by different soils in Guanzhong basin based on response surface method

    Surface morphology analysisSEM images were shown in Fig. 1. It showed that the contour of three soils were fairly clear before adsorption. But it became fuzzier and the degree of cementation was increased when phenanthrene was adsorbed on the soils. According to the surface morphology, the silty sand (A) had furrows on the surface before adsorption compared with the fairly smooth without any furrows after adsorption (B). The silts (C) were flaky and the lamellar accumulation decreased (D). The loess (E) had a smooth surface with some flaky and rod like structure, after adsorption (F), the surface of loess increased in clay-like structure.Figure 1SEM micrographs of the three soil samples. (A) Silty sand; (B) Adsorbing 5 h of Silty sand; (C) Silts; (D) Adsorbing 5 h of Silts; (E) Loess; (F) Adsorbing 5 h of Loess.Full size imageAdsorption and desorption experimentsAdsorption and desorption kineticsAdsorption kinetics is one of the most important characteristics governing solute uptake rate and represents adsorption efficiency33. The sorption and desorption kinetics of phenanthrene in three soils were shown in Fig. 2. The results showed that the adsorption processes among all soils were similar. The kinetics of phenanthrene in soils was completed in two steps: a “fast” adsorption and a “slow” adsorption. The adsorption amount increased during 0-18h. It was a rapid reaction from 0 to 200 minutes. From 200 to 600 minutes, the adsorption amount increased slightly into balance. This phenomenon was due to the adsorption of phenanthrene occurred on the surface of soil organic matter. With the increase of time, soil surface adsorption sites were gradually saturated, causing the decrease of adsorption rate until reaching the equilibrium. Phenanthrene was a hydrophobic substance. It was easy to reach the soil surface and adhere to the grain surface. The results were consistent with the study of had also found that the balance time was approximately 18h and the adsorption amount increased with the adsorption reaction time34. Under the same conditions, loess had the highest adsorption capacity, which was mainly due to the highest organic content 18. The maximum phenanthrene sorption capacities ranked as follows: loess > silty sand > silts. As shown in Fig. 2, phenanthrene desorption in soils was relatively quick and its desorption equilibrium time was 3h. To reach an adequate desorption balance while remaining consistent with the adsorption reaction time, the balance time of the adsorption–desorption experiment was set at 18h. Generally, PAHs below 4 cycles could reach the adsorption equilibrium for about 16~24h.Figure 2(a)Adsorption equilibration curves of phenanthrene sorption in soils. (b) Desorption equilibration curves of phenanthrene sorption in soils.Full size imagePseudo-second-order and Elovich models were used to study the phenanthrene adsorption mechanism (Table 3). Phenanthrene sorption kinetics were satisfactorily described by a pseudo-second-order model with coefficients of determination (R2) ranging from 0.99875 to 0.99847, compared with R2 values of 0.26508–0.73901 for the Elovich model. This well-fitting pseudo-second-order model indicated that the rate-limiting step was chemical adsorption, including electronic forces through sharing or exchange of electrons35,36. Moreover, it suggested that sorption was governed by the availability of sorption sites on the soil surfaces instead of by the phenanthrene concentration in solution.Table 3 Constants and coeffients of determination of Pseudo-second-order kinetics and Elovich models of sorption.Full size tableAdsorption and desorption isothermsThe isotherm was used for quantitative analysis of phenanthrene transport from liquid to solid phase and for understanding the nature of interactions between phenanthrene and the soil matrix. The sorption and desorption isotherms of phenanthrene in soils were shown in Fig. 3. The data showed that phenanthrene adsorption and desorption capacities of three soils varied markedly due to their different physicochemical properties. With the increase of phenanthrene concentration, the adsorbed amount increased. At the same temperature, the adsorption capacity of silty sand was minimum while loess was maximum. This is mainly related to the soil physicochemical properties. At the same initial concentration, the temperature increase from 20 °C to 40 °C showed that the adsorption and desorption capacity decreased with temperature increase. On the one hand, the rise of temperature can increase the phenanthrene solubility in the liquid phase. On the other hand, it could reduce various forces between the soil surface and phenanthrene37.Figure 3(a)20 °C adsorption isotherms for phenanthrene in soils. (b)30 °C adsorption isotherms for phenanthrene in soils. (c)40 °C adsorption isotherms for phenanthrene in soils. (d) 20 °C desorption isotherms for phenanthrene in soils. (e) 30 °C desorption isotherms for phenanthrene in soils. (f) 40 °C desorption isotherms for phenanthrene in soils.Full size imageThe Freundlich isotherm was used mainly for adsorption surfaces with nonuniform energy distribution, and the Langmuir isotherm was used for monolayer adsorption on perfectly smooth and homogeneous surfaces38. The experimental data were fitted with the Langmuir and Freundlich adsorption models, and the isotherm parameters logKF, 1/n, KL, qmax and the coefficient of determination (R2) of phenanthrene in soils were listed in Table 4.Table 4 Isotherm parameters for Phenanthrene sorption in soils.Full size tableAs shown in Table 4, according to the coefficients of determination (R2), all soils were better fitted with the Freundlich model, which assumes that phenanthrene sorption and desorption occurs on a heterogeneous surface with the possibility of sorption being multi-layered39. This phenomenon has also been observed in humic acid and nanometer clay mineral40. It showed that the soil adsorption of organic matter was not only surface adsorption but also the process of soil organic matter distribution41,42,43 reached the equilibrium isotherm fitted well with the Freundlich equation when studying the adsorption behavior of aromatic compounds by solids.Adsorption and desorption thermodynamicsTo clarify the adsorption mechanisms, the thermodynamic parameters mentioned earlier were calculated and presented in Table 5. Generally, the value of Gibbs free energy changeΔG0 indicated the spontaneity of a chemical reaction. Therefore, it could evaluate whether sorption was relate to spontaneous interaction44. Negative values of ΔG0 indicated that the feasibility and spontaneous nature. The research was under the temperature range about 293–313 K. For adsorption process, all soils ΔG0 was  0 and desorption ΔH  1, P  temperature  > phenanthrene concentration  > pH. In the interaction, the phenanthrene concentration and organic matter have a significant effect on the silt adsorption rate. The coefficient of determination of the silt complex correlation is R2 = 0.9464, indicating that the response model has a good fit, and the experimental error is within the acceptable range. Adjusting the complex correlation coefficient R2 = 0.8982 indicates that the regression relationship can explain 89.82% of the change in the dependent variable. Therefore, this The model can be used to analyze and predict the effect of different factors on the adsorption rate of phenanthrene.3D response surface analysisIn response surface optimization, the three-dimensional response surface graph reflects the influence of the interaction of the other two variables on the response value, and the slope of the response surface reflects the significance of the interaction of the two variables on the response value. The more significant the interaction effect is on the response value, when the slope is gentle, the effect is not significant. If the contour map is elliptical, it indicates that the interaction between the two variables is significant, and if the contour map is circular, it is not significant46. In addition, the slope and density of the contour line also reflect the influence of the variable on the response value. The steeper the contour line and the greater the density, the greater the influence of the variable on the response value47.

    (1) Loess Fig. 5 is a three-dimensional response surface diagram of the interaction between initial phenanthrene concentration and pH to phenanthrene adsorption on loess. It can be seen from the figure that the slope of the response surface graph is steep, and the contour line is an approximate circle, indicating that the interaction between phenanthrene concentration and pH is not significant for the response value. With the increase of pH, the adsorption rate of phenanthrene on loess showed a slow decline at first to the lowest point at 6, and then gradually increased. When the soil pH was close to 6, with the increase of the initial phenanthrene concentration, the adsorption rate of loess also showed a trend of first decreasing and then increasing. According to the F value, F = 0.337, P = 0.5532  > 0.05, it can be concluded that soil pH and initial phenanthrene concentration of the solution have no significant interaction on the adsorption rate of loess.

    Figure 6 shows the effects of initial phenanthrene concentration and organic matter on phenanthrene adsorption on Loess under the condition that pH value and temperature are at the central point. It can be seen from the figure that the initial phenanthrene concentration and soil organic matter contour are steep, indicating that their interaction is significant. The range of phenanthrene adsorption rate is 70 ~ 95, and the change of surface is steep. From the Loess error analysis, it can be seen that if f value is 6.05 and P value is 0.0275  More

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    Climate, currents and species traits contribute to early stages of marine species redistribution

    Pecl, G. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 
    PubMed 

    Google Scholar 
    Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).Article 

    Google Scholar 
    Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).Article 
    PubMed 

    Google Scholar 
    Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 
    PubMed 

    Google Scholar 
    Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).Article 

    Google Scholar 
    Sanford, E., Sones, J. L., García-Reyes, M., Goddard, J. H. & Largier, J. L. Widespread shifts in the coastal biota of northern California during the 2014–2016 marine heatwaves. Sci. Rep. 9, 4216 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Molinos, J. G., Burrows, M. & Poloczanska, E. Ocean currents modify the coupling between climate change and biogeographical shifts. Sci. Rep. 7, 1–9 (2017).
    Google Scholar 
    Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean‐warming hotspot. Ecol. Lett. 18, 944–953 (2015).Article 
    PubMed 

    Google Scholar 
    Figueira, W. F., Curley, B. & Booth, D. J. Can temperature-dependent predation rates regulate range expansion potential of tropical vagrant fishes? Mar. Biol. 166, 73 (2019).Article 

    Google Scholar 
    Champion, C. & Coleman, M. A. Seascape topography slows predicted range shifts in fish under climate change. Limnol. Oceanogr. Lett. 6, 143–153 (2021).Article 

    Google Scholar 
    Roberts, S. M., Boustany, A. M. & Halpin, P. N. Substrate-dependent fish have shifted less in distribution under climate change. Commun. Biol. 3, 1–7 (2020).Article 

    Google Scholar 
    Engelhard, G. H., Righton, D. A. & Pinnegar, J. K. Climate change and fishing: a century of shifting distribution in North Sea cod. Glob. Change Biol. 20, 2473–2483 (2014).Article 

    Google Scholar 
    Twiname, S. et al. A cross‐scale framework to support a mechanistic understanding and modelling of marine climate‐driven species redistribution, from individuals to communities. Ecography 43, 1764–1778 (2020).Article 

    Google Scholar 
    Bates, A. E. et al. Defining and observing stages of climate-mediated range shifts in marine systems. Glob. Environ. Change 26, 27–38 (2014).Article 

    Google Scholar 
    Fogarty, H. E., Burrows, M. T., Pecl, G. T., Robinson, L. M. & Poloczanska, E. S. Are fish outside their usual ranges early indicators of climate‐driven range shifts? Glob. Change Biol. 23, 2047–2057 (2017).Article 

    Google Scholar 
    Jiguet, F. & Barbet‐Massin, M. Climate change and rates of vagrancy of Siberian bird species to Europe. Ibis 155, 194–198 (2013).Article 

    Google Scholar 
    Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).Article 
    PubMed 

    Google Scholar 
    Peck, M. A. et al. Projecting changes in the distribution and productivity of living marine resources: a critical review of the suite of modeling approaches used in the large European project VECTORS. Estuar., Coast. Shelf Sci. 201, 40–55 (2016).Article 

    Google Scholar 
    Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).Article 
    PubMed 

    Google Scholar 
    Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).Article 
    PubMed 

    Google Scholar 
    Champion, C., Hobday, A. J., Zhang, X., Pecl, G. T. & Tracey, S. R. Changing windows of opportunity: past and future climate-driven shifts in temporal persistence of kingfish (Seriola lalandi) oceanographic habitat within south-eastern Australian bioregions. Mar. Freshw. Res. 70, 33–42 (2019).Article 

