Philippa Kaur

IPCC. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al) (Cambridge University Press. In Press, 2021).Otto, F. E. L. et al. Toward an inventory of the impacts of human-induced climate change. Bull. Am. Meteorol. Soc. 101, E1972–E1979 (2020).
Google Scholar 
Stanners, D. et al. in Sustainability Indicators. A Scientific Assessment (eds Moldan, B., Hak, T. & Dahl, A. L.) 127–144 (Island Press, 2007).Cohen-Shacham, E. et al. Core principles for successfully implementing and upscaling Nature-based Solutions. Environ. Sci. Policy 98, 20–29 (2019).
Google Scholar 
Seddon, N. et al. Getting the message right on nature-based solutions to climate change. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15513 (2021).Keesstra, S. et al. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610-611, 997–1009 (2018).CAS 

Google Scholar 
Seddon, N. et al. Understanding the value and limits of nature-based solutions to climate change and other global challenges. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190120 (2020).
Google Scholar 
Gómez Martín, E., Máñez Costa, M. & Schwerdtner Máñez, K. An operationalized classification of Nature Based Solutions for water-related hazards: from theory to practice. Ecol. Econ. 167 https://doi.org/10.1016/j.ecolecon.2019.106460 (2020).Doswald, N. et al. Effectiveness of ecosystem-based approaches for adaptation: review of the evidence-base. Clim. Dev. 6, 185–201 (2014).
Google Scholar 
Chausson, A. et al. Mapping the effectiveness of nature-based solutions for climate change adaptation. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15310 (2020).Rebelo, A. J., Holden, P. B., Esler, K. & New, M. G. Benefits of water-related ecological infrastructure investments to support sustainable land-use: a review of evidence from critically water-stressed catchments in South Africa. R. Soc. Open Sci. 8, 201402 (2021).
Google Scholar 
Berrang-Ford, L. et al. A systematic global stocktake of evidence on human adaptation to climate change. Nat. Clim. Change 11, 989–1000 (2021).
Google Scholar 
Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 

Google Scholar 
Bastin, J.-F. et al. The global tree restoration potential. Science 365, 76–79 (2019).CAS 

Google Scholar 
Koch, A., Brierley, C. & Lewis, S. L. Effects of Earth system feedbacks on the potential mitigation of large-scale tropical forest restoration. Biogeosciences 18, 2627–2647 (2021).CAS 

Google Scholar 
Girardin, C. A. J. et al. Nature-based solutions can help cool the planet – if we act now. Nature 593, 191–194 (2021).CAS 

Google Scholar 
Sudmeier-Rieux, K. et al. Scientific evidence for ecosystem-based disaster risk reduction. Nat. Sustain. 4, 803–810 (2021).
Google Scholar 
Otto, F. E. L. Attribution of weather and climate events. Annu. Rev. Environ. Resources 42, 627–646 (2017).
Google Scholar 
Philip, S. et al. A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Climatol. Meteorol. Oceanogr. 6, 177–203 (2020).
Google Scholar 
Herring, S. C., Christidis, N., Hoell, A., Hoerling, M. P. & Stott, P. A. Explaining extreme events of 2019 from a climate perspective. Bull. Amer. Meteorol. Soc. 102, S1–S112 (2021).Otto, F. E. L. et al. Challenges to understanding extreme weather changes in lower income countries. Bull. Am. Meteorol. Soc. https://doi.org/10.1175/bams-d-19-0317.1 (2020).Pall, P. et al. Anthropogenic greenhouse gas contribution to flood risk in England and Wales in autumn 2000. Nature 470, 382–385 (2011).CAS 

Google Scholar 
Kay, A. L., Crooks, S. M., Pall, P. & Stone, D. A. Attribution of Autumn/Winter 2000 flood risk in England to anthropogenic climate change: a catchment-based study. J. Hydrol. 406, 97–112 (2011).
Google Scholar 
Schaller, N. et al. Human influence on climate in the 2014 southern England winter floods and their impacts. Nat. Clim. Change 6, 627–634 (2016).
Google Scholar 
Wolski, P., Stone, D., Tadross, M., Wehner, M. & Hewitson, B. Attribution of floods in the Okavango basin, Southern Africa. J. Hydrol. 511, 350–358 (2014).
Google Scholar 
Ross, A. C. et al. Anthropogenic influences on extreme annual streamflow into Chesapeake Bay from the Susquehanna River. Bull. Am. Meteorol. Soc. 102, S25–S32 (2021).Mitchell, D. et al. Attributing human mortality during extreme heat waves to anthropogenic climate change. Environ. Res. Lett. 11, 074006 (2016).
Google Scholar 
Botai, C., Botai, J., de Wit, J., Ncongwane, K. & Adeola, A. Drought Characteristics over the Western Cape Province, South Africa. Water https://doi.org/10.3390/w9110876 (2017).Wolski, P. How severe is Cape Town’s “Day Zero” drought? Significance 15, 24–27 (2018).
Google Scholar 
Stafford, L., Shemie, D., Kroeger, T., Baker, T. & Apse, C. The Greater Cape Town Water Fund. Assessing the return on investment for Ecological Infrastructure restoration. Business case. (The Nature Conservancy, 2018).Otto, F. E. L. et al. Anthropogenic influence on the drivers of the Western Cape drought 2015–2017. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aae9f9 (2018).Pascale, S., Kapnick, S. B., Delworth, T. L. & Cooke, W. F. Increasing risk of another Cape Town “Day Zero” drought in the 21st century. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2009144117 (2020).Van Wilgen, B. W., Measey, J., Richardson, D. M., Wilson, J. R. & Zengeya, T. A. Biological Invasions in South Africa (Springer Nature, 2020).Le Maitre, D. et al. Impacts of plant invasions on terrestrial water flows in South Africa in Biological Invasions in South Africa (eds van Wilgen, B. W., Measey. J., Richardson, D. M., Wilson, J. R. & Zengeya, T. A.) 431–457 (Springer, 2020).Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W. & Vertessy, R. A. A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J. Hydrol. 310, 28–61 (2005).
Google Scholar 
Dennedy-Frank, P. J. & Gorelick, S. M. Insights from watershed simulations around the world: watershed service-based restoration does not significantly enhance streamflow. Glob. Environ. Change https://doi.org/10.1016/j.gloenvcha.2019.101938 (2019).Calder, I. D. & Dye, P. Hydrological impacts of invasive alien plants. Land Use Water Resour. Res. 7, 1–12 (2001).
Google Scholar 
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).
Google Scholar 
Farley, K. A., Jobbagy, E. G. & Jackson, R. B. Effects of afforestation on water yield: a global synthesis with implications for policy. Glob. Change Biol. 11, 1565–1576 (2005).
Google Scholar 
Jackson, R. B. Trading water for carbon with biological carbon sequestration. Science 310, 1944–1947 (2005).Filoso, S., Bezerra, M. O., Weiss, K. C. B. & Palmer, M. A. Impacts of forest restoration on water yield: a systematic review. PLoS ONE 12, e0183210 (2017).
Google Scholar 
Sitzia, T., Campagnaro, T., Kowarik, I. & Trentanovi, G. Using forest management to control invasive alien species: helping implement the new European regulation on invasive alien species. Biol. Invasions 18, 1–7 (2015).
Google Scholar 
Richardson, D. M. & Rejmánek, M. Trees and shrubs as invasive alien species – a global review. Divers. Distrib. 17, 788–809 (2011).
Google Scholar 
Everard, M. et al. Can control of invasive vegetation improve water and rural livelihood security in Nepal? Ecosyst. Serv. 32, 125–133 (2018).
Google Scholar 
Everard, M. Can management of ‘thirsty’ alien trees improve water security in semi-arid India? Sci. Total Environ. 704, 135451 (2020).CAS 

Google Scholar 
Archer, S. R. et al. Woody plant encroachment: causes and consequences in Rangeland Systems Springer Series on Environmental Management (ed. Briske, D. D.) Chapter 2, 25–84 (2017).Wood, M. Bootstrapped confidence intervals as an approach to statistical inference. Organ. Res. Methods 8, 454–470 (2016).
Google Scholar 
Tan, S. H. The correct interpretation of confidence intervals. Proc. Singapore Healthc. 19 (2010).Coetsee, C., Gray, E. F., Wakeling, J., Wigley, B. J. & Bond, W. J. Low gains in ecosystem carbon with woody plant encroachment in a South African savanna. J. Trop. Ecol. 29, 49–60 (2012).
Google Scholar 
Stevens, N., Erasmus, B. F., Archibald, S. & Bond, W. J. Woody encroachment over 70 years in South African savannahs: overgrazing, global change or extinction aftershock? Philos. Trans. R. Soc. Lond. B Biol. Sci. https://doi.org/10.1098/rstb.2015.0437 (2016).Venter, Z. S., Cramer, M. D. & Hawkins, H. J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).CAS 

Google Scholar 
Forsyth, G. G., Le Maitre, D. C., Smith, J. & Lotter, D. Upper Berg River Catchment (G10A) Management Unit Control Plan. (Natural Resources Management (NRM) Department of Environmental Affairs, 2016).Dirmeyer, P. A., Balsamo, G., Blyth, E. M., Morrison, R. & Cooper, H. M. Land‐atmosphere interactions exacerbated the drought and heatwave over northern Europe during summer 2018. AGU Adv. 2, e2020AV000283 (2021).
Google Scholar 
Rejmánek, M., Richardson, D. M. & Pysek, P. Trees and shrubs as invasive alien species – 2013 update of the global database. Divers. Distrib. 19, 1093–1094 (2013).
Google Scholar 
Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–603 (2021).CAS 

