Philippa Kaur

Ritchie, H. & Roser, M. Urbanization. Our World in Data (2018). https://ourworldindata.org/urbanization.Chen, H., Weersink, A., Beaulieu, M., Lee, Y. N. & Nagelschmitz, K. A historical review of changes in farm size in canada. Tech. Rep., University of Guelph, Institute for the Advanced Study of Food and and Agricultural Policy (2019).Gudelj, I. & White, K. Spatial heterogeneity, social structure and disease dynamics of animal populations. Theor. Popul. Biol. 66, 139–149 (2004).CAS 
MATH 
Article 

Google Scholar 
Augustin, N., Mugglestone, M. A. & Buckland, S. T. An autologistic model for the spatial distribution of wildlife. J. Appl. Ecol. 339–347 (1996).Karlsson, E. K., Kwiatkowski, D. P. & Sabeti, P. C. Natural selection and infectious disease in human populations. Nat. Rev. Genetics 15, 379–393 (2014).CAS 
Article 

Google Scholar 
Fornaciari, A. Environmental microbial forensics and archaeology of past pandemics. Microbiol. Spect. 5, 5–1 (2017).Article 

Google Scholar 
Thèves, C., Crubézy, E. & Biagini, P. History of smallpox and its spread in human populations. Microbiol. Spect. 4, 4–4 (2016).Article 

Google Scholar 
Coltart, C. E., Lindsey, B., Ghinai, I., Johnson, A. M. & Heymann, D. L. The ebola outbreak, 2013–2016: old lessons for new epidemics. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160297 (2017).Article 

Google Scholar 
Colizza, V., Barrat, A., Barthelemy, M., Valleron, A.-J. & Vespignani, A. Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLOS Med. 4, e13 (2007).Article 

Google Scholar 
Lüthy, I. A., Ritacco, V. & Kantor, I. N. One hundred years after the “Spanish” flu. Medicina 78, 113–118 (2018).
Google Scholar 
Zhang, Y., Zhang, A. & Wang, J. Exploring the roles of high-speed train, air and coach services in the spread of COVID-19 in China. Transport Policy 94, 34–42 (2020).Article 

Google Scholar 
Coelho, M. T. P. et al. Global expansion of COVID-19 pandemic is driven by population size and airport connections. PeerJ 8, e9708 (2020).Article 

Google Scholar 
Tompkins, D. M., Carver, S., Jones, M. E., Krkošek, M. & Skerratt, L. F. Emerging infectious diseases of wildlife: A critical perspective. Trends Parasitol. 31, 149–159 (2015).Article 

Google Scholar 
Soulsbury, C. D. & White, P. C. Human-wildlife interactions in urban areas: A review of conflicts, benefits and opportunities. Wildl. Res. 42, 541–553 (2015).Article 

Google Scholar 
VanderWaal, K. L. et al. Network analysis of cattle movements in Uruguay: Quantifying heterogeneity for risk-based disease surveillance and control. Prevent. Vet. Med. 123, 12–22 (2016).Article 

Google Scholar 
Rossi, G. et al. The potential role of direct and indirect contacts on infection spread in dairy farm networks. PLOS Comput. Biol. 13, e1005301 (2017).Article 

Google Scholar 
Stoddard, S. T. et al. The role of human movement in the transmission of vector-borne pathogens. PLOS Neg. Trop. Dis. 3, e481 (2009).Article 

Google Scholar 
Cosner, C. Models for the effects of host movement in vector-borne disease systems. Math. Biosci. 270, 192–197 (2015).MathSciNet 
MATH 
Article 

Google Scholar 
Scherer, P.C. Infection on the move: individual host movement drives disease persistence in spatially structured landscapes. Dr. rer. nat. thesis, Universität Potsdam (2019).Riley, S. Large-scale spatial-transmission models of infectious disease. Science 316, 1298–1301 (2007).ADS 
CAS 
Article 

Google Scholar 
Dougherty, E. R., Seidel, D. P., Carlson, C. J., Spiegel, O. & Getz, W. M. Going through the motions: Incorporating movement analyses into disease research. Ecol. Lett. 21, 588–604 (2018).Article 

Google Scholar 
Daversa, D., Fenton, A., Dell, A., Garner, T. & Manica, A. Infections on the move: How transient phases of host movement influence disease spread. Proc. R. Soc. B Biol. Sci. 284, 20171807 (2017).Article 

Google Scholar 
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton University Press, 2001).Book 

Google Scholar 
Kobayashi, K. & Okumura, M. The growth of city systems with high-speed railway systems. Ann. Region. Sci. 31, 39–56 (1997).Article 

Google Scholar 
VanderWaal, K., Perez, A., Torremorrell, M., Morrison, R. M. & Craft, M. Role of animal movement and indirect contact among farms in transmission of porcine epidemic diarrhea virus. Epidemics 24, 67–75 (2018).Article 

Google Scholar 
Hanski, I. Metapopulation dynamics. Nature 396, 41–49 (1998).ADS 
CAS 
Article 

Google Scholar 
Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: Theory and simulations. J. Theor. Biol. 251, 450–467 (2008).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Wang, L. & Li, X. Spatial epidemiology of networked metapopulation: An overview. Chin. Sci. Bull. 59, 3511–3522 (2014).Article 

Google Scholar 
Ruxton, G. D. Low levels of immigration between chaotic populations can reduce system extinctions by inducing asynchronous regular cycles. Proc. R. Soc. Lond. Seri. B Biol. Sci. 256, 189–193 (1994).ADS 
Article 

Google Scholar 
Earn, D. J. D., Rohani, P. & Grenfell, B. T. Persistence chaos and synchrony in ecology and epidemiology. Proc. R. Soc. Lond. Seri. B Biol. Sci. 265, 7–10 (1998).CAS 
Article 

Google Scholar 
Rosenzweig, M. L. Paradox of enrichment: Destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).ADS 
CAS 
Article 

Google Scholar 
Hilker, F. M. & Schmitz, K. Disease-induced stabilization of predator-prey oscillations. J. Theor. Biol. 255, 299–306 (2008).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).Article 

Google Scholar 
Philipson, T. Economic epidemiology and infectious diseases. Handb. Health Econ. 1, 1761–1799 (2000).Article 

Google Scholar 
Murdoch, W. W., Briggs, C. J. & Nisbet, R. M. Consumer-Resource Dynamics, Monographs in Population Biology Vol. 36 (Princeton University Press, 2003).
Google Scholar 
Murdoch, W. W. & Oaten, A. Predation and population stability. In Advances in Ecological Research, vol. 9, 1–131 (Elsevier, 1975).Bolker, B. & Grenfell, B. T. Space, persistence and dynamics of measles epidemics. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 348, 309–320 (1995).ADS 
CAS 
Article 

Google Scholar 
Keeling, M. J. & Rohani, P. Estimating spatial coupling in epidemiological systems: a mechanistic approach. Ecol. Lett. 5, 20–29 (2002).Article 

Google Scholar 
Arino, J. Spatio-temporal spread of infectious pathogens of humans. Infect. Dis. Model. 2, 218–228 (2017).
Google Scholar 
Keeling, M. J. & Rohani, P. Modeling Infectious Diseases in Humans and Animals (Princeton University Press, 2011).MATH 
Book 

Google Scholar 
Wilson, E. B. & Worcester, J. The spread of an epidemic. Proc. Nat. Acad. Sci. 31, 327 (1945).ADS 
CAS 
Article 

Google Scholar 
Rushton, S. & Mautner, A. The deterministic model of a simple epidemic for more than one community. Biometrika 42, 126–132 (1955).MathSciNet 
MATH 
Article 

Google Scholar 
Lourenço, J. & Recker, M. Natural, persistent oscillations in a spatial multi-strain disease system with application to dengue. PLOS Comput. Biol. 9, e1003308 (2013).ADS 
Article 

Google Scholar 
Wikramaratna, P. S., Pybus, O. G. & Gupta, S. Contact between bird species of different lifespans can promote the emergence of highly pathogenic avian influenza strains. Proc. Natl. Acad. Sci. 111, 10767–10772 (2014).ADS 
CAS 
Article 

Google Scholar 
Xiao, Y., Zhou, Y. & Tang, S. Modelling disease spread in dispersal networks at two levels. Math. Med. Biol. J. IMA 28, 227–244 (2011).MathSciNet 
MATH 
Article 

Google Scholar 
Arino, J., Ducrot, A. & Zongo, P. A metapopulation model for malaria with transmission-blocking partial immunity in hosts. J. Math. Biol. 64, 423–448 (2012).MathSciNet 
MATH 
Article 

Google Scholar 
De Roos, A. M., Mccauley, E. & Wilson, W. G. Mobility versus density-limited predator-prey dynamics on different spatial scales. Proc. R. Soc. Lond. Ser. B Biol. Sci. 246, 117–122 (1991).ADS 
Article 

Google Scholar 
Dey, S., Goswami, B. & Joshi, A. Effects of symmetric and asymmetric dispersal on the dynamics of heterogeneous metapopulations: Two-patch systems revisited. J. Theor. Biol. 345, 52–60 (2014).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Anderson, R. M., Jackson, H. C., May, R. M. & Smith, A. M. Population dynamics of fox rabies in Europe. Nature 289, 765–771 (1981).ADS 
CAS 
Article 

Google Scholar 
Gupta, S., Ferguson, N. & Anderson, R. Chaos persistence, and evolution of strain structure in antigenically diverse infectious agents. Science 280, 912–915 (1998).ADS 
CAS 
Article 

Google Scholar 
Holland, M. D. & Hastings, A. Strong effect of dispersal network structure on ecological dynamics. Nature 456, 792–794 (2008).ADS 
CAS 
Article 

Google Scholar 
McCann, K., Hastings, A. & Huxel, G. R. Weak trophic interactions and the balance of nature. Nature 395, 794–798 (1998).ADS 
CAS 
Article 

Google Scholar 
Singh, A. & Gakkhar, S. Synchronization of chaos in a food web in ecological systems. World Acad. Sci. Eng. Technol. 70, 94–98 (2010).
Google Scholar 
Gotelli, N. J. Metapopulation models: The rescue effect, the propagule rain, and the core-satellite hypothesis. American Naturalist 138, 768–776 (1991).Article 

Google Scholar 
Heino, M., Kaitala, V., Ranta, E. & Lindström, J. Synchronous dynamics and rates of extinction in spatially structured populations. Proc. R. Soc. Lond. Ser. B Biol. Sci. 264, 481–486 (1997).ADS 
Article 

Google Scholar 
Molofsky, J. & Ferdy, J.-B. Extinction dynamics in experimental metapopulations. Proc. Natl. Acad. Sci. 102, 3726–3731 (2005).ADS 
CAS 
Article 

Google Scholar 
Saxena, G., Prasad, A. & Ramaswamy, R. Amplitude death: The emergence of stationarity in coupled nonlinear systems. Phys. Rep. 521, 205–228 (2012).ADS 
Article 

Google Scholar 
Majhi, S. & Ghosh, D. Amplitude death and resurgence of oscillation in networks of mobile oscillators. Europhys. Lett. 118, 40002 (2017).ADS 
Article 

Google Scholar 
Shen, C., Chen, H. & Hou, Z. Mobility and density induced amplitude death in metapopulation networks of coupled oscillators. Chaos 24, 043125 (2014).MATH 
Article 