    Google Scholar 
    Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: scaling from organisms to communities. Annu. Rev. Mar. Sci. 12, 153–179 (2020).Article 

    Google Scholar 
    Lonhart, S. I., Jeppesen, R., Beas-Luna, R., Crooks, J. A. & Lorda, J. Shifts in the distribution and abundance of coastal marine species along the eastern Pacific Ocean during marine heatwaves from 2013 to 2018. Mar. Biodivers. Rec. 12, 1–15 (2019).Article 

    Google Scholar 
    Wernberg, T. et al. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Change 3, 78–82 (2013).Article 

    Google Scholar 
    Lenanton, R., Dowling, C., Smith, K., Fairclough, D. & Jackson, G. Potential influence of a marine heatwave on range extensions of tropical fishes in the eastern Indian Ocean—Invaluable contributions from amateur observers. Regional Stud. Mar. Sci. 13, 19–31 (2017).Article 

    Google Scholar 
    Leriorato, J. C. & Nakamura, Y. Unpredictable extreme cold events: a threat to range-shifting tropical reef fishes in temperate waters. Mar. Biol. 166, 1–10 (2019).Article 

    Google Scholar 
    Hobday, A. J. & Pecl, G. T. Identification of global marine hotspots: sentinels for change and vanguards for adaptation action. Rev. Fish. Biol. Fish. 24, 415–425 (2014).Article 

    Google Scholar 
    Pecl, G. T. et al. Redmap Australia: challenges and successes with a large-scale citizen science-based approach to ecological monitoring and community engagement on climate change. Front. Mar. Sci. 6, 349 (2019).Article 

    Google Scholar 
    Jacox, M. G., Alexander, M. A., Bograd, S. J. & Scott, J. D. Thermal displacement by marine heatwaves. Nature 584, 82–86 (2020).Article 
    PubMed 

    Google Scholar 
    Brown, C. J. et al. Ecological and methodological drivers of species’ distribution and phenology responses to climate change. Glob. Change Biol. 22, 1548–1560 (2016).Article 

    Google Scholar 
    Fuchs, H. L. et al. Wrong-way migrations of benthic species driven by ocean warming and larval transport. Nat. Clim. Change 10, 1052–1056 (2020).Article 

    Google Scholar 
    Rooney, N., McCann, K. S. & Moore, J. C. A landscape theory for food web architecture. Ecol. Lett. 11, 867–881 (2008).Article 
    PubMed 

    Google Scholar 
    Feary, D. A. et al. Latitudinal shifts in coral reef fishes: why some species do and others do not shift. Fish. Fish. 15, 593–615 (2014).Article 

    Google Scholar 
    Beissinger, S. R. & Riddell, E. A. Why are species’ traits weak predictors of range shifts? Ann. Rev. Ecol. Evol. Syst. 52, 47–66 (2021).Pearce, A. F. & Feng, M. The rise and fall of the “marine heat wave” off Western Australia during the summer of 2010/2011. J. Mar. Syst. 111, 139–156 (2013).Article 

    Google Scholar 
    Oliver, E. C. et al. The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun. 8, 16101 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gervais, C. R., Champion, C. & Pecl, G. T. Species on the move around the Australian coastline: a continental scale review of climate‐driven species redistribution in marine systems. Glob. Change Biol. 27, 3200–3217 (2021).Article 

    Google Scholar 
    Nursey-Bray, M., Palmer, R. & Pecl, G. Spot, log, map: assessing a marine virtual citizen science program against Reed’s best practice for stakeholder participation in environmental management. Ocean Coast. Manag. 151, 1–9 (2018).Article 

    Google Scholar 
    Pecl, G. T. et al. Ocean warming hotspots provide early warning laboratories for climate change impacts. Rev. Fish. Biol. Fish. 24, 409–413 (2014).Article 

    Google Scholar 
    Stuart-Smith, J. et al. Southernmost records of two Seriola species in an Australian ocean-warming hotspot. Mar. Biodivers. 48, 1579–1582 (2018).Article 

    Google Scholar 
    Provoost, P. & Bosch, S. robis: R Client to access data from the OBIS API. Ocean Biogeographic Information System. Intergovernmental Oceanographic Commission of UNESCO. R package version 2.1.8, https://cran.r-project.org/package=robis (2019).Froese, R. & Pauly, D. (eds). FishBase. World Wide Web electronic publication. www.fishbase.org. (2022). Accessed 14 July 2019.ABRS. Australian Faunal Directory. Australian Biological Resources Study, Canberra. https://biodiversity.org.au/afd/home. (2020). Accessed 15 July 2019.Robinson, L. M. et al. Rapid assessment of an ocean warming hotspot reveals “high” confidence in potential species’ range extensions. Glob. Environ. Change 31, 28–37 (2015).Article 

    Google Scholar 
    Hijmans, R. J. raster: geographic data analysis and modeling. R package version 3.4-5. https://CRAN.R-project.org/package=raster (2020).van Etten, J. R package gdistance: distances and routes on geographical grids. J. Stat. Softw. 76, 1–21 (2017).
    Google Scholar 
    Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).Article 

    Google Scholar 
    Molinos, J. G., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an r package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).Article 

    Google Scholar 
    Schlegel, R. W. & Smit, A. J. heatwaveR: a central algorithm for the detection of heatwaves and cold-spells. J. Open Source Softw. 3, 821 (2018).Article 

    Google Scholar 
    Venables, W. N. & Ripley, B. D. Modern applied statistics with S. 4th edn, (Springer, 2002).Lüdecke, D. sjPlot: data visualization for statistics in social science. R package version 2.8.6. https://CRAN.R-project.org/package=sjPlot (2020). More

  • in

    The impact of the first United Kingdom COVID-19 lockdown on environmental air pollution, digital display device use and ocular surface disease symptomatology amongst shielding patients

    Knight, H. et al. Impacts of the COVID-19 Pandemic and Self-Isolation on Students and Staff in Higher Education: A Qualitative Study. Int. J. Environ. Res. Public Health 18, 10675 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Higham, J. E., Ramírez, C. A., Green, M. A. & Morse, A. P. UK COVID-19 lockdown: 100 days of air pollution reduction? Air Quality. Atmosphere & Health https://doi.org/10.1007/s11869-020-00937-0 (2020).Article 

    Google Scholar 
    Office, P. M. s. Slides and datasets to accompany coronavirus press conference. (2020).Organization, W. H. WHO global air quality guidelines: particulate matter (PM2. 5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary. (2021).Singh, A. et al. Impacts of emergency health protection measures upon air quality, traffic and public health: evidence from Oxford UK. Environ. Pollut. 293, 118584. https://doi.org/10.1016/j.envpol.2021.118584 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Lee, J. D., Drysdale, W. S., Finch, D. P., Wilde, S. E. & Palmer, P. I. UK surface NO2 levels dropped by 42% during the COVID-19 lockdown: impact on surface O3. Atmos. Chem. Phys. 20, 15743–15759. https://doi.org/10.5194/acp-20-15743-2020 (2020).Article 
    CAS 

    Google Scholar 
    Shi, Z. et al. Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns. Science Advances 7, eabd6696, doi:https://doi.org/10.1126/sciadv.abd6696 (2021).Ropkins, K. & Tate, J. E. Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK. Sci. Total Environ. 754, 142374. https://doi.org/10.1016/j.scitotenv.2020.142374 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nwanaji-Enwerem, J. C., Allen, J. G. & Beamer, P. I. Another invisible enemy indoors: COVID-19, human health, the home, and United States indoor air policy. J Expo Sci Environ Epidemiol 30, 773–775. https://doi.org/10.1038/s41370-020-0247-x (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rasha, A., Karan Jetly, J. & Shqran, S. Indoor Air Quality Monitoring Systems: A Comprehensive Review of Different IAQM Systems. International Journal of Knowledge-Based Organizations (IJKBO) 11, 1–14, doi:https://doi.org/10.4018/ijkbo.2021070101 (2021).World Health Organization. Regional Office for, E. WHO guidelines for indoor air quality: selected pollutants. xxv, 454 p. (World Health Organization. Regional Office for Europe, 2010).Stafoggia, M. et al. Long-term exposure to ambient air pollution and incidence of cerebrovascular events: Results from 11 European cohorts within the ESCAPE project. Environ. Health Perspect 122, 919–925. https://doi.org/10.1289/ehp.1307301 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brook, R. D. et al. Particulate matter air pollution and cardiovascular disease: An update to the scientific statement from the American heart association. Circulation 121, 2331–2378. https://doi.org/10.1161/CIR.0b013e3181dbece1 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Raaschou-Nielsen, O. et al. Air pollution and lung cancer incidence in 17 European cohorts: prospective analyses from the European study of cohorts for air pollution effects (ESCAPE). Lancet Oncol. 14, 813–822. https://doi.org/10.1016/s1470-2045(13)70279-1 (2013).Article 
    PubMed 

    Google Scholar 
    Guan, W. J., Zheng, X. Y., Chung, K. F. & Zhong, N. S. Impact of air pollution on the burden of chronic respiratory diseases in China: Time for urgent action. Lancet 388, 1939–1951. https://doi.org/10.1016/s0140-6736(16)31597-5 (2016).Article 
    PubMed 

    Google Scholar 
    Atkinson, R. W. et al. Acute effects of particulate air pollution on respiratory admissions: Results from APHEA 2 project. Air pollution and health: A European approach. Am. J. Respir. Crit. Care Med. 164, 1860–1866. https://doi.org/10.1164/ajrccm.164.10.2010138 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Stapleton, F. et al. TFOS DEWS II epidemiology report. Ocular Surf. 15, 334–365. https://doi.org/10.1016/j.jtos.2017.05.003 (2017).Article 

    Google Scholar 
    Starr, C. E. et al. Dry eye disease flares: A rapid evidence assessment. Ocul. Surf. 22, 51–59. https://doi.org/10.1016/j.jtos.2021.07.001 (2021).Article 
    PubMed 

    Google Scholar 
    Torricelli, A. A. et al. Correlation between signs and symptoms of ocular surface dysfunction and tear osmolarity with ambient levels of air pollution in a large metropolitan area. Cornea 32, e11-15. https://doi.org/10.1097/ICO.0b013e31825e845d (2013).Article 
    PubMed 

    Google Scholar 
    Hwang, S. H. et al. Potential importance of ozone in the association between outdoor air pollution and dry eye disease in South Korea. JAMA Ophthalmol. 134, 503–510. https://doi.org/10.1001/jamaophthalmol.2016.0139 (2016).Article 
    PubMed 

    Google Scholar 
    Wiwatanadate, P. Acute air pollution-related symptoms among residents in Chiang Mai Thailand. J. Environ. Health 76, 76–84 (2014).CAS 
    PubMed 

    Google Scholar 
    Alves, M., Novaes, P., Morraye Mde, A., Reinach, P. S. & Rocha, E. M. Is dry eye an environmental disease? Arq. Bras. Oftalmol. 77, 193–200 https://doi.org/10.5935/0004-2749.20140050 (2014).Bourcier, T. et al. Effects of air pollution and climatic conditions on the frequency of ophthalmological emergency examinations. Br. J. Ophthalmol. 87, 809–811. https://doi.org/10.1136/bjo.87.7.809 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hao, R. et al. Impact of air pollution on the ocular surface and tear cytokine levels: A multicenter prospective cohort study. Front. Med. (Lausanne) 9, 909330. https://doi.org/10.3389/fmed.2022.909330 (2022).Article 
    PubMed 

    Google Scholar 
    Vehof, J., Snieder, H., Jansonius, N. & Hammond, C. J. Prevalence and risk factors of dry eye in 79,866 participants of the population-based lifelines cohort study in the Netherlands. Ocul. Surf. 19, 83–93. https://doi.org/10.1016/j.jtos.2020.04.005 (2021).Article 
    PubMed 