Google Scholar 
Ziervogel, G. et al. Climate change impacts and adaptation in South Africa. Wiley Interdiscip. Rev. Clim. Change 5, 605–620 (2014).
Google Scholar 
Thomas, A. et al. Global evidence of constraints and limits to human adaptation. Reg. Environ. Change https://doi.org/10.1007/s10113-021-01808-9 (2021).Dow, K., Berkhout, F. & Preston, B. L. Limits to adaptation to climate change: a risk approach. Curr. Opin. Environ. Sustain. 5, 384–391 (2013).
Google Scholar 
Manning, J. & Goldblatt, P. Plants of the greater Cape Floristic Region 1: the Core Cape Flora., (South African National Biodiversity Institute, 2012).Nel, J. L. et al. Strategic water source areas for urban water security: Making the connection between protecting ecosystems and benefiting from their services. Ecosyst. Serv. 28, 251–259 (2017).
Google Scholar 
Wolski, P. What Cape Town learned from its drought. Bull. At. Sci. https://thebulletin.org/2018/04/what-cape-town-learned-from-its-drought/ (2018).D. W. S. Cape Town River Systems State of Dams on 2021-08-16. Department of Water and Sanitation. Republic of South Africa. https://www.dws.gov.za/Hydrology/Weekly/RiverSystems.aspx?river=CT (2021).Rebelo, A. J. et al. The hydrological benefits of restoration: a modelling study of alien tree clearing in four mountain catchments in South Africa. Preprint at J. Hydrol. https://doi.org/10.21203/rs.3.rs-1316834/v1.DWAF. The Assessment of Water Availability in the Berg Catchment (WMA 19) by Means of Water Resource Related Models: Report 9 (Groundwater Model): Volume 9 (Breede River Alluvium Aquifer Model). (Department of Water Affairs and Forestry, 2008).DWAF. The Assessment of Water Availability in the Berg Catchment (WMA 19) by Means of Water Resource Related Models: Report 9 (Groundwater Model): Volume 3 (Regional Conceptual Model). (Department of Water Affairs and Forestry, 2008).Blake, D., Mlisa, A. & Hartnady, C. Large scale quantification of aquifer storage and volumes from the Peninsula and Skurweberg Formations in the southwestern Cape. Water SA 36, 177–184 (2010).
Google Scholar 
Holden, P. B., Rebelo, A. J. & New, M. G. Mapping invasive alien trees in water towers: a combined approach using satellite data fusion, drone technology and expert engagement. Remote Sens. Appl.: Soc. Environ. https://doi.org/10.1016/j.rsase.2020.100448 (2021).Midgley, J. & Scott, D. The use of stable isotopes of water in hydrological studies in the Jonkershoek Valley. Water SA 20, 151–154 (1994).
Google Scholar 
Van Genuchten, M. T. A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980).
Google Scholar 
Moriasi, D. N., Gitau, M. W., Pai, N. & Daggupati, P. Hydrologic and water quality models: performance measures and evaluation criteria. Trans. ASABE 58, 1763–1785 (2015).
Google Scholar 
Stone, D. A. et al. A basis set for exploration of sensitivity to prescribed ocean conditions for estimating human contributions to extreme weather in CAM5.1-1degree. Weather Clim. Extremes 19, 10–19 (2018).
Google Scholar 
Risser, M. D., Stone, D. A., Paciorek, C. J., Wehner, M. F. & Angélil, O. Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence. Clim. Dyn. 49, 3051–3073 (2017).
Google Scholar 
Jones, G. S., Stott, P. A. & Christidis, N. Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations. J. Geophys. Res. Atmos. 118, 4001–4024 (2013).
Google Scholar 
Sun, L. et al. Drivers of 2016 record Arctic warmth assessed using climate simulations subjected to factual and counterfactual forcing. Weather Clim. Extremes 19, 1–9 (2018).CAS 

Google Scholar 
Guillod, B. P. et al. weather@home 2: validation of an improved global–regional climate modelling system. Geosci. Model Dev. 10, 1849–1872 (2017).
Google Scholar 
Massey, N. et al. weather@home—development and validation of a very large ensemble modelling system for probabilistic event attribution. Q. J. R. Meteorol. Soc. 141, 1528–1545 (2014).
Google Scholar 
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Google Scholar 
Flato, G. et al. in Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 741–866 (Cambridge University Press, 2014).Hargreaves, G. H. & Samani, Z. A. Reference crop evapotranspiration from temperature. Appl. Eng. Agriculture 1, 96–99 (1985).
Google Scholar 
Cayan, D. R., Maurer, E. P., Dettinger, M. D., Tyree, M. & Hayhoe, K. Climate change scenarios for the California region. Clim. Change 87, 21–42 (2008).
Google Scholar 
Cannon, A. J., Sobie, S. R. & Murdock, T. Q. Bias correction of GCM precipitation by quantile mapping: how well do methods preserve changes in quantiles and extremes? J. Clim. 28, 6938–6959 (2015).
Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing. https://www.R-project.org/, 2020).Paciorek, C. J., Stone, D. A. & Wehner, M. F. Quantifying statistical uncertainty in the attribution of human influence on severe weather. Weather Clim. Extremes 20, 69–80 (2018).
Google Scholar 
Tadono, T. et al. Generation of the 30 M-Mesh Global Digital Surface Model by Alos Prism. ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4, 157–162, https://developers.google.com/earth-engine/datasets/catalog/JAXA_ALOS_AW3D30_V3_2#description (2016).
Google Scholar 
Takaku, J., Tadono, T., Tsutsui, K. & Ichikawa, M. Validation of “Aw3d” Global Dsm Generated from Alos Prism. ISPRS Ann. Photogramm. III-4, 25–31 (2016).
Google Scholar 
Viviroli, D. Increasing dependence of lowland population on mountain water resources. Nat. Sustain. 3, 917–928 (2020).
Google Scholar 
Meybeck, M. A New typology for mountains and other relief classes: an application to global continental water resources and population distribution. Mt. Res. Dev. 21, 34–45 (2001).DWS. Surface water home. Department of Water and Sanitation. Republic of South Africa. https://www.dws.gov.za/Hydrology/Unverified/UnverifiedDataFlowInfo.aspx (2021). More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

Du, E. et al. Global patterns of terrestrial nitrogen and phosphorus limitation. Nat. Geosci. 13, 221–226 (2020).ADS 
CAS 

Google Scholar 
Schulze, E. Ueber einige stickstoffhaltige Bestandtheile der Keimlinge von Vicia sativa. Z. Phys. Chem. 17, 193–216 (1893).
Google Scholar 
Wishart, D. S. et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 46, D608–D617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kato, T., Yamagata, M. & Tsukahara, S. Guanidine compounds in fruit trees and their seasonal variations in citrus (Citrus unshiu Marc.). J. Jpn. Soc. Hortic. Sci. 55, 169–173 (1986).CAS 

Google Scholar 
Gund, P. Guanidine, trimethylenemethane, and “Y-delocalization.” Can acyclic compounds have “aromatic” stability? J. Chem. Educ. 49, 100 (1972).CAS 

Google Scholar 
Güthner, T., Mertschenk, B. & Schulz, B. In Ullmann’s Fine Chemicals vol. 2, 657–672 (Wiley-VCH, 2014).Strecker, A. Untersuchungen über die chemischen Beziehungen zwischen Guanin, Xanthin, Theobromin, Caffeïn und Kreatinin. Justus Liebigs Ann. Chem. 118, 151–177 (1861).
Google Scholar 
Iwanoff, N. N. & Awetissowa, A. N. The fermentative conversion of guanidine in urea. Biochem. Z. 231, 67–78 (1931).
Google Scholar 
Lenkeit, F., Eckert, I., Hartig, J. S. & Weinberg, Z. Discovery and characterization of a fourth class of guanidine riboswitches. Nucleic Acids Res. 48, 12889–12899 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Salvail, H., Balaji, A., Yu, D., Roth, A. & Breaker, R. R. Biochemical validation of a fourth guanidine riboswitch class in bacteria. Biochemistry 59, 4654–4662 (2020).CAS 
PubMed 

Google Scholar 
Nelson, J. W., Atilho, R. M., Sherlock, M. E., Stockbridge, R. B. & Breaker, R. R. Metabolism of free guanidine in bacteria is regulated by a widespread riboswitch class. Mol. Cell 65, 220–230 (2017).CAS 
PubMed 

Google Scholar 
Sherlock, M. E. & Breaker, R. R. Biochemical validation of a third guanidine riboswitch class in bacteria. Biochemistry 56, 359–363 (2016).
Google Scholar 
Sherlock, M. E., Malkowski, S. N. & Breaker, R. R. Biochemical validation of a second guanidine riboswitch class in bacteria. Biochemistry 56, 352–358 (2016).
Google Scholar 
Kermani, A. A., Macdonald, C. B., Gundepudi, R. & Stockbridge, R. B. Guanidinium export is the primal function of SMR family transporters. Proc. Natl Acad. Sci. USA 115, 3060–3065 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sinn, M., Hauth, F., Lenkeit, F., Weinberg, Z. & Hartig, J. S. Widespread bacterial utilization of guanidine as nitrogen source. Mol. Microbiol. 116, 200–210 (2021).CAS 
PubMed 

Google Scholar 
Schneider, N. O. et al. Solving the conundrum: widespread proteins annotated for urea metabolism in bacteria are carboxyguanidine deiminases mediating nitrogen assimilation from guanidine. Biochemistry 59, 3258–3270 (2020).CAS 
PubMed 

Google Scholar 
Zhao, J., Zhu, L., Fan, C., Wu, Y. & Xiang, S. Structure and function of urea amidolyase. Biosci. Rep. 38, BSR20171617 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mobley, H. L., Island, M. D. & Hausinger, R. P. Molecular biology of microbial ureases. Microbiol. Rev. 59, 451–480 (1995).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mazzei, L., Musiani, F. & Ciurli, S. The structure-based reaction mechanism of urease, a nickel dependent enzyme: tale of a long debate. J. Biol. Inorg. Chem. 25, 829–845 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Uribe, E. et al. Functional analysis of the Mn2+ requirement in the catalysis of ureohydrolases arginase and agmatinase – a historical perspective. J. Inorg. Biochem. 202, 110812 (2020).CAS 
PubMed 

Google Scholar 
Perozich, J., Hempel, J. & Morris, S. M. Jr Roles of conserved residues in the arginase family. Biochim. Biophys. Acta 1382, 23–37 (1998).CAS 
PubMed 