Google Scholar 
Karnatak, R., Ramaswamy, R. & Feudel, U. Conjugate coupling in ecosystems: Cross-predation stabilizes food webs. Chaos Solitons Fractals 68, 48–57 (2014).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Bolker, B. M. & Grenfell, B. T. Chaos and biological complexity in measles dynamics. Proc. R. Soc. Lond. Ser. B Biol. Sci. 251, 75–81 (1993).ADS 
CAS 
Article 

Google Scholar 
Olsen, L. F., Truty, G. L. & Schaffer, W. M. Oscillations and chaos in epidemics: A nonlinear dynamic study of six childhood diseases in Copenhagen, Denmark. Theor. Popul. Biol. 33, 344–370 (1988).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Lundberg, P., Ranta, E., Ripa, J. & Kaitala, V. Population variability in space and time. Trends Ecol. Evolut. 15, 460–464 (2000).CAS 
Article 

Google Scholar 
Dekker, A. Realistic social networks for simulation using network rewiring. In International Congress on Modelling and Simulation, 677–683 (2007).Milgram, S. The small world problem. Psychol. Today 2, 60–67 (1967).
Google Scholar 
Sallaberry, A., Zaidi, F. & Melançon, G. Model for generating artificial social networks having community structures with small-world and scale-free properties. Soc. Netw. Anal. Min. 3, 597–609 (2013).Article 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. 104, 19891–19896 (2007).ADS 
CAS 
MATH 
Article 

Google Scholar 
Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl. Acad. Sci. 108, 3648–3652 (2011).ADS 
CAS 
Article 

Google Scholar 
Girvan, M. & Newman, M. E. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002).ADS 
MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
Krause, A. E., Frank, K. A., Mason, D. M., Ulanowicz, R. E. & Taylor, W. W. Compartments revealed in food-web structure. Nature 426, 282–285 (2003).ADS 
CAS 
Article 

Google Scholar 
Rezende, E. L., Albert, E. M., Fortuna, M. A. & Bascompte, J. Compartments in a marine food web associated with phylogeny, body mass, and habitat structure. Ecol. Lett. 12, 779–788 (2009).Article 

Google Scholar 
Pastor-Satorras, R. & Vespignani, A. Epidemics and immunization in scale-free networks. In Handbook of Graphs and Networks, 111–130 (Wiley Online Library, 2002).Lloyd-Smith, J. O., Schreiber, S. J., Kopp, P. E. & Getz, W. M. Superspreading and the effect of individual variation on disease emergence. Nature 438, 355–359 (2005).ADS 
CAS 
Article 

Google Scholar 
Shirley, M. D. & Rushton, S. P. The impacts of network topology on disease spread. Ecol. Complex. 2, 287–299 (2005).Article 

Google Scholar 
Keeling, M. J. & Eames, K. T. Networks and epidemic models. J. R. Soc. Interface 2, 295–307 (2005).Article 

Google Scholar 
Godfrey, S. S., Bull, C. M., James, R. & Murray, K. Network structure and parasite transmission in a group living lizard the gidgee skink, Egernia stokesii. Behav. Ecol. Sociobiol. 63, 1045–1056 (2009).Article 

Google Scholar 
VanderWaal, K. L., Atwill, E. R., Hooper, S., Buckle, K. & McCowan, B. Network structure and prevalence of Cryptosporidium in Belding’s ground squirrels. Behav. Ecol. Sociobiol. 67, 1951–1959 (2013).Article 

Google Scholar 
Proulx, S. R., Promislow, D. E. & Phillips, P. C. Network thinking in ecology and evolution. Trends Ecol. Evolut. 20, 345–353 (2005).Article 

Google Scholar 
Craft, M. E. & Caillaud, D. Network models: An underutilized tool in wildlife epidemiology?. Interdiscip. Perspect. Infect. Dis. 2011, (2011).Bajardi, P. et al. Human mobility networks, travel restrictions, and the global spread of 2009 h1n1 pandemic. PloS one 6, e16591 (2011).ADS 
CAS 
Article 

Google Scholar 
Gog, J. R. et al. Seven challenges in modeling pathogen dynamics within-host and across scales. Epidemics 10, 45–48 (2015).Article 

Google Scholar 
Cen, X., Feng, Z. & Zhao, Y. Emerging disease dynamics in a model coupling within-host and between-host systems. J. Theor. Biol. 361, 141–151 (2014).ADS 
MathSciNet 
MATH 
Article 

Google Scholar 
Meakin, S. R. & Keeling, M. J. Correlations between stochastic epidemics in two interacting populations. Epidemics 26, 58–67 (2019).Article 

Google Scholar 
Machado, G. et al. Identifying outbreaks of porcine epidemic diarrhea virus through animal movements and spatial neighborhoods. Sci. Rep. 9, 1–12 (2019).
Google Scholar 
Tonkin, J. D. et al. The role of dispersal in river network metacommunities: Patterns, processes, and pathways. Freshwater Biol. 63, 141–163 (2018).Article 

Google Scholar 
Pedersen, T. L. tidygraph: a tidy API for graph manipulation (2019). R package version 1.1.2.Rackauckas, C. & Nie, Q. Differentialequations.jl–a performant and feature-rich ecosystem for solving differential equations in Julia. J. Open Res. Softw. 5 (2017).Rackauckas, C. & Nie, Q. Confederated modular differential equation APIS for accelerated algorithm development and benchmarking. Adv. Eng. Softw. 132, 1–6 (2019).Article 

Google Scholar 
Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. Julia: A fresh approach to numerical computing. SIAM Rev. 59, 65–98 (2017).MathSciNet 
MATH 
Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 
Book 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020). More

Lal Mohan, R. S., Dey, S. C., Bairagi, S. P. & Roy, S. On a survey of the Ganges River dolphin Platanista gangetica of Bramaputra River, Assam. J. Bombay Nat. Hist. Soc. 94, 483–495 (1997).
Google Scholar 
Sinha, R.K., et al. Status and distribution of the Ganges susu (Platanista gangetica) in Ganges River system of India and Nepal in Biology and conservation of freshwater cetaceans in Asia (eds. Reeves, R. R., Smith, B. D. & Kasuya, T). 42–48 (Switzerland: Occasional Paper of the IUCN Species Survival Commission, 2000)Sinha, R. K. & Kannan, K. Ganges River dolphin: an overview of biology, ecology, and conservation status in India. Ambio. 43,1029–1046 (2014).
Google Scholar 
Anderson, J. Anatomical and Zoological Researches: Comprising an Account of the Zoological Results of the Two Expeditions to Western Yunnan in 1868 and 1875; and A Monograph of the Two Cetacean Genera, Platanista and Orcella-Vol. 1 (Text). Vol. 1 (Bernard Quaritch, 1878).Herald, E. S. et al. Blind river dolphin: first side-swimming cetacean. Science 166, 1408–1410 (1969).ADS 
CAS 

Google Scholar 
Herald, E. S. Field and aquarium study of the blind River Dolphin (Platanista Gangetica) (California Academy of Sciences San Fransico Steinhart Aquarium, 1969).Pilleri, G., Zbinden, K., Gihr, M. & Kraus, C. Sonar clicks, directionality of the emission field and echolocating behaviour of the Indus dolphin (Platanista indi, Blyth, 1859). Invest. Cetacea Brain Anat. Inst. Berne Switzerl. 13–43 (1976).Jensen, F. H. et al. Clicking in shallow rivers: short-range echolocation of Irrawaddy and Ganges river dolphins in a shallow, acoustically complex habitat. PLoS ONE 8, e59284 (2013).ADS 
CAS 

Google Scholar 
Pence, E.A. Monofilament gill net acoustic study. (National Mammal Laboratory, Contract 40-ABNF-5-1988,1986)Jefferson, T. A., Würsig, B. & Fertl, D. Cetacean Detection and Responses to Fishing Gear in Marine Mammal Sensory Systems (eds. Thomas, J.A., Kastelein, R.A. & Supin, A.Y.) 663–684 (Springer, 1992)
Google Scholar 
Mansur, E. F., Smith, B. D., Mowgli, R. M. & Diyan, M. A. A. Two incidents of fishing gear entanglement of Ganges River dolphins (Platanista gangetica gangetica) in waterways of the Sundarbans mangrove forest, Bangladesh. Aquat. Mamm. 34, 362 (2008).
Google Scholar 
Sinha, R. K. An alternative to dolphin oil as a fish attractant in the Ganges River system: conservation of the Ganges River dolphin.
Biol. Conserv. 107, 253–257 https://doi.org/10.1016/S0006-3207(02)00058-7 (2002).Article 

Google Scholar 
Qureshi, Q. et al. Development of conservation action plan for river dolphin. 228 (Wildlife Institute of India, Dehradun, Uttarakhand, 2018).Kolipakam, V. et al. Evidence for the continued use of river dolphin oil for bait fishing and traditional medicine: implications for conservation. Heliyon 6, e04690 (2020).
Google Scholar 
Wakid, A. Initiative to reduce the fishing pressures in and around identified habitats of endangered Gangetic dolphin in Brahmaputra River system. (Assam, 2010).Braulik, G.T. & Smith, B.D. Platanista gangetica (amended version of 2017
assessment). The IUCN Red List of Threatened Species, e.T41758A151913336. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T41758A151913336.en (2019).Dawson, S. M., Northridge, S., Waples, D. & Read, A. J. To ping or not to ping: the use of active acoustic devices in mitigating interactions between small cetaceans and gillnet fisheries. Endanger. Species Res. 19, 201–221 (2013)
Google Scholar 
Reeves, R. R., McClellan, K. & Werner, T. B. Marine mammal bycatch in gillnet and other entangling net fisheries, 1990 to 2011. Endanger. Species Res. 20, 71–97 (2013).
Google Scholar 
Moore, M. J. et al. Fatally entangled right whales can die extremely slowly in OCEANS 2006. 1–3 (IEEE, 2006).Meÿer, M.A. et al. Trends and interventions in large whale entanglement along the South African coast. Afr. J. Mar. Sci. 33, 429–439 (2011).
Google Scholar 
Knowlton, A. R., Hamilton, P. K., Marx, M. K., Pettis, H. M. & Kraus, S. D. Monitoring North Atlantic right whale Eubalaena glacialis entanglement rates: a 30 year retrospective. Mar. Ecol. Prog. Ser. 466, 293–302 (2012).ADS 

Google Scholar 
Knowlton, A. R. et al. Effects of fishing rope strength on the severity of large whale entanglements. Conserv. Biol. 30, 318–328 (2016).
Google Scholar 
Pace, R. M. III., Cole, T. V. & Henry, A. G. Incremental fishing gear modifications fail to significantly reduce large whale serious injury rates. Endanger. Species Res. 26, 115–126 (2014).
Google Scholar 
Salvador, G., Kenney, J. & Higgins, J. 2008 Supplement to the Large whale gear research summary. NOAA/Fisheries Northeast Regional Office, Protected Resources Division, Gloucester, MA (2008).van der Hoop, J. M. et al. Assessment of management to mitigate anthropogenic effects on large whales. Conserv. Biol. 27, 121–133 (2013).
Google Scholar 
Hamilton, S. & Baker, G. B. Technical mitigation to reduce marine mammal bycatch and entanglement in commercial fishing gear: lessons learnt and future directions. Rev. Fish Biol. Fish. 29, 223–247 (2019).
Google Scholar 
Bordino, P., Mackay, A. I., Werner, T. B., Northridge, S. & Read, A. Franciscana bycatch is not reduced by acoustically reflective or physically stiffened gillnets. Endanger. Species Res. 21, 1–12 (2013).
Google Scholar 
Dawson, S. M. Incidental catch of Hector’s dolphin in inshore gillnets. Mar. Mamm. Sci. 7, 283–295 (1991).
Google Scholar 
Mooney, T. A., Nachtigall, P. E. & Au, W. W. Target strength of a nylon monofilament and an acoustically enhanced gillnet: predictions of biosonar detection ranges. Aquat. Mamm. 30, 220–226 (2004).
Google Scholar 
Northridge, S., Sanderson, D., Mackay, A. & Hammond, P. Analysis and mitigation of cetacean bycatch in UK fisheries. Final Report
to DEFRA, Project MF0726, Sea Mammal Research Unit, School of Biology, University of St. Andrews (2003).Mangel, J. C. et al. Illuminating gillnets to save seabirds and the potential for multi-taxa bycatch mitigation. R. Soc. Open Sci. 5, 180254 (2018).ADS 