    Google Scholar 
    Wolffsohn, J. S. et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. Ocul. Surf. 21, 58–63. https://doi.org/10.1016/j.jtos.2021.05.001 (2021).Article 
    PubMed 

    Google Scholar 
    Núñez-Álvarez, C. & Osborne, N. N. Enhancement of corneal epithelium cell survival, proliferation and migration by red light: Relevance to corneal wound healing. Exp. Eye Res. 180, 231–241. https://doi.org/10.1016/j.exer.2019.01.003 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Marek, V. et al. Blue light phototoxicity toward human corneal and conjunctival epithelial cells in basal and hyperosmolar conditions. Free Radic. Biol. Med. 126, 27–40. https://doi.org/10.1016/j.freeradbiomed.2018.07.012 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Talens-Estarelles, C., García-Marqués, J. V., Cerviño, A. & García-Lázaro, S. Determining the best management strategy for preventing short-term effects of digital display use on dry eyes. Eye Contact Lens 48, 416–423. https://doi.org/10.1097/icl.0000000000000921 (2022).Article 
    PubMed 

    Google Scholar 
    GOV.UK. COVID-19: guidance on protecting people defined on medical grounds as extremely vulnerable, (2020).Joy, M. et al. Reorganisation of primary care for older adults during COVID-19: A cross-sectional database study in the UK. Br. J. Gen. Pract. 70, e540–e547. https://doi.org/10.3399/bjgp20X710933 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schiffman, R. M., Christianson, M. D., Jacobsen, G., Hirsch, J. D. & Reis, B. L. Reliability and validity of the ocular surface disease index. Arch. Ophthalmol. 118, 615–621. https://doi.org/10.1001/archopht.118.5.615 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Amparo, F. & Dana, R. Web-based longitudinal remote assessment of dry eye symptoms. Ocul. Surf. 16, 249–253. https://doi.org/10.1016/j.jtos.2018.01.002 (2018).Article 
    PubMed 

    Google Scholar 
    Inomata, T. et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmol. 138, 58–68. https://doi.org/10.1001/jamaophthalmol.2019.4815 (2020).Article 
    PubMed 

    Google Scholar 
    Toth, M. & Jokić-Begić, N. Psychological contribution to understanding the nature of dry eye disease: A cross-sectional study of anxiety sensitivity and dry eyes. Health Psychol. Behav. Med. 8, 202–219. https://doi.org/10.1080/21642850.2020.1770093 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mehra, D. & Galor, A. Digital screen use and dry eye: A review. Asia-Pacific J. Ophthalmol. 9, 491–497. https://doi.org/10.1097/apo.0000000000000328 (2020).Article 

    Google Scholar 
    Galor, A., Kumar, N., Feuer, W. & Lee, D. J. Environmental factors affect the risk of dry eye syndrome in a United States veteran population. Ophthalmology 121, 972–973. https://doi.org/10.1016/j.ophtha.2013.11.036 (2014).Article 
    PubMed 

    Google Scholar 
    Courtin, R. et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open 6, e009675. https://doi.org/10.1136/bmjopen-2015-009675 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Torricelli, A. A. et al. Effects of ambient levels of traffic-derived air pollution on the ocular surface: Analysis of symptoms, conjunctival goblet cell count and mucin 5AC gene expression. Environ. Res. 131, 59–63. https://doi.org/10.1016/j.envres.2014.02.014 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gupta, S. K., Gupta, V., Joshi, S. & Tandon, R. Subclinically dry eyes in urban Delhi: An impact of air pollution?. Ophthalmologica 216, 368–371. https://doi.org/10.1159/000066183 (2002).Article 
    CAS 
    PubMed 

    Google Scholar 
    Berg, E. J. et al. Climatic and environmental correlates of dry eye disease severity: A report from the dry eye assessment and management (DREAM) study. Trans. Vision Sci. Technol. 9, 25–25. https://doi.org/10.1167/tvst.9.5.25 (2020).Article 

    Google Scholar 
    Lang, S.-J., Abel, G. A., Mant, J. & Mullis, R. Impact of socioeconomic deprivation on screening for cardiovascular disease risk in a primary prevention population: A cross-sectional study. BMJ Open 6, e009984. https://doi.org/10.1136/bmjopen-2015-009984 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Denniston, A. K. et al. United Kingdom diabetic retinopathy electronic medical record (UK DR EMR) users group: Report 4, real-world data on the impact of deprivation on the presentation of diabetic eye disease at hospital services. Br. J. Ophthalmol. 103, 837–843. https://doi.org/10.1136/bjophthalmol-2018-312568 (2019).Article 
    PubMed 

    Google Scholar 
    Nessim, M., Denniston, A. K., Nolan, W., Holder, R. & Shah, P. Research into Glaucoma and Ethnicity (ReGAE) 8: Is there a relationship between social deprivation and acute primary angle closure?. Br. J. Ophthalmol. 94, 1304–1306. https://doi.org/10.1136/bjo.2009.160721 (2010).Article 
    PubMed 

    Google Scholar 
    Sharma, H. E. et al. The role of social deprivation in severe neovascular age-related macular degeneration. Br. J. Ophthalmol. 98, 1625–1628. https://doi.org/10.1136/bjophthalmol-2014-304959 (2014).Article 
    PubMed 

    Google Scholar 
    Bo, M., Salizzoni, P., Clerico, M. & Buccolieri, R. Assessment of indoor-outdoor particulate matter air pollution: A review. Atmosphere 8, 136 (2017).Article 

    Google Scholar 
    Strøm-Tejsen, P., Zukowska, D., Fang, L., Space, D. R. & Wyon, D. P. Advantages for passengers and cabin crew of operating a gas-phase adsorption air purifier in 11-h simulated flights. Indoor Air 18, 172–181. https://doi.org/10.1111/j.1600-0668.2007.00511.x (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mandell, J. T., Idarraga, M., Kumar, N. & Galor, A. Impact of air pollution and weather on dry eye. J. Clin. Med. https://doi.org/10.3390/jcm9113740 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Navarro, D. Learning Statistics with R. (Daniel Joseph Navarro, 2015). More

  • in

    Multi-proxy dentition analyses reveal niche partitioning between sympatric herbivorous dinosaurs

    Versluys, J. Die Kaubewegungen von Trachodon. Palaontol. Z. 4, 80–87 (1922).
    Google Scholar 
    Kripp, D. Die Kaubewegung und Lebensweise von Edmontosaurus spec. auf Grund der mechanischkonstruktiven analyse. Palaeobiologica 5, 409–422 (1933).
    Google Scholar 
    Ostrom, J. H. Cranial morphology of the hadrosaurian dinosaurs of North America. Bull. Am. Mus. Nat. Hist. 122, 39–186 (1961).
    Google Scholar 
    Ostrom, J. H. A functional analysis of jaw mechanics in the dinosaur. Triceratops. Postilla. 88, 1–35 (1964).MathSciNet 

    Google Scholar 
    Galton, P. M. The cheeks of ornithischian dinosaurs. Lethaia 6, 67–89. https://doi.org/10.1111/j.1502-3931.1973.tb00873.x (1973).Article 

    Google Scholar 
    Galton, P. M. Herbivorous adaptations of Late Triassic and Early Jurassic dinosaurs. In The Beginning of the Age of Dinosaurs (ed. Padian, K.) 203–221 (Cambridge University Press, 1986).
    Google Scholar 
    Weishampel, D. B. Hadrosaurid jaw mechanics. Acta Palaeontol. Pol. 28, 271–280 (1983).
    Google Scholar 
    Weishampel, D. B. The evolution of jaw mechanisms in ornithopod dinosaurs. Adv. Anat. Embryol. Cell. Biol. 87, 1–2 (1984).Article 
    CAS 
    PubMed 

    Google Scholar 
    Weishampel, D. B. Interactions between Mesozoic plants and vertebrates: fructifications and seed predation. Neues Jahrb. Geol. Paläontol. Abh. 167, 224–250 (1984).
    Google Scholar 
    Weishampel, D. B. & Norman, D. B. Vertebrate herbivory in the Mesozoic: Jaws, plants, and evolutionary metrics. In Paleobiology of the Dinosaurs Special Papers 238 (ed. Farlow, J. O.) 87–100 (Geological Society of America, 1989).Chapter 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Feeding mechanisms in some small herbivorous dinosaurs: processes and patterns. In Biomechanics and Evolution (eds Rayner, J. M. V. & Wooton, R. J.) 161–181 (Cambridge University Press, 1991).
    Google Scholar 
    Sereno, P., Zijin, Z. & Lin, T. A new psittacosaur from Inner Mongolia and the parrot-like structure and function of the psittacosaur skull. Proc. Roy. Soc. B. 277, 199–209. https://doi.org/10.1098/rspb.2009.0691 (2010).Article 

    Google Scholar 
    Barrett, P. M. Paleobiology of herbivorous dinosaurs. Annu. Rev. Earth Planet. Sci. 42(1), 207–230. https://doi.org/10.1146/annurev-earth-042711-105515 (2014).Article 
    CAS 

    Google Scholar 
    Erickson, G. M. et al. Wear biomechanics in the slicing dentition of the giant horned dinosaur Triceratops. Sci. Adv. 1(5), e1500055. https://doi.org/10.1126/sciadv.1500055 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nabavizadeh, A. Hadrosauroid jaw mechanics and the functionalsignificance of the predentary bone. In The hadrosaurs: Proceedings of the International Hadrosaur Symposium (eds Evans, D. & Eberth, D.) 467–482 (Indiana University Press, 2014).
    Google Scholar 
    Nabavizadeh, A. Evolutionary trends in the jaw adductor mechanics of ornithischian dinosaurs. Anat. Rec. 299(3), 271–294. https://doi.org/10.1002/ar.23306 (2016).Article 

    Google Scholar 
    Nabavizadeh, A. new reconstruction of cranial musculature in ceratopsian dinosaurs: Implications for jaw mechanics and ‘cheek’anatomy. FASEB J. 30, lb27–lb27. https://doi.org/10.1096/fasebj.30.1_supplement.lb27 (2016).Article 

    Google Scholar 
    Nabavizadeh, A. new reconstruction of cranial musculature in ornithischian dinosaurs: Implications for feeding mechanismsand buccal anatomy. Anat. Rec. 303, 347–362. https://doi.org/10.1002/ar.23988 (2020).Article 

    Google Scholar 
    Varriale, F. J. Dental microwear reveals mammal-like chewing in the neoceratopsian dinosaur Leptoceratops gracilis. PeerJ 4, e2132. https://doi.org/10.7717/peerj.2132 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Melstrom, K. M., Chiappe, L. M. & Smith, N. D. Exceptionally simple, rapidly replaced teeth in sauropod dinosaurs demonstrate a novel evolutionary strategy for herbivory in Late Jurassic ecosystems. BMC Evol. Biol. 21(1), 1–12. https://doi.org/10.1186/s12862-021-01932-4 (2021).Article 