Google Scholar 
Sekowska, A., Danchin, A. & Risler, J. L. Phylogeny of related functions: the case of polyamine biosynthetic enzymes. Microbiology 146, 1815–1828 (2000).CAS 
PubMed 

Google Scholar 
Sekula, B. The neighboring subunit is engaged to stabilize the substrate in the active site of plant arginases. Front. Plant Sci. 11, 987 (2020).PubMed 
PubMed Central 

Google Scholar 
Quintero, M. J., Muro-Pastor, A. M., Herrero, A. & Flores, E. Arginine catabolism in the cyanobacterium Synechocystis sp. strain PCC 6803 involves the urea cycle and arginase pathway. J. Bacteriol. 182, 1008–1015 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lacasse, M. J., Summers, K. L., Khorasani-Motlagh, M., George, G. N. & Zamble, D. B. Bimodal nickel-binding site on Escherichia coli [NiFe]-hydrogenase metallochaperone HypA. Inorg. Chem. 58, 13604–13618 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoffmann, D., Gutekunst, K., Klissenbauer, M., Schulz-Friedrich, R. & Appel, J. Mutagenesis of hydrogenase accessory genes of Synechocystis sp. PCC 6803. FEBS J. 273, 4516–4527 (2006).CAS 
PubMed 

Google Scholar 
Dowling, D. P., Di Costanzo, L., Gennadios, H. A. & Christianson, D. W. Evolution of the arginase fold and functional diversity. Cell. Mol. Life Sci. 65, 2039–2055 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dutta, A., Mazumder, M., Alam, M., Gourinath, S. & Sau, A. K. Metal-induced change in catalytic loop positioning in Helicobacter pylori arginase alters catalytic function. Biochem. J. 476, 3595–3614 (2019).CAS 
PubMed 

Google Scholar 
Di Costanzo, L. et al. Crystal structure of human arginase I at 1.29-Å resolution and exploration of inhibition in the immune response. Proc. Natl Acad. Sci. USA 102, 13058–13063 (2005).ADS 
PubMed 
PubMed Central 

Google Scholar 
Suzek, B. E. et al. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics 31, 926–932 (2014).PubMed 
PubMed Central 

Google Scholar 
Alfano, M. & Cavazza, C. Structure, function, and biosynthesis of nickel-dependent enzymes. Protein Sci. 29, 1071–1089 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, B. et al. A guanidine-degrading enzyme controls genomic stability of ethylene-producing cyanobacteria. Nat. Commun. 12, 5150 (2021).ADS 
PubMed 
PubMed Central 

Google Scholar 
McGee, D. J. et al. Purification and characterization of Helicobacter pylori arginase, RocF: unique features among the arginase superfamily. Eur. J. Biochem. 271, 1952–1962 (2004).CAS 
PubMed 

Google Scholar 
Arakawa, N., Igarashi, M., Kazuoka, T., Oikawa, T. & Soda, K. d-Arginase of Arthrobacter sp. KUJ 8602: characterization and its identity with Zn2+-guanidinobutyrase. J. Biochem. 133, 33–42 (2003).CAS 
PubMed 

Google Scholar 
Saragadam, T., Kumar, S. & Punekar, N. S. Characterization of 4-guanidinobutyrase from Aspergillus niger. Microbiology 165, 396–410 (2019).CAS 
PubMed 

Google Scholar 
Viator, R. J., Rest, R. F., Hildebrandt, E. & McGee, D. J. Characterization of Bacillus anthracis arginase: effects of pH, temperature, and cell viability on metal preference. BMC Biochem. 9, 15 (2008).PubMed 
PubMed Central 

Google Scholar 
D’Antonio, E. L., Hai, Y. & Christianson, D. W. Structure and function of non-native metal clusters in human arginase I. Biochemistry 51, 8399–8409 (2012).PubMed 

Google Scholar 
Andresen, E., Peiter, E. & Küpper, H. Trace metal metabolism in plants. J. Exp. Bot. 69, 909–954 (2018).CAS 
PubMed 

Google Scholar 
Eisenhut, M. Manganese homeostasis in cyanobacteria. Plants 9, 18 (2019).PubMed Central 

Google Scholar 
Burnat, M. & Flores, E. Inactivation of agmatinase expressed in vegetative cells alters arginine catabolism and prevents diazotrophic growth in the heterocyst-forming cyanobacterium Anabaena. MicrobiologyOpen 3, 777–792 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. P., Yuan, Y. & Wolfenden, R. The burden borne by urease. J. Am. Chem. Soc. 127, 10828–10829 (2005).CAS 
PubMed 

Google Scholar 
Lewis, C. A. Jr & Wolfenden, R. The nonenzymatic decomposition of guanidines and amidines. J. Am. Chem. Soc. 136, 130–136 (2014).CAS 
PubMed 

Google Scholar 
Grobben, Y. et al. Structural insights into human Arginase-1 pH dependence and its inhibition by the small molecule inhibitor CB-1158. J. Struct. Biol. X 4, 100014 (2020).CAS 
PubMed 

Google Scholar 
Mills, L. A., McCormick, A. J. & Lea-Smith, D. J. Current knowledge and recent advances in understanding metabolism of the model cyanobacterium Synechocystis sp. PCC 6803. Biosci. Rep. 40, BSR20193325 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Giner-Lamia, J. et al. Identification of the direct regulon of NtcA during early acclimation to nitrogen starvation in the cyanobacterium Synechocystis sp PCC 6803. Nucleic Acids Res. 45, 11800–11820 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martinez, S. & Hausinger, R. P. Biochemical and spectroscopic characterization of the non-heme Fe(II)- and 2-oxoglutarate-dependent ethylene-forming enzyme from Pseudomonas syringae pv. phaseolicola PK2. Biochemistry 55, 5989–5999 (2016).CAS 
PubMed 

Google Scholar 
Copeland, R. A. et al. An iron(IV)-oxo intermediate initiating l-arginine oxidation but not ethylene production by the 2-oxoglutarate-dependent oxygenase, ethylene-forming enzyme. J. Am. Chem. Soc. 143, 2293–2303 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rippka, R., Deruelles, J., Waterbury, J. B., Herdman, M. & Stanier, R. Y. Generic assignments, strain histories and properties of pure cultures of cyanobacteria. Microbiology 111, 1–61 (1979).
Google Scholar 
Geyer, J. W. & Dabich, D. Rapid method for determination of arginase activity in tissue homogenates. Anal. Biochem. 39, 412–417 (1971).CAS 
PubMed 

Google Scholar 
van Anken, H. C. & Schiphorst, M. E. A kinetic determination of ammonia in plasma. Clin. Chim. Acta 56, 151–157 (1974).PubMed 

Google Scholar 
Kabsch, W. XDS. Acta Crystallogr. D 66, 125–132 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lamzin, V. S. P. A., Wilson, K. S. In International Tables for Crystallography Vol. F (eds Arnold, E. et al.) 525–528 (Kluwer, 2012).Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D 66, 486–501 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Adams, P. D. et al. The Phenix software for automated determination of macromolecular structures. Methods 55, 94–106 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).CAS 
PubMed 

Google Scholar 
Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).ADS 
PubMed 

Google Scholar 
Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).CAS 
PubMed 

Google Scholar 
Trott, O. & Olson, A. J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lemoine, F. et al. NGPhylogeny.fr: new generation phylogenetic services for non-specialists. Nucleic Acids Res. 47, W260–W265 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucleic Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar  More

NATURE PODCAST
09 March 2022

The AI that deciphers ancient Greek graffiti

An artificial intelligence that restores illegible inscriptions, and the project that’s reintroducing lost species in Argentina.

Nick Petrić Howe

&

Benjamin Thompson

Nick Petrić Howe

View author publications

You can also search for this author in PubMed
 Google Scholar

Benjamin Thompson

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Subscribe
Subscribe

iTunes
Google Podcast
acast
RSS

Listen to the latest from the world of science, with Nick Petrić Howe and Benjamin Thompson.

Your browser does not support the audio element.

Download MP3

In this episode:00:46 The AI helping historians read ancient textsResearchers have developed an artificial intelligence that can restore and date ancient Greek inscriptions. They hope that it will help historians by speeding up the process of reconstructing damaged texts. Research article: Assael et al.News and Views: AI minds the gap and fills in missing Greek inscriptionsVideo: The AI historian: A new tool to decipher ancient textsIthaca platform08:53 Research HighlightsPollinators prefer nectar with a pinch of salt, and measurements of a megacomet’s mighty size.Research Highlight: Even six-legged diners can’t resist sweet-and-salty snacksResearch Highlight: Huge comet is biggest of its kind11:10 Rewilding ArgentinaThis week Nature publishes a Comment article from a group who aim to reverse biodiversity loss by reintroducing species to areas where they are extinct. We speak to one of the Comment’s authors about the project and their hopes that it might kick start ecosystem restoration.Comment: Rewilding Argentina: lessons for the 2030 biodiversity targets21:02 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, giant bacteria that can be seen with the naked eye, and how record-breaking rainfall has caused major floods in Australia.Science: Largest bacterium ever discovered has an unexpectedly complex cellNew Scientist: Record flooding in Australia driven by La Niña and climate changeThe Conversation: The east coast rain seems endless. Where on Earth is all the water coming from?Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.

doi: https://doi.org/10.1038/d41586-022-00701-7

Related Articles

Read the paper: Restoring and attributing ancient texts using deep neural networks

Rewilding Argentina: lessons for the 2030 biodiversity targets

Subjects

Machine learning

History

Ecology

Latest on:

Machine learning

AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22

The evolution, evolvability and engineering of gene regulatory DNA
Article 09 MAR 22

Gran Turismo champion, reimagined urine — the week in infographics
News 15 FEB 22

History

AI minds the gap and fills in missing Greek inscriptions
News & Views 09 MAR 22

Police rely on radiocarbon dating to identify forged paintings
News 09 MAR 22

Restoring and attributing ancient texts using deep neural networks
Article 09 MAR 22

Ecology

Discovery of a Ni2+-dependent guanidine hydrolase in bacteria
Article 09 MAR 22

Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22

How itchy vicuñas remade a vast wilderness
Research Highlight 04 MAR 22

Jobs

Principal Investigator / Faculty

San Diego Biomedical Research Institute (SDBRI)
Multiple locations

Research Associate / Light Microscopy Imaging Specialist (m/f/x)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Bioinformatician (m/f/div)