Google Scholar 
Stephenson, P. C. & Wells, S. Evaluation of the effectiveness of reducing dolphin catches with pingers and exclusion grids in the Pilbara trawl fishery. (Department of Fisheries, Western Australia, 2006).Santana-Garcon, J. et al. Risk versus reward: Interactions, depredation rates, and bycatch mitigation of dolphins in demersal fish trawls. Can. J. Fish. Aquat. Sci. 75, 2233–2240 (2018).
Google Scholar 
Carretta, J., Barlow, J. & Enriquez, L. Acoustic pingers eliminate beaked whale bycatch in a gill net fishery. Mar. Mamm. Sci. 24, 956–961 (2008).
Google Scholar 
Bordino, P. et al. Reducing incidental mortality of Franciscana dolphin Pontoporia blainvillei with acoustic warning devices attached to fishing nets. Mar. Mamm. Sci. 18, 833–842 (2002).
Google Scholar 
Khan, U. & Willems, D. Report of the Trinational workshop on the Irrawaddy Dolphin, 1st to 4th December 2020. 41 (WWF, Pakistan & Netherlands, 2021).Deori, S. et al. PINGERS: can be the eyes of blind ganges dolphins (Platanista Gangetica Gangetica, Roxburgh 1801). J. Sci. Trans. Environ. Technov. 11, 169–178 (2018).
Google Scholar 
Kraus, S. D. The once and future ping: challenges for the use of acoustic deterrents in fisheries. Mar. Technol. Soc. J. 33, 90 (1999).
Google Scholar 
Mate, B. R. & Harvey, J. T. Acoustical deterrents in marine mammal conflicts with fisheries. a workshop held February 17–18, 1986 at Newport, Oregon. NTIS, SPRINGFIELD, VA(USA) (1987).Favaro, L., Gnone, G. & Pessani, D. Postnatal development of echolocation abilities in a bottlenose dolphin (Tursiops truncatus): Temporal organization. Zoo Biol. 32, 210–215 (2013).
Google Scholar 
Dey, M., Krishnaswamy, J., Morisaka, T. & Kelkar, N. Interacting effects of vessel noise and shallow river depth elevate metabolic stress in Ganges river dolphins. Sci. Rep. 9, 15426. https://doi.org/10.1038/s41598-019-51664-1 (2019).ADS 

Google Scholar 
Kastelein, R. A. et al. Effects of acoustic alarms, designed to reduce small cetacean bycatch in gillnet fisheries, on the behaviour of North Sea fish species in a large tank. Mar. Environ. Res. 64, 160–180 (2007).CAS 

Google Scholar 
Kraus, S. et al. Acoustic alarms reduce porpoise mortality. Nature 388, 525 (1997).ADS 
CAS 

Google Scholar 
Roberts, B. L. & Read, A. J. Field assessment of C-POD performance in detecting echolocation click trains of bottlenose dolphins (Tursiops truncatus). Mar. Mamm. Sci. 31, 169–190 (2015).
Google Scholar 
Wickham, H. ggplot2: elegant graphics for data analysis. (Springer-Verlag, New York, 2009).RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. http://www.rstudio.com/ (2021).Crawley, M. J. Statistics: An Introduction using R (Wiley, 2005).MATH 

Google Scholar 
Perrin, W. F., Donovan, G.P. & Barlow, J. Report of the workshop on mortality of cetaceans in passive fishing nets and traps. Rep. Int. Whal. Commn. 1–71 (Cambridge: IWC, 1994).Read, A. J., Drinker, P. & Northridge, S. Bycatch of marine mammals in US and global fisheries. Conserv. Biol. 20, 163–169 (2006).
Google Scholar 
Reeves, R. & Leatherwood, S. Action plan for the conservation of cetaceans: dolphins, porpoises, and whales. IUCN/SSC Cetacean Specialist Group (IUCN Cambridge, 1998).Smith, B. D. & Braulik, G. Susu and Bhulan : Platanista gangetica gangetica and P. g. minor in Encyclopedia of Marine Mammals. 1135–1139 (Academic Press Ltd – Elsevier Science Ltd, 2009).Wakid, A. Status and distribution of the endangered Gangetic dolphin (Platanista gangetica gangetica) in the Brahmaputra River within India in 2005. Curr. Sci., 97, 1143–1151 (2009).
Google Scholar 
D’agrosa, C., Lennert-Cody, C. E. & Vidal, O. Vaquita bycatch in Mexico’s artisanal gillnet fisheries: driving a small population to extinction. Conserv. Biol. 14, 1110–1119 (2000).
Google Scholar 
Jaramillo-Legorreta, A. et al. Saving the vaquita: immediate action, not more data. Conserv. Biol., 21, 1653–1655 (2007).
Google Scholar 
Turvey, S. T. et al. First human-caused extinction of a cetacean species?. Biol. Lett. 3, 537–540 (2007).
Google Scholar 
Bashir, T., Khan, A., Gautam, P. & Behera, S. K. Abundance and prey availability assessment of Ganges River dolphin (Platanista gangetica gangetica) in a stretch of Upper Ganges River, India. Aquat. Mamm. 36, 19–26 (2010).
Google Scholar 
Braulik, G.T. & Smith, B.D. Platanista gangetica. The IUCN Red List of Threatened Species, e.T41758A50383612. https://doi.org/10.2305/IUCN.UK.2017-3.RLTS.T41758A50383612.en (2017).Hastie, G. D., Wilson, B., Wilson, L., Parsons, K. M. & Thompson, P. M. Functional mechanisms underlying cetacean distribution patterns: hotspots for bottlenose dolphins are linked to foraging. Mar. Biol. 144, 397–403 (2004).
Google Scholar 
Smith, A. M. & Smith, B. D. Review of status and threats to river cetaceans and recommendations for their conservation. Environ. Rev. 6, 189–206 (1998).
Google Scholar 
Wedekin, L., Daura-Jorge, F., Piacentini, V. & Simões-Lopes, P. Seasonal variations in spatial usage by the estuarine dolphin, Sotalia guianensis (van Bénéden, 1864)(Cetacea; Delphinidae) at its southern limit of distribution. Brazil. J. Biol. 67, 1–8 (2007).CAS 

Google Scholar 
Omeyer, L. et al. Assessing the effects of banana pingers as a bycatch mitigation device for harbour porpoises (Phocoena phocoena). Front. Mar. Sci. 285 (2020).Barlow, J. & Cameron, G. A. Field experiments show that acoustic pingers reduce marine mammal bycatch in the California drift gill net fishery. Mar. Mamm. Sci. 19, 265–283 (2003).
Google Scholar 
Amano, M., Kusumoto, M., Abe, M. & Akamatsu, T. Long-term effectiveness of pingers on a small population of finless porpoises in Japan. Endanger. Species Res. 32, 35–40 (2017).
Google Scholar 
Clay, T. A., Alfaro-Shigueto, J., Godley, B. J., Tregenza, N. & Mangel, J. C. Pingers reduce the activity of Burmeister’s porpoise around small-scale gillnet vessels. Mar. Ecol. Prog. Ser. 626, 197–208 (2019).ADS 

Google Scholar 
Kyhn, L. A. et al. Pingers cause temporary habitat displacement in the harbour porpoise Phocoena phocoena. Mar. Ecol. Prog. Ser. 526, 253–265 (2015).ADS 

Google Scholar 
Sugimatsu, H. et al. Study of acoustic characteristics of Ganges river dolphin calf using echolocation clicks recorded during long-term in-situ observation in 2012 OCEANS. 1–7 (IEEE, 2012).Ayadi, A., Ghorbel, M. & Bradai, M. N. Do pingers reduce interactions between bottlenose dolphins and trammel nets around the Kerkennah Islands (Central Mediterranean Sea)?. Cahiers Biol. Mar. 54, 375–383 (2013).
Google Scholar 
Carretta, J. V. & Barlow, J. Long-term effectiveness, failure rates, and “dinner bell” properties of acoustic pingers in a gillnet fishery. Mar. Technol. Soc. J. 45, 7–19 (2011).
Google Scholar 
Read, A. J., Waples, D. M., Urian, K. W. & Swanner, D. Fine-scale behaviour of bottlenose dolphins around gillnets. Proc. R. Soc. Lond. Ser. B Biol. Sci. 270, S90–S92 (2003).
Google Scholar 
Olesiuk, P. F., Nichol, L. M., Sowden, M. J. & Ford, J. K. Effect of the sound generated by an acoustic harassment device on the relative abundance and distribution of harbor porpoises (Phocoena phocoena) in Retreat Passage, British Columbia. Mar. Mamm. Sci. 18, 843–862 (2002).
Google Scholar 
Cox, T. M., Read, A. J., Solow, A. & Tregenza, N. Will harbour porpoises (Phocoena phocoena) habituate to pingers?. J. Cetacean Res. Manag. 3, 81–86 (2001).
Google Scholar 
Bruno, C. A. et al. Acoustic deterrent devices as mitigation tool to prevent dolphin-fishery interactions in the Aeolian Archipelago (Southern Tyrrhenian Sea, Italy). Mediterr. Mar. Sci. 22, 408–421 (2021).
Google Scholar 
Enger, P. S. Frequency discrimination in teleosts—central or peripheral in Hearing and sound communication in fishes (eds. Tavolga, W. N. et al.) 243–255 (Springer-Verlag, New York, 1981).
Google Scholar 
Halvorsen, M. B., Casper, B. M., Matthews, F., Carlson, T. J. & Popper, A. N. Effects of exposure to pile-driving sounds on the lake sturgeon, Nile tilapia and hogchoker. Proc. R. Soc. B Biol. Sci. 279, 4705–4714 (2012).
Google Scholar 
Ladich, F. Sound communication in fishes and the influence of ambient and anthropogenic noise. Bioacoustics 17, 34–38 (2008).
Google Scholar 
McCauley, R. D., Fewtrell, J. & Popper, A. N. High intensity anthropogenic sound damages fish ears. J. Acoust. Soc. Am. 113, 638–642 (2003).ADS 

Google Scholar 
Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends Ecol. Evol. 25, 419–427 (2010).
Google Scholar 
Gazo, M., Gonzalvo, J. & Aguilar, A. Pingers as deterrents of bottlenose dolphins interacting with trammel nets. Fish. Res. 92, 70–75 (2008).
Google Scholar 
Waples, D. M. et al. A field test of acoustic deterrent devices used to reduce interactions between bottlenose dolphins and a coastal gillnet fishery. Biol. Conserv. 157, 163–171 (2013).
Google Scholar 
Leaper, R. & Calderan, S. Review of methods used to reduce risks of cetacean bycatch and entanglements. CMS Tech. Ser. 38 (UNEP/CMS Secretariat, Bonn, Germany, 2018). More