    Google Scholar 
    Norman, D. B. On the cranial morphology and evolution of ornithopod dinosaurs. Proc. Zool. Soc. Lond. 52, 521–547 (1984).
    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Ornithopod feeding mechanisms: Their bearing on the evolution of herbivory. Am. Nat. 126, 151–164. https://doi.org/10.1086/284406 (1985).Article 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Vegetarian dinosaurs chew it differently-living mammals can chew plants for more effectively than reptiles. Yet some dinosaurs were surprisingly adept chewers. This unexpected ability may have been crucial in their evolution. New Sci. 114(1559), 42–45 (1987).
    Google Scholar 
    Rybczynski, N., Tirabasso, A., Bloskie, P., Cuthbertson, R. & Holliday, C. A three-dimensional animation model of Edmontosaurus (Hadrosauridae) for testing chewing hypotheses. Palaeontol. Electron. 11(2), 9A (2008).
    Google Scholar 
    Williams, V. S., Barrett, P. M. & Purnell, M. A. Quantitative analysis of dental microwear in hadrosaurid dinosaurs, and the implications for hypotheses of jaw mechanics and feeding. PNAS 106(27), 11194–11199. https://doi.org/10.1073/pnas.0812631106 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cuthbertson, R. S., Tirabasso, A., Rybczynski, N. & Holmes, R. B. Kinetic limitations of intracranial joints in Brachylophosaurus canadensis and Edmontosaurus regalis (Dinosauria: Hadrosauridae), and their implications for the chewing mechanics of hadrosaurids. Anat. Rec. 295, 968–979. https://doi.org/10.1002/ar.22458 (2012).Article 

    Google Scholar 
    Erickson, G. M. & Zelenitsky, D. K. Osteohistology and occlusal morphology of Hypacrosaurus stebengeri teeth throughout ontogeny with comments on wear-induced form and function. In Hadrosaurs (eds Eberth, D. A. & Evans, D. C.) 422–432 (Indiana University Press, 2014).
    Google Scholar 
    Barrett, P. M. Tooth wear and possible jaw action of Scelidosaurus harrisonii Owen and a review of feeding mechanisms in other thyreophoran dinosaurs. In The Armored Dinosaurs (ed. Carpenter, K.) 25–52 (Indiana University Press, 2001).
    Google Scholar 
    Rybczynski, N. & Vickaryous, M. K. Evidence of complex jaw movement in the Late Cretaceous ankylosaurid Euoplocephalus tutus (Dinosauria: Thyreophora). In The Armored Dinosaurs (ed. Carpenter, K.) 299–317 (Indiana University Press, 2001).
    Google Scholar 
    Mallon, J. C. & Anderson, J. S. The functional and palaeoecological implications of tooth morphology and wear for the megaherbivorous dinosaurs from the Dinosaur Park Formation (Upper Campanian) of Alberta, Canada. PLoS ONE 9(6), e98605. https://doi.org/10.1371/journal.pone.0098605 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mallon, J. C. & Anderson, J. S. Implications of beak morphology for the evolutionary paleoecology of the megaherbivorous dinosaurs from the Dinosaur Park Formation (upper Campanian) of Alberta, Canada. Palaeogeogr. Palaeoclimatol. Palaeoecol. 394, 29–41. https://doi.org/10.1016/j.palaeo.2013.11.014 (2014).Article 

    Google Scholar 
    Ősi, A., Barrett, P. M., Földes, T. & Tokai, R. Wear pattern, dental function, and jaw mechanism in the Late Cretaceous ankylosaur Hungarosaurus. Anat. Rec. 297(7), 1165–1180. https://doi.org/10.1002/ar.22910 (2014).Article 

    Google Scholar 
    Ősi, A., Prondvai, E., Mallon, J. & Bodor, E. R. Diversity and convergences in the evolution of feeding adaptations in ankylosaurs (Dinosauria: Ornithischia). Hist. Biol. 29(4), 539–570. https://doi.org/10.1080/08912963.2016.1208194 (2017).Article 

    Google Scholar 
    Hill, R. V., D’Emic, M. D., Bever, G. S. & Norell, M. A. A complex hyobranchial apparatus in a Cretaceous dinosaur and the antiquity of avian paraglossalia. Zool. J. Linn. Soc. 175(4), 892–909. https://doi.org/10.1111/zoj.12293 (2015).Article 

    Google Scholar 
    Lautenschlager, S., Brassey, C. A., Button, D. J. & Barrett, P. M. Decoupled form and function in disparate herbivorous dinosaur clades. Sci. Rep. 6(1), 1–10. https://doi.org/10.1038/srep26495 (2016).Article 
    CAS 

    Google Scholar 
    Skutschas, P. P. et al. Wear patterns and dental functioning in an Early Cretaceous stegosaur from Yakutia, Eastern Russia. PLoS ONE 16(3), e0248163. https://doi.org/10.1371/journal.pone.0248163 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Strickson, E., Prieto-Márquez, A., Benton, M. J. & Stubbs, T. L. Dynamics of dental evolution in ornithopod dinosaurs. Sci. Rep. 6, 28904. https://doi.org/10.1038/srep28904 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Virág, A. & Ősi, A. Morphometry, microstructure, and wear pattern of neornithischian dinosaur teeth from the Upper Cretaceous Iharkút locality (Hungary). Anat. Rec. 300(8), 1439–1463. https://doi.org/10.1002/ar.23592 (2017).Article 

    Google Scholar 
    Mallon, J. C. & Anderson, J. S. Skull ecomorphology of megaherbivorous dinosaurs from the Dinosaur Park Formation (Upper Campanian) of Alberta, Canada. PLoS ONE 8(7), e67182. https://doi.org/10.1371/journal.pone.0067182 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Botfalvai, G., Ősi, A. & Mindszenty, A. Taphonomic and paleoecologic investigations of the Late Cretaceous (Santonian) Iharkút vertebrate assemblage (Bakony Mts, northwestern Hungary). Palaeogeogr. Palaeoclimatol. Palaeoecol. 417, 379–405. https://doi.org/10.1016/j.palaeo.2014.09.032 (2015).Article 

    Google Scholar 
    Botfalvai, G., Haas, J., Bodor, E. R., Mindszenty, A. & Ősi, A. Facies architecture and palaeoenvironmental implications of the upper Cretaceous (Santonian) Csehbánya formation at the Iharkút vertebrate locality (Bakony Mountains, Northwestern Hungary). Palaeogeogr. Palaeoclimatol. Palaeoecol. 441, 659–678. https://doi.org/10.1016/j.palaeo.2015.10.018 (2016).Article 

    Google Scholar 
    Ősi, A. et al. The Late Cretaceous continental vertebrate fauna from Iharkút, western Hungary: A review. In Bernissart Dinosaurs and Early Cretaceous Terrestrial Ecosystems (ed. Godefroit, P.) 532–569 (Indiana University Press, 2012).
    Google Scholar 
    Wells, N. A. Making thin sections. In Paleotechniques (eds Feldmann, R. M. et al.) 120–129 (University of Tennessee, 1989).
    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675. https://doi.org/10.1038/nmeth.2089 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, A. R. Surfer Manipulator. http://evomorph.org/surfermanipulator (2011).Evans, A. R., Wilson, G. P., Fortelius, M. & Jernvall, J. High-level similarity of dentitions in carnivorans and rodents. Nature 445, 78–81. https://doi.org/10.1038/nature05433 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wilson, G. P. et al. Adaptive radiation of multituberculate mammals before the extinction of dinosaurs. Nature 483, 457–460. https://doi.org/10.1038/nature10880 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ungar, P. S. Dental microwear of European Miocene catarrhines: Evidence for diets and tooth use. J. Hum. Evol. 31, 355–366. https://doi.org/10.1006/jhev.1996.0065 (1996).Article 

    Google Scholar 
    Ungar, P. S. A semiautomated image analysis procedure for the quantification of dental microwear II. Scanning. 17, 57–59. https://doi.org/10.1002/sca.4950170108 (1995).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ungar, P. S., Brown, C. A., Bergstrom, T. S. & Walker, A. Quantification of dental microwear by tandem scanning confocal microscopy and scale-sSensitive fractal analyses. Scanning 25, 185–193. https://doi.org/10.1002/sca.4950250405 (2003).Article 
    PubMed 

    Google Scholar 
    Ungar, P. S., Merceron, G. & Scott, R. S. Dental microwear texture analysis of Varswater bovids and Early Pliocene paleoenvironments of langebaanweg, Western Cape Province, South Africa. J. Mammal. Evol. 14, 163–181. https://doi.org/10.1007/s10914-007-9050-x (2007).Article 

    Google Scholar 
    Scott, J. R. Dental microwear texture analysis of extant African Bovidae. Mammalia 76, 157–217. https://doi.org/10.1515/mammalia-2011-0083 (2012).Article 

    Google Scholar 
    Merceron, G., Hofman-Kaminska, E. & Kowalczyk, R. 3D dental microwear texture analysis of feeding habits of sympatric ruminants in the Białowieza Primeval Forest, Poland. For. Ecol. Manag. 328, 262–269. https://doi.org/10.1016/j.foreco.2014.05.041 (2014).Article 

    Google Scholar 
    Caporale, S. S. & Ungar, P. S. Rodent incisor microwear as a proxy for ecological reconstruction. Palaeogeog. Palaeocl. Palaeoecol. 446, 225–233. https://doi.org/10.1016/j.palaeo.2016.01.013 (2016).Article 

    Google Scholar 
    R Core Team. R. A language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2021).Erickson, G. M. Incremental lines of von Ebner in dinosaurs and the assessment of tooth replacement rates using growth line counts. PNAS 93(25), 14623–14627. https://doi.org/10.1073/pnas.93.25.14623 (1996).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Godefroit, P. et al. Extreme tooth enlargement in a new Late Cretaceous rhabdodontid dinosaur from Southern France. Sci. Rep. 7(1), 1–9. https://doi.org/10.1038/s41598-017-13160-2 (2017).Article 
    CAS 

    Google Scholar 
    Edmund, G. Tooth replacement phenomena in the lower vertebrates. Life. Sci. Contrib. R. Ont. Mus. 52, 1–190 (1960).
    Google Scholar 
    D’Emic, M. D., Whitlock, J. A., Smith, K. M., Fisher, D. C. & Wilson, J. A. Evolution of high tooth replacement rates in sauropod dinosaurs. PLoS ONE 8(7), e69235. https://doi.org/10.1371/journal.pone.0069235 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ősi, A., Prondvai, E., Butler, R. & Weishampel, D. B. Phylogeny, histology and inferred body size evolution in a new rhabdodontid dinosaur from the Late Cretaceous of Hungary. PLoS ONE 7(9), e44318. https://doi.org/10.1371/journal.pone.0044318 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Weishampel, D. B., Jianu, C. M., Csiki, Z. & Norman, D. B. Osteology and phylogeny of Zalmoxes (ng), an unusual euornithopod dinosaur from the latest Cretaceous of Romania. J. Syst. Palaeontol. 1(2), 65–123. https://doi.org/10.1017/S1477201903001032 (2003).Article 

    Google Scholar 
    Melstrom, K. M. The relationship between diet and tooth complexity in living dentigerous saurians. J. Morphol. 278, 500–522 (2017).Article 
    PubMed 

    Google Scholar 
    LeBlanc, A. R. H., Reisz, R. R., Evans, D. C. & Bailleul, A. M. Ontogeny reveals function and evolution of the hadrosaurid dinosaur dental battery. BMC Evol. Biol. 16(1), 1–13. https://doi.org/10.1186/s12862-016-0721-1 (2016).Article 

    Google Scholar 
    Erickson, G. M. et al. Complex dental structure and wear biomechanics in hadrosaurid dinosaurs. Science 338(6103), 98–101. https://doi.org/10.1126/science.1224495 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Norman, D. B. & Weishampel, D. B. Iguanodontidae and related Ornithopoda. In The Dinosauria (eds Weishampel, D. B. et al.) 510–533 (University of California Press, 1990).
    Google Scholar 
    Hulke, J. W. An attempt at a complete osteology of Hypsilophodon foxii, a British Wealden dinosaur. Philos. Trans. R. Soc. Lond. 172, 1035–1062. https://doi.org/10.1098/rstl.1882.0025 (1882).Article 