Max Planck Institute for Plant Breeding Research (MPIPZ)
Cologne, Germany

Research Coordinator / Institute Manager (div/f/m)

Max Planck Institute for Astrophysics (MPA)
Garching near Munich, Germany More

Beck, M. W. et al. The role of near shore ecosystems as fish and shellfish nurseries. Issues Ecol. 11, 1–12 (2003).
Google Scholar 
De la Torre-Castro, M., Di Carlo, G. & Jiddawi, N. S. Seagrass importance for a small-scale fishery in the tropics: The need for seascape management. Mar. Poll. Bull. 83, 398–407 (2014).
Google Scholar 
Sheaves, M., Baker, R., Nagelkerken, I. & Connolly, R. M. True value of estuarine and coastal nurseries for fish: incorporating complexity and dynamics. Estuar. Coasts 38, 401–414 (2014).
Google Scholar 
Nordlund, L. M., Unsworth, R. K. F., Gullström, M. & Cullen-Unsworth, L. C. Global significance of seagrass fishery activity. Fish. Fish. 19, 399–412 (2018).
Google Scholar 
Kimirei, I. A., Nagelkerken, I., Griffioen, B., Wagner, C. & Mgaya, Y. D. Ontogenetic habitat use by mangrove/seagrass-associated coral reef fishes shows flexibility in time and space. Estuar. Coast. Shelf Sci. 92, 47–58 (2011).ADS 

Google Scholar 
Unsworth, R. K. F. et al. Structuring of Indo-Pacific fish assemblages along the mangrove-seagrass continuum. Aquat. Biol. 5, 85–95 (2009).
Google Scholar 
Cocheret De La Morinière, E., Pollux, B. J. A., Nagelkerken, I. & van Der Velde, G. Post-settlement life cycle migration patterns and habitat preference of coral reef fish that use seagrass and mangrove habitats as nurseries. Estuar. Coast. Shelf Sci. 55, 309–321 (2002).Berkström, C., Lindborg, R., Thyresson, M. & Gullström, M. Assessing connectivity in a tropical embayment: fish migrations and seascape ecology. Biol. Conserv. 166, 43–53 (2013).
Google Scholar 
Saenger, P., Gartside, D. & Funge-Smith, S. A review of mangrove and seagrass ecosystems and their linkage to fisheries and fisheries management. FAO Regional Office for Asia and the Pacific, Bangkok, Thailand, 74 (RAP Publication, 2013).King, A. J. Density and distribution of potential prey for larval fish in the main channel of a floodplain river: pelagic versus epibenthic meiofauna. River Res. Appl. 20, 883–897 (2004).
Google Scholar 
Carassou, L., Ponton, D., Mellin, C. & Galzin, R. Predicting the structure of larval fish assemblages by a hierarchical classification of meteorological and water column forcing factors. Coral Reefs 27, 867–880 (2008).ADS 

Google Scholar 
Pinho Costa, A. C., Martins Garcia, T., Pereira Paiva, B., Ximenes Neto, A. R. & de Oliveira Soares, M. Seagrass and rhodolith beds are important seascapes for the development of fish eggs and larvae in tropical coastal areas. Mar. Environ. Res. 161, 105064 (2020).Muzaki, F. K., Giffari, A. & Saptarini, D. Community structure of fish larvae in mangroves with different root types in Labuhan coastal area, Sepulu–Madura. AIP Conf. Proc. 1854, 020025 (2017).Isari, S. et al. Exploring the larval fish community of the central Red Sea with an integrated morphological and molecular approach. PLoS ONE, 12, e0182503 (2017).Levin, P. S. Fine-scale temporal variation in recruitment of a temperate demersal fish: the importance of settlement versus post-settlement loss. Oecologia 97, 124–133 (1994).ADS 
CAS 
PubMed 

Google Scholar 
Mwaluma, J. M., Boaz Kaunda-Arara, B., Rasowo, J., Osore, M. K. & Vidar Øresland V. Seasonality in fish larval assemblage structure within marine reef National Parks in coastal Kenya. Environ. Biol. Fish. 90, 393–404 (2011).Reglero, P., Tittensor, D. P., Álvarez-Berastegui, D., Aparicio-González, A. & Worm, B. Worldwide distributions of tuna larvae: revisiting hypotheses on environmental requirements for spawning habitats. Mar. Ecol. Prog. Ser. 501, 207–224 (2014).ADS 

Google Scholar 
Leis, J. M. Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyol. Res. 57, 325–342 (2010).
Google Scholar 
Tzeng, W. N. & Wang, Y. T. Hydrography and distribution dynamics of larval and juvenile fishes in the coastal waters of the Tanshui River estuary, Taiwan, with reference to estuarine larval transport. Mar. Biol. 116, 205–217 (1993).
Google Scholar 
Leis, J. M., Sweatman, H. P. A. & Reader, S. E. What the pelagic stages of coral reef fishes are doing out in blue water: Daytime field observations of larval behavioural capabilities. Mar. Freshw. Res. 47, 401–411 (1996).
Google Scholar 
Leis, J. M. & Carson-Ewart, B. M. Complex behaviour by coral-reef fish larvae in open-water and near-reef pelagic environments. Environ. Biol. Fish. 53, 259–266 (1998).
Google Scholar 
Leis, J. M. Are larvae of demersal fishes plankton or nekton?. Adv. Mar. Biol. 51, 57–141 (2006).PubMed 

Google Scholar 
Faillettaz, R., Paris, C. B. & Irisson, J. O. Larval fish swimming behavior alters dispersal patterns from marine protected areas in the North-Western Mediterranean Sea. Front. Mar. Sci. 5, 1–12 (2018).ADS 

Google Scholar 
Azeiteiro, U. M., Bacelar-Nicolau, L., Resende, P., Gonçalves, F. & Pereira, M. J. Larval fish distribution in shallow coastal waters off North Western Iberia (NE Atlantic). Estuar. Coast. Shelf Sci. 69, 554–566 (2006).ADS 

Google Scholar 
Irisson, J. O. & Lecchini, D. In situ observation of settlement behaviour in larvae of coral reef fishes at night. J. Fish Biol. 72, 2707–2713 (2008).
Google Scholar 
Teixeira Bonecker, F., de Castro, M. S. & Teixeira Bonecker, A. C. Larval fish assemblage in a tropical estuary in relation to tidal cycles, day/night and seasonal variations. Pan-Am. J. Aquat. Sci. 4, 239–246 (2009).Strydom, N. A. Patterns in larval fish diversity, abundance, and distribution in temperate South African estuaries. Estuar. Coasts 38, 268–284 (2014).
Google Scholar 
Lana, P. C. & Bernardino, A. F. (Eds). Brazilian estuaries: a benthic perspective. Brazilian Marine Biodiversity series. 212 (Springer, Cham, 2018).Donahue, M. J., Karnauskas, M., Toews, C. & Paris, C. B. Location isn’t everything: Timing of spawning aggregations optimizes larval replenishment. PLoS ONE 10, 1–15 (2015).
Google Scholar 
Reynalte-Tataje, D. A., Zaniboni-Filho, E., Bialetzki, A. & Agostinho, A. A. Temporal variability of fish larvae assemblages: influence of natural and anthropogenic disturbances. Neotrop. Ichthyol. 10, 837–846 (2012).
Google Scholar 
Somarakis, S., Tsoukali, S., Giannoulaki, M., Schismenou, E. & Nikolioudakis, N. Spawning stock, egg production and larval survival in relation to small pelagic fish recruitment. Mar. Ecol. Prog. Ser. 2018, 113–136 (2018).
Google Scholar 
Sampey, A., Meekan, M. G., Carleton, J. H., McKinnon, A. D. & McCormick, M. I. Temporal patterns in distributions of tropical fish larvae on the North West Shelf of Australia. Mar. Freshw. Res. 55, 473–487 (2004).
Google Scholar 
Rezagholinejad, S., Arshad, A., Nurul Amin, S. M. & Ehteshami, F. The influence of environmental parameters on fish larval distribution and abundance in the mangrove estuarine area of Marudu bay, Sabah, Malaysia. J. Surv. Fish. Sci. 2, 67–78 (2016).Shuai, F. et al. Temporal patterns of larval fish occurrence in a large subtropical river. PLoS ONE 11, e0156556 (2016).Nordlund, L. M. et al. Intertidal zone management in the Western Indian Ocean: assessing current status and future possibilities using expert opinions. Ambio 43, 1006–1019 (2014).PubMed 

Google Scholar 
De Oliveira, E. C. & Ferreira, E. J. G. Spawning areas, dispersion and microhabitats of fish larvae in the Anavilhanas Ecological Station, rio Negro, Amazonas State Brazil. Neotrop. Ichthyol. 6, 559–566 (2008).
Google Scholar 
Caley, M. J. et al. Recruitment and the local dynamics of open marine populations. Ann. Rev. Ecol. Syst. 27, 477–500 (1996).
Google Scholar 
Crochelet, E. et al. Validation of a fish larvae dispersal model with otolith data in the Western Indian Ocean and implications for marine spatial planning in data-poor regions. Ocean Coast Manag. 86, 13–21 (2013).
Google Scholar 
Gilroy, J. J. & Edwards, D. P. Source-sink dynamics: a neglected problem for landscape-scale biodiversity conservation in the tropics. Curr. Landsc. Ecol. Rep. 2, 51–60 (2017).
Google Scholar 
Little, M. C., Reay, P. J. & Grove, S. J. Distribution gradients of ichthyoplankton in an East African mangrove creek. Estuar. Coast. Shelf Sci. 26, 669–677 (1988).ADS 

Google Scholar 
Hedberg, P., Rybak, F. F., Gullström, M., Jiddawi, N. S. & Winder, M. Fish larvae distribution among different habitats in coastal East Africa. J. Fish Biol. 94, 29–39 (2019).CAS 
PubMed 