Gibbs, J. P. Wetland loss and biodiversity conservation. Conserv. Biol. 14, 314–317 (2000).Article 

Google Scholar 
Turner, R. K. et al. Ecological-economic analysis of wetlands: Scientific integration for management and policy. Ecol. Econ. 35, 7–23 (2000).Article 

Google Scholar 
Zedler, J. B. & Kercher, S. Wetland resources: Status trends ecosystem services and restorability. Annu. Rev. Environ. Resour. 15, 39–74 (2005).Article 

Google Scholar 
Euliss, N. H., Smith, L. M., Wilcox, D. A. & Browne, B. A. Lining ecosystem processes with wetland management goals: Chartering a course for a sustainable future. Wetlands 28, 553–562 (2008).Article 

Google Scholar 
Costanza, R. et al. Changes in the global value of ecosystem services. Glob. Environ. Change. 26, 152–158 (2014).Article 

Google Scholar 
Macreadie, P. J. et al. The future of blue carbon. Nat. Commun. 10, 3998 (2019).ADS 
Article 

Google Scholar 
RAMSAR. Wise use of wetlands, Ramsar Handbooks, 4th edition (2010).Kingsford, R. T., Basset, A. & Jackson, L. Wetlands: Conservation’s poor cousins. Aquat. Conserv. 26, 892–916 (2016).Article 

Google Scholar 
Beck, M. W., Heck, K. L. & Able, K. W. The Identification, Conservation, and Management of Estuarine and Marine Nurseries for Fish and Invertebrates: A better understanding of the habitats that serve as nurseries for marine species and the factors that create site-specific variability in nursery quality will improve conservation and management of these areas. Bioscience 51, 633–641 (2001).Article 

Google Scholar 
Canu, D. M. et al. Adressing sustainability of clam farming in the Venice Lagoon. Ecol. Soc. 16, 26 (2010).
Google Scholar 
Canu, D. M., Solidoro, C., Cossarini, G. & Giorgi, F. Effect of global change on bivalve rearing activity and the need for adaptive management. Clim. Res. 42, 13–26 (2011).Article 

Google Scholar 
Newton, A. et al. Anthropogenic pressures on Coastal Wetlands. Front. Ecol. Evol. 8, 144 (2020).Article 

Google Scholar 
Ayache, F. et al. Environmental characteristics landscape history and pressures on three coastal lagoons in the Southern Mediterranean Region: Merja Zerga (Morocco) Ghar El Melh (Tunisia) and Lake Manzala (Egypt). Hydrobiologia 622, 15–43 (2009).CAS 
Article 

Google Scholar 
Solidoro, C. et al. Response of Venice Lagoon ecosystem to natural and anthropogenic pressures over the last 50 years. In Coastal Lagoons—Critical Habitats of Environmental Change (eds. Kennish, M. J. & Paerl, H. W.) 483–511 (2010).Newton, A. et al. Assessing quantifying and valuing the ecosystem services of coastal lagoons. J. Nat. Conserv. 44, 50–65 (2018).Article 

Google Scholar 
Newton, A. et al. An overview of ecological status vulnerability and future perspectives of European large shallow semi-enclosed coastal systems lagoons and transitional waters. Estuar. Coast. Shelf Sci. 140, 95–122 (2014).ADS 
Article 

Google Scholar 
Béjaoui, B. et al. Random Forest model and TRIX used in combination to assess and diagnose the trophic status of Bizerte Lagoon, southern Mediterranean. Ecol. Indic. 71, 293–301 (2016).Article 

Google Scholar 
Ramdani, M. et al. North African wetland lakes: Characterization of nine sites included in the CASSARINA Project. Aquat. Ecol. 35, 281–302 (2001).Article 

Google Scholar 
Junk, W. J. et al. Current state of knowledge regarding the world’s wetlands and their future under global climate change: A synthesis. Aquat. Sci. 75, 151–167 (2013).CAS 
Article 

Google Scholar 
Ouni, H. et al. Numerical modeling of hydrodynamic circulation in Ichkeul Lake-Tunisia. Energy Rep. 6, 208–213 (2020).Article 

Google Scholar 
Hollis, G. E. et al. Modeling and management of the internationally important wetland at Garaet Ichkeul Tunisia. Numéro 4 de IWRB special publication, International Waterfowl Research Bureau, ISSN 0962–6271 Volume 4 de International Waterfowl Research Bureau Slimbridge: IWRB special publication (ed. International Waterfowl Research Bureau) 1–121 (1986).Casagranda, C. & Boudouresque, C. F. A first quantification of the overall biomass, from phytoplankton to birds, of a Mediterranean brackish lagoon: Revisiting the ecosystem of Lake Ichkeul (Tunisia). Hydrobiologia 637, 73–85 (2010).Article 

Google Scholar 
Hamdi, N., Touihri, M. & Charfi, F. Diagnostic Écologique du Parc National Ichkeul (Tunisie) après la construction des barrages: Cas des oiseaux d’eau. Rev. Ecol-Terre Vie. 67, 41–62 (2012).
Google Scholar 
UNESCO. Biosphere Reserve Information Tunisia Ichkeul, UNESCO-MAB. Biosphere Reserves Directory. (2009a).UNESCO. Ichkeul National Park http://whc.unesco.org/en/list/8/ (2009b).RAMSAR. Convention and Wetlands International. Information Sheet on Ramsar Wetlands Tunisia Ichkeul, Ramsar Sites Information Service. (2009).Tamisier, A., et al. Modelling aquatic ecosystems: Benefits, costs and risks, for a field biologist. Ichkeul Lake, Tunisia, a case study. In Limnology and Aquatic birds, Monitoring, modeling and management (eds. Comin, F. A., Herrera, J. A. & Ramirez, J.) 185–203 (2001).Giordani, G. et al. Nutrient fluxes in transitional zones of the Italian coast. LOICZ Reports & Studies No. 28, ii+157 pages, LOICZ, Texel, the Netherlands. (2005).Thomson, A. J., Giannopoulos, G., Pretty, J., Baggs, E. M. & Richardson, D. J. Biological sources and sinks of nitrous oxide and strategies to mitigate emissions. Phil. Trans. R. Soc. B367, 1157–1168 (2012).Article 

Google Scholar 
Chen, N., Wu, J., Chen, Z., Lu, T. & Wang, L. Spatial-temporal variation of dissolved N2 and denitrification in an agricultural river network, southeast China. Agric. Ecosyst. Environ. 189, 1–10 (2014).ADS 
CAS 
Article 

Google Scholar 
Loeks, B. M. & Cotner, J. B. Upper Midwest lakes are supersaturated with N2. Proc. Natl. Acad. Sci. USA 117, 17063–17067 (2020).Article 

Google Scholar 
Thomann, R. V., DiToro, D. M., Winfield, R. P. & O’Connor, D. J. Mathematical modelling of phytoplankton in Lake Ontario. Part 1. Model development and verification. U.S. Environmental Protection Agency, EPA-660/3-75-005, Corvallis, Oreg. 77 (1975).DiToro, D. M. & Connolly, J. P. Mathematical models of water quality in large lakes. Part 2: Lake Erie. U.S. Environmental Protection Agency, Duluth, Minnesota. EPA-600/3-80-065. 231. (1980)Jacobsen, O. S. & Jorgensen, S. E. A submodel for nitrogen release from sediments. Ecol. Model. 1, 147–151 (1975).CAS 
Article 

Google Scholar 
Jorgensen, S. E., Kamp-Neilsen, L. & Jacobsen, O. S. A submodel for anaerobic mud-water exchange of phosphate. Ecol. Model. 1, 133–146 (1975).Article 

Google Scholar 
Jorgensen, S. E. An Eutrophication model for a lake. Ecol. Model. 2, 147–165 (1976).Article 

Google Scholar 
Jorgensen, S. E., Mejer, H. & Friis, M. Examination of a Lake model. Ecol. Model. 4, 253–278 (1978).Article 

Google Scholar 
Chapelle, A., Mesnage, V., Mazouni, N., Deslous-Paoli, J. M. & Picot, B. Modélisation des cycles de l’azote et du phosphore dans les sédiments d’une lagune soumise à une exploitation conchylicole. Oceanol. Acta. 17, 609–620 (1994).CAS 

Google Scholar 
Raillard, O. & Ménesguen, A. An ecosystem box model for estimating the carrying capacity of a macrotidal shellfish system. Mar. Ecol. Prog. Ser. 115, 117–130 (1994).ADS 
Article 

Google Scholar 
Kremer, H. H. et al. Land–ocean interactions in the coastal zone: Science plan and implementation strategy, IGBP Report 51, IHDP Report 18. International Geosphere-Biosphere Programme. (2005).Strobl, R., Zaldivar, C. J., Somma, F., Stips, A. & Garcia, G. E. Application of the LOICZ Methodology to the Mediterranean Sea EUR 23936 EN. Luxembourg (Luxembourg): OPOCE. JRC52454. (2009).Swaney, D. P. & Giordani, G (Eds.). Proceedings of the LOICZ Workshop on Biogeochemical Budget Methodology and Applications, Providence RI, November 9–10, 2007. LOICZ Reports and Studies no. 37. GKSS Research Centre, Geesthacht. http://www.loicz.org/imperia/md/content/loicz/print/rsreports/biogeochemical_budget_methodology_and_applications.pdf (2011).Swaney, D. P., Smith, S. V. & Wulff, F. The LOICZ Biogeochemical Modeling Protocol and its Application to Estuarine Ecosystems. In Teratise on Estuarine and Coastal Ecosystem Science, Academic Press, Elsevier (eds. Bauer, J. E. & Bianchi, T. S.) 136–159 (2011).Glaeser, B., Kannen, A. & Kremer, H. Introduction: The future of coastal areas. Challenges for planning practice and research. Gaia-Ecol. Perspect. Sci. Soc. 18, 145–149 (2009).
Google Scholar 
Glaeser, B., Bruckmeier, K., Glaser, M. & Krause, G. Social-ecological systems analysis in coastal and marine areas: A path toward integration of interdisciplinary knowledge. In Current Trends in Human Ecology. Cambridge Scholars Publishing (eds. Lopes, P. & Begossi, A.) 183–203 (2009b).Glaser, M. & Glaeser, B. The social dimension in the management of social ecological change. In Treatise on Estuarine and Coastal Science, Vol. 11: Integrated Management of Estuaries and Coasts. München: Elsevier (eds. Kremer, H. & Pinckney, J.) 59 (2011).Glaser, M. & Glaeser, B. Towards a framework for cross-scale and multi-level analysis of coastal and marine social-ecological systems dynamics. Reg. Environ. Change. 14, 2039–2052 (2014).Article 

Google Scholar 
Vybernaite-Lubiene, I. et al. Biogeochemical budgets of nutrients and metabolism in the curonian lagoon (Southeast Baltic Sea): Spatial and temporal variations. Water 14, 164 (2022).CAS 
Article 

Google Scholar 
Yazidi, A., Saidi, S., Ben, M. N. & Darragi, F. Contribution of GIS to evaluate surface water pollution by heavy metals: Case of Ichkeul Lake (Northern Tunisia). J. Afr. Earth. Sci. 134, 166–173 (2017).ADS 
CAS 
Article 