    Google Scholar 
    Sternberg, C. H. Thescelosaurus edmontonensis, n. sp., and classification of the Hypsilophodontidae. J. Paleontol. 14, 481–494 (1940).
    Google Scholar 
    Galton, P. M. The ornithischian dinosaur Hypsilophodon from the Wealden of the Isle of Wight. Bull. Br. Mus. Nat. Hist. 25(1), 1–152 (1974).
    Google Scholar 
    Norman, D. B. On the anatomy of Iguanodon atherfieldensis (Ornithischia: Ornithopoda). Bull. Inst. Roy. Sci. Nat. Belgique 56, 281–372 (1986).
    Google Scholar 
    Norman, D. B. & Barrett, P. M. Ornithischian dinosaurs from the lower Cretaceous (Berriasian) of England. Spec. Pap. Palaeontol. 68, 161–190 (2002).
    Google Scholar 
    Kosch, J. C. & Zanno, L. E. Sampling impacts the assessment of tooth growth and replacement rates in archosaurs: Implications for paleontological studies. PeerJ 8, e9918. https://doi.org/10.7717/peerj.9918 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Janis, C. M. & Fortelius, M. On the means whereby mammals achieve increased functional durability of their dentitions with special reference to limiting factors. Biol. Rev. 63, 197–230. https://doi.org/10.1111/j.1469-185X.1988.tb00630.x (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    You, H., Ji, Q. & Li, D. Lanzhousaurus magnidens gen. et sp. nov. from Gansu Province, China: The largest-toothed herbivorous dinosaur in the world. Geol. Bull. Chi 24(9), 785–794 (2005).
    Google Scholar 
    Suarez, C. A., You, H. L., Suarez, M. B., Li, D. Q. & Trieschmann, J. B. Stable isotopes reveal rapid enamel elongation (amelogenesis) rates for the Early Cretaceous iguanodontian dinosaur Lanzhousaurus magnidens. Sci. Rep. 7, 15319. https://doi.org/10.1038/s41598-017-15653-6 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Upchurch, P. & Barrett, P. M. The evolution of sauropod feeding mechanisms. In Evolution of Herbivory in Terrestrial Vertebrates: Perspectives from the Fossil Record (ed. Sues, H. D.) 79–122 (Cambridge University Press, 2000).Chapter 

    Google Scholar 
    Sereno, P. C. & Wilson, J. A. Structure and evolution of a sauropod tooth battery in Curry. In The Sauropods: Evolution and Paleobiology (eds Rogers, K. A. & Wilson, J. A.) 157–177 (University of California Press, 2005).
    Google Scholar 
    Brown, B. & Schlaikjer, E. M. The structure and relationships of Protoceratops. Ann. N. Y. Acad. Sci. 40(3), 133–265. https://doi.org/10.1111/j.1749-6632.1940.tb57047.x (1940).Article 

    Google Scholar 
    Solounias, N., Teaford, M. & Walker, A. Interpreting the diet of extinct ruminants-the case of a non-browsing giraffid. Paleobiology 14, 287–300. https://doi.org/10.1017/S009483730001201X (1988).Article 

    Google Scholar 
    Walker, A. & Teaford, M. Inferences from quantitative analysis of dental microwear. Folia Primatol. 53, 177–189. https://doi.org/10.1159/000156415 (1989).Article 
    CAS 

    Google Scholar 
    Ungar, P. S. Mammalian dental function and wear: A review. Biosurf. Biotribol. 1(1), 25–41. https://doi.org/10.1016/j.bsbt.2014.12.001 (2015).Article 
    MathSciNet 

    Google Scholar 
    Janis, C. M. An estimation of tooth volume and hypsodonty indices in ungulate mammals, and the correlation of these factors with dietary preferences. Mém. Mus. Natl. Hist. Nat. Sér. C Géol. 53, 367–387 (1988).
    Google Scholar 
    Lucas, P. W. et al. The role of dust, grit and phytoliths in tooth wear. Ann. Zool. Fenn. 51(1–2), 143–152. https://doi.org/10.5735/086.051.0215 (2014).Article 

    Google Scholar 
    Winkler, D. E. et al. Shape, size, and quantity of ingested external abrasives influence dental microwear texture formation in guinea pigs. Proc. Nat. Acad. Sci. 117, 22264–22273. https://doi.org/10.1073/pnas.2008149117 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kaiser, T. M. et al. Nano-indentation of native phytoliths and dental tissues: Implications for herbivore-plant combat and dental wear proxies. Evol. Syst. 2, 55–63. https://doi.org/10.3897/evolsyst.2.22678 (2018).Article 

    Google Scholar 
    Winkler, D. E. et al. Forage silica and water content control dental surface texture in guinea pigs and provide implications for dietary reconstruction. Proc. Nat. Acad. Sci. 116, 1325–1330. https://doi.org/10.1073/pnas.1814081116 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ősi, A. & Makádi, L. New remains of Hungarosaurus tormai (Ankylosauria, Dinosauria) from the Upper Cretaceous of Hungary: Skeletal reconstruction and body mass estimation. Palaontol. Z. 83(2), 227–245. https://doi.org/10.1007/s12542-009-0017-5 (2009).Article 

    Google Scholar 
    Winkler, D. E., Schulz-Kornas, E., Kaiser, T. M. & Tütken, T. Dental microwear texture reflects dietary tendencies in extant Lepidosauria despite their limited use of oral food processing. Proc. R. Soc. B 286, 20190544. https://doi.org/10.1098/rspb.2019.0544 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bestwick, J., Unwin, D. M., Butler, R. J. & Purnell, M. A. Dietary diversity and evolution of the earliest flying vertebrates revealed by dental microwear texture analysis. Nat. Commun. 11, 1–9. https://doi.org/10.1038/s41467-020-19022-2 (2020).Article 
    CAS 

    Google Scholar 
    Sakaki, H. et al. Non-occlusal dental microwear texture analysis of a titanosauriform sauropod dinosaur from the Upper Cretaceous (Turonian) Tamagawa Formation, northeastern Japan. Cret. Res. 136, 105218. https://doi.org/10.1016/j.cretres.2022.105218 (2022).Article 

    Google Scholar 
    Fiorillo, A. R. Dental microwear on the teeth of Camarasaurus and Diplodocus; implications for sauropod paleoecology. In Fifth Symposium on Mesozoic Terrestrial Ecosystems and Biota (eds Kielan-Jaworowska, Z. et al.) 23–24 (Paleontologisk Museum, 1991).
    Google Scholar 
    Mallon, J. C., Cuthbertson, R. S. & Tirabasso, A. Hadrosaurid jaw mechanics as revealed by cranial joint limitations and dental microwear analysis. In Hadrosaur Symposium Abstract Volume (eds Braman, D. R. et al.) 87–90 (Royal Tyrrell Museum of Palaeontology, 2011).
    Google Scholar 
    Fiorillo, A. R. Dental microwear patterns of the sauropod dinosaurs Camarasaurus and Diplodocus: Evidence for resource partitioning in the Late Jurassic of North America. Hist. Biol. 13, 1–16. https://doi.org/10.1080/08912969809386568 (1998).Article 

    Google Scholar 
    Sereno, P. C. et al. Structural extremes in a Cretaceous dinosaur. PLoS ONE 2(11), e1230. https://doi.org/10.1371/journal.pone.0001230 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Whitlock, J. A. Inferences of diplodocoid (Sauropoda: Dinosauria) feeding behavior from snout shape and microwear analyses. PLoS ONE 6(4), e18304. https://doi.org/10.1371/journal.pone.0018304 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fiorillo, A. R. Microwear patterns on the teeth of northern high latitude hadrosaurs with comments on microwear patterns in hadrosaurs as a function of latitude and seasonal ecological constraints. Palaeontol. Electron. 14(3), 20A (2011).
    Google Scholar 
    Bell, P. R., Snively, E. & Shychoski, L. A comparison of the jaw mechanics in hadrosaurid and ceratopsid dinosaurs using finite element analysis. Anat. Rec. 292(9), 1338–1351. https://doi.org/10.1002/ar.20978 (2009).Article 

    Google Scholar 
    Chin, K. & Gill, B. D. Dinosaurs, dung beetles, and conifers: Participants in a Cretaceous food web. Palaios 11, 280–285. https://doi.org/10.2307/3515235 (1996).Article 

    Google Scholar 
    Brown, C. M. et al. Dietary palaeoecology of an early Cretaceous armoured dinosaur (Ornithischia; Nodosauridae) based on floral analysis of stomach contents. Roy. Soc. Open Sci. 7(6), 200305. https://doi.org/10.1098/rsos.200305 (2020).Article 
    CAS 

    Google Scholar 
    Crane, P. C., Friis, E. M. & Pedersen, K. R. The origin and early diversification of angiosperms. Nature 374, 27–33 (1995).Article 
    CAS 

    Google Scholar 
    Friis, E. M., Crane, P. R. & Pedersen, K. R. Early Flowers and Angiosperm Evolution 1–596 (Cambridge University Press, 2011). https://doi.org/10.1017/CBO9780511980206.Book 

    Google Scholar 
    Benson, R. B., Hunt, G., Carrano, M. T. & Campione, N. Cope’s rule and the adaptive landscape of dinosaur body size evolution. Palaeontology 61, 13–48. https://doi.org/10.1111/pala.12329 (2018).Article 

    Google Scholar 
    Hummel, J. et al. In vitro digestibility of fern and gymnosperm foliage: Implications for sauropod feeing ecology and diet selection. Proc. Royal Soc. B 275, 1015–1021. https://doi.org/10.1098/rspb.2007.1728 (2008).Article 

    Google Scholar 
    Gee, C. T. Dietary options for the sauropod dinosaurs from an integrated botanical and paleobotanical perspective. In Biology of the Sauropod Dinosaurs: Understanding the Life of Giants (eds Klein, K. et al.) 34–56 (Indiana University Press, 2011).
    Google Scholar 
    Peters, R. H. The Ecological Implications of Body Size 1–329 (Cambridge University Press, 1983).Book 

    Google Scholar 
    Jarman, P. J. The social organisation of antelope in relation to their ecology. Behaviour 48, 215–267 (1974).Article 

    Google Scholar  More

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    Alternative stable ecological states observed after a biological invasion