Google Scholar 
Heylen, B. C. & Nachtsheim, D. A. Bio-telemetry as an essential tool in movement ecology and marine conservation. In: Jungblut, S., Liebich, V. & Bode, M. (Eds), YOUMARES 8–Oceans Across Boundaries: Learning From Each Other. 83–107 (Springer, 2018).Parrish, J. Fish communities of interacting shallow-water habitats in tropical oceanic regions. Mar. Ecol. Prog. Ser. 58, 143–160 (1989).ADS 

Google Scholar 
McMahon, K. W., Berumen, M. L. & Thorrold, S. R. Linking habitat mosaics and connectivity in a coral reef seascape. Proc. Natl. Acad. Sci. USA 109, 15372–15376 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Carlson, R. R. et al. Synergistic benefits of conserving land-sea ecosystems. Glob. Ecol. Conserv. 28, e01684 (2021).Mwaluma, J. M. et al. Assemblage structure and distribution of fish larvae on the North Kenya Banks during the Southeast Monsoon season. Ocean Coast. Manag. 212, 105800 (2021).Joyeux, J. C. The abundance of fish larvae in estuaries: Within-tide variability at inlet and immigration. Estuaries 22, 889–904 (1999).
Google Scholar 
Able, K. W., Valenti, J. L. & Grothues, T. M. Fish larval supply to and within a lagoonal estuary: Multiple sources for Barnegat Bay New Jersey. Environ. Biol. Fish. 100, 663–683 (2017).
Google Scholar 
McClanahan, T. R. Seasonality in East Africa’s coastal waters. Mar. Ecol. Prog. Ser. 44, 191–199 (1988).ADS 

Google Scholar 
Aceves-Medina, G. et al. Distribution and abundance of the ichthyoplankton assemblages and its relationships with the geostrophic flow along the southern region of the California current. Lat. Am. J. Aquat. Res. 46, 104–119 (2018).
Google Scholar 
Gray, C. A. & Miskiewicz, A. G. Larval fish assemblages in south-east Australian coastal waters: Seasonal and spatial structure. Estuar. Coast. Shelf Sci. 50, 549–570 (2000).ADS 

Google Scholar 
Jiménez, M. P., Sánchez-Leal, R. F., González, C., García-Isarch, E. & García, A. Oceanographic scenario and fish larval distribution off Guinea-Bissau (north-west Africa). J. Mar. Biolog. Assoc. UK 95, 435–452.Mwaluma, J. M., Kaunda-Arara, B. & Rasowo, J. Diel and lunar variations in larval supply to Malindi Marine Park, Kenya. West Ind. Ocean J. Mar. Sci. 13, 57–67 (2014).
Google Scholar 
Stephens, J. S. Jr., Jordan, G. A., Morris, P. A., Singer, M. M. & McGowen, G. E. Can we relate larval fish abundance to recruitment or population stability? A preliminary analysis of recruitment to a temperate rocky reef. CalCOFI Rep. 27, 65–83 (1986).
Google Scholar 
Green, B. C., Smith, D. J., Grey, J. & Underwood, G. J. C. High site fidelity and low site connectivity in temperate salt marsh fish populations: A stable isotope approach. Oecologia 168, 245–255 (2012).ADS 
PubMed 

Google Scholar 
Green, J. M. & Wroblewski, J. S. Movement patterns of Atlantic cod in Gilbert Bay, Labrador: Evidence for bay residency and spawning site fidelity. J. Mar. Biolog. Assoc. UK 80, 1077–1085 (2000).
Google Scholar 
Grüss, A., Kaplan, D. M. & Hart, D. R. Relative impacts of adult movement, larval dispersal and harvester movement on the effectiveness of reserve networks. PLoS ONE 6, e19960 (2011).Luiz, O. J. et al. Adult and larval traits as determinants of geographic range size among tropical reef fishes. Proc. Natl. Acad. Sci. USA 110, 16498–16502 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Macpherson, E. & Raventos, N. Relationship between pelagic larval duration and geographic distribution of Mediterranean littoral fishes. Mar. Ecol. Prog. Ser. 327, 257–265 (2006).ADS 

Google Scholar 
Green, A. L. et al. Larval dispersal and movement patterns of coral reef fishes, and implications for marine reserve network design. Biol. Rev. 90, 1215–1247 (2015).PubMed 

Google Scholar 
Taylor, M. D., Laffan, S. D., Fielder, D. S. & Suthers, I. M. Key habitat and home range of mulloway Argyrosomus japonicus in a south-east Australian estuary: Finding the estuarine niche to optimise stocking. Mar. Ecol. Prog. Ser. 328, 237–247 (2006).ADS 

Google Scholar 
Manson, F. J., Loneragan, N. R., Skilleter, G. A. & Phinn, S. R. An evaluation of the evidence for linkages between mangroves and fisheries: A synthesis of the literature and identification of research directions. Oceanogr. Mar. Biol. 43, 483–513 (2005).
Google Scholar 
Pattrick, P. & Strydom, N. A. Composition, abundance, distribution and seasonality of larval fishes in the shallow nearshore of the proposed Greater Addo Marine Reserve, Algoa Bay South Africa. Estuar. Coast. Shelf Sci. 79, 251–262 (2008).ADS 

Google Scholar 
Sato, N., Asahida, T., Terashima, H., Hurbungs, M. D. & Ida, H. Species composition and dynamics of larval and juvenile fishes in the surf zone of Mauritius. Environ. Biol. Fish. 81, 229–238 (2008).
Google Scholar 
Jaonalison, H., Mahafina, J. & Ponton, D. Fish post-larvae assemblages at two contrasted coral reef habitats in southwest Madagascar. Reg. Stud. Mar. Sci 6, 62–74 (2016).
Google Scholar 
Azmir, I. A., Esa, Y., Amin, S. M. N., Yasin, I. S. & Yusof, F. ZMd. Identification of larval fish in mangrove areas of Peninsular Malaysia using morphology and DNA barcoding methods. J. Appl. Ichthyol. 33, 998–1006 (2017).CAS 

Google Scholar 
Macedo-Soares, L. C. P., Freire, A. S. & Muelbert, J. H. Small-scale spatial and temporal variability of larval fish assemblages at an isolated oceanic island. Mar. Ecol. Prog. Ser. 444, 207–222 (2012).ADS 

Google Scholar 
Monteleone, D. M. Seasonality and abundance of ichthyoplankton in great South Bay, New York. Estuaries 15, 230–238 (1992).
Google Scholar 
Ara, R., Arshad, A., Amin, S. M. & Mazlan, A. G. Temporal and spatial distribution of fish larvae in different ecological habitats. Asian J. Anim. Vet. Adv. 8, 53–62 (2013).
Google Scholar 
Abu El-Regal, M. Abundance and diversity of coral reef fish larvae at Hurghada, Egyptian Red Sea. Egypt. J. Aquat. Biol. Fish. 12, 17–33 (2008).
Google Scholar 
Bialetzki, A., Nakatani, K., Sanches, P. V., Baumgartner, G. & Gomes, L. C. Larval fish assemblage in the Baía River (Mato Grosso do Sul State, Brazil): temporal and spatial patterns. Environ. Biol. Fish. 73, 37–47 (2005).
Google Scholar 
Dudley, B., Tolimieri, N. & Montgomery, J. Swimming ability of the larvae of some reef fishes from New Zealand waters. Mar. Freshw. Res. 51, 783–787. https://doi.org/10.1071/MF00062 (2000).Article 

Google Scholar 
Hare, J. A. et al. Biophysical mechanisms of larval fish ingress into Chesapeake Bay. Mar. Ecol. Prog. Ser. 303, 295–310 (2005).ADS 

Google Scholar 
Watt-pringle, P. & Strydom, N. A. Habitat use by larval fishes in a temperate South African surf zone. Estuar. Coast. Shelf Sci. 58, 765–774 (2003).ADS 

Google Scholar 
Picapedra, P. H. S., Sanches, P. V. & Lansac-Tôha, F. A. Effects of light-dark cycle on the spatial distribution and feeding activity of fish larvae of two co-occurring species (Pisces: Hypophthalmidae and Sciaenidae) in a neotropical floodplain lake. Braz. J. Biol. 78, 763–772 (2018).CAS 
PubMed 

Google Scholar 
Cederlöf, U., Rydberg, L., Mgendi, M. & Mwaipopo, O. Tidal exchange in a warm tropical lagoon: Chwaka Bay, Zanzibar. Ambio 24, 458–464 (1995).
Google Scholar 
Gullström, M. et al. Assessment of changes in the seagrass-dominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing. Estuar. Coast. Shelf Sci. 67, 399–408 (2006).ADS 

Google Scholar 
Gullström, M. et al. Seagrass meadows of Chwaka Bay: ecological, social and management aspects. In: de la Torre-Castro, M., Lyimo, T. J. (Eds) People, nature and research: past, present and future of Chwaka Bay, Zanzibar. ISBN: 978-9987-9559-1-6, Zanzibar Town: 89–109 (WIOMSA, 2012a)Gullström, M. et al. Connectivity and nursery function of shallow-water habitats in Chwaka Bay. In: de la Torre-Castro, M., Lyimo, T. J. (Eds) People, nature and research: past, present and future of Chwaka Bay, Zanzibar. ISBN: 978-9987-9559-1-6, Zanzibar Town: 175–192 (WIOMSA, 2012b)Rehren, J., Wolff, M. & Jiddawi, N. Holistic assessment of Chwaka Bay’s multi-gear fishery—using a trophic modeling approach. J. Mar. Syst. 180, 265–278 (2018).
Google Scholar 
Torell, E., Mmochi, A. & Palmigiano, K. Menai Bay Convernance Baseline. Coastal Resources Center, 1–18 (University of Rhode Island, 2006).Torell, E., Shalli, M., Francis, J., Kalangahe, B. & Munubi, R. Tanzania biodiversity threats assessment: Biodiversity threats and management opportunities for Fumba, Bagamoyo, and Mkuranga. 1–47 (University of Rhode Island, Narragansett, 2007).Jeyaseelan, M. J. P. Manual of fish eggs and larvae from Asian mangrove waters.193 (Paris: UNESCO Publishing, 1998).Mwaluma, J. M., Kaunda-Arara, B. & Strydom, N. A. A guide to commonly occurring larval stages of fishes in Kenyan Coastal Waters. WIOMSA Book Series No. 15. xvi + 73 (WIOMSA, 2014).Leis, J. M. & Carson-Ewart, B. M. (Eds.). The larvae of Indo-Pacific coastal fishes: an identification guide to marine fish larvae (Fauna Malesiana Handbooks 2), 804 (Brill, Leiden, 2000).Strickland, J. D. H. & Parsons, T. R. A practical handbook of seawater analysis, 2nd edn. Vol. 167. 21–26 (Bull. Fish. Res. Bd. Canada, 1972).Clarke, K. R. & Warwick, R. M. Change in Marine Communities: An Approach to Statistical Analysis and Interpretation (PRIMER-E). Plymouth Marine Laboratory, (Plymouth, UK, 2001). More