Google Scholar 
Goudling, M. et al. Ecosystem-based management of Amazon fisheries and wetlands. Fish Fish. 20, 138–158 (2018).
Google Scholar 
Mitsch, W. J. & Gosselink, J. G. Wetlands 5th edn. (Wiley, 2015).
Google Scholar 
World Bank 2022.Affouri, H. & Sahraoui, O. The sedimentary organic matter from a Lake Ichkeul core (far northern Tunisia): Rock-Eval and biomarker approach. J. Afr. Earth. Sci. 129, 248–259 (2017).ADS 
CAS 
Article 

Google Scholar 
Vanderkelen, I., van Lipzig, N. P. M. & Thiery, A. Modelling the water balance of Lake Victoria (East Africa)–Part 1: Observational analysis. Hydrol. Earth Syst. Sci. 22, 1–17 (2018).Article 

Google Scholar 
Coe, M. T. & Foley, J. A. Human and natural impacts on the water resources of the Lake Chad basin. J. Geophys. Res. Atmos. 106, 3349–3356 (2001).ADS 
Article 

Google Scholar 
Gao, H., Bohn, T. J., Podest, E., McDonald, K. C. & Lettenmaier, D. P. On the causes of the shrinking of Lake Chad. Environ. Res. Lett. 6, 34021 (2011).Article 

Google Scholar 
Prange, M., Wilke, T. & Wesselingh, F. P. The other side of sea level change. Commun. Earth Environ. 1, 69 (2020).ADS 
Article 

Google Scholar 
Glausiusz, J. Environmental Science: New life for the DeaSea?. Nature 464, 1118–1120 (2010).CAS 
Article 

Google Scholar 
Gronewold, A. D. & Stow, C. A. Water Loss from the Great Lakes. Science 343, 1084–1085 (2014).ADS 
Article 

Google Scholar 
Mei, X., Dai, Z., Du, J. & Chen, J. Linkage between Three Gorges Dam impacts and the dramatic recessions in China’s largest freshwater lake, Poyang Lake. Sci. Rep. 5, 18197 (2015).ADS 
CAS 
Article 

Google Scholar 
Micklin, P. The aral sea disaster. Ann. Rev. Earth Planet. Sci. 35, 47–72 (2007).ADS 
CAS 
Article 

Google Scholar 
Feng, L., Han, X., Hu, C. & Chen, X. Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam?. Remote Sens. Environ. 176, 43–55 (2016).ADS 
Article 

Google Scholar 
Downing, J. A. et al. The global abundance and size distribution of lakes, ponds, and impoundments. Limnol. Oceanogr. 51, 2388–2397 (2006).ADS 
Article 

Google Scholar 
Awange, J. L. et al. The falling lake victoria water level: GRACE, TRIMM and CHAMP satellite analysis of the lake basin. Water Resour. Manag. 22, 775–796 (2008).Article 

Google Scholar 
Carroll, M. L., Townshend, R. H. G., DiMiceli, C. M., Loboda, T. & Sohlberg, R. A. Shrinkage lakes of the Artic: Spatial relationships and trajectory of change. Geophys. Res. Lett. 38, 20406 (2011).ADS 
Article 

Google Scholar 
Lefebvre, G. et al. Predicting the vulnerability of seasonally-flooded wetlands to climate change across the Mediterranean Basin. Sci. Total Environ. 692, 546–555 (2019).ADS 
CAS 
Article 

Google Scholar 
Touaylia, S., Ghannem, S., Toumi, H., Béjaoui, M. & Garrido, J. Assessment of heavy metals status in northern Tunisia using contamination indices: Case of the Ichkeul steams system. Int. J. Environ. Res. Public Health. 3, 209–217 (2016).
Google Scholar 
Aouissi, J., Benabdallah, S., Lili, C. Z. & Cudennec, C. Modelling water quality to improve agricultural practices and land management in a Tunisian catchment using soil and water assessment tool. J. Environ. Qual. 43, 18–25 (2014).Article 

Google Scholar 
Aouissi, J., Lili, C. Z., Benabdallah, S. & Cudennec, C. Assessing the hydrological impacts of agricultural changes upstream of the Tunisian World Heritage sea-connected Ichkeul Lake. Proc. Int. Assoc. Hydrol. Sci. 365, 61–65 (2015).
Google Scholar 
Fathalli, A. et al. Molecular and phylogenetic characterization of potentially toxic cyanobacteria in Tunisian freshwaters. Syst. Appl. Microbiol. 34, 303–310 (2011).CAS 
Article 

Google Scholar 
Ouchir, N., Morin, S., Ben, A. L., Boughdiri, M. & Aydi, A. Periphytic diatom communities in tributaries around Lake Ichkeul, northern Tunisia: A preliminary assessment. Afr. J. Aquat. Sci. 42, 65–73 (2017).Article 

Google Scholar 
Chislock, M. F., Doster, E., Zitomer, R. A. & Wilson, A. E. Eutrophication: Causes, consequences, and controls in aquatic ecosystems. Nat. Educ. Knowl. 4, 10 (2013).
Google Scholar 
Paerl, H. W. & Huisman, J. Climate change: A catalyste for global expansion of harmful cyanobacteria blooms. Environ. Microb. Rep. 1, 27–37 (2009).CAS 
Article 

Google Scholar 
Paerl, H. W., Nathan, S. H. & Calandrino, E. S. Controlling harmful cyanobacteria blooms in a world experiencing anthropogenic and climatic-induced change. Sci. Total Environ. 409, 1739–1745 (2011).ADS 
CAS 
Article 

Google Scholar 
O’Neil, J. M., Davis, T. M., Burford, M. A. & Gobler, C. J. The rise of harmful cyanobacteria blooms: The potential roles of eutrophication and climate change. Harmful Algae 14, 313–334 (2012).Article 

Google Scholar 
Ben, S. F. et al. Pesticides in Ichkeul Lake-Bizerte Lagoon Watershed in Tunisia: Use, occurrence, and effects on bacteria and free-living marine nematodes. Environ. Sci. Pollut. Res. 23, 36–48 (2016).Article 

Google Scholar 
Bourhane, Z. et al. Microbial diversity alteration reveals biomarkers of contamination in soil-river-lake continuum. J. Hazard. Mater. 421, 126789 (2022).CAS 
Article 

Google Scholar 
Kolzau, S. et al. Seasonal patterns of nitrogen and phosphorus limitation in four German lakes and the predictability of limitation status from Ambient nutrient concentrations. PLoS ONE 9, e96065 (2014).ADS 
Article 

Google Scholar 
Abidi, M., Ben, A. R. & Gueddari, M. Assessment of the trophic status of the South Lagoon of Tunis (Tunisia, Mediterranean Sea); a Geochemical and Statistical Approaches. J. Chem. (2018).Saunders, D. L. & Kaffl, J. Denitrification rates in the sediments of Lake Memphremagog, Canda–USA. Water Res. 35, 1897–1904 (2001).CAS 
Article 

Google Scholar 
Davidson, E. A. & Seitzinger, S. The enigma of progress in denitrification research. Ecol. Appl. 16, 2057–2063 (2006).Article 

Google Scholar 
Medina-Galvan, J. et al. Comparing the biogeochemical functioning of two arid subtropical coastal lagoons: The effect of wastewater discharges. Ecosyst. Health Sustain. 7, 1 (2021).Article 

Google Scholar 
Piehler, M. F. & Smyth, A. R. Habitat-specific distinctions in estuarine denitrification affect both ecosystem function and services. Ecosphere. 2, 1–17 (2011).ADS 
Article 

Google Scholar 
Loeks-Johson, B. M. & Cotner, J. B. Upper Midwest lakes are supersaturated with N2. Proc. Natl. Acad. Sci. U S A. 117, 17063–17067 (2020).Reddy, K. R., Patrick, W. H. & Lindau, C. W. Nitrification-denitrification at the plant root sediment interface in Wetlands. Limnol. Oceanogr. 34, 1004–1013 (1989).ADS 
CAS 
Article 

Google Scholar 
Adrian, A. et al. Lakes as sentinels of climate change. Limnol. Oceanogr. 54, 2283–2297 (2009).ADS 
Article 

Google Scholar 
Seo, C. D. & DeLaune, R. D. Fungal and bacterial mediated denitrification in wetlands: Influence of sediment redox condition. Water Res. 44, 2441–2450 (2010).CAS 
Article 

Google Scholar 
Montzka, S. A., Dlugokencky, I. J. & Butler, J. H. Non-CO2 greenhouse gases and climate change. Nature 476, 43–50 (2011).CAS 
Article 

Google Scholar 
Sferratore, A., Billen, G. & Garnier, J. The S Modeling nutrient (N, P, Si ) budget in the Seine watershed: Application of the River Strahler model using data from local to global scale resolution Modeling nutrient (N, P, Si) budget in the Seine watershed: Application of the River Strahler model using data from local to global scale resolution. Glob. Biogeochem. Cycles. 19, 20 (2005).Article 

Google Scholar 
Béjaoui, B. et al. 3D modeling of phytoplankton seasonal variation and nutrient budget in a Southern Mediterranean Lagoon. Mar. Pollut. Bull. 114, 962–976 (2017).Article 

Google Scholar 
Shaiek, M., Fassatoui, C. & Romdhane, M. S. Past and present fish species recorded in the estuarine Lake Ichkeul, northern Tunisia. Afr. J. Aquat. Sci. 41, 171–180 (2016).Article 

Google Scholar 
INM. Données climatiques de la région de Bizerte. Institut National de Météorologie, Tunis, Tunisie. (2017).Rodier, J. et al. L’analyse de l’eau, Eaux naturelles, eaux résiduaires, eau de mer, Dunod Paris. (1996).Lorenzen, C. J. Determination of chlorophyll and pheopigments by spectrophotometric equations. Limnol. Oceanogr. 12, 343–346 (1967).ADS 
CAS 
Article 

Google Scholar 
Parsons, T. R., Maita, Y. & Lalli, C. M. A manual of chemical and biological methods for seawater analysis. Geol. Mag. 122, 570–570 (1980).
Google Scholar 
Redfield, A. C. The biological control of chemical factors in the environment. Sci. Prog. 11, 150–170 (1960).CAS 

Google Scholar 
Gordon, D. C. et al. LOICZ biogeochemical modelling guidelines. LOICZ Rep and Stud. 5, 1–96 (1996).
Google Scholar 
Seitzinger, S. P. Denitrification in freshwater and coastal marine ecosystems: Ecological and geochemical significance. Limnol. Oceanogr. 33, 702–724 (1988).ADS 
CAS 

Google Scholar 
Atkinson, M. J. & Smith, S. V. C:N: P ratios of benthic marine plants. Limnol. Oceanogr. 28, 568–574 (1983).ADS 
CAS 
Article 

Google Scholar 
APHA (American Public Health Association) Standard Methods for the Examination of Water and Wastewater. 18th Edition, American Public Health Association (APHA), American Water Works Association (AWWA) and Water Pollution Control Federation (WPCF), Washington DC (1992). More

We express our gratitude to Lukáš Falteisek, Richard Dorrell, Jan Petrášek, Stanislav Volsobě, Kateřina Schwarzerová and Jana Krtková for constructive discussions. English has been kindly corrected by William Bourland. Furthermore, we thank to Dovilė Barcytė, William Bourland, Antonio Calado, Dora Čertnerová, Yana Eglit, Ivan Fiala, Martina Hálová, Miroslav Hyliš, Dagmar Jirsová, Petr Kaštánek, Viktorie Kolátková, Alena Kubátová, Alexander Kudryavtsev, Frederik Leliaert, Julius Lukeš, Jan Mach, Joost Mansour, Jan Mourek, Yvonne Němcová, Fabrice Not, Vladimír Scholtz, Alastair Simpson, Pavel Škaloud, Jan Šťastný, Róbert Šuťák, Daria Tashyreva, Dana Savická, Jan Šobotník, Zdeněk Verner, Jan Votýpka for kindly providing cultures and taxonomic identifications. More