    Study systemOur focal ecosystem is in Selvíria, state of Mato Grosso do Sul, Brazil ((hbox {20}^{circ }) (22′) (41.86”) S, (hbox {51}^{circ }) (24′) (58.90”) W), on a property owned by the São Paulo State University (UNESP). The location covers 350 ha of pasture composed of liverseed grass (Urochloa decumbens). The native vegetation was removed, pasture areas were implemented, and livestock was introduced in the 1970s, maintaining this configuration during the following 50 years. The climate of this area is categorized as equatorial savanna, with dry periods concentrated mostly during the winter, from April to August. During our sampling period (from November 23th, 1989, to November 19th, 2015), no vermifuges and insecticides that could affect negatively the community of dung beetles associated with cow pads were used1.The native dung beetle community at this site is composed of dwellers and tunnelers. Dwellers comprise the Aphodiinae subfamily, whereas all the tunnelers belong to the Scarabaeinae subfamily31. In total, there were eight species classified as dwellers (Ataenius crenulatus, A. picinus and Atanius aequalis-platensis grouped as one species, Blackburneus furcatus, Genieridium bidens, Labarrus pseudolividus, Nialaphodius nigrita and Trichillum externepunctatum) and ten native tunnelers (Ateuchus nr. puncticollis, A. vividus, Canthidium nr. pinotoides, Dichotomius bos, D. semiaeneus, D. sexdentatus, Ontherus appendiculatus, O. dentatus, O. sulcator). These species were chosen for our study because, as the invasive tunneler D. gazella (also from the Scarabaeinae subfamily), they all co-occur in pasture and exploit the same resource (cow pad)32. The initial establishment of D. gazella caused the loss of most of the native tunnelers from the community, with the invader becoming the overwhelming representative of the functional group, and an initial decrease of abundance for dwellers. Differently from native tunnelers, however, dwellers were able to recover their number a few years after invasion (Fig. 1a, Fig. S1).As reported in1, the abundance of dung beetles was significantly affected by both local minimum temperature and relative humidity. The influence of these two factors is expected, as they determine egg and larval survival and development of dung beetles. For example, because dung beetles are poikilotherms, environmental temperature is key to their development and fecundity33. One of the main dweller species, Labarrus pseudolividus, is widely found in locations with temperature averages ranging between (hbox {12},^{circ }hbox {C}) and (hbox {18},^{circ }hbox {C})34, making it tolerant to colder local temperatures. On the other hand, for D. gazella the lower developmental threshold is (hbox {15.5},^{circ }hbox {C}) (individuals cannot survive below this temperature), and the optimum temperature for population growth is (hbox {28},^{circ }hbox {C})35. For both groups, physiological growth and reproduction rates are maintained even when outside temperatures are close to the lower developmental threshold; dwellers, for example, live inside the dung pile, where temperature is higher and less variable than outside36,37. However, while tunnelers oviposit deep in the soil to protect the eggs, warmer and drier conditions reduce dweller egg viability on dung piles since they are exposed38. Low humidity conditions lead to drier dung and can cause egg and insect dessication. In addition, dwellers from our focal system have Palearctic evolutionary origins39; D. gazella’s natural distribution ranges from central to southern Africa40, presenting high physiological plasticity that allows it to tolerate high temperatures and low relative humidity better than other tunneler species41.Functional-group data collection and community structure characterizationDung beetles were collected once a week in a black-light flight intercept trap42, which guarantees the collection of coprophagic beetles. During all collection periods, climate variables were also collected from a meteorological station located within 2 km of our collecting site. See1 for the complete description of the collection process and database. For our purposes, we retained the species, number of individuals per species, and climate variables for each week sampled (Supplementary Information, SI, Figs. S1–S2).We focused first on the weekly abundance data, which we needed to process in order to avoid spurious results in our analyses stemming from the measurement protocol. Specifically, we filtered out seasonal low values associated with sampling in the coldest periods, when few beetles are captured because the reduced activity in all functional groups restricts their spatio-temporal distribution43. Including such samples would not be representative of the community and could bias the analysis since we are investigating community composition (i.e. proportions, very sensitive to low sampling). Thus, we considered only samples with a total number of beetles (that is, summing up all groups together) higher than the value of the median of all data, a conservative threshold that retains observations that allow for as much representation of the community as possible. As will become evident in the Results section and Supplementary Information, less conservative choices for the threshold did not alter our main conclusions.Following Mesquita -Filho et al.1, we categorized all sampled species into either dwellers or tunnelers. D. gazella is a tunneler and, as explained above, the native tunneler species experienced massive declines in abundance after its establishment, leaving D. gazella as almost the single representative in the tunneler functional group during the period of observation1. Thus, given the sharp contrast in community composition, we also separated the data into before and after invasion using to that end the 200th week, when D. gazella was first observed at the study site (September 11th, 1993, starting date for what we will call “after invasion”, our focal period henceforth).To describe community functional composition (i.e. system state) through time, we derived a normalized functional group ratio. First, because the abundance of each functional group spanned up to four orders of magnitude, we performed a logarithmic transformation of the number of captured insects from each group i, (log _{10}(N_{i}+K)), following  Yamamura44. Here, we chose (K=1), but the value of K did not alter our results qualitatively. In addition, the original data showed random mismatches in the phenology of each group, which gave the wrong impression of extreme short-term shifts in functional group dominance within the community. To avoid such artifacts, we used nonparametric local regression (LOESS)45 to smooth the dynamics of each group46. For this smoothing, we employed the loess function in the R software 3.6.147 with a smooth parameter equal to 0.25, but other moderate values (or an optimal value calculated with Bayesian inference by the R function optimal_span) did not alter our conclusions. Finally, we extracted back from the smoothed curve the number of beetles within each functional group to calculate the fraction (f_{dwell}) that measures the relative abundance of dwellers:$$begin{aligned} f_{dwell} = frac{N_D}{N_D+N_T} end{aligned}$$
    (1)
    where (N_D) corresponds to the number of dwellers per week and (N_T) corresponds to the number of native tunnelers (for the period before invasion), or only the number of D. gazella observed per week (after invasion), using their corresponding smoothed curves. Including also native tunnelers after invasion did not alter our conclusions.Climate driverWe devised a single climatic driver variable that merges the weekly measurement of temperature and relative humidity over the years, abiotic factors key to the survival and reproduction of both groups (see above). We first converted minimum temperatures and relative humidity to normalized climate variables using a min-max normalization (a feature scaling that uses the total range of temperatures or relative humidity, respectively, as normalization factor):$$begin{aligned} T = frac{T_{week} – T_{min}}{T_{max}-T_{min}};;,~ ~ ~ ~ ~ ~ RH = frac{RH_{week} – RH_{min}}{RH_{max}-RH_{min}};;, end{aligned}$$
    (2)
    where T corresponds to the normalized temperature, (T_{week}) is the weekly temperature, and (T_{max}) and (T_{min}) are the absolute maximum and minimum temperatures observed during the whole sampling period, respectively. We used a similar notation for relative humidity, RH. Based on the information above regarding beetle response to climate, the merged climate factor c was defined as the relationship:$$begin{aligned} c = frac{T}{RH};;, end{aligned}$$
    (3)
    for (RHne 0). That is, higher temperatures and/or drier conditions (expected to favor D. gazella) lead to higher values for c. On the other hand, lower temperatures and/or more humid conditions (expected to favor dwellers) imply lower values for c. Intermediate values of c can represent either moderate or extreme values for both T and RH.Identifying ecological states and quantifying resilienceWith our (f_{dwell}) data as an index of community composition (i.e. system state), we calculated kernel density functions to interpolate a continuous probability distribution of the relative fraction of dwellers in the community, (p_{n}(f_{dwell})) (function density, R software 3.1.647) for a given range of climatic driver c values. We grouped the (f_{dwell}) data using ranges for c of size 0.4, to ensure a significant amount of weekly samples that allowed for the reconstruction of these probability distributions (see Table S1, first column). Note that bins with extreme values showed few data points (see first and last rows in Table S1), and thus were rejected to prevent misleading results due to reduced sampling. Also note that, for the density function, we used the default Gaussian kernel with a smoothing bandwidth adjusted to be (50%) larger than the default value (“adjust” argument set to 1.5). This conservative choice aims to reduce the effect of the different sampling across c bins and to ensure that differences among distributions across c values are not the result of spurious sampling noise.Further, we transformed the kernel density function:$$begin{aligned} V(f_{dwell}) = -ln (p_{n}(f_{dwell})) end{aligned}$$
    (4)
    This (V(f_{dwell})) function, called potential (e.g.48), shows by design well-defined minima for the most frequently observed values of (f_{dwell}) (i.e. configurations most frequently observed for the community, which conform the modes of the probability distribution) in a given group of data. At these points, the potential exhibits a change of trend from decreasing to increasing, and therefore its derivative shows a change of sign. Eq. (4), thus, provides a simple criterion to identify possible system states, which is a reason why potentials have been used extensively across disciplines49,50,51. Nonetheless, because the position of extrema is invariant under the transformation, using probability distributions instead would not alter our conclusions.Representing the potential obtained from all the (f_{dwell}) system states associated with a same range of climatic driver c values allowed us to identify stable community configurations associated with a specific climate. The comparison of the potentials obtained for different c ranges enabled the description of how the community changed in response to climatic variation. The location of the minima revealed which states were stable for a given value of the climatic driver; the presence of two minima, then, flagged the existence of bistability (i.e. two different community compositions possible for the same c value).These minima are materialized as wells in the potential’s landscape, which provides an easy way to understand the concept of stability: the dynamics of the system for the given value of the driver will eventually “fall” into a well (either a state dominated by dwellers or a state dominated by tunnelers), with the shape of the well (e.g. its depth) determining how difficult it is for the system to “escape” that state. Therefore, the area inside a well provides quantification of the tendency of a system to stay in that specific state, i.e. the resilience of the associated ecological state or how strong a perturbation has to be to move the system from such an ecological state to another2,3,50,51,52,53. Thus, in addition to number and location of wells, measuring their associated area allowed us to further characterize the resilience of the community. To this end, we first set a visualization window common to all potentials. Specifically, we plotted the potentials within a range for the vertical variable (the potential, V) given by ([-1.5,1.5]); the horizontal variable (fraction of dwellers, (f_{dwell})) is by definition bounded between 0 and 1. For potentials that showed one single well, the area of the well was measured as the area above the potential curve within this visualization window. For potentials that showed two wells (bistability), we measured the value of the potential at the local maximum separating the two wells, and established that value as the upper (horizontal) line closing the area of each well. To ensure all cases were comparable and eliminate any arbitrariness of the choices above, we expressed resilience as a relative area; in other words, we further normalized the well area by the total area across wells for that potential, which means that any single-well case will show a resilience (or relative area) of 1, and the resilience of the two wells when there is bistability adds up to 1.Figure 1Left: Community composition by functional group for all weeks of observation1. Green represents dwellers, blue represents tunnelers, and orange represents the invader D. gazella. Right: Sketch of responses of the community composition to the climatic driver (i.e. phase diagram) expected from the physiological and behavioral characteristics of the functional groups in the community as described in text: linear (red), or non-linear but monotonic without (blue) or with (brown) hysteresis.Full size imageIdentifying ecological transitionsMeasuring a state variable, (f_{dwell}), and a driver, c (order and control parameter, respectively, in the jargon of regime shift theory), allowed us to study how their observed behavior over time materializes in a driver-state relationship (the so-called phase diagram) defining the possible shifts in dominance (i.e. regime shifts) that the community may undergo as climate changes12. The non-monotonic temporal behavior of the components of the order parameter (i.e. dwellers and tunneler availability) and the components of the control parameter (i.e. temperature and relative humidity) makes it difficult to predict the shape of the phase diagram, and therefore whether we can expect alternative stable states in the focal example. For such cases, the dominance of the dung beetle community could (1) shift in a linear fashion toward the functional group favored by climatic conditions; (2) shift between functional groups in non-linear threshold response to climatic conditions without hysteresis; or (3) shift between functional groups in non-linear threshold response to climatic conditions with hysteresis –and thus showing bistability (see Fig. 1b, or12). Other possibilities, e.g. a non-linear shift between functional groups where one group is favored at intermediate climatic conditions12 are discarded as the invader is better suited for warmer and drier conditions. To evaluate which of these possibilities occurred, we represented (f_{dwell}) as a function of c, as well as the location of the minima shown by the potentials above. In addition to the emerging shape of this relationship, this plot can reveal the presence of alternative stable states if two or more different points occur for the same value of the control parameter, c. More

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    Photosynthetic usable energy explains vertical patterns of biodiversity in zooxanthellate corals

    Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237–240. https://doi.org/10.1126/science.281.5374.237 (1998).Article 
    CAS 
    PubMed 

    Google Scholar 
    Valladares, F. In Progress in Botany Vol. 64 (eds Esser, K. et al.) 439–471 (Springer, 2003).Chapter 

    Google Scholar 
    Anthony, K. R. N., Ridd, P. V., Orpin, A. R., Larcombe, P. & Lough, J. Temporal variation of light availability in coastal benthic habitats: Effects of clouds, turbidity, and tides. Limnol. Oceanogr. 49, 2201–2211. https://doi.org/10.4319/lo.2004.49.6.2201 (2004).Article 