Following the work in36, we construct an epidemiological model which tracks the disease dynamics and population of two species of hosts following the introduction of a pathogen. The native host (hereafter simply referred to as “type 1”) is vulnerable to the disease, but due to being well adapted to the native habitat has high fecundity when uninfected. The invasive host (hereafter referred to as “type 2”), has coevolved defenses to the pathogen that increase both its tolerance of and resistance to the disease, but is not inherently as well-adapted to the habitat in the absence of infection (i.e., its intrinsic rate of growth in the new habitat is lower than that of the native).Our initial conditions correspond to a population of uninfected type 1 hosts with a small number of both uninfected and infected type 2 hosts, representing an invasion by a novel competitor carrying a novel pathogen into the type 1 population. We consider a vector-borne pathogen, and make the simplifying assumption that there is an already abundant competent vector species in the habitat. (For this initial formulation, we considered a scenario of mosquito-borne infections in birds, such as avian malaria37 or West Nile virus38, to motivate concrete choices.)The model couples two biological dynamics: the daily vector-borne spread of the disease among hosts, and a yearly host breeding cycle. We simulate in discrete time-steps that represent days using an SIR model taking into account the interactions between the disease, the two species of host, and the vectors. The model also includes a passive death rate for hosts of vectors, which increases for hosts while infected. While the vectors are assumed to breed daily, the hosts reproduce as part of an assumed annual breeding season, every (t_c) time-steps (typically equal to 365). These dynamics were informed by considering an annually breeding bird population in a tropical environment, however, they are not meant to reflect the realism of any one biological system. They are chosen here merely to allow a clean interpretation of modeled scenarios. Future models should explore the impact of greater variety in the dynamics of possible vector and host reproductive patterns.Epidemiological modelThe model tracks eight variables corresponding to combinations of host species and vectors with their infection status. Hosts may be of type 1 or 2, and are either susceptible to the disease ((S_1, S_2)), currently infected ((I_1, I_2)), or recovered ((R_1, R_2)). We assume that recovery is complete and recovered individuals suffer no residual effects from their infection aside from a lifelong immunity to becoming reinfected. (We later set the recovery rate for host type 1 to 0, so (R_1 = 0) at all times, but leave it defined for the sake of generality.) For simplicity, we model using only one stage of infection in which individuals are both infectious and symptomatic. The model also tracks the status of the vector population, which may either be susceptible ((S_v)) or infected ((I_v)). We assume that vectors do not recover from the disease, but also suffer no negative effects from being infected, acting only as carriers.For convenience of notation, we denote the total number of hosts$$begin{aligned} H = S_1 + I_1 + R_1 + S_2 + I_2 + R_2 end{aligned}$$and the relative frequencies of infection within their respective population$$begin{aligned} F_1 = frac{I_1}{H}, F_2 = frac{I_2}{H},F_v = frac{I_v}{S_v+I_v} end{aligned}$$which allows some equations to be written more compactly. Table 1 shows a summary of these variables.Table 1 Variables.Full size tableThe model also has several constant parameters that affect the dynamics. (beta _j) determines the probability that hosts of type j become infected when bitten by a single infected vector. We typically set (beta _1 > beta _2), making type 2 hosts less likely to become infected.Likewise, (delta _j) determines the probability that a vector becomes infected when biting an infected host of type j.(b_j) determines the bite rate for vectors on host type j. We assume that each vector bites the same number of hosts per day, so each vector’s probability of becoming infected depends only on the frequency of infection among hosts, while each host will be bitten more if there are more vectors.(gamma _j) determines the proportion of infected hosts of type j that recover from the disease each day. We typically set (gamma _1 = 0 < gamma _2), meaning infected hosts of type 1 do not recover, while infected type 2 recover after an average of (1/gamma _2) days.(mu _{j-}) determines the daily death rate for uninfected hosts of type j and (mu _{j+}) determines the death rate for infected host of type j. We typically set (mu _{1-} = mu _{2-}< mu _{2+} < mu _{1+}), meaning uninfected hosts have the same death rate regardless of type, infected type 2 have a higher death rate than uninfected hosts, and infected type 1 have the highest. (Both susceptible and recovered hosts are considered to be uninfected.) Table 2 shows a summary of parameters related to the SIR dynamics.Equation 1 shows continuous ordinary differential equations approximating the dynamics. Note that the actual model instantiates these in discrete time-steps using the forward Euler method with (h = 1).$$ begin{aligned}&frac{dS_1}{dt} = - S_1 beta _1 b_1 I_v /H - S_1 mu _{1-} \&frac{dI_1}{dt} = S_1 beta _1 b_1 I_v /H - gamma _1 I_1 - I_1 mu _{1+} \&frac{dR_1}{dt} = I_1 gamma _1 - R_1 mu _{1-} \&frac{dS_2}{dt} = -S_2 beta _2 b_2 I_v /H - S_2 mu _{2-} \&frac{dI_2}{dt} = S_2 beta _2 b_2 I_v /H - I_2 gamma _2 - I_2 mu _{2+} \&frac{dR_2}{dt} = I_2 gamma _2 - R_2 mu _{2-}\&frac{dS_v}{dt} = alpha _v H -S_v delta _1 b_1 F_1 -S_v delta _2 b_2 F_2 -S_v mu _v\&frac{dI_v}{dt} = S_v delta _1 b_1 F_1 + S_v delta _2 b_2 F_2 - I_v mu _v\ end{aligned} $$ (1) Table 2 Parameters for SIR dynamics.Full size tableFollowing a standard SIR model, susceptible hosts can become infected, and infected hosts become recovered, but each equation also contains a negative term corresponding to deaths. Thus, the total population of hosts is strictly decreasing in this time-frame. We assume that the vectors breed on a much shorter timescale than hosts, so we include a term for their births here, while host births are implemented by a yearly breeding event. We assume no vertical disease transmission, so all new vectors begin in the susceptible category. We assume that the daily birthrate for each vector increases with access to hosts, and decreases with competition among other vectors for hosts and breeding sites, so we set it equal to (frac{alpha _v H}{S_v + I_v}), where (alpha _v) is a constant scaling factor. Since the birthrate for each vector contains the total number of vectors in its denominator, the total number of vector births in the population will simply be (alpha _v H).A population with a larger number of hosts will be able to sustain a larger number of vectors. For a population with a constant number of hosts, the equilibrium vector population will be proportional to the number hosts: aH where (a = frac{alpha _v}{mu _v}) is the equilibrium vector density (number of vectors per host). For instance if (a = 2), then in equilibrium there will be twice as many vectors as hosts. Given a fixed number of hosts, the population of vectors will asymptotically approach the equilibrium value. In practice the total number of hosts is constantly changing, so the population of vectors will chase after this moving equilibrium, though for our standard parameters (alpha _v) and (mu _v) are sufficiently large such that this will occur on a short timescale, and the population of vectors remains close to the current equilibrium value.Breeding eventTable 3 shows a summary of parameters related to the breeding event. Every (t_c) days (typically 365), a breeding event occurs according to the following process.Table 3 Parameters for breeding event.Full size tableLet$$begin{aligned}&Delta S_1 = t_c alpha _{1-}(S_1+R_1)+t_calpha _{1+} I_1 \&Delta S_2 = t_c alpha _{2-}(S_2+R_2)+t_calpha _{2+} I_2 \ end{aligned}$$be the number of new host offspring of each type born this generation. In order to maintain consistency of temporal units among the parameters, each birthrate parameter is multiplied by (t_c). Let H be the current total number of hosts. Let$$begin{aligned} c = {left{ begin{array}{ll} 0 &{} hbox {if } H ge kappa \ 1 &{} hbox {if } H + Delta S_1 + Delta S_2 le kappa \ frac{kappa -H}{Delta S_1 + Delta S_2} &{} hbox {otherwise} \ end{array}right. } end{aligned}$$be the proportion of offspring that survive to adulthood. (None, if the population is already above carrying capacity. All, if the difference between the reproducing population size and the carrying capacity exceeds the new births. If the population is approaching carrying capacity, juvenile mortality scales proportionally so that the population will hit carrying capacity but not exceed it.)Then$$begin{aligned}&S_1 + c Delta S_1 rightarrow S_1 \&S_2 + c Delta S_2 rightarrow S_2 \ end{aligned}$$We assume there is no vertical disease transmission, so all new hosts begin in the susceptible category. We assume that the host population is iteroparous, such that the new offspring and the existing adult population both carry over to the next generation. If the new population would exceed the carrying capacity, we assume the limited space or supplies reduces the number of successful offspring so that the population exactly reaches the carry capacity by reduction in juvenile survival rather than population-wide competition that could also reduce the adult population.The carrying capacity is therefore what drives the interspecific host competition. Because births of both species are summed and then normalized by the total number of births, the higher the birthrate of one host, the larger a fraction of the available space it will capture during the breeding event. Similarly, the lower the death-rate of a host, the less space it frees up for the next breeding event. Even if one host species would be able to sustain a stable population on its own, the presence of a more fit competitor can lead to the extinction of the less fit type by driving its effective birth rate down.Immune-reproductive trade-offs and boundary conditionsWe assume that host type 1 is evolutionarily stable in the absence of the disease; an uninfected monoculture population below the carrying capacity will have at least as many births as deaths each cycle. In a continuous version of this model where births and deaths happened simultaneously, this might be defined by (alpha _{1-} ge mu _{1-}) . However in our model, the population spends many days decreasing due to deaths before the next breeding event occurs. The population exponentially decays throughout the cycle, and then jumps up during the breeding event. The number of new host births is proportional to the number of hosts at the start of the breeding event, which will be the lowest value of any other time during the cycle. Thus, the birth rate needs to be high enough that the surviving hosts can compensate despite their diminished numbers. Taking this into account, we get the condition$$begin{aligned}&alpha _{1-} ge frac{1-(1- mu _{1-})^{t_c}}{(1-mu _{1-})^{t_c}} \ end{aligned}$$Which is a higher bound on (alpha _{1-}) than the simpler one above, but will be close to it if (mu _{1-}) and (t_c) are small.To implement the scenario in which type 2 has increased resistance and tolerance to the disease at the expense of overall fecundity, we implement the following boundary conditions:$$begin{aligned}&beta _1 > beta _2 \&0 = gamma _1< gamma _2 \&mu _{1-} = mu _{2-}< mu _{2+} < mu _{1+} \&alpha _{1-} > alpha _{2-} > alpha _{2+} > alpha _{1+} end{aligned}$$Type 2 hosts are less likely to contract the disease, and are able to recover from it, while type 1 lack the immunological strength to eradicate it completely. Additionally, while both types of host are weakened by the disease, type 2 suffer fewer negative effects. However, this stronger immune response comes at the cost of reducing their birth rate when compared to healthy type 1 hosts.Due to the heterogeneous population, there is ambiguity in defining (R_0) for the disease. The two types of host have different transmission rates and durations of infection, and will therefore be responsible for different amounts of disease spread. To resolve this, we define several related values. Let (R_0^j) be the (R_0) of the disease in a homogeneous population of type j hosts: the average number of hosts infected (indirectly, through vectors) from a single infected host in a population consisting entirely of type j hosts.$$begin{aligned}&R_0^1 = frac{delta _1 beta _1 a b_1^2}{mu _v mu _{1+}} \&R_0^2 = frac{delta _2 beta _2 a b_2^2}{mu _v (mu _{2+}+gamma _2)} end{aligned}$$We simplify the equation for (R_0^1) since (gamma _1 = 0). We define w to be the frequency of host type 1: (w := (S_1 + I_1)/H). Then (R_0) for the vectors is$$begin{aligned} R_0^v = R_0^1 w + R_0^2 (1-w) end{aligned}$$which will also be the effective (R_0) of the disease for the hosts in the mixed population.For simplicity of results, we restrict to the case where type 1 is more infectious overall than type 2, in particular (R_0^1 > R_0^2). This allows us to avoid edge cases in simulation outcomes which are beyond the scope of this paper. We intend to lift this restriction and study these outcomes in future work.NoteAlthough usual epidemiological model formulations can rely on the value 1 as the boundary condition for (R_0) to determine the epidemic potential of an outbreak, in this case we are calculating effective (R_0) in a dynamic host population, such that the decrease in disease spread due to saturation from recovered hosts and already infected hosts increases the actual thresholds. More accurate criteria require a technical and somewhat cumbersome analysis, which we leave for a future paper. More