Study areaThe study was conducted on two rivers in north-eastern Amazonia sensu lato, including the Guiana Shield and the Amazon River drainage (Fig. 2). The climate of the entire study area is homogeneous and the region is covered by dense, uniform lowland primary rainforest51. The altitude is in the range of 0–860 m a.s.l. The regional climate is equatorial, and the annual rainfall ranges from 3600 mm in the northeast to 2000 mm in the southwest. The Maroni River is 612 km long from its source to its estuary, and its watershed covers a surface of >68,000 km2 in Suriname and French Guiana. The Oyapock River (length, 404 km; area, 26,800 km2) is located in the state of Amapa in Brazil and in French Guiana.The foregoing river basins host nearly 400 freshwater fish species and more than 180 mammal species52,53. Most of the mammal species have a large distribution range, covering the entire study area53. The fish species have a less homogeneous distribution and a distinct upstream-downstream composition gradient54,55. Here, only large rivers were considered and most fish species were widespread over the whole study area. As habitat availability increases with river size, species richness is expected to increase upstream to dowsntream31,32. The Oyapock and Maroni river basins are among the last remaining wilderness areas on Earth17. Nevertheless, ecological disturbances are increasing there because of a growing human population and the development of small-scale gold mining activity. These disturbances have caused limited but diffuse deforestation23,56. The deforested areas currently comprise 0.67% of all Maroni and Oyapock catchments.SamplingEnvironmental DNA (eDNA) was collected from water samples at 74 locations (hereafter, sites) along the main channel and the large tributaries of the Maroni and Oyapock rivers (Fig. 2). Thirty-seven sites were sampled at each river basin. The minimum and maximum distances between adjacent sites were 1.07 and 50.20 km, respectively. The mean and median distances between adjacent sites were 10.18 and 9.14 km, respectively, and the standard deviation (SD) was 7.79 km. The sites were located from sea level to 157 m a.s.l. At all sites, the river was wider than 20 m and deeper than 1 m (Strahler orders 4–8; Supplementary Fig. 5). The physicochemical properties of the water slightly varied among sites. The temperature, pH, and conductivity were in the ranges of 28.4–33.2 °C, 6.5–7.6, and 16.9–54.6 µS/cm, respectively, at all sites except two estuarine locations where the conductivity was relatively high because of seawater incursion (Supplementary Data 2).The eDNA samples were collected during the dry seasons (October–November) of 2017 and 2018 for Maroni and Oyapock, respectively. At both rivers, the sites were sequentially sampled from downstream to upstream at a rate of 1–4 sites per day depending on the distance and travel time between sites. Following the protocol of ref. 45, we collected the eDNA by filtering two replicates of 34 L of water per site. A peristaltic pump (Vampire Sampler; Buerkle GmbH, Bad Bellingen, Germany) and single-use tubing were used to pump the water into a single-use filtration capsule (VigiDNA, pore size 0.45 μm; filtration surface 500 cm2, SPYGEN, Bourget-du-Lac, France). The tubing input was placed a few centimetres below the water surface in zones with high water flow as recommended by Cilleros et al.43. Sampling was performed in turbulent areas with rapid hydromorphologic units to ensure optimal eDNA homogeneity throughout the water column. To avoid eDNA cross-contamination among sites, the operator remained on emerging rocks downstream from the filtration area. At the end of filtration, the capsule was voided, filled with 80 mL CL1 preservation buffer (SPYGEN), and stored in the dark up to one month before the DNA extraction. No permits were required for the eDNA sampling and the access to all sites was legally permitted. The study complies with access and benefit permits ABSCH-IRCC-FR-246820-1 and ABSCH-IRCC-FR-245902-1, authorizing collection, transport and analysis of all environmental DNA samples used in this study.Laboratory procedures and bioinformatic analysesFor the DNA extraction, each filtration capsule was agitated on an S50 shaker (Ingenieurbüro CAT M. Zipperer GmbH, Ballrechten-Dottingen, Germany) at 800 rpm for 15 min, decanted into a 50 mL tube, and centrifuged at 15,000 × g and 6 °C for 15 min. The supernatant was removed with a sterile pipette, leaving 15 mL of liquid at the bottom of the tube. Subsequently, 33 mL of ethanol and 1.5 mL of 3 M sodium acetate were added to each 50 mL tube, and the mixtures were stored at −20 °C for at least one night. The tubes were then centrifuged at 15,000 × g and 6 °C for 15 min, and the supernatants were discarded. Then, 720 µL of ATL buffer from a DNeasy Blood & Tissue Extraction Kit (Qiagen, Hilden, Germany) was added. The tubes were vortexed, and the supernatants were transferred to 2 mL tubes containing 20 µL proteinase K. The tubes were then incubated at 56 °C for 2 h. DNA extraction was performed using a NucleoSpin Soil kit (Macherey-Nagel GmbH, Düren, Germany) starting from step six of the manufacturer’s instructions. Elution was performed by adding 100 µL of SE buffer twice. After the DNA extraction, the samples were tested for inhibition by qPCR following the protocol in ref. 57. Briefly, quantitative PCR was performed in duplicate for each sample. If at least one of the replicates showed a different Ct (Cycle threshold) than expected (at least 2 Cts), the sample was considered inhibited and diluted 5-fold before the amplification.For the fish, the “teleo” primers58 (forward: 3ʹ-ACACCGCCCGTCACTCT-5ʹ; reverse: 3ʹ-CTTCCGGTACACTTACCATG-5ʹ) were used as they efficiently discriminated local fish species43,45. For the mammals, the 12S-V5 vertebrate marker59 (forward: 3ʹ-TAGAACAGGCTCCTCTAG-5ʹ; reverse: 3ʹ-TTAGATACCCCACTATGC-5ʹ) was used as it also effectively distinguishes local mammal species44,60. The DNA amplifications were performed in a final volume of 25 μL containing 1 U AmpliTaq Gold DNA Polymerase (Applied Biosystems, Foster City, CA, USA), 0.2 μM of each primer, 10 mM Tris-HCl, 50 mM KCl, 2.5 mM MgCl2, 0.2 mM of each dNTP, and 3 μL DNA template. Human blocking primer was added to the mixture for the “teleo”58 (5′-ACCCTCCTCAAGTATACTTCAAAGGAC-C3-3′) and the “12S-V5” primers61 (5′-CTATGCTTAGCCCTAAACCTCAACAGTTAAATCAACAAAACTGCT-C3-3′) at final concentrations of 4 μM and 0.2 μg/μL bovine serum albumin (BSA; Roche Diagnostics, Basel, Switzerland). Twelve PCR replicates were performed per field sample. The forward and reverse primer tags were identical within each PCR replicate. The PCR mixture was denatured at 95 °C for 10 min, followed by 50 cycles of 30 s at 95 °C, 30 s at 55 °C for the “teleo” primers and 50 °C for the 12S-V5 primers, 1 min at 72 °C, and a final elongation step at 72 °C for 7 min. This step was conducted in a dedicated room for DNA amplification that is kept under negative air pressure and is physically separated from the DNA extraction rooms maintained under positive air pressure. The purified PCR products were pooled in equal volumes to achieve an expected sequencing depth of 500,000 reads per sample before DNA library preparation.For the fish analyses, 10 libraries were prepared using a PCR-free library protocol (https://www.fasteris.com/metafast) at Fasteris, Geneva, Switzerland. Four libraries were sequenced on an Illumina HiSeq 2500 (2 × 125 bp) (Illumina, San Diego, CA, USA) with a HiSeq SBS Kit v4 (Illumina), three were sequenced on a MiSeq (2 × 125 bp) (Illumina) with a MiSeq Flow Cell Kit Version3 (Illumina), and three libraries were sequenced on a NextSeq (2 × 150 bp + 8) (Illumina) with a NextSeq Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. Sequencing was performed according to the manufacturer’s instructions at Fasteris. For the mammal analyses, eight libraries were prepared using a PCR-free library protocol (https://www.fasteris.com/metafast) at Fasteris. Two libraries were sequenced on an Illumina HiSeq 2500 (2 × 125 bp) (Illumina) using a HiSeq Rapid Flow Cell v2 and a HiSeq Rapid SBS Kit v2 (Illumina), three libraries were prepared on a MiSeq (2 × 125 bp) (Illumina) with a MiSeq Flow Cell Kit Version3 (Illumina), and three libraries were prepared using a NextSeq (2 × 150 bp + 8) (Illumina) and a NextSeq Mid kit (Illumina). The libraries run on the NextSeq were equally distributed in four lanes. As different sequencing platforms were used (MiSeq and NextSeq for the Maroni and HiSeq 2500 and MiSeq for the Oyapock; Supplementary Fig. 6 and Supplementary Data 3), the possible influences of the platforms on the sequencing results were verified. To this end, we compared the differences in species numbers between the sample replicates assigned to the same platform (accounting for replicate effect only) against those of the sample replicates assigned to different platforms (accounting for replicate and platform effects). As there were more sites with their two replicates sequenced with the same platform than sites with their replicates sequenced with different platforms (see Supplementary Fig. 6), sites with replicates on the same platform were randomly selected for the comparisons. We repeated this procedure 50 times. The number of species between replicates sequenced on the same platform and those sequenced on different platforms did not differ for >98.5% of all fish and mammal samples (Supplementary Fig. 7 and Supplementary Note 2). Similar to these results, a previous study on 16 S rRNA amplicon has shown that the samples were not influenced by the Illumina sequencing platform used62.To monitor for contaminants, 13 negative extraction controls were performed for each of the primers (“teleo” and “12S-V5”); one control was amplified twice. All of them were amplified and sequenced by the same methods as the samples and in parallel to them. Therefore, for the negative extraction controls, 168 amplifications were prepared with the “teleo” primers (13 negative controls; one amplified and sequenced twice) and 156 amplifications with the “12S-V5” primers (13 negative controls). Fourteen negative PCR controls (ultrapure water; 12 replicates) were amplified and sequenced in parallel to the samples. Eight were amplified with the “teleo” primers and six were amplified with the “12S-V05” primers. Thus, for the PCR negative controls, there were 96 amplifications with the “teleo” primers and 72 amplifications with the Vert01 primers. Sequencing information for the controls is shown in Supplementary Data 3c.An updated version of the reference database from ref. 43 was used. There were 265 Guianese species for the fish analyses (ref. 47). The GenBank nucleotide database was consulted, but it contained little information on the Guianese fish species. Most of the sequences were derived from ref. 43. For the mammal analyses, the vertebrate database was built using ecoPCR software63 from the releases 134 and 138 of the European Nucleotide Archive (ENA), for the Maroni and Oyapock river samples, respectively. The two releases were compared, and it was established that the new mammal species added to each version did not originate from French Guiana. Hence, the results were not influenced by the EMBL release number. The relevant metabarcoding fragment was extracted from this database with ecoPCR63 and OBITools64. Therefore, the reference database comprised the local database of French Guianese mammals60, which references 576 specimens from 164 species as well as all available vertebrate species in EMBL.The sequence reads were analyzed with the OBITools package according to the protocol described by Valentini et al.58. Briefly, the forward and reverse reads were assembled with the illuminapairedend programme. The ngsfilter programme was then used to assign the sequences to each sample. A separate dataset was created for each sample by splitting the original dataset into several files with obisplit. Sequences shorter than 20 bp or occurring less than 10 times per sample were discarded. The obiclean program was used to identify amplicon sequence variants (ASVs) that have likely arisen due to PCR or sequencing errors. It uses the information of sequence counts and sequence similarities to classify whether a sequence is a variant (“internal”) of a more abundant (“head”) ASV64. After this step, we matched the ASV with the reference database to obtain the taxonomic assignation for each ASV. Sequences labelled by the obiclean programme as ‘internal’’ and probably corresponding to PCR errors were discarded. The ecotag programme was then used for taxonomic assignment of molecular operational taxonomic units (MOTUs). The taxonomic assignments from ecotag were corrected to avoid overconfidence in assignments. Species-level assignments were validated only for ≥98% sequence identity with the reference database. Sequences below this threshold were discarded.Measuring disturbance intensity using GIS dataIn riverine systems, the disturbances may accumulate because of hydrologic connectivity, which is the downstream transfer of matter and pollutants4. Hence, the upstream sub-basin drainage network was considered to determine the size of the upstream sub-basin affecting local biodiversity (Fig. 1). The sub-basins were delineated by applying a flow accumulation algorithm to the SRTM global 30 m digital elevation model65. Deforestation was measured over 14 upstream spatial extents with radii of 0.5, 1.5, 3, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, and 90 km for each sampling site. Then, these 14 upstream spatial extents were intersected with the sub-basin drainage network. In addition, mammals and fish can also be affected by disturbances other than those mediated by hydrologic connectivity. Thus, deforestation was also measured upstream and downstream from the eDNA sampling sites using the same foregoing 14 radii.At each sampling site, deforestation intensity was quantified for each of the 14 spatial extents. We summed upstream (only accounting for disturbances mediated by river hydrologic connectivity) or upstream and downstream (not only considering disturbances mediated by hydrologic connectivity) deforested surfaces from Landsat satellite image datasets. Forest loss surfaces were obtained from the Global Forest Change dataset66. The Global Forest Change dataset identifies areas deforested between 2001 and 2017 on a 30 m spatial scale. To incorporate deforested areas prior to 2000, tree canopy cover data for that year were also used. Except for river courses, all pixels with More