    Google Scholar 
    Gattuso, J. P. et al. Light availability in the coastal ocean: Impact on the distribution of benthic photosynthetic organisms and their contribution to primary production. Biogeosciences 3, 489–513. https://doi.org/10.5194/bg-3-489-2006 (2006).Article 

    Google Scholar 
    Wright, D. H. Species-energy theory: An extension of species-area theory. Oikos 41, 496–506 (1983).Article 

    Google Scholar 
    Cusens, J., Wright, S. D., McBride, P. D. & Gillman, L. N. What is the form of the productivity–animal-species-richness relationship? A critical review and meta-analysis. Ecology 93, 2241–2252. https://doi.org/10.1890/11-1861.1 (2012).Article 
    PubMed 

    Google Scholar 
    Rosenzweig, M. L. & Abramsky, Z. in Species Diversity in Ecological Communities. Historical and Geographical Perspectives (eds Ricklefs, R. E. & Schluter, D.) Ch. 5, 52–65 (The University of Chicago Press, 1993).Abrams, P. A. Monotonic or unimodal diversity-productivity gradients: What does competition theory predict?. Ecology 76, 2019–2027 (1995).Article 

    Google Scholar 
    Huston, M. A. Disturbance, productivity, and species diversity: Empiricism vs. logic in ecological theory. Ecology 95, 2382–2396 (2014).Article 

    Google Scholar 
    Roberts, T. E. et al. Testing biodiversity theory using species richness of reef-building corals across a depth gradient. Biol. Lett. 15, 20190493. https://doi.org/10.1098/rsbl.2019.0493 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Frankowiak, K. et al. Photosymbiosis and the expansion of shallow-water corals. Sci. Adv. 2, e1601122. https://doi.org/10.1126/sciadv.1601122 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goreau, T. F. & Goreau, N. I. The physiology of skeleton formation in corals. II. Calcium deposition by hermatypic corals under various conditions in the reef. Biol. Bull. 117, 239–250. https://doi.org/10.2307/1538903 (1959).Article 
    CAS 

    Google Scholar 
    Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems 3rd edn. (Cambridge University Press, 2011).
    Google Scholar 
    Stoddart, D. R. Ecology and morphology of recent coral reefs. Biol. Rev. 44, 433–498. https://doi.org/10.1111/j.1469-185X.1969.tb00609.x (1969).Article 

    Google Scholar 
    Lesser, M. P., Slattery, M. & Leichter, J. J. Ecology of mesophotic coral reefs. J. Exp. Mar. Biol. Ecol. 375, 1–8 (2009).Article 

    Google Scholar 
    Ackleson, S. G. Light in shallow waters: A brief research review. Limnol. Oceanogr. 48, 323–328. https://doi.org/10.4319/lo.2003.48.1_part_2.0323 (2003).Article 

    Google Scholar 
    Connell, J. H. Diversity in tropical rain forests and coral reefs. High diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310. https://doi.org/10.1126/science.199.4335.1302 (1978).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dollar, S. J. Wave stress and coral community structure in Hawaii. Coral Reefs 1, 71–81. https://doi.org/10.1007/BF00301688 (1982).Article 

    Google Scholar 
    Hughes, T. P. Community structure and diversity of coral reefs: The role of history. Ecology 70, 275–279. https://doi.org/10.2307/1938434 (1989).Article 

    Google Scholar 
    Fraser, R. H. & Currie, D. J. The species richness-energy hypothesis in a system where historical factors are thought to prevail: Coral reefs. Am. Nat. 148, 138–159 (1996).Article 

    Google Scholar 
    Cornell, H. V. & Karlson, R. H. Coral species richness: Ecological versus biogeographical influences. Coral Reefs 19, 37–49 (2000).Article 

    Google Scholar 
    Bellwood, D. R., Hughes, T., Connolly, S. & Tanner, J. Environmental and geometric constraints on Indo-Pacific coral reef biodiversity. Ecol. Lett. 8, 643–651. https://doi.org/10.1111/j.1461-0248.2005.00763.x (2005).Article 

    Google Scholar 
    Brown, B. E. et al. Diurnal changes in photochemical efficiency and xanthophyll concentrations in shallow water reef corals: Evidence for photoinhibition and photoprotection. Coral Reefs 18, 99–105 (1999).Article 

    Google Scholar 
    Hoegh-Guldberg, O. & Jones, R. J. Photoinhibition and photoprotection in symbiotic dinoflagellates from reef-building corals. Mar. Ecol. Prog. Ser. 183, 73–86. https://doi.org/10.3354/meps183073 (1999).Article 

    Google Scholar 
    Lesser, M. P. & Gorbunov, M. Y. Diurnal and bathymetric changes in chlorophyll fluorescence yields of reef corals measured in situ with a fast repetition rate fluorometer. Mar. Ecol. Prog. Ser. 212, 69–77. https://doi.org/10.3354/meps212069 (2001).Article 
    CAS 

    Google Scholar 
    Hoogenboom, M. O., Anthony, K. R. N. & Connolly, S. R. Energetic cost of photoinhibition in corals. Mar. Ecol. Prog. Ser. 313, 1–12. https://doi.org/10.3354/meps313001 (2006).Article 
    CAS 

    Google Scholar 
    Huot, Y. & Babin, M. Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications 31–74 (Springer, 2010).Book 

    Google Scholar 
    Warner, M. E., Lesser, M. P. & Ralph, P. J. Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications Ch. Chapter 10, 209–222 (Springer Science+Business Media B.V., 2010).Skirving, W. et al. Remote sensing of coral bleaching using temperature and light: Progress towards an operational algorithm. Remote Sens. 10, 18 (2018).Article 

    Google Scholar 
    Enríquez, S., Merino, M. & Iglesias-Prieto, R. Variations in the photosynthetic performance along the leaves of the tropical seagrass Thalassia testudinum. Mar. Biol. 140, 891–900. https://doi.org/10.1007/s00227-001-0760-y (2002).Article 
    CAS 

    Google Scholar 
    Sundby, C., McCaffery, S. & Anderson, J. M. Turnover of the photosystem II D1 protein in higher plants under photoinhibitory and nonphotoinhibitory irradiance. J. Biol. Chem. 268, 25476–25482 (1993).Article 
    CAS 
    PubMed 

    Google Scholar 
    Tyystjärvi, E. & Aro, E. M. The rate constant of photoinhibition, measured in lincomycin-treated leaves, is directly proportional to light intensity. Proc. Natl. Acad. Sci. U. S. A. 93, 2213–2218. https://doi.org/10.1073/pnas.93.5.2213 (1996).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Iglesias-Prieto, R., Beltrán, V. H., LaJeunesse, T. C., Reyes-Bonilla, H. & Thomé, P. E. Different algal symbionts explain the vertical distribution of dominant reef corals in the eastern Pacific. Proc. R. Soc. Lond. B 271, 1757–1763. https://doi.org/10.1098/rspb.2004.2757 (2004).Article 
    CAS 

    Google Scholar 
    Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).Article 
    CAS 

    Google Scholar 
    Long, S. P., Humphries, S. & Falkowski, P. G. Photoinhibition of photosynthesis in nature. Annu. Rev. Plant Physiol. Plant Mol. Biol. 45, 633–662. https://doi.org/10.1146/annurev.pp.45.060194.003221 (1994).Article 
    CAS 

    Google Scholar 
    Huner, N. P. A., Öuist, G. & Sarhan, F. Energy balance and acclimation to light and cold. Trends Plant Sci. 3, 224–230 (1998).Article 

    Google Scholar 
    Sheppard, C. R. C. Coral cover, zonation and diversity on reef slopes of Chagos Atolls, and population structures of the major species. Mar. Ecol. Prog. Ser. 2, 193–205 (1980).Article 

    Google Scholar 
    Huston, M. A. Patterns of species diversity in relation to depth at Discovery Bay, Jamaica. Bull. Mar. Sci. 37, 928–935 (1985).
    Google Scholar 
    Loya, Y. Community structure and species diversity of hermatypic corals at Eilat, Red Sea. Mar. Biol. 13, 100–123. https://doi.org/10.1007/BF00366561 (1972).Article 

    Google Scholar 
    Chow, G. S. E., Chan, Y. K. S., Jain, S. S. & Huang, D. Light limitation selects for depth generalists in urbanised reef coral communities. Mar. Environ. Res. 147, 101–112. https://doi.org/10.1016/j.marenvres.2019.04.010 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kahng, S. E. et al. Community ecology of mesophotic coral reef ecosystems. Coral Reefs 29, 255–275. https://doi.org/10.1007/s00338-010-0593-6 (2010).Article 

    Google Scholar 
    Iglesias-Prieto, R. Temperature-dependent inactivation of Photosystem II in symbiotic dinoflagellates. in Proc. 8th Int. Coral Reef Sym, 1313–1318 (1997).Jones, R. J., Hoegh-Guldberg, O., Larkum, A. W. D. & Schreiber, U. Temperature-induced bleaching of corals begins with impairment of the CO2 fixation mechanism in zooxanthellae. Plant Cell Environ. 21, 1219–1230. https://doi.org/10.1046/j.1365-3040.1998.00345.x (1998).Article 
    CAS 

    Google Scholar 
    Hennige, S. J., Suggett, D. J., Warner, M. E., McDougall, K. E. & Smith, D. J. Photobiology of Symbiodinium revisited: Bio-physical and bio-optical signatures. Coral Reefs 28, 179–195. https://doi.org/10.1007/s00338-008-0444-x (2008).Article 

    Google Scholar 
    Quigg, A. & Beardall, J. Protein turnover in relation to maintenance metabolism at low photon flux in two marine microalgae. Plant Cell Environ. 26, 693–703. https://doi.org/10.1046/j.1365-3040.2003.01004.x (2003).Article 
    CAS 

    Google Scholar 
    Järvi, S., Suorsa, M. & Aro, E. M. Photosystem II repair in plant chloroplasts—Regulation, assisting proteins and shared components with photosystem II biogenesis. Biochim. Biophys. Acta Bioenerg. 900–909, 2015. https://doi.org/10.1016/j.bbabio.2015.01.006 (1847).Article 
    CAS 

    Google Scholar 
    Jokiel, P. L. Solar ultraviolet radiation and coral reef epifauna. Science 207, 1069–1071 (1980).Article 
    CAS 
    PubMed 

    Google Scholar 
    López-Londoño, T. et al. Physiological and ecological consequences of the water optical properties degradation on reef corals. Coral Reefs 40, 1243–1256. https://doi.org/10.1007/s00338-021-02133-7 (2021).Article 

    Google Scholar 
    Vermeij, M. J. A. & Bak, R. P. M. How are coral populations structured by light? Marine light regimes and the distribution of Madracis. Mar. Ecol. Prog. Ser. 233, 105–116. https://doi.org/10.3354/meps233105 (2002).Article 

    Google Scholar 
    Hoogenboom, M. O., Connolly, S. R. & Anthony, K. R. N. Interactions between morphological and physiological plasticity optimize energy acquisition in corals. Ecology 89, 1144–1154. https://doi.org/10.1890/07-1272.1 (2008).Article 
    PubMed 

    Google Scholar 
    Kaniewska, P., Anthony, K., Sampayo, E., Campbell, P. & Hoegh-Guldberg, O. Implications of geometric plasticity for maximizing photosynthesis in branching corals. Mar. Biol. 161, 313–328 (2014).Article 
    CAS 

    Google Scholar 
    Kramer, N., Tamir, R., Eyal, G. & Loya, Y. Coral morphology portrays the spatial distribution and population size-structure along a 5–100 m depth gradient. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00615 (2020).Article 