Since the bone discontinuities noted in the three vertebrae analyzed show no clear sign of bone overgrowth, it is pivotal to rule out the possibility that we are dealing with preservation damages before proposing an accurate diagnosis for the lesions. The close-up view examination of the abnormalities shows that their edges have clear signs of smoothing and rounding (Fig. 1), which represent important evidence of osteoblastic activity18,19. Additionally, the similar color of the cortical damage and normal bone can be used as secondary evidence to rule out post-mortem processes as a possible origin of the alterations, since recent destructive processes are lighter than the rest of the bone19. Therefore, as taphonomic processes can be ruled out, the pointed evidence strongly suggests that the discontinuities observed are of pathological origin. More specifically, these breaks found in all three vertebrae are indicative of bone fracture.Based on fracture analysis criteria applied here20, which consider the location and morphological pattern of the fractures, we classified the fractures noted in all vertebrae as traumas belonging to Type A (vertebral body compression), Group A2 (split fractures), and subgroup A2.1 (sagittal split fracture). This diagnosis implies that the traumatic episode was likely caused by a compressive force on the vertebral column, which split the vertebral bodies in the sagittal plane. This type of injury is considered stable—i.e., the fracture does not have a tendency to displace after reduction—and neurological deficit is uncommon20,22,23. Although stable traumas cause only moderate pain, without generating significant movement limitations20, the Eremotherium individual here analyzed died with unhealed bones, as there is no evidence of callus formation.The absence of other skeletal signs that point to the presence of another type of disease concomitantly to the fractures allows us to reject the possibility that they have been generated as a result of a pre-existing disease (e.g., infection, neoplasm). We also consider that the vertebral injuries were not caused by repetitive force (stress fractures) because this type of injury is commonly characterized as a nondisplaced line or crack in the bone, called hairline fracture3. Those refer to situations where the broken bone fragments are not visibly out of alignment and exhibit very little relative displacement21. Although the Eremotherium vertebrae fractures’ can be described as nondisplaced, they also have a noticeable gap between their edges that is mostly narrow with wider parts in the middle, something found in split fractures20 but that is not characteristic of hairline fractures. Lastly, the subgroup C1.2.1 (rotational sagittal split fracture) might be a source of confusion due to similar morphological pattern with subgroup A2.1 (sagittal split fracture). However, in subgroup C1.2.1 there are compressive and rotational forces acting simultaneously, producing total separation into two parts20, which clearly did not occur in the vertebrae analyzed here.In humans, compression fractures are most commonly caused by osteoporosis, although infection, neoplasm and trauma can also be etiological factors23,24,25. However, as aforementioned, the absence of other pathological skeletal marks is an important characteristic to take note as it serves to disregard the possibility of the fractures’ genesis to be secondary to another pathology. As such, in this case, osteoporosis, infection and neoplasm are unlikely etiologies. On the other hand, a compression fracture in a healthy individual is commonly generated after a severe traumatic event such as a fall from great height23,26. This scenario seems to better explain the origin of the vertebral fractures in the case of the Eremotherium ground sloth herein studied.The three fractured vertebrae were recovered in the Toca das Onças site (Fig. 2), a small cave considered as one of the richest paleontological sites of the Brazilian Quaternary15. Two complete skeletons of Eremotherium laurillardi and fragments belonging to at least thirteen other individuals, together with several other bones assigned to different smaller species are known to this cave14. It comprises of a single dry chamber that can only be entered through vertical entrances approximately 4.5 m high (Figs. 2b–d and 3). Two different hypotheses concerning the depositional process of Toca da Onças were previously proposed: (1) the animals climbed down into the cave in search of water14; or (2) due to the vertical character of the cave entrance, it could have functioned as a natural trap where animals accidentally fell into the cave15.Figure 2Location map of the Toca das Onças site and images of the cave. (a) Detail of the location, (b) cave entrance area view, (c) view from inside the cave, (d) Cave entrance detail. Scale bars 10 m in (b) and 5 m in (c). This figure was generated by Adobe Photoshop CS6 software (https://www.adobe.com/br/products/photoshop.html).Full size imageFigure 3Schematic representation of the Toca das Onças site. (a) Ground plan of the cave illustrating its morphology and dimension, (b) Cross-section illustrating the abyss-shaped entrance.Full size imageThe first hypothesis would indicate that the animal fell into the cave during an attempt to climb down. However, there is no report in the literature indicating that Eremotherium laurillardi could have been a climbing animal. In addition, the vertical morphology of the cave entrance would be a limiting factor for climbing behavior (see Fig. 3).Therefore, based on the type of fracture (compression sagittal split fracture) observed in the three vertebrae of Eremotherium as well as the inferred origin mechanism (fall from a great height), the presence of the individual here analyzed in the fossil accumulation of Toca das Onças is more likely explained by the second hypothesis. This idea is not particularly new as ‘entrapment due to fall’ has been described as a fossil accumulation mode to several other caves worldwide (e.g.,27,28). However, the use of bones fractures as an indicator of fossil accumulation mode is an interesting novelty. Of course, a detailed taphonomic investigation in the Toca das Onças still needs to be conducted in order to accurately interpret the formation of this important Quaternary fossil accumulation from Brazil.In sum, we suggest that the animal accidentally fell into the cave, fractured at least three sequential vertebrae (12th, 13th thoracic vertebrae and 1st lumbar vertebra) after the impact on the ground, survived for a while, but succumbed trapped inside the cave without food and water (Fig. 4). Other animals found in the cave, but without signs of bone fracture, may have fallen and not fractured their bones or not survived after the fall, especially the smaller ones. Finally, the proposal of falls to explain the unusual record of giant ground sloth fossils preserving much of its skeleton in caves, as reported for Toca das Onças site, contrasts with the better-documented pattern of skeletal accumulation via hydraulic action.Figure 4Artistic reconstruction of the suggested fall of the individual Eremotherium laurillardi into the cave. Artwork by Júlia d’Oliveira.Full size image More