Site descriptionThe respective sites were classified into five climatic regions in California, containing San Cruz and San Rose in region 1, Saint Helena and San Jose in region 2, Livermore and Cloverdale in region 3, Davis, Lodi and Fontana in region 4, Fresno and Bakerfield in region 5 (Fig. 1). There were differences in annual mean temperature among five climatic regions, ranging from 14.3°C to 18.6°C. In each region, the GHDs, quality of musts and wines, and wine tasting notes were recorded for 148 cultivars from 1935 to 1941. Meanwhile, in region 2, namely in Napa, the GHDs and must sugar content (in °Brix) were recorded for four representative cultivars (Cabernet Sauvignon, Chardonnay, Merlot and Sauvignon Blanc) during 1991–2018.Fig. 1The locations of five climatic regions for wine grape classed by Winkler index in California. The insert plot represents the distinct Winkler index (WI) during 1935–1941 in five climatic regions.Full size imageClimate dataThe climate data was collected from five stations for over one hundred year-period (1911–2018), including daily average, maximum and minimum temperature (Table 1). Climate data was retrieved from the National Oceanic and Atmospheric Administration (NOAA)’s National Centers for Environmental Information (NCEI). The database from which the data was retrieved was the “Global Historical Climatology Network – Daily (GHCN-Daily), Version 3” (https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_station/)25,26. Table 1 showed the search codes and names of five stations in the website. The climate data of region 1 and region 5 were for the periods of 1911–2011 and 1938–2018, respectively.Table 1 Description of weather stations and time-span in five climatic regions.Full size tableBioclimatic indicesHere, we presented seven temperature-related indices to explore the changing climate in five climatic regions during the last 100 years. We compared the changes of these indices between the past (1935–1941) and current climate conditions (1991–2018). Thereafter, four indices were chosen to describe annual changes, including average, maximum, minimum temperature and diurnal temperature range (DTR). Furthermore, other indices were used to analyse growing season temperature (GST), Winkler index (WI) and Huglin index (HI) for the grape-growing season5,27,28. The equations used to calculate the bioclimatic indices of grape-growing season are:$$GST=frac{{sum }_{Apr1}^{Oct31}frac{{T}_{max}+{T}_{min}}{2}}{n}$$
(1)
$$WI={sum }_{Apr1}^{Oct31}left(frac{{T}_{max}+{T}_{min}}{2}-10right)$$
(2)
$$HI={sum }_{Apr1}^{Sep30}left(frac{{T}_{max}+{T}_{ave}}{2}-10right)times K$$
(3)
where Tmax, Tmin and Tave represent daily maximum, minimum and average temperatures, respectively. K is a length of day coefficient ranging from 1.02 to 1.06 between 40 and 50 of latitude in the northern hemisphere.Sample collection, harvest dates, quality of musts and wines measurementSample collection, harvest dates, quality of musts and wines measurement were detailed in the report of Amerine and Winkler24. Briefly, grape berries (22–220 kg) were picked in the morning from representative vines of variety collections or commercial vineyards by Amerine and Winkler24, as well as numerous vineyard owners. The harvest dates were recorded after picking. All grapes picked were crushed within 24 hours except for a few samples in 1935. The clear juice was taken after the coarse sediment had settled, in order to measure total soluble solids (°Brix), total acid (grams per 100 cc), and pH of must. The must was placed in an open oak fermenting tank. After fermentation, it was completed in a closed oak container. Then, the alcohol (percent by volume), extract (grams per 100 cc), tannin (grams per 100 cc), and fixed acid (grams per 100 cc) of wine were measured. The must °Brix was measured with a Brix hydrometer floating in a cylinder, must total acid was determined by titration with sodium hydroxide to a phenolphthalein end point, and must pH was measured with a quinhydrone electrode or a Beckman pH meter. In addition, wine alcohol was measured by the hydrometer and reported as percentage by volume, the extract and tannin of wine were measured by means of a special 0° to 8° Balling hydrometer and the Association of Official Agricultural Chemists method24. Note that the fixed acid of wine are equal to total acid minus volatile acid, where the total acid was measured by titration with phenolphthalein as an indicator while the volatile acid was determined also by titration with pretreated wines by method II of the Association of Official Agricultural Chemists24.Wine tasting notesThe purpose of wine tasting was to evaluate the cultivars based on the merits and defects of wine. The descriptive terms used for recording the results of the organoleptic examination contained appearance, color, odors, volatile acidity, total acidity, dryness, body, taste, smoothness and astringency, and general quality. More

Yuuka Fukui
Yuuka Fukui received Ph.D. degree from Keio University in 2012 under the supervision of Prof. Keiji Fujimoto. She was a JSPS research fellow (DC2) from 2010 to 2012. She joined the laboratory of Prof. Keiji Fujimoto at Keio university as a research associate in 2012 and was promoted to an assistant professor in 2017. Her research interests focus on the design and synthesis of polymeric materials (particles, membranes, porous structures) and organic-inorganic hybrid materials inspired from biological systems.About the award article: The authors reported a new technique to prepare nanoparticles from biomass-derived polymers, which will be utilized as an eco-friendly alternative to synthetic particulate plastics. Nanosized agarose gel particles were produced via sol-to-gel transition of agarose inside water nanodroplets prepared by W/O miniemulsion method. Subsequently, the water evaporation was carried out to generate xerogel nanoparticles (AgarX). The morphologies and crystal structure of AgarX were controlled by changing the pressure and temperature during the water evaporation. The resultant AgarX possessed high crystallinity and exhibited a water dispersibility and a water resistance.

Mikihiro Hayashi
Mikihiro Hayashi received his Ph.D. degree from Nagoya University (Prof. Yushu Matsushita group) in 2015. During his doctor course, he had been selected as a JSPS research fellow (DC2) and experienced researches in ESPCI Paris-Tech (Prof. Ludwik Leibler) and in Shanghai Jiao Tong University (Prof. Xinyuan Zhu). He then re-joined Ludwik Leibler’s group as a postdoc, and experienced another postdoc in Prof. Masatoshi Tokita in Tokyo institute of technology. In 2017, he became an assistant professor in Prof. Akinori Takasu group (Nagoya institute of technology), and currently manages his own laboratory as a PI. His research interest is the design of functional cross-linked materials.About the award article: the authors reported a preparation vitrimer-like elastomers with dynamic bond-exchangeable cross-links. A poly(ethyl acrylate)-based copolymer bearing random pyridine groups was synthesized, which was cross-linked by quaternization reaction with dibromo cross-linkers. In this system, the bond exchange was operated via trans-N-alkylation of the quaternized pyridine groups, showing useful sustainable functions, such as reprocessability, recyclability, and dissolution ability in some selective solvents.

Ryohei Ishige
Ryohei Ishige received his Ph.D. from Tokyo Institute of Technology in 2011 under the supervision of Prof. Junji Watanabe. He joined Prof. Atsushi Takahara’s laboratory at Kyushu University (2011–2013) and Prof. Yoshinobu Tsujii’s laboratory at Kyoto University (2013–2014). From 2014, he joined Prof. Shinji Ando’s laboratory at Tokyo Institute of Technology as an assistant professor and was promoted to an associate professor in 2021. His research interests are liquid-crystalline aromatic polymers and those structure-property relationships.About the award article: the authors developed a novel analytical technique integrating spectroscopies (infrared pMAIRS, and spectroscopic ellipsometry) and scattering methods (GI-WAXS), applied to the process where thin film polyimide, PI, is generated from linear poly(amic ester), PAE, precursors whose backbone consists of para-linkage. They revealed that PAE-based thin PI films form heterogeneous structure composed of non-oriented amorphous region and oriented ordered region which includes anisotropic nanopores causing structural birefringence. This method enables comprehensive evaluation of the evolution in complex hierarchical structures following chemical reactions for every noncrystalline thin film polymers.