    Google Scholar 
    Lesser, M. P., Mobley, C. D., Hedley, J. D. & Slattery, M. Incident light on mesophotic corals is constrained by reef topography and colony morphology. Mar. Ecol. Prog. Ser. 670, 49–60. https://doi.org/10.3354/meps13756 (2021).Article 

    Google Scholar 
    Prada, C. et al. Linking photoacclimation responses and microbiome shifts between depth-segregated sibling species of reef corals. R. Soc. Open Sci. 9, 211591. https://doi.org/10.1098/rsos.211591 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rowan, R., Knowlton, N., Baker, A. & Jara, J. Landscape ecology of algal symbionts creates variation in episodes of coral bleaching. Nature 388, 265–269. https://doi.org/10.1038/40843 (1997).Article 
    CAS 
    PubMed 

    Google Scholar 
    Warner, M. E., LaJeunesse, T. C., Robison, J. D. & Thur, R. M. The ecological distribution and comparative photobiology of symbiotic dinoflagellates from reef corals in Belize: Potential implications for coral bleaching. Limnol. Oceanogr. 51, 1887–1897. https://doi.org/10.4319/lo.2006.51.4.1887 (2006).Article 

    Google Scholar 
    Anthony, K. R. N. & Fabricius, K. E. Shifting roles of heterotrophy and autotrophy in coral energetics under varying turbidity. J. Exp. Mar. Biol. Ecol. 252, 221–253 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hoogenboom, M., Rodolfo-Metalpa, R. & Ferrier-Pagès, C. Co-variation between autotrophy and heterotrophy in the Mediterranean coral Cladocora caespitosa. J. Exp. Biol. 213, 2399–2409 (2010).Article 
    PubMed 

    Google Scholar 
    Carlson, R. R., Foo, S. A. & Asner, G. P. Land use impacts on coral reef health: A ridge-to-reef perspective. Front. Mar. Sci 6, 562. https://doi.org/10.3389/fmars.2019.00562 (2019).Article 

    Google Scholar 
    Wang, M. et al. The great Atlantic Sargassum belt. Science 365, 83–87. https://doi.org/10.1126/science.aaw7912 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Alvarez-Filip, L., González-Barrios, F. J., Pérez-Cervantes, E., Molina-Hernández, A. & Estrada-Saldívar, N. Stony coral tissue loss disease decimated Caribbean coral populations and reshaped reef functionality. Commun. Biol. 5, 440. https://doi.org/10.1038/s42003-022-03398-6 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611. https://doi.org/10.4319/lo.1981.26.4.0601 (1981).Article 
    CAS 

    Google Scholar 
    Jørgensen, S. E. & Bendoricchio, G. Fundamentals of Ecological Modelling 3rd edn, Vol. 21 (Elsevier Sceince B. V., 2001).
    Google Scholar 
    Hennige, S. J. et al. Acclimation and adaptation of scleractinian coral communities along environmental gradients within an Indonesian reef system. J. Exp. Mar. Biol. Ecol. 391, 143–152. https://doi.org/10.1016/j.jembe.2010.06.019 (2010).Article 

    Google Scholar 
    Scheufen, T., Iglesias-Prieto, R. & Enríquez, S. Changes in the number of symbionts and Symbiodinium cell pigmentation modulate differentially coral light absorption and photosynthetic performance. Front. Mar. Sci 4, 309. https://doi.org/10.3389/fmars.2017.00309 (2017).Article 

    Google Scholar 
    Veron, J. E. N. Corals in Space and Time. The Biogeography and Evolution of the Scleractinia 321 (Cornell University Press, 1995).
    Google Scholar 
    Nelder, J. A. & Mead, R. A simplex method for function minimization. J. Comput. 7, 308–313. https://doi.org/10.1093/comjnl/7.4.308 (1965).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    R: A languate and environment for statistical computing. Retrieved from http://www.R-project.org (R Foundation for Statistical Computing, Vienna, Austria, 2010). More

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    Evidence for a consistent use of external cues by marine fish larvae for orientation

    General methodological approachTo examine if larvae utilize external cues (i.e., oriented movement) to swim in a directional manner (i.e., significant mean vector length), we develop two complementary analyses that compare the empirically observed directional precision (i.e., mean vector length) with the null distribution expected under a strict use of internal cues (i.e., unoriented movement). The empirically observed directional precision is quantified as the mean vector length (R) of larval bearings (θ) (Fig. 2a), herein ({hat{R}}_{theta }). The angular differences between consecutive bearings, herein turning angles (Fig. 2a; Δθt = θt-θt-1), are used to generate two null distributions of Rθ expected under the unoriented movement of Correlated Random Walk (CRW; ({R}_{{theta }_{0}})), based on the two analyses: Correlated Random Walk-von Mises (CRW-vm) and Correlated Random Walk- resampling (CRW-r), described below. The first is theoretical and is based on a von Mises distribution of simulated Δθ (Fig. 2b, c); the second is empirical, and is based on resampling the Δθ within each trial (Fig. 2d, e). These two analyses are complementary because the first can generate an unlimited number of trajectories but is based on a theoretical distribution rather than on observations, whereas the second is based on a finite number of observations. In addition to these two main analyses, we apply a third analysis, the Correlated Random Walk-wrapped Cauchy, herein CRW-wc, which is similar to CRW-vm, with the only difference of using wrapped Cauchy distribution instead of von Mises. The reason for applying CRW-wc is that it was shown to represent well animal movement in some cases33. Notably, we consider the simple cases of undirected movement pattern with a turning angle distribution centered at 0 (CRW), testing if the mean vector length of the trial’s sequence is higher than that expected under CRW. If true, that would be an indication for a directed movement pattern (i.e., BRW or BCRW), or an indication for more complex behaviors (discussed in Supplementary note 4).Statistics and reproducibilityQuantitative analyses are applied to directional trials, i.e., larval bearing sequences ((hat{theta })) that are significantly different from a uniform distribution based on the Rayleigh’s test8 (p  81, 162, 270). Trials with Nobs higher than the maximal Nobs were trimmed to contain the maximal Nobs per species, retaining the later-in-time data. For the scuba-following trials, the number of observations had to be Nobs  > 20 due to the sensitivity of the analysis to a low number of observations. In other words, a low number of observations limits the capacity of the quantitative analyses to distinguish between oriented and unoriented movement patterns (see Supplementary note 3, Supplementary Figure S3). Importantly, both methods were shown to be robust in terms of artifacts and biases55,56, and have been tested together demonstrating high consistency in larval orientation results16,48.Each orientation trial includes a sequence of larval swimming directions, termed bearings (θ) (Fig. 2a). For the DISC trials, θ are the cardinal directions of larval positions within the DISC’s chamber55. The angular differences between θ of consecutive time steps (t) are defined as Δθ (Δθt = θt-θt-1), such that for every θ sequence of a given length (N), there is a respective Δθ sequence of length N-1 (Fig. 2a). Directional precision with respect to external and internal cues is computed as the mean vector length of bearings (Rθ) and of turning angles (RΔθ), respectively54. Values of mean vector length (R) range from 0 to 1, with 0 indicating a uniform distribution of angles and 1 indicating that all angles are the same.We used two quantitative approaches to examine if larvae exhibit oriented movement: the Correlated Random Walk- von Mises and Correlated Random Walk- wrapped Cauchy (CRW-vm and CRW-wc) analyses and the CRW resampling (CRW-r) analysis. Both types of analyses are based on the assumption that trajectories of animals that strictly use internal cues for directional movement are characterized by a CRW pattern. Hence, their capacity for directional movement is exclusively dependent on the distribution of their turning angles (Δθ)57. In contrast, for an external-cues orienting animal, for which movement directions are correlated with an external fixed direction, the mean vector length of the observed bearings, ({hat{R}}_{theta }), is expected to exceed that of a CRW, ({R}_{{theta }_{0}})6. Both analyses compare ({hat{R}}_{theta }) against the expected ({R}_{{theta }_{0}}), but the first type computes ({R}_{{theta }_{0}^{{vm}}})and ({R}_{{theta }_{0}^{{wc}}})using theoretical von Mises and wrapped Cauchy distributions of Δθ, and the second type computes ({R}_{{theta }_{0}^{r}}) by producing 100 new θ sequences per individual trial (larva) by multiple resampling-without-replacement of the Δθ.A key principle for both analyses types stems from the fact that the mean vector length of bearings (Rθ) is inherently dependent on the mean vector length of turning angles (RΔθ)28. In other words, an animal with a high capacity for unoriented directional movement, i.e., a narrow distribution of Δθ, is likely to yield a high Rθ, even if it makes absolutely no use of external cues for oriented movement. Hence, in both analyses ({hat{R}}_{theta }) is gauged against a distribution of ({R}_{{theta }_{0}}), given its respective mean vector length of turning angles ({hat{R}}_{triangle theta }). The open-source software R58 with the package circular59 is used for all analyses in this study.Correlated Random Walk-von Mises (CRW-vm)In this analysis, we first generate the directional precision (R), expected for unoriented CRW movement using the theoretical von Mises distribution (({R}_{{theta }_{0}^{{vm}}})). The CRW bearings sequences (({theta }_{0}^{{vm}})) are generated by choosing a random initial bearing, followed by a series of Nobs-1 turning angles (({triangle theta }_{0}^{{vm}})) in bearing direction; drawn at random (with replacement) from a von Mises distribution (Nrep = 1000). The length of ({theta }_{0}^{{vm}}) sequence is according to the number of observations in our four types of experimental trials: Nobs = 21 for the scuba-following, and 90, 180 and 300 for the DISC (Table 1). The directional precision of the von Mises distribution is dependent on the concentration parameter, kappa. Kappa values ranging from 0 to 399 are applied at 1-unit increments to cover the entire range of directional precision from completely random (kappa = 0), to highly directional (kappa = 399). Next, the directional precision of the bearings (Rθ) and the turning angles (RΔθ) are computed for each simulated sequence of θ (Fig. 2a–c).These respective pairs of values (RΔθ, Rθ) provide the basis for generating the expected relationship between ({R}_{{theta }_{0}^{{vm}}}) and ({R}_{{triangle theta }_{0}^{{vm}}}). Then, for any given kappa value, the following quantiles are computed: 5th, 10th, 20th,….,90th, and 95th (grey vertical distributions in Fig. 2c). Next, smooth spline functions are fitted through all respective quantiles, generating the ({R}_{{theta }_{0}^{{vm}}})quantile contours, which represent the null expectation under CRW. This expected (RΔθ, Rθ) correspondence creates a phase diagram (Fig. 2c), based on which the observed θ patterns are gauged. The procedure is repeated four times to match the among-study differences in the number of θ observations per trial (i.e., Nobs = 21, 90, 180, and 300; see Table 1).To examine if the observed larval movement patterns differ from those expected for unoriented movement (CRW-vm), we compute RΔθ and Rθ for each individual trial (({hat{R}}_{triangle theta }) and ({hat{R}}_{theta })). We then place these values in the phase diagram and examine their positions with respect to ({R}_{{theta }_{0}^{{vm}}}) (Fig. 2c). Larvae with ({hat{R}}_{theta }) substantially higher than ({bar{R}}_{{theta }_{0}^{{vm}}}), are considered to have a higher tendency for a straighter movement than expected under CRW, suggesting oriented movement such as BRW and BCRW (Fig. 2b, c)6,28. Larvae with ({hat{R}}_{theta }) values substantially below ({bar{R}}_{{theta }_{0}^{{vm}}})indicate irregular patterns such as a one-sided drift (right or left). A larva is considered directional if the bearing sequence ((hat{theta })) is significantly different from a uniform distribution based on the Rayleigh’s test (p  More