Our compiled dataset consisted of 1674 records of marine mammal records after removing duplicate reports. It included 660 reports of sightings, 59 reports of induced mortalities or hunting records, 240 reports of incidental mortalities, 632 unique stranding records (live / dead), and 83 records which could not be categorised because of incomplete information.SightingsA total of 660 opportunistic sightings (number of individuals, ni = 3299) were recorded throughout the Indian coastline between 1748 and 2017 (Fig. 1a, 2a, 3a). Sighting data on the east coast (species = 18, ni = 1105) was mostly restricted to Odisha and Tamil Nadu (representing 97% of total east coast sightings). On the west coast (ni = 1297), Maharashtra (ni = 549), Gujarat (ni = 248) and Karnataka (ni = 307) contributed to highest sighting records (representing 85% of total west coast sightings). Sightings from the islands also contributed to 24.85% of the dataset (Andaman & Nicobar Islands = 24.37%, Lakshadweep = 0.48%). Highest incidence of sightings was for DFP (ni = 1894) followed by dugongs (ni = 959), BW (ni = 58) and SBW (ni = 17).Figure 1Marine mammal records obtained from data compiled between years 1748 – 2017 along the east coast, west coast and the islands of India for the groups i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, given as color-coded stacked bars where (a) sighting records—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) stranding records—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue.Full size imageFigure 2Marine mammal records obtained every year from the data compiled between years 1748–2017 along Indian coastline given as cumulative numbers for each group i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, as color-coded stacked bars, where (a) sighting records—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) stranding records—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue.Full size imageFigure 3Bubble plots showing distribution of marine mammal records obtained from data compiled between years 1748–2017 along the Indian coastline for each group i.e., baleen whales (BW), dolphins and finless porpoise (DFP), sperm and beaked whales (SBW) and dugongs, as color-coded stacked bars, where (a) sighting—records where live animals were sighted (b) induced mortalities—records where animals were reported hunted or killed or were driven ashore, (c) incidental mortalities—records where animals were found dead after entanglement in fishing nets or being struck by vessels and (d) strandings—records where dead or live animals were found washed ashore, or floating near shore or stranded alive and were attempted for rescue. Size of the bubble indicates number of individuals. These maps were created using ArcGIS 10.5 (https://desktop.arcgis.com/en/arcmap/10.3/map/working-with-layers/about-symbolizing-layers-to-represent-quantity.htm).Full size imageInduced mortalitiesA total of 59 incidences (ni = 102) were recorded of marine mammals being hunted/ captured between the years 1748–2017 (Fig. 1b, 2b, 3b). The total number of animals hunted/ captured deliberately is similar along east coast (ni = 33), west coast (ni = 29) and islands (ni = 36). Out of all marine mammal species, 90% of the animals hunted at the east coast were dugong D. dugon (ni = 30, all from Tamil Nadu). On the west coast, records of hunting incidences of finless porpoise Neophocaena phocaenoides were highest (79% of total records on west coast, Goa ni = 17, Kerala ni = 4, Karnataka and Maharashtra ni = 1). In the islands (i.e., Andaman and Nicobar Islands), 94% of the hunting records were of dugongs (ni = 34).Incidental mortalitiesA total of 240 net entanglements (ni = 1356) were reported along the Indian coast between the years 1748 and 2017 (Fig. 1c, 2c, 3c). Similar counts of individuals entangled along east (ni = 670) and west coast (ni = 654) were obtained with low reporting from the islands (ni = 26). Fourteen species were reported entangled from both east and west coast with only 4 species recorded from the islands. D. dugon was found to be most frequently entangled along the east coast (63 incidences, ni = 594, contributing to 56% of the total numbers on east coast), followed by Tursiops sp. (11 incidences, ni = 14, 9% of the east coast dataset). On the west coast, Tursiops sp. was the most frequently entangled (18 incidences, ni = 117, contributing to 18% of the west coast dataset), followed by N. phocaenoides (17 incidences, ni = 34, contributing to 17% of the dataset). The total number of DFP being entangled from west coast (ni = 623) were higher than east coast (ni = 68). More dugong individuals were entangled along east coast (i.e., from Tamil Nadu; ni = 594) as compared to the west coast (i.e., Gujarat; ni = 3) and Islands (i.e., Andaman and Nicobar; ni = 19). D. dugon was the most frequently entangled species in the islands (19 incidences, ni = 19, contributing to 79% of the total numbers in islands dataset) followed by false killer whale Pseudorca crassidens (3 incidences, ni = 5, contributing to 12% of the islands dataset). Very few BW or SBW (11 incidences, ni = 11) were recorded accidently entangled throughout the Indian coastline.StrandingsMarine mammals stranding reports consisted of 91.93% dead (ni = 581) and 8.07% live strandings (ni = 51) (Figs. 1d, 2d, 3d). Considering mass strandings as strandings with ni  > 2 (excluding mother and calf;33,34), 8.5% of all reports were mass strandings (21 strandings, ni = 1054). Most of the records did not have information about the sex of the stranded animal (83%), the age class (88%) or the state of decomposition of the carcass (53%). Highest strandings were reported of dugongs (strandings = 190, ni = 228), followed by BW (strandings = 178, ni =  = 190), DFP (strandings = 157, ni =  = 552) and SBW (strandings = 47, individuals = 48). There were 54 incidences (ni = 54, 9% of total stranding data) where the animal was not identified reliably to include in either of the groups.Species composition and frequencies of strandings were different on east coast, west coast and in the islands (Fig. 1, Table 1). Twenty-two species were reported as stranded on the east coast with D. dugon as the most frequently stranded species (83 incidences, ni = 107, ~ 29% of all records), followed by Indo-Pacific humpback dolphin Sousa chinensis, (31 incidences, ni = 108, ~ 10% of all records). On the west coast, out of 20 species reported as stranded, Balaenoptera musculus was most frequent (28 incidences, ni = 29, ~ 12% of all records) followed by N. phocaenoides (23 incidences, ni = 39, ~ 10% of all records). In the islands, 13 species were reported as stranded, D. dugon (93 incidences, ni = 102, contributing to 77% of the total animals found on the islands) followed by strandings of sperm whale Physeter macrocephalus (8 incidences, ni = 8, contributing to 6% of the data; Table 1).

a. Baleen whales

Table 1 Number of stranding events reported for marine mammals between 1748–2017 in India from the east coast, the west coast and Lakshadweep and Andaman & Nicobar archipelagos.Full size tableA total of 178 BW strandings (ni = 190) were reported. Most species were unidentified (east coast ni= 27, west coast ni = 58, islands ni = 4; i.e., 47% of the data). Identified strandings comprised of 6 species (see Table 1), some of which were later found to be misidentification (no confirmed evidence for common Minke Whale Balaenoptera acutorostrata, Sei Whale Balaenoptera borealis and Fin Whale Balaenoptera physalus from Indian waters; MMRCNI, 2018). Higher number of strandings occurred on the west coast (ni = 126), as compared to east coast (ni = 60). The east and west coast reported all six species of BW, whereas only three species stranded on the islands. B. borealis (misidentified) was the most stranded species across the east coast (12 incidences, ni = 12, contributing to 11% of the data) whereas blue whale Balaenoptera musculus was the most frequent across the west coast (28 incidences, ni = 29, contributing to 11% of the data). Baleen whale strandings were rare in the islands (4 incidences, ni = 4).Forty-seven SBW strandings (ni = 48) were reported along the Indian coast. More SBW stranded on the east coast (ni = 23) as compared to the west coast (ni = 13) and the islands (ni = 12). P. macrocephalus was most frequently reported (70% of all SBW records, east coast ni = 20, west coast ni = 6, islands ni = 8).There were 157 strandings (ni =552) of DFP belonging to 14 species. Twenty-one of these events were mass strandings (ni  > 2). The largest mass stranding event (ni = 147) occurred of short-finned pilot whale Globicephala macrorhynchus along the west coast (Tamil Nadu). Higher number of DFP strandings were recorded from east coast (ni = 418) as compared to west coast (ni = 83) and the islands (ni = 51; Table 1). East coast received a higher diversity of stranded DFP (number of species = 11) as compared to west coasts (number of species = 9) and the islands (number of species = 3). S. chinensis was the most frequently stranded species along the east coast (31 incidences, ni = 108, contributing to 33% of the data) whereas N. phocaenoides was the most frequent along the west coast (23 incidences, ni = 39, contributing to 37% of the data; Table 1).

d. Dugongs

The current distribution of dugongs in India is in the shallow coastal waters of Gujarat, Tamil Nadu and Andaman & Nicobar Islands37,38. There are 190 stranding events recorded between the years 1893 and 2017. The highest number of stranded dugongs were recorded from Tamil Nadu (ni = 107) closely followed by Andaman and Nicobar Islands (ni = 102) and few records from Gujarat (ni = 19).Temporal stranding patternsOur analysis of temporal trends for the last 42 years (1975–2017) showed that the mean number of strandings along the Indian coast was 11.25 ± SE 1.39 / year. The number of stranding reports show an increasing trend for two decades after 1975, dropping between 1995 and 2004. We observed a distinct rise in strandings post 2005 (18.23 ± SE 2.98 / year) with the highest reports from 2015–17 (27.66 ± SE 8.51/year) (Fig. 4).

a. Baleen whales

Figure 4A beanplot of decadal trends in marine mammal stranding in India from data compiled between years 1975–2017. Data prior to 1975 was discontinuous over the years to be considered for decadal trends. The data for last decade considered here includes only two years (2015–17) where increased reporting is evident. The bold horizontal lines indicate the mean number of strandings in each decade whereas the smaller horizontal lines indicate stranding numbers recorded for each year within the decade.Full size imageOn the west coast, mean stranding rate throughout the years (1975–2017) was 0.0010 ± SE 0.0014 strandings/km, and a steady rise was observed in rate of reported strandings after 2010. A seasonal trend was observed as well, with a peak in the month of September (sr = 0.0061 ± SE 0.0016 strandings/km), i.e., towards the end of monsoon season, and lowest strandings were recorded in the month of June (sr = 0.0016 ± SE 0.006 strandings/ km) (Fig. 5).Figure 5Temporal patterns (annual and monthly stranding rates / 100 km of coastline) in strandings of marine mammal records obtained from data compiled between years 1975–2017 along east and west coast of India for each group where (a) annual stranding rate and (b) monthly stranding rate for baleen whales (BW); (c) annual stranding rate and (d) monthly stranding rate for dolphins and finless porpoise (DFP); (e) annual stranding rate and (f) monthly stranding rate for sperm and beaked whales (SBW) and (g) annual stranding rate and (h) monthly stranding rate for dugongs.Full size imageThe mean stranding rate of BW on the east coast through 1975–2017 was 0.0013 ± SE 0.0017 strandings/km, but no specific trends were observed according to years or seasons. Stranding rates of BW did not differ between east and west coast (Mann–Whitney U test, U = 390, U standardized = -0.025, p value  > 0.05).The stranding rates of SBW differed significantly along both the coasts (Mann Whitney U test, U = 192, U standardized = 0.0, p value  More