Ryohei Kakuchi
Ryohei Kakuchi received his Ph.D. degree from the Hokkaido University in 2009 with a JSPS (Japan Society for Promotion of Science) research fellowship. After the Ph.D., he has made postdoctoral works in Germany from 2009 to 2014 and joined Kanazawa University as a research assistant professor in 2014. Based on the Leading Initiative for Excellent Young Researchers program, he was then appointed as an assistant professor (PI) at Gunma University in 2017. His research interests are the novel polymer synthesis based on unique organic transformation reactions including multicomponent reactions.About the award article: The authors proposed a new synthetic strategy to utilize wood-biomass sourced compounds in a green fashion. To achieve sustainable material chemistry, the intrinsic reactivity of lignin-derived poly(methacrylated vanillin) (PMV) was spotlighted because many multicomponent reactions employ aldehydes as a reactant. First, the Passerini three-component reaction (Passerini-3CR) of the PMV was revealed to proceed with >90% aldehyde conversions. Taking advantage of this high reactivity of the PMV, its immobilized cellulose fabric, a wood-biomass sourced organic hybrid, was revealed to accept the surface Passerini-3CR with amino acid derivatives, thereby demonstrating a fully bio-based material fabrication. More

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 (1998).Article 

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

Google Scholar 
Ptacnik, R. et al. Diversity predicts stability and resource use efficiency in natural phytoplankton communities. Proc. Natl.Acad. Sci. USA 105, 5134–5138 (2008).Article 

Google Scholar 
Corcoran, A. A. & Boeing, W. J. Biodiversity increases the productivity and stability of phytoplankton communities. PLoS ONE 7, e49397 (2012).Article 

Google Scholar 
Arteaga, L., Pahlow, M. & Oschlies, A. Global patterns of phytoplankton nutrient and light colimitation inferred from an optimality-based model. Glob. Biogeochem. Cycles 28, 648–661 (2014).Article 

Google Scholar 
Lewis, M., Hebert, D., Harrison, W. G., Platt, T. & Oakey, N. S. Vertical nitrate fluxes in the oligotrophic ocean. Science 234, 870–873 (1986).Article 

Google Scholar 
McGillicuddy, D. J. J. et al. Eddy/wind interactions stimulate extraordinary mid-ocean plankton blooms. Science 316, 1021–1026 (2007).Article 

Google Scholar 
Duce, R. A. et al. Impacts of atmospheric anthropogenic nitrogen on the open ocean. Science 320, 893–897 (2008).Article 

Google Scholar 
Tang, W. et al. Revisiting the distribution of oceanic N2 fixation and estimating diazotrophic contribution to marine production. Nat. Commun. 10, 831 (2019).Article 

Google Scholar 
Letscher, R. T., Primeau, F. & Moore, J. K. Nutrient budgets in the subtropical ocean gyres dominated by lateral transport. Nat. Geosci. 9, 815–819 (2016).Article 

Google Scholar 
Righetti, D., Vogt, M., Gruber, N., Psomas, A. & Zimmermann, N. E. Global pattern of phytoplankton diversity driven by temperature and environmental variability. Sci. Adv. 5, eaau6253 (2019).Article 

Google Scholar 
Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell 179, 1084–1097 (2019).Article 

Google Scholar 
Lévy, M., Franks, P. J. S. & Smith, K. S. The role of submesoscale currents in structuring marine ecosystems. Nat. Commun. 9, 4758 (2018).Article 

Google Scholar 
Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences 17, 609–634 (2020).Article 

Google Scholar 
Gove, J. M. et al. Near-island biological hotspots in barren ocean basins. Nat. Commun. 7, 10581 (2016).Article 

Google Scholar 
Doty, M. S. & Oguri, M. The island mass effect. ICES J. Mar. Sci. 22, 33–37 (1956).Article 

Google Scholar 
Bell, J. D. et al. Planning the use of fish for food security in the Pacific. Mar. Policy 33, 64–76 (2009).Article 

Google Scholar 
Bakker, D. C., Nielsdóttir, M. C., Morris, P. J., Venables, H. J. & Watson, A. J. The island mass effect and biological carbon uptake for the subantarctic Crozet Archipelago. Deep Sea Res. Pt II 54, 2174–2190 (2007).Article 

Google Scholar 
Heywood, K. J., Stevens, D. P. & Bigg, G. R. Eddy formation behind the tropical island of Aldabra. Deep Sea Res. Pt I 43, 555–578 (1996).Article 

Google Scholar 
Palacios, D. M. Factors influencing the island-mass effect of the Galapagos archipelago. Geophys. Res. Lett. 29, 2134 (2002).Article 

Google Scholar 
Gilmartin, M. & Revelante, N. The ‘island mass’ effect on the phytoplankton and primary production of the Hawaiian Islands. J. Exp. Mar. Biol. Ecol. 16, 181–204 (1974).Article 

Google Scholar 
Signorini, S. C., McClain, C. R. & Dandonneau, Y. Mixing and phytoplankton bloom in the wake of the Marquesas Islands. Geophys. Res. Lett. 26, 3121–3124 (1999).Article 

Google Scholar 
Messié, M., Radenac, M.-H., Lefèvre, J. & Marchesiello, P. Chlorophyll bloom in the western Pacific at the end of the 1997-98 El Niño: the role of the Kiribati Islands. Geophys. Res. Lett. 33, L14601 (2006).Article 

Google Scholar 
Messié, M. & Radenac, M.-H. Seasonal variability of the surface chlorophyll in the western tropical Pacific from SeaWiFS data. Deep Sea Res. Pt I 53, 1581–1600 (2006).Article 

Google Scholar 
Le Borgne, R., Dandonneau, Y. & Lemasson, L. The problem of the island mass effect on chlorophyll and zooplankton standing crops around Mare (Loyalty Islands) and New Caledonia. Bull. Mar. Sci. 37, 450–459 (1985).
Google Scholar 
Messié, M. et al. The delayed island mass effect: how islands can remotely trigger blooms in the oligotrophic ocean. Geophys. Res. Lett. 47, e2019GL085282 (2020).Article 

Google Scholar 
Dandonneau, Y. & Charpy, L. An empirical approach to the island mass effect in the south tropical Pacific based on sea surface chlorophyll concentrations. Deep Sea Res. Pt A 32, 707–721 (1985).Article 

Google Scholar 
Shiozaki, T., Kodama, T. & Furuya, K. Large-scale impact of the island mass effect through nitrogen fixation in the western South Pacific Ocean. Geophys. Res. Lett. 41, 2907–2913 (2014).Article 

Google Scholar 
Caputi, L. et al. Community-level responses to iron availability in open ocean plankton ecosystems. Glob. Biogeochem. Cycles 33, 391–419 (2019).Article 

Google Scholar 
Martinez, E., Rodier, M., Pagano, M. & Sauzède, R. Plankton spatial variability within the Marquesas archipelago, South Pacific. J. Mar. Syst. 212, 103432 (2020).Article 

Google Scholar 
Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).Article 

Google Scholar 
Laws, E. A., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).Article 

Google Scholar 
Messié, M. & Chavez, F. P. A global analysis of ENSO synchrony: the oceans’ biological response to physical forcing. J. Geophys. Res. 117, C09001 (2012).
Google Scholar 
Luo, Y.-W., Lima, I. D., Karl, D. M., Deutsch, C. A. & Doney, S. C. Data-based assessment of environmental controls on global marine nitrogen fixation. Biogeosciences 11, 691–708 (2014).Article 

Google Scholar 
Messié, M. & Chavez, F. P. Seasonal regulation of primary production in eastern boundary upwelling systems. Prog. Oceanogr. 134, 1–18 (2015).Article 

Google Scholar 
Mouw, C. B. et al. A consumer’s guide to satellite remote sensing of multiple phytoplankton groups in the global ocean. Front. Mar. Sci. 4, 41 (2017).Article 

Google Scholar 
Alvain, S., Moulin, C., Dandonneau, Y. & Bréon, F. M. Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery. Deep Sea Res. Pt I 52, 1989–2004 (2005).Article 

Google Scholar 
Rêve-Lamarche, A.-H. et al. Ocean color radiance anomalies in the North Sea. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00408 (2017).Alvain, S., Loisel, H. & Dessailly, D. Theoretical analysis of ocean color radiances anomalies and implications for phytoplankton groups detection in case 1 waters. Opt. Express 20, 1070–1083 (2012).Article 

Google Scholar 
Mackey, D. J., Blanchot, J., Higgins, H. W. & Neveux, J. Phytoplankton abundances and community structure in the equatorial Pacific. Deep Sea Res. Pt II 49, 2561–2582 (2002).Article 

Google Scholar 
Johnson, Z. I. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).Article 

Google Scholar 
Martiny, A. C., Kathuria, S. & Berube, P. M. Widespread metabolic potential for nitrite and nitrate assimilation among Prochlorococcus ecotypes. Proc. Natl. Acad. Sci. USA 106, 10787–10792 (2009).Article 

Google Scholar 
Vallina, S. M. et al. Global relationship between phytoplankton diversity and productivity in the ocean. Nat. Commun. 5, 4299 (2014).Article 

Google Scholar 
Dai, S. et al. The seamount effect on phytoplankton in the tropical western Pacific. Mar. Environ. Res. 162, 105094 (2020).Article 

Google Scholar 
Leitner, A. B., Neuheimer, A. B. & Drazen, J. C. Evidence for long-term seamount-induced chlorophyll enhancements. Sci. Rep. 10, 12729 (2020).Article 

Google Scholar 
Bowen, B. W., Rocha, L. A., Toonen, R. J. & Karl, S. A. The origins of tropical marine biodiversity. Trends Ecol. Evol. 28, 359–366 (2013).Article 

Google Scholar 
Worm, B., Lotze, H. K. & Myers, R. A. Predator diversity hotspots in the blue ocean. Proc. Natl. Acad. Sci. USA 100, 9884–9888 (2003).Article 

Google Scholar 
Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).Article 

Google Scholar 
Harrison, A.-L. et al. The political biogeography of migratory marine predators. Nat. Ecol. Evol. 2, 1571–1578 (2018).Article 

Google Scholar 
Pompa, S., Ehrlich, P. R. & Ceballos, G. Global distribution and conservation of marine mammals. Proc. Natl. Acad. Sci. USA 108, 13600–13605 (2011).Article 

Google Scholar 
Wessel, P. & Smith, W. H. F. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741––8743 (1996).Article 

Google Scholar 
Nunn, P. D., Kumar, L., Eliot, I. & McLean, R. F. Classifying Pacific islands. Geosci. Lett 3, 7 (2016).Article 

Google Scholar 
Hasegawa, D., Lewis, M. R. & Gangopadhyay, A. How islands cause phytoplankton to bloom in their wakes. Geophys. Res. Lett. 36, L20605 (2009).Article 

Google Scholar 
Platt, T. & Sathyendranath, S. Oceanic primary production: estimation by remote sensing at local and regional scales. Science 241, 1613–1620 (1988).Article 

Google Scholar 
Hasegawa, D., Yamazaki, H., Ishimaru, T., Nagashima, H. & Koike, Y. Apparent phytoplankton bloom due to island mass effect. J. Mar. Syst. 69, 238–246 (2008).Article 

Google Scholar 
Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: a net production model for global ocean phytoplankton. Glob. Biogeochem. Cycles 30, 1756–1777 (2016).Article 

Google Scholar 
Ben Mustapha, Z., Alvain, S., Jamet, C., Loisel, H. & Dessailly, D. Automatic classification of water-leaving radiance anomalies from global SeaWiFS imagery: application to the detection of phytoplankton groups in open ocean waters. Remote Sens. Environ. 146, 97–112 (2014).Article 

Google Scholar 
Alvain, S., Moulin, C., Dandonneau, Y. & Loisel, H. Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: a satellite view. Glob. Biogeochem. Cycles 22, GB3001 (2008).Article 

Google Scholar 
Bray, J. R. & Curtis, J. T. An ordination of the upland forest communities of Southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).Article 

Google Scholar 
Pielou, E. The measurement of diversity in different types of biological collections. J. Theor. Biol. 13, 131–144 (1966).Article 

Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).Article 

Google Scholar 
Colwell, R. K., Mao, C. X. & Chang, J. Interpolating, extrapolating, and comparing incidence-based species accumulation curves. Ecology 85, 2717–2727 (2004).Article 

Google Scholar 
De Monte, S., Soccodato, A., Alvain, S. & d’Ovidio, F. Can we detect oceanic biodiversity hotspots from space? ISME J. 7, 2054–2056 (2013).Article 

Google Scholar 
Soccodato, A. et al. Estimating planktonic diversity through spatial dominance patterns in a model ocean. Mar. Geonom. 29, 9–17 (2016).Article 

Google Scholar 
Messié, M., Petrenko, A., Doglioli, A., Martinez, E. & Alvain, S. Data from: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.6416130 (2022).Messié, M. Code for: Basin-scale biogeochemical and ecological impacts of islands in the tropical Pacific Ocean (v1.0.0). Zenodo https://doi.org/10.5281/zenodo.6494328 (2022